Patent Publication Number: US-2021173887-A1

Title: Sytem and method for filtering and creating points-of-interest

Description:
RELATED APPLICATIONS 
     This application claims the benefit of provisional patent application Ser. No. 61/149,205, filed Feb. 2, 2009, provisional patent application Ser. No. 61/227,192, filed Jul. 21, 2009, and provisional patent application Ser. No. 61/236,296, filed Aug. 24, 2009, the disclosures of which are hereby incorporated by reference in their entireties. 
    
    
     FIELD OF THE DISCLOSURE 
     The present disclosure relates to filtering and creating Points-of-Interest (POIs). 
     BACKGROUND OF THE DISCLOSURE 
     Points-of-Interest (POIs) are usually displayed by a system statically or based on a user&#39;s input. For example, using the search feature of Google® Maps on the Apple® iPhone, a user is enabled to search for POIs using one or more search terms (e.g., restaurants). Other devices, such as portable navigation devices, allow the user to filter static POIs in a POI database stored by the device using predefined categories (e.g., restaurants, gas stations, etc.). However, in certain cases, a user may be interested not only in finding POIs but also in finding POIs having desirable surroundings, or those that have an affinity with their surroundings, such as an Italian restaurant in the Little Italy district of a city. In addition, current systems are not generally aware of all locations that may be considered POIs. For example, current systems may not be aware of newly established restaurants, night clubs, or the like. As such, there is a need for a system and method of filtering POIs in a manner that takes into account at least some information regarding the surroundings that a user will experience at the POIs. In addition, there is a need for a system and method for creating POIs based upon these surroundings or an affinity to these surroundings. 
     SUMMARY OF THE DISCLOSURE 
     Systems and methods are provided for filtering and/or creating Points-of-Interest (POIs). With respect to filtering, in general, a list of POIs is obtained and then filtered based on crowd data related to the list of POIs to provide a filtered list of POIs. More specifically, in one embodiment, a list of POIs is obtained, crowds at POIs in the list of POIs are identified, and the list of POIs is filtered such that POIs having crowds with attributes that do not satisfy one or more crowd-based filtering criteria are removed from the list of POIs to provide a filtered list of POIs. In another embodiment, a list of POIs is obtained, crowds relevant to a bounding region for the list of POIs are identified, and the list of POIs is filtered based on aggregate profiles for the crowds to provide a filtered list of POIs. With respect to creating POIs, in one embodiment, a crowd-sourced POI request is received. One or more crowds relevant to a bounding region for the crowd-sourced POI request are identified. One or more crowd-sourced POIs are then created based on the one or more crowds relevant to the bounding region for the crowd-sourced POI request. 
     Those skilled in the art will appreciate the scope of the present invention and realize additional aspects thereof after reading the following detailed description of the preferred embodiments in association with the accompanying drawing figures. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING FIGURES 
       The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the invention, and together with the description serve to explain the principles of the invention. 
         FIG. 1  illustrates a Mobile Aggregate Profile (MAP) system according to one embodiment of the present disclosure; 
         FIG. 2  is a block diagram of the MAP server of  FIG. 1  according to one embodiment of the present disclosure; 
         FIG. 3  is a block diagram of the MAP client of one of the mobile devices of  FIG. 1  according to one embodiment of the present disclosure; 
         FIG. 4  illustrates the operation of the system of  FIG. 1  to provide user profiles and current locations of the users of the mobile devices to the MAP server according to one embodiment of the present disclosure; 
         FIG. 5  illustrates the operation of the system of  FIG. 1  to provide user profiles and current locations of the users of the mobile devices to the MAP server according to another embodiment of the present disclosure; 
         FIGS. 6 and 7  graphically illustrate bucketization of users according to location for purposes of maintaining a historical record of anonymized user profile data by location according to one embodiment of the present disclosure; 
         FIG. 8  is a flow chart illustrating the operation of a foreground bucketization process performed by the MAP server to maintain the lists of users for location buckets for purposes of maintaining a historical record of anonymized user profile data by location according to one embodiment of the present disclosure; 
         FIG. 9  is a flow chart illustrating the anonymization and storage process performed by the MAP server for the location buckets in order to maintain a historical record of anonymized user profile data by location according to one embodiment of the present disclosure; 
         FIG. 10  graphically illustrates anonymization of a user record according to one embodiment of the present disclosure; 
         FIG. 11  is a flow chart for a quadtree based storage process that may be used to store anonymized user profile data for location buckets according to one embodiment of the present disclosure; 
         FIG. 12  is a flow chart illustrating a quadtree algorithm that may be used to process the location buckets for storage of the anonymized user profile data according to one embodiment of the present disclosure; 
         FIGS. 13A through 13E  graphically illustrate the process of  FIG. 12  for the generation of a quadtree data structure for one exemplary base quadtree region; 
         FIG. 14  illustrates the operation of the system of  FIG. 1  wherein a mobile device is enabled to request and receive historical data from the MAP server according to one embodiment of the present disclosure; 
         FIGS. 15A and 15B  illustrate a flow chart for a process for generating historical data in a time context in response to a historical request from a mobile device according to one embodiment of the present disclosure; 
         FIG. 16  is an exemplary Graphical User Interface (GUI) that may be provided by the MAP application of one of the mobile devices of  FIG. 1  in order to present historical aggregate profile data in a time context according to one embodiment of the present disclosure; 
         FIGS. 17A and 17B  illustrate a flow chart for a process for generating historical data in a geographic context in response to a historical request from a mobile device according to one embodiment of the present disclosure; 
         FIG. 18  illustrates an exemplary GUI that may be provided by the MAP application of one of the mobile devices of  FIG. 1  to present historical data in the geographic context according to one embodiment of the present disclosure; 
         FIG. 19  illustrates the operation of the system of  FIG. 1  wherein the subscriber device is enabled to request and receive historical data from the MAP server according to one embodiment of the present disclosure; 
         FIGS. 20A and 20B  illustrate a process for generating historical data in a time context in response to a historical request from a subscriber device according to one embodiment of the present disclosure; 
         FIGS. 21A and 21B  illustrate a process for generating historical data in a geographic context in response to a historical request from a subscriber device according to one embodiment of the present disclosure. 
         FIG. 22  is a flow chart for a spatial crowd formation process according to one embodiment of the present disclosure; 
         FIGS. 23A through 23D  graphically illustrate the crowd formation process of  FIG. 22  for an exemplary bounding box; 
         FIGS. 24A through 24D  illustrate a flow chart for a spatial crowd formation process according to another embodiment of the present disclosure; 
         FIGS. 25A through 25D  graphically illustrate the crowd formation process of  FIGS. 24A through 24D  for a scenario where the crowd formation process is triggered by a location update for a user having no old location; 
         FIGS. 26A through 26F  graphically illustrate the crowd formation process of  FIGS. 24A through 24D  for a scenario where the new and old bounding boxes overlap; 
         FIGS. 27A through 27E  graphically illustrate the crowd formation process of  FIGS. 24A through 24D  in a scenario where the new and old bounding boxes do not overlap; 
         FIG. 28  illustrates the operation the system of  FIG. 1  to enable the mobile devices to request crowd data for currently formed crowds according to one embodiment of the present disclosure; 
         FIG. 29A  is a flow chart for a process for generating aggregate profiles for crowds identified in response to a crowd request from a mobile device according to one embodiment of the present disclosure; 
         FIG. 29B  is a flow chart for a process for generating aggregate profiles for crowds identified in response to a crowd request from a mobile device according to another embodiment of the present disclosure; 
         FIG. 30  illustrates the operation of the system of  FIG. 1  to enable a subscriber device to request crowd data for current crowds according to one embodiment of the present disclosure; 
         FIG. 31  is a flow chart for a process for generating aggregate profiles for crowds identified for a crowd request in response to a crowd request from a subscriber device according to one embodiment of the present disclosure; 
         FIGS. 32A through 32E  illustrate a GUI for an exemplary embodiment of the MAP application of one of the mobile devices of  FIG. 1  according to one embodiment of the present disclosure; 
         FIGS. 33A through 33C  illustrate an exemplary web interface provided by the MAP server and presented to the subscriber at the subscriber device according to one embodiment of the present disclosure; 
         FIG. 34  is a flow chart illustrating a spatial crowd fragmentation process according to one embodiment of the present disclosure; 
         FIGS. 35A and 35B  graphically illustrate the spatial crowd fragmentation process of  FIG. 34  for an exemplary crowd; 
         FIG. 36  illustrates a connectivity-based crowd fragmentation process according to one embodiment of the present disclosure; 
         FIGS. 37A and 37B  graphically illustrate the connectivity-based crowd fragmentation process of  FIG. 36  for an exemplary crowd; 
         FIG. 38  is a flow chart illustrating a recursive crowd fragmentation that uses both spatial crowd formation and connectivity-based crowd formation according to one embodiment of the present disclosure; 
         FIG. 39  is a flow chart illustrating a recursive crowd fragmentation that uses both spatial crowd formation and connectivity-based crowd formation according to another embodiment of the present disclosure; 
         FIGS. 40A and 40B  illustrate an exemplary graphical representation of the degree of fragmentation for a crowd according to one embodiment of the present disclosure; 
         FIG. 41  is a flow chart for a process for determining a best-case and worst-case average degree of separation (DOS) for a crowd fragment of a crowd according to one embodiment of the present disclosure; 
         FIG. 42  is a more detailed flow chart illustrating the process for determining a best-case and worst-case average DOS for a crowd fragment according to one embodiment of the present disclosure; 
         FIGS. 43A through 43D  illustrate an exemplary graphical representation of the best-case and worst-case average DOS for a crowd fragment according to one embodiment of the present disclosure; 
         FIG. 44  is a flow chart for a process of determining a degree of bidirectionality of relationships between users in a crowd fragment according to one embodiment of the present disclosure; 
         FIGS. 45A through 45C  illustrate an exemplary graphical representation of the degree of bidirectionality of friendship relationships for a crowd fragment according to one embodiment of the present disclosure; 
         FIG. 46  is a flow chart for a process for generating a quality level for an aggregate profile for a crowd according to one embodiment of the present disclosure; 
         FIG. 47  illustrates an exemplary GUI for presenting an aggregate profile for a crowd and a quality level of the aggregate profile generated using the process of  FIG. 46  according to one embodiment of the present disclosure; 
         FIG. 48  illustrates another exemplary GUI for presenting an aggregate profile for a crowd and a quality level of the aggregate profile generated using the process of  FIG. 46  according to another embodiment of the present disclosure; 
         FIG. 49  illustrates a flow chart for a process for generating confidence factors for keywords included in an aggregate profile for a crowd based on confidence levels for current locations of users in the crowd according to one embodiment of the present disclosure; 
         FIG. 50  illustrates an exemplary GUI for presenting an aggregate profile for a crowd including an indication of a confidence level for each of a number of keywords in the aggregate profile according to one embodiment of the present disclosure; 
         FIG. 51  graphically illustrates modification of the confidence level of the current location of a user according to one embodiment of the present disclosure; 
         FIG. 52  illustrates the operation of the system of  FIG. 1  to perform a process for efficiently handling requests for crowd data for large geographic areas according to one embodiment of the present disclosure; 
         FIGS. 53A through 53E  illustrate an exemplary series of outwardly radiating, concentric geographic regions for a number of hotspots identified for a bounding region established by the MAP server in response to a request for crowd data according to one embodiment of the present disclosure; 
         FIG. 54  graphically illustrates one exemplary variation to the follow-up request regions illustrated in  FIGS. 53A through 53E ; 
         FIG. 55  illustrates exemplary data records that may be used to represent crowds, users, crowd snapshots, and anonymous users according to one embodiment of the present disclosure; 
         FIGS. 56A through 56D  illustrate one embodiment of a spatial crowd formation process that may be used to enable crowd tracking according to one embodiment of the present disclosure; 
         FIG. 57  illustrates a process for creating crowd snapshots according to one embodiment of the present disclosure; 
         FIG. 58  illustrates a process that may be used to re-establish crowds and detect crowd splits according to one embodiment of the present disclosure; 
         FIG. 59  graphically illustrates the process of re-establishing a crowd for an exemplary crowd according to one embodiment of the present disclosure; 
         FIG. 60  graphically illustrates the process for capturing a crowd split for an exemplary crowd according to one embodiment of the present disclosure; 
         FIG. 61  graphically illustrates the merging of two exemplary pre-existing crowds according to one embodiment of the present disclosure; 
         FIG. 62  illustrates the operation of the MAP server of  FIG. 1  to serve a request for crowd tracking data for a crowd according to one embodiment of the present disclosure; 
         FIG. 63  illustrates the operation of the MAP server of  FIG. 1  to enable alerts according to one embodiment of the present disclosure; 
         FIG. 64  illustrates an embodiment of the MAP server that further includes a Point-of-Interest (POI) request processing function and a POI filtering function; 
         FIG. 65  illustrates the operation of the system of  FIG. 1  including the MAP server of  FIG. 64  to provide POI filtering according to one embodiment of the present disclosure; 
         FIG. 66  illustrates an embodiment of the MAP server that further includes a POI filtering function; 
         FIG. 67  illustrates the operation of the system of  FIG. 1  including the MAP server of  FIG. 66  to provide POI filtering according to another embodiment of the present disclosure; 
         FIG. 68  is a flow chart illustrating a process for filtering POIs based on attributes of crowds at or near the POIs according to one embodiment of the present disclosure; 
         FIG. 69  is a flow chart illustrating process for filtering POIs based on aggregate profiles of crowds relevant to a bounding region encompassing locations of the POIs according to another embodiment of the present disclosure; 
         FIG. 70  illustrates an embodiment of the MAP server that further includes a crowd-sourced POI creation filtering function; 
         FIG. 71  illustrates the operation of the system of  FIG. 1  including the MAP server of  FIG. 70  to create crowd-sourced POIs according to one embodiment of the present disclosure; 
         FIG. 72  is a flow chart for a process for promoting a crowd-sourced POI to a permanent POI according to one embodiment of the present disclosure; 
         FIG. 73  is a flow chart for a process for augmenting metadata describing a POI based on historical data according to one embodiment of the present disclosure; 
         FIG. 74  is a flow chart for a process for augmenting metadata describing a POI based on historical data according to another embodiment of the present disclosure; 
         FIG. 75  is a block diagram of the MAP server of  FIG. 1  according to one embodiment of the present disclosure; 
         FIG. 76  is a block diagram of one of the mobile devices of  FIG. 1  according to one embodiment of the present disclosure; 
         FIG. 77  is a block diagram of the subscriber device of  FIG. 1  according to one embodiment of the present disclosure; and 
         FIG. 78  is a block diagram of a computing device operating to host the third-party service of  FIG. 1  according to one embodiment of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     The embodiments set forth below represent the necessary information to enable those skilled in the art to practice the invention and illustrate the best mode of practicing the invention. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the invention and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure and the accompanying claims. 
       FIG. 1  illustrates a Mobile Aggregate Profile (MAP) system  10  according to one embodiment of the present disclosure. In this embodiment, the system  10  includes a MAP server  12 , one or more profile servers  14 , a location server  16 , a number of mobile devices  18 - 1  through  18 -N having associated users  20 - 1  through  20 -N, a subscriber device  22  having an associated subscriber  24 , and a third-party service  26  communicatively coupled via a network  28 . The network  28  may be any type of network or any combination of networks. Specifically, the network  28  may include wired components, wireless components, or both wired and wireless components. In one exemplary embodiment, the network  28  is a distributed public network such as the Internet, where the mobile devices  18 - 1  through  18 -N are enabled to connect to the network  28  via local wireless connections (e.g., WiFi or IEEE 802.11 connections) or wireless telecommunications connections (e.g., 3G or 4G telecommunications connections such as GSM, LTE, W-CDMA, or WiMAX connections). 
     As discussed below in detail, the MAP server  12  operates to obtain current locations, including location updates, and user profiles of the users  20 - 1  through  20 -N of the mobile devices  18 - 1  through  18 -N. The current locations of the users  20 - 1  through  20 -N can be expressed as positional geographic coordinates such as latitude-longitude pairs, and a height vector (if applicable), or any other similar information capable of identifying a given physical point in space in a two-dimensional or three-dimensional coordinate system. Using the current locations and user profiles of the users  20 - 1  through  20 -N, the MAP server  12  is enabled to provide a number of features such as, but not limited to, maintaining a historical record of anonymized user profile data by location, generating aggregate profile data over time for a Point of Interest (POI) or Area of Interest (AOI) using the historical record of anonymized user profile data, identifying crowds of users using current locations and/or user profiles of the users  20 - 1  through  20 -N, generating aggregate profiles for crowds of users at a POI or in an AOI using the current user profiles of users in the crowds, and crowd tracking. Note that while the MAP server  12  is illustrated as a single server for simplicity and ease of discussion, it should be appreciated that the MAP server  12  may be implemented as a single physical server or multiple physical servers operating in a collaborative manner for purposes of redundancy and/or load sharing. 
     In general, the one or more profile servers  14  operate to store user profiles for a number of persons including the users  20 - 1  through  20 -N of the mobile devices  18 - 1  through  18 -N. For example, the one or more profile servers  14  may be servers providing social network services such the Facebook® social networking service, the MySpace® social networking service, the LinkedIN® social networking service, or the like. As discussed below, using the one or more profile servers  14 , the MAP server  12  is enabled to directly or indirectly obtain the user profiles of the users  20 - 1  through  20 -N of the mobile devices  18 - 1  through  18 -N. The location server  16  generally operates to receive location updates from the mobile devices  18 - 1  through  18 -N and make the location updates available to entities such as, for instance, the MAP server  12 . In one exemplary embodiment, the location server  16  is a server operating to provide Yahoo!&#39;s FireEagle service. 
     The mobile devices  18 - 1  through  18 -N may be mobile smart phones, portable media player devices, mobile gaming devices, or the like. Some exemplary mobile devices that may be programmed or otherwise configured to operate as the mobile devices  18 - 1  through  18 -N are the Apple® iPhone, the Palm Pre, the Samsung Rogue, the Blackberry Storm, and the Apple® iPod Touch® device. However, this list of exemplary mobile devices is not exhaustive and is not intended to limit the scope of the present disclosure. 
     The mobile devices  18 - 1  through  18 -N include MAP clients  30 - 1  through  30 -N, MAP applications  32 - 1  through  32 -N, third-party applications  34 - 1  through  34 -N, and location functions  36 - 1  through  36 -N, respectively. Using the mobile device  18 - 1  as an example, the MAP client  30 - 1  is preferably implemented in software. In general, in the preferred embodiment, the MAP client  30 - 1  is a middleware layer operating to interface an application layer (i.e., the MAP application  32 - 1  and the third-party applications  34 - 1 ) to the MAP server  12 . More specifically, the MAP client  30 - 1  enables the MAP application  32 - 1  and the third-party applications  34 - 1  to request and receive data from the MAP server  12 . In addition, the MAP client  30 - 1  enables applications, such as the MAP application  32 - 1  and the third-party applications  34 - 1 , to access data from the MAP server  12 . For example, as discussed below in detail, the MAP client  30 - 1  enables the MAP application  32 - 1  to request anonymized aggregate profiles for crowds of users located at a POI or within an AOI and/or request anonymized historical user profile data for a POI or AOI. 
     The MAP application  32 - 1  is also preferably implemented in software. The MAP application  32 - 1  generally provides a user interface component between the user  20 - 1  and the MAP server  12 . More specifically, among other things, the MAP application  32 - 1  enables the user  20 - 1  to initiate historical requests for historical data or crowd requests for crowd data (e.g., aggregate profile data and/or crowd characteristics data) from the MAP server  12  for a POI or AOI. The MAP application  32 - 1  also enables the user  20 - 1  to configure various settings. For example, the MAP application  32 - 1  may enable the user  20 - 1  to select a desired social networking service (e.g., Facebook, MySpace, LinkedIN, etc.) from which to obtain the user profile of the user  20 - 1  and provide any necessary credentials (e.g., username and password) needed to access the user profile from the social networking service. 
     The third-party applications  34 - 1  are preferably implemented in software. The third-party applications  34 - 1  operate to access the MAP server  12  via the MAP client  30 - 1 . The third-party applications  34 - 1  may utilize data obtained from the MAP server  12  in any desired manner. As an example, one of the third party applications  34 - 1  may be a gaming application that utilizes historical aggregate profile data to notify the user  20 - 1  of POIs or AOIs where persons having an interest in the game have historically congregated. 
     The location function  36 - 1  may be implemented in hardware, software, or a combination thereof. In general, the location function  36 - 1  operates to determine or otherwise obtain the location of the mobile device  18 - 1 . For example, the location function  36 - 1  may be or include a Global Positioning System (GPS) receiver. 
     The subscriber device  22  is a physical device such as a personal computer, a mobile computer (e.g., a notebook computer, a netbook computer, a tablet computer, etc.), a mobile smart phone, or the like. The subscriber  24  associated with the subscriber device  22  is a person or entity. In general, the subscriber device  22  enables the subscriber  24  to access the MAP server  12  via a web browser  38  to obtain various types of data, preferably for a fee. For example, the subscriber  24  may pay a fee to have access to historical aggregate profile data for one or more POIs and/or one or more AOIs, pay a fee to have access to crowd data such as aggregate profiles for crowds located at one or more POIs and/or located in one or more AOIs, pay a fee to track crowds, or the like. Note that the web browser  38  is exemplary. In another embodiment, the subscriber device  22  is enabled to access the MAP server  12  via a custom application. 
     Lastly, the third-party service  26  is a service that has access to data from the MAP server  12  such as a historical aggregate profile data for one or more POIs or one or more AOIs, crowd data such as aggregate profiles for one or more crowds at one or more POIs or within one or more AOIs, or crowd tracking data. Based on the data from the MAP server  12 , the third-party service  26  operates to provide a service such as, for example, targeted advertising. For example, the third-party service  26  may obtain anonymous aggregate profile data for one or more crowds located at a POI and then provide targeted advertising to known users located at the POI based on the anonymous aggregate profile data. Note that while targeted advertising is mentioned as an exemplary third-party service  26 , other types of third-party services  26  may additionally or alternatively be provided. Other types of third-party services  26  that may be provided will be apparent to one of ordinary skill in the art upon reading this disclosure. 
     Before proceeding, it should be noted that while the system  10  of  FIG. 1  illustrates an embodiment where the one or more profile servers  14  and the location server  16  are separate from the MAP server  12 , the present disclosure is not limited thereto. In an alternative embodiment, the functionality of the one or more profile servers  14  and/or the location server  16  may be implemented within the MAP server  12 . 
       FIG. 2  is a block diagram of the MAP server  12  of  FIG. 1  according to one embodiment of the present disclosure. As illustrated, the MAP server  12  includes an application layer  40 , a business logic layer  42 , and a persistence layer  44 . The application layer  40  includes a user web application  46 , a mobile client/server protocol component  48 , and one or more data Application Programming Interfaces (APIs)  50 . The user web application  46  is preferably implemented in software and operates to provide a web interface for users, such as the subscriber  24 , to access the MAP server  12  via a web browser. The mobile client/server protocol component  48  is preferably implemented in software and operates to provide an interface between the MAP server  12  and the MAP clients  30 - 1  through  30 -N hosted by the mobile devices  18 - 1  through  18 -N. The data APIs  50  enable third-party services, such as the third-party service  26 , to access the MAP server  12 . 
     The business logic layer  42  includes a profile manager  52 , a location manager  54 , a history manager  56 , a crowd analyzer  58 , and an aggregation engine  60 , each of which is preferably implemented in software. The profile manager  52  generally operates to obtain the user profiles of the users  20 - 1  through  20 -N directly or indirectly from the one or more profile servers  14  and store the user profiles in the persistence layer  44 . The location manager  54  operates to obtain the current locations of the users  20 - 1  through  20 -N including location updates. As discussed below, the current locations of the users  20 - 1  through  20 -N may be obtained directly from the mobile devices  18 - 1  through  18 -N and/or obtained from the location server  16 . 
     The history manager  56  generally operates to maintain a historical record of anonymized user profile data by location. The crowd analyzer  58  operates to form crowds of users. In one embodiment, the crowd analyzer  58  utilizes a spatial crowd formation algorithm. However, the present disclosure is not limited thereto. In addition, the crowd analyzer  58  may further characterize crowds to reflect degree of fragmentation, best-case and worst-case degree of separation (DOS), and/or degree of bi-directionality, as discussed below in more detail. Still further, the crowd analyzer  58  may also operate to track crowds. The aggregation engine  60  generally operates to provide aggregate profile data in response to requests from the mobile devices  18 - 1  through  18 -N, the subscriber device  22 , and the third-party service  26 . The aggregate profile data may be historical aggregate profile data for one or more POIs or one or more AOIs or aggregate profile data for crowd(s) currently at one or more POIs or within one or more AOIs. 
     The persistence layer  44  includes an object mapping layer  62  and a datastore  64 . The object mapping layer  62  is preferably implemented in software. The datastore  64  is preferably a relational database, which is implemented in a combination of hardware (i.e., physical data storage hardware) and software (i.e., relational database software). In this embodiment, the business logic layer  42  is implemented in an object-oriented programming language such as, for example, Java. As such, the object mapping layer  62  operates to map objects used in the business logic layer  42  to relational database entities stored in the datastore  64 . Note that, in one embodiment, data is stored in the datastore  64  in a Resource Description Framework (RDF) compatible format. 
     In an alternative embodiment, rather than being a relational database, the datastore  64  may be implemented as an RDF datastore. More specifically, the RDF datastore may be compatible with RDF technology adopted by Semantic Web activities. Namely, the RDF datastore may use the Friend-Of-A-Friend (FOAF) vocabulary for describing people, their social networks, and their interests. In this embodiment, the MAP server  12  may be designed to accept raw FOAF files describing persons, their friends, and their interests. These FOAF files are currently output by some social networking services such as Livejournal and Facebook. The MAP server  12  may then persist RDF descriptions of the users  20 - 1  through  20 -N as a proprietary extension of the FOAF vocabulary that includes additional properties desired for the MAP system  10 . 
       FIG. 3  illustrates the MAP client  30 - 1  of  FIG. 1  in more detail according to one embodiment of the present disclosure. This discussion is equally applicable to the other MAP clients  30 - 2  through  30 -N. As illustrated, in this embodiment, the MAP client  30 - 1  includes a MAP access API  66 , a MAP middleware component  68 , and a mobile client/server protocol component  70 . The MAP access API  66  is implemented in software and provides an interface by which the MAP client  30 - 1  and the third-party applications  34 - 1  are enabled to access the MAP client  30 - 1 . The MAP middleware component  68  is implemented in software and performs the operations needed for the MAP client  30 - 1  to operate as an interface between the MAP application  32 - 1  and the third-party applications  34 - 1  at the mobile device  18 - 1  and the MAP server  12 . The mobile client/server protocol component  70  enables communication between the MAP client  30 - 1  and the MAP server  12  via a defined protocol. 
       FIG. 4  illustrates the operation of the system  10  of  FIG. 1  to provide the user profile of the user  20 - 1  of the mobile device  18 - 1  according to one embodiment of the present disclosure. This discussion is equally applicable to user profiles of the other users  20 - 2  through  20 -N of the other mobile devices  18 - 2  through  18 -N. First, an authentication process is performed (step  1000 ). For authentication, in this embodiment, the mobile device  18 - 1  authenticates with the profile server  14  (step  1000 A) and the MAP server  12  (step  1000 B). In addition, the MAP server  12  authenticates with the profile server  14  (step  1000 C). Preferably, authentication is preformed using OpenID or similar technology. However, authentication may alternatively be performed using separate credentials (e.g., username and password) of the user  20 - 1  for access to the MAP server  12  and the profile server  14 . Assuming that authentication is successful, the profile server  14  returns an authentication succeeded message to the MAP server  12  (step  1000 D), and the profile server  14  returns an authentication succeeded message to the MAP client  30 - 1  of the mobile device  18 - 1  (step  1000 E). 
     At some point after authentication is complete, a user profile process is performed such that a user profile of the user  20 - 1  is obtained from the profile server  14  and delivered to the MAP server  12  (step  1002 ). In this embodiment, the MAP client  30 - 1  of the mobile device  18 - 1  sends a profile request to the profile server  14  (step  1002 A). In response, the profile server  14  returns the user profile of the user  20 - 1  to the mobile device  18 - 1  (step  1002 B). The MAP client  30 - 1  of the mobile device  18 - 1  then sends the user profile of the user  20 - 1  to the MAP server  12  (step  1002 C). Note that while in this embodiment the MAP client  30 - 1  sends the complete user profile of the user  20 - 1  to the MAP server  12 , in an alternative embodiment, the MAP client  30 - 1  may filter the user profile of the user  20 - 1  according to criteria specified by the user  20 - 1 . For example, the user profile of the user  20 - 1  may include demographic information, general interests, music interests, and movie interests, and the user  20 - 1  may specify that the demographic information or some subset thereof is to be filtered, or removed, before sending the user profile to the MAP server  12 . 
     Upon receiving the user profile of the user  20 - 1  from the MAP client  30 - 1  of the mobile device  18 - 1 , the profile manager  52  of the MAP server  12  processes the user profile (step  1002 D). More specifically, in the preferred embodiment, the profile manager  52  includes social network handlers for the social network services supported by the MAP server  12 . Thus, for example, if the MAP server  12  supports user profiles from Facebook, MySpace, and LinkedIN, the profile manager  52  may include a Facebook handler, a MySpace handler, and a LinkedIN handler. The social network handlers process user profiles to generate user profiles for the MAP server  12  that include lists of keywords for each of a number of profile categories. The profile categories may be the same for each of the social network handlers or different for each of the social network handlers. Thus, for this example assume that the user profile of the user  20 - 1  is from Facebook. The profile manager  52  uses a Facebook handler to process the user profile of the user  20 - 1  to map the user profile of the user  20 - 1  from Facebook to a user profile for the MAP server  12  including lists of keywords for a number of predefined profile categories. For example, for the Facebook handler, the profile categories may be a demographic profile category, a social interaction profile category, a general interests profile category, a music interests profile category, and a movie interests profile category. As such, the user profile of the user  20 - 1  from Facebook may be processed by the Facebook handler of the profile manager  52  to create a list of keywords such as, for example, liberal, High School Graduate, 35-44, College Graduate, etc. for the demographic profile category, a list of keywords such as Seeking Friendship for the social interaction profile category, a list of keywords such as politics, technology, photography, books, etc. for the general interests profile category, a list of keywords including music genres, artist names, album names, or the like for the music interests profile category, and a list of keywords including movie titles, actor or actress names, director names, move genres, or the like for the movie interests profile category. In one embodiment, the profile manager  52  may use natural language processing or semantic analysis. For example, if the Facebook user profile of the user  20 - 1  states that the user  20 - 1  is 20 years old, semantic analysis may result in the keyword of 18-24 years old being stored in the user profile of the user  20 - 1  for the MAP server  12 . 
     After processing the user profile of the user  20 - 1 , the profile manager  52  of the MAP server  12  stores the resulting user profile for the user  20 - 1  (step  1002 E). More specifically, in one embodiment, the MAP server  12  stores user records for the users  20 - 1  through  20 -N in the datastore  64  ( FIG. 2 ). The user profile of the user  20 - 1  is stored in the user record of the user  20 - 1 . The user record of the user  20 - 1  includes a unique identifier of the user  20 - 1 , the user profile of the user  20 - 1 , and, as discussed below, a current location of the user  20 - 1 . Note that the user profile of the user  20 - 1  may be updated as desired. For example, in one embodiment, the user profile of the user  20 - 1  is updated by repeating step  1002  each time the user  20 - 1  activates the MAP application  32 - 1 . 
     Note that the while the discussion herein focuses on an embodiment where the user profiles of the users  20 - 1  through  20 -N are obtained from the one or more profile servers  14 , the user profiles of the users  20 - 1  through  20 -N may be obtained in any desired manner. For example, in one alternative embodiment, the user  20 - 1  may identify one or more favorite websites. The profile manager  52  of the MAP server  12  may then crawl the one or more favorite websites of the user  20 - 1  to obtain keywords appearing in the one or more favorite websites of the user  20 - 1 . These keywords may then be stored as the user profile of the user  20 - 1 . 
     At some point, a process is performed such that a current location of the mobile device  18 - 1  and thus a current location of the user  20 - 1  is obtained by the MAP server  12  (step  1004 ). In this embodiment, the MAP application  32 - 1  of the mobile device  18 - 1  obtains the current location of the mobile device  18 - 1  from the location function  36 - 1  of the mobile device  18 - 1 . The MAP application  32 - 1  then provides the current location of the mobile device  18 - 1  to the MAP client  30 - 1 , and the MAP client  30 - 1  then provides the current location of the mobile device  18 - 1  to the MAP server  12  (step  1004 A). Note that step  1004 A may be repeated periodically or in response to a change in the current location of the mobile device  18 - 1  in order for the MAP application  32 - 1  to provide location updates for the user  20 - 1  to the MAP server  12 . 
     In response to receiving the current location of the mobile device  18 - 1 , the location manager  54  of the MAP server  12  stores the current location of the mobile device  18 - 1  as the current location of the user  20 - 1  (step  1004 B). More specifically, in one embodiment, the current location of the user  20 - 1  is stored in the user record of the user  20 - 1  maintained in the datastore  64  of the MAP server  12 . Note that only the current location of the user  20 - 1  is stored in the user record of the user  20 - 1 . In this manner, the MAP server  12  maintains privacy for the user  20 - 1  since the MAP server  12  does not maintain a historical record of the location of the user  20 - 1 . As discussed below in detail, historical data maintained by the MAP server  12  is anonymized in order to maintain the privacy of the users  20 - 1  through  20 -N. 
     In addition to storing the current location of the user  20 - 1 , the location manager  54  sends the current location of the user  20 - 1  to the location server  16  (step  1004 C). In this embodiment, by providing location updates to the location server  16 , the MAP server  12  in return receives location updates for the user  20 - 1  from the location server  16 . This is particularly beneficial when the mobile device  18 - 1  does not permit background processes, which is the case for the Apple® iPhone. As such, if the mobile device  18 - 1  is an Apple® iPhone or similar device that does not permit background processes, the MAP application  32 - 1  will not be able to provide location updates for the user  20 - 1  to the MAP server  12  unless the MAP application  32 - 1  is active. 
     Therefore, when the MAP application  32 - 1  is not active, other applications running on the mobile device  18 - 1  (or some other device of the user  20 - 1 ) may directly or indirectly provide location updates to the location server  16  for the user  20 - 1 . This is illustrated in step  1006  where the location server  16  receives a location update for the user  20 - 1  directly or indirectly from another application running on the mobile device  18 - 1  or an application running on another device of the user  20 - 1  (step  1006 A). The location server  16  then provides the location update for the user  20 - 1  to the MAP server  12  (step  1006 B). In response, the location manager  54  updates and stores the current location of the user  20 - 1  in the user record of the user  20 - 1  (step  1006 C). In this manner, the MAP server  12  is enabled to obtain location updates for the user  20 - 1  even when the MAP application  32 - 1  is not active at the mobile device  18 - 1 . 
       FIG. 5  illustrates the operation of the system  10  of  FIG. 1  to provide the user profile of the user  20 - 1  of the mobile device  18 - 1  according to another embodiment of the present disclosure. This discussion is equally applicable to user profiles of the other users  20 - 2  through  20 -N of the other mobile devices  18 - 2  through  18 -N. First, an authentication process is performed (step  1100 ). For authentication, in this embodiment, the mobile device  18 - 1  authenticates with the MAP server  12  (step  1100 A), and the MAP server  12  authenticates with the profile server  14  (step  1100 B). Preferably, authentication is performed using OpenID or similar technology. However, authentication may alternatively be performed using separate credentials (e.g., username and password) of the user  20 - 1  for access to the MAP server  12  and the profile server  14 . Assuming that authentication is successful, the profile server  14  returns an authentication succeeded message to the MAP server  12  (step  1100 C), and the MAP server  12  returns an authentication succeeded message to the MAP client  30 - 1  of the mobile device  18 - 1  (step  1100 D). 
     At some point after authentication is complete, a user profile process is performed such that a user profile of the user  20 - 1  is obtained from the profile server  14  and delivered to the MAP server  12  (step  1102 ). In this embodiment, the profile manager  52  of the MAP server  12  sends a profile request to the profile server  14  (step  1102 A). In response, the profile server  14  returns the user profile of the user  20 - 1  to the profile manager  52  of the MAP server  12  (step  1102 B). Note that while in this embodiment the profile server  14  returns the complete user profile of the user  20 - 1  to the MAP server  12 , in an alternative embodiment, the profile server  14  may return a filtered version of the user profile of the user  20 - 1  to the MAP server  12 . The profile server  14  may filter the user profile of the user  20 - 1  according to criteria specified by the user  20 - 1 . For example, the user profile of the user  20 - 1  may include demographic information, general interests, music interests, and movie interests, and the user  20 - 1  may specify that the demographic information or some subset thereof is to be filtered, or removed, before sending the user profile to the MAP server  12 . 
     Upon receiving the user profile of the user  20 - 1 , the profile manager  52  of the MAP server  12  processes to the user profile (step  1102 C). More specifically, as discussed above, in the preferred embodiment, the profile manager  52  includes social network handlers for the social network services supported by the MAP server  12 . The social network handlers process user profiles to generate user profiles for the MAP server  12  that include lists of keywords for each of a number of profile categories. The profile categories may be the same for each of the social network handlers or different for each of the social network handlers. 
     After processing the user profile of the user  20 - 1 , the profile manager  52  of the MAP server  12  stores the resulting user profile for the user  20 - 1  (step  1102 D). More specifically, in one embodiment, the MAP server  12  stores user records for the users  20 - 1  through  20 -N in the datastore  64  ( FIG. 2 ). The user profile of the user  20 - 1  is stored in the user record of the user  20 - 1 . The user record of the user  20 - 1  includes a unique identifier of the user  20 - 1 , the user profile of the user  20 - 1 , and, as discussed below, a current location of the user  20 - 1 . Note that the user profile of the user  20 - 1  may be updated as desired. For example, in one embodiment, the user profile of the user  20 - 1  is updated by repeating step  1102  each time the user  20 - 1  activates the MAP application  32 - 1 . 
     Note that the while the discussion herein focuses on an embodiment where the user profiles of the users  20 - 1  through  20 -N are obtained from the one or more profile servers  14 , the user profiles of the users  20 - 1  through  20 -N may be obtained in any desired manner. For example, in one alternative embodiment, the user  20 - 1  may identify one or more favorite websites. The profile manager  52  of the MAP server  12  may then crawl the one or more favorite websites of the user  20 - 1  to obtain keywords appearing in the one or more favorite websites of the user  20 - 1 . These keywords may then be stored as the user profile of the user  20 - 1 . 
     At some point, a process is performed such that a current location of the mobile device  18 - 1  and thus a current location of the user  20 - 1  is obtained by the MAP server  12  (step  1104 ). In this embodiment, the MAP application  32 - 1  of the mobile device  18 - 1  obtains the current location of the mobile device  18 - 1  from the location function  36 - 1  of the mobile device  18 - 1 . The MAP application  32 - 1  then provides the current location of the user  20 - 1  of the mobile device  18 - 1  to the location server  16  (step  1104 A). Note that step  1104 A may be repeated periodically or in response to changes in the location of the mobile device  18 - 1  in order to provide location updates for the user  20 - 1  to the MAP server  12 . The location server  16  then provides the current location of the user  20 - 1  to the MAP server  12  (step  1104 B). The location server  16  may provide the current location of the user  20 - 1  to the MAP server  12  automatically in response to receiving the current location of the user  20 - 1  from the mobile device  18 - 1  or in response to a request from the MAP server  12 . 
     In response to receiving the current location of the mobile device  18 - 1 , the location manager  54  of the MAP server  12  stores the current location of the mobile device  18 - 1  as the current location of the user  20 - 1  (step  1104 C). More specifically, in one embodiment, the current location of the user  20 - 1  is stored in the user record of the user  20 - 1  maintained in the datastore  64  of the MAP server  12 . Note that only the current location of the user  20 - 1  is stored in the user record of the user  20 - 1 . In this manner, the MAP server  12  maintains privacy for the user  20 - 1  since the MAP server  12  does not maintain a historical record of the location of the user  20 - 1 . As discussed below in detail, historical data maintained by the MAP server  12  is anonymized in order to maintain the privacy of the users  20 - 1  through  20 -N. 
     As discussed above, the use of the location server  16  is particularly beneficial when the mobile device  18 - 1  does not permit background processes, which is the case for the Apple® iPhone. As such, if the mobile device  18 - 1  is an Apple® iPhone or similar device that does not permit background processes, the MAP application  32 - 1  will not provide location updates for the user  20 - 1  to the location server  16  unless the MAP application  32 - 1  is active. However, other applications running on the mobile device  18 - 1  (or some other device of the user  20 - 1 ) may provide location updates to the location server  16  for the user  20 - 1  when the MAP application  32 - 1  is not active. This is illustrated in step  1106  where the location server  16  receives a location update for the user  20 - 1  from another application running on the mobile device  18 - 1  or an application running on another device of the user  20 - 1  (step  1106 A). The location server  16  then provides the location update for the user  20 - 1  to the MAP server  12  (step  1106 B). In response, the location manager  54  updates and stores the current location of the user  20 - 1  in the user record of the user  20 - 1  (step  1106 C). In this manner, the MAP server  12  is enabled to obtain location updates for the user  20 - 1  even when the MAP application  32 - 1  is not active at the mobile device  18 - 1 . 
     Using the current locations of the users  20 - 1  through  20 -N and the user profiles of the users  20 - 1  through  20 -N, the MAP server  12  can provide a number of features. A first feature that may be provided by the MAP server  12  is historical storage of anonymized user profile data by location. This historical storage of anonymized user profile data by location is performed by the history manager  56  of the MAP server  12 . More specifically, as illustrated in  FIG. 6 , in the preferred embodiment, the history manager  56  maintains lists of users located in a number of geographic regions, or “location buckets.” Preferably, the location buckets are defined by floor(latitude, longitude) to a desired resolution. The higher the resolution, the smaller the size of the location buckets. For example, in one embodiment, the location buckets are defined by floor(latitude, longitude) to a resolution of 1/10,000 th  of a degree such that the lower left-hand corners of the squares illustrated in  FIG. 6  are defined by the floor(latitude, longitude) values at a resolution of 1/10,000 th  of a degree. In the example of  FIG. 6 , users are represented as dots, and location buckets  72  through  88  have lists of 1, 3, 2, 1, 1, 2, 1, 2, and 3 users, respectively. 
     As discussed below in detail, at a predetermined time interval such as, for example, 15 minutes, the history manager  56  makes a copy of the lists of users in the location buckets, anonymizes the user profiles of the users in the lists to provide anonymized user profile data for the corresponding location buckets, and stores the anonymized user profile data in a number of history objects. In one embodiment, a history object is stored for each location bucket having at least one user. In another embodiment, a quadtree algorithm is used to efficiently create history objects for geographic regions (i.e., groups of one or more adjoining location buckets). 
       FIG. 7  graphically illustrates a scenario where a user moves from one location bucket to another, namely, from the location bucket  74  to the location bucket  76 . As discussed below in detail, assuming that the movement occurs during the time interval between persistence of the historical data by the history manager  56 , the user is included on both the list for the location bucket  74  and the list for the location bucket  76 . However, the user is flagged or otherwise marked as inactive for the location bucket  74  and active for the location bucket  76 . As discussed below, after making a copy of the lists for the location buckets to be used to persist the historical data, users flagged as inactive are removed from the lists of users for the location buckets. Thus, in sum, once a user moves from the location bucket  74  to the location bucket  76 , the user remains in the list for the location bucket  74  until the predetermined time interval has expired and the anonymized user profile data is persisted. The user is then removed from the list for the location bucket  74 . 
       FIG. 8  is a flow chart illustrating the operation of a foreground “bucketization,” or sorting, process performed by the history manager  56  to maintain the lists of users for location buckets according to one embodiment of the present disclosure. First, the history manager  56  receives a location update for a user (step  1200 ). For this discussion, assume that the location update is received for the user  20 - 1 . The history manager  56  then determines a location bucket corresponding to the updated location (i.e., the current location) of the user  20 - 1  (step  1202 ). In the preferred embodiment, the location of the user  20 - 1  is expressed as latitude and longitude coordinates, and the history manager  56  determines the location bucket by determining floor values of the latitude and longitude coordinates, which can be written as floor(latitude, longitude) at a desired resolution. As an example, if the latitude and longitude coordinates for the location of the user  20 - 1  are 32.24267381553987 and −111.9249213502935, respectively, and the floor values are to be computed to a resolution of 1/10,000 th  of a degree, then the floor values for the latitude and longitude coordinates are 32.2426 and −111.9249. The floor values for the latitude and longitude coordinates correspond to a particular location bucket. 
     After determining the location bucket for the location of the user  20 - 1 , the history manager  56  determines whether the user  20 - 1  is new to the location bucket (step  1204 ). In other words, the history manager  56  determines whether the user  20 - 1  is already on the list of users for the location bucket. If the user  20 - 1  is new to the location bucket, the history manager  56  creates an entry for the user  20 - 1  in the list of users for the location bucket (step  1206 ). Returning to step  1204 , if the user  20 - 1  is not new to the location bucket, the history manager  56  updates the entry for the user  20 - 1  in the list of users for the location bucket (step  1208 ). At this point, whether proceeding from step  1206  or  1208 , the user  20 - 1  is flagged as active in the list of users for the location bucket (step  1210 ). 
     The history manager  56  then determines whether the user  20 - 1  has moved from another location bucket (step  1212 ). More specifically, the history manager  56  determines whether the user  20 - 1  is included in the list of users for another location bucket and is currently flagged as active in that list. If the user  20 - 1  has not moved from another location bucket, the process proceeds to step  1216 . If the user  20 - 1  has moved from another location bucket, the history manager  56  flags the user  20 - 1  as inactive in the list of users for the other location bucket from which the user  20 - 1  has moved (step  1214 ). 
     At this point, whether proceeding from step  1212  or  1214 , the history manager  56  determines whether it is time to persist (step  1216 ). More specifically, as mentioned above, the history manager  56  operates to persist history objects at a predetermined time interval such as, for example, every 15 minutes. Thus, the history manager  56  determines that it is time to persist if the predetermined time interval has expired. If it is not time to persist, the process returns to step  1200  and is repeated for a next received location update, which will typically be for another user. If it is time to persist, the history manager  56  creates a copy of the lists of users for the location buckets and passes the copy of the lists to an anonymization and storage process (step  1218 ). In this embodiment, the anonymization, or anonymizing, and storage process is a separate process performed by the history manager  56 . The history manager  56  then removes inactive users from the lists of users for the location buckets (step  1220 ). The process then returns to step  300  and is repeated for a next received location update, which will typically be for another user. 
       FIG. 9  is a flow chart illustrating the anonymization and storage process performed by the history manager  56  at the predetermined time interval according to one embodiment of the present disclosure. First, the anonymization and storage process receives the copy of the lists of users for the location buckets passed to the anonymization and storage process by the bucketization process of  FIG. 8  (step  1300 ). Next, anonymization is performed for each of the location buckets having at least one user in order to provide anonymized user profile data for the location buckets (step  1302 ). Anonymization prevents connecting information stored in the history objects stored by the history manager  56  back to the users  20 - 1  through  20 -N or at least substantially increases a difficulty of connecting information stored in the history objects stored by the history manager  56  back to the users  20 - 1  through  20 -N. Lastly, the anonymized user profile data for the location buckets is stored in a number of history objects (step  1304 ). In one embodiment, a separate history object is stored for each of the location buckets, where the history object of a location bucket includes the anonymized user profile data for the location bucket. In another embodiment, as discussed below, a quadtree algorithm is used to efficiently store the anonymized user profile data in a number of history objects such that each history object stores the anonymized user profile data for one or more location buckets. 
       FIG. 10  graphically illustrates one embodiment of the anonymization process of step  1302  of  FIG. 9 . In this embodiment, anonymization is performed by creating anonymous user records for the users in the lists of users for the location buckets. The anonymous user records are not connected back to the users  20 - 1  through  20 -N. More specifically, as illustrated in  FIG. 10 , each user in the lists of users for the location buckets has a corresponding user record  90 . The user record  90  includes a unique user identifier (ID) for the user, the current location of the user, and the user profile of the user. The user profile includes keywords for each of a number of profile categories, which are stored in corresponding profile category records  92 - 1  through  92 -M. Each of the profile category records  92 - 1  through  92 -M includes a user ID for the corresponding user which may be the same user ID used in the user record  90 , a category ID, and a list of keywords for the profile category. 
     For anonymization, an anonymous user record  94  is created from the user record  90 . In the anonymous user record  94 , the user ID is replaced with a new user ID that is not connected back to the user, which is also referred to herein as an anonymous user ID. This new user ID is different than any other user ID used for anonymous user records created from the user record of the user for any previous or subsequent time periods. In this manner, anonymous user records for a single user created over time cannot be linked to one another. 
     In addition, anonymous profile category records  96 - 1  through  96 -M are created for the profile category records  92 - 1  through  92 -M. In the anonymous profile category records  96 - 1  through  96 -M, the user ID is replaced with a new user ID, which may be the same new user ID included in the anonymous user record  94 . The anonymous profile category records  96 - 1  through  96 -M include the same category IDs and lists of keywords as the corresponding profile category records  92 - 1  through  92 -M. Note that the location of the user is not stored in the anonymous user record  94 . With respect to location, it is sufficient that the anonymous user record  94  is linked to a location bucket. 
     In another embodiment, the history manager  56  performs anonymization in a manner similar to that described above with respect to  FIG. 10 . However, in this embodiment, the profile category records for the group of users in a location bucket, or the group of users in a number of location buckets representing a node in a quadtree data structure (see below), may be selectively randomized among the anonymous user records of those users. In other words, each anonymous user record would have a user profile including a selectively randomized set of profile category records (including keywords) from a cumulative list of profile category records for all of the users in the group. 
     In yet another embodiment, rather than creating anonymous user records  94  for the users in the lists maintained for the location buckets, the history manager  56  may perform anonymization by storing an aggregate user profile for each location bucket, or each group of location buckets representing a node in a quadtree data structure (see below). The aggregate user profile may include a list of all keywords and potentially the number of occurrences of each keyword in the user profiles of the corresponding group of users. In this manner, the data stored by the history manager  56  is not connected back to the users  20 - 1  through  20 -N. 
       FIG. 11  is a flow chart illustrating the storing step (step  1304 ) of  FIG. 9  in more detail according to one embodiment of the present disclosure. First, the history manager  56  processes the location buckets using a quadtree algorithm to produce a quadtree data structure, where each node of the quadtree data structure includes one or more of the location buckets having a combined number of users that is at most a predefined maximum number of users (step  1400 ). The history manager  56  then stores a history object for each node in the quadtree data structure having at least one user (step  1402 ). 
     Each history object includes location information, timing information, data, and quadtree data structure information. The location information included in the history object defines a combined geographic area of the location bucket(s) forming the corresponding node of the quadtree data structure. For example, the location information may be latitude and longitude coordinates for a northeast corner of the combined geographic area of the node of the quadtree data structure and a southwest corner of the combined geographic area for the node of the quadtree data structure. The timing information includes information defining a time window for the history object, which may be, for example, a start time for the corresponding time interval and an end time for the corresponding time interval. The data includes the anonymized user profile data for the users in the list(s) maintained for the location bucket(s) forming the node of the quadtree data structure for which the history object is stored. In addition, the data may include a total number of users in the location bucket(s) forming the node of the quadtree data structure. Lastly, the quadtree data structure information includes information defining a quadtree depth of the node in the quadtree data structure. 
       FIG. 12  is a flow chart illustrating a quadtree algorithm that may be used to process the location buckets to form the quadtree data structure in step  1400  of  FIG. 11  according to one embodiment of the present disclosure. Initially, a geographic area served by the MAP server  12  is divided into a number of geographic regions, each including multiple location buckets. These geographic regions are also referred to herein as base quadtree regions. The geographic area served by the MAP server  12  may be, for example, a city, a state, a country, or the like. Further, the geographic area may be the only geographic area served by the MAP server  12  or one of a number of geographic areas served by the MAP server  12 . Preferably, the base quadtree regions have a size of 2 n ×2 n  location buckets, where n is an integer greater than or equal to 1. 
     In order to form the quadtree data structure, the history manager  56  determines whether there are any more base quadtree regions to process (step  1500 ). If there are more base quadtree regions to process, the history manager  56  sets a current node to the next base quadtree region to process, which for the first iteration is the first base quadtree region (step  1502 ). The history manager  56  then determines whether the number of users in the current node is greater than a predefined maximum number of users and whether a current quadtree depth is less than a maximum quadtree depth (step  1504 ). In one embodiment, the maximum quadtree depth may be reached when the current node corresponds to a single location bucket. However, the maximum quadtree depth may be set such that the maximum quadtree depth is reached before the current node reaches a single location bucket. 
     If the number of users in the current node is greater than the predefined maximum number of users and the current quadtree depth is less than a maximum quadtree depth, the history manager  56  creates a number of child nodes for the current node (step  1506 ). More specifically, the history manager  56  creates a child node for each quadrant of the current node. The users in the current node are then assigned to the appropriate child nodes based on the location buckets in which the users are located (step  1508 ), and the current node is then set to the first child node (step  1510 ). At this point, the process returns to step  1504  and is repeated. 
     Once the number of users in the current node is not greater than the predefined maximum number of users or the maximum quadtree depth has been reached, the history manager  56  determines whether the current node has any more sibling nodes (step  1512 ). Sibling nodes are child nodes of the same parent node. If so, the history manager  56  sets the current node to the next sibling node of the current node (step  1514 ), and the process returns to step  1504  and is repeated. Once there are no more sibling nodes to process, the history manager  56  determines whether the current node has a parent node (step  1516 ). If so, since the parent node has already been processed, the history manager  56  determines whether the parent node has any sibling nodes that need to be processed (step  1518 ). If the parent node has any sibling nodes that need to be processed, the history manager  56  sets the next sibling node of the parent node to be processed as the current node (step  1520 ). From this point, the process returns to step  1504  and is repeated. Returning to step  1516 , if the current node does not have a parent node, the process returns to step  1500  and is repeated until there are no more base quadtree regions to process. Once there are no more base quadtree regions to process, the finished quadtree data structure is returned to the process of  FIG. 11  such that the history manager  56  can then store the history objects for nodes in the quadtree data structure having at least one user (step  1522 ). 
       FIGS. 13A through 13E  graphically illustrate the process of  FIG. 12  for the generation of the quadtree data structure for one exemplary base quadtree region  98 .  FIG. 13A  illustrates the base quadtree region  98 . As illustrated, the base quadtree region  98  is an 8×8 square of location buckets, where each of the small squares represents a location bucket. First, the history manager  56  determines whether the number of users in the base quadtree region  98  is greater than the predetermined maximum number of users. In this example, the predetermined maximum number of users is 3. Since the number of users in the base quadtree region  98  is greater than 3, the history manager  56  divides the base quadtree region  98  into four child nodes  100 - 1  through  100 - 4 , as illustrated in  FIG. 13B . 
     Next, the history manager  56  determines whether the number of users in the child node  100 - 1  is greater than the predetermined maximum, which again for this example is 3. Since the number of users in the child node  100 - 1  is greater than 3, the history manager  56  divides the child node  100 - 1  into four child nodes  102 - 1  through  102 - 4 , as illustrated in  FIG. 13C . The child nodes  102 - 1  through  102 - 4  are children of the child node  100 - 1 . The history manager  56  then determines whether the number of users in the child node  102 - 1  is greater than the predetermined maximum number of users, which again is 3. Since there are more than 3 users in the child node  102 - 1 , the history manager  56  further divides the child node  102 - 1  into four child nodes  104 - 1  through  104 -N, as illustrated in  FIG. 13D . 
     The history manager  56  then determines whether the number of users in the child node  104 - 1  is greater than the predetermined maximum number of users, which again is 3. Since the number of users in the child node  104 - 1  is not greater than the predetermined maximum number of users, the child node  104 - 1  is identified as a node for the finished quadtree data structure, and the history manager  56  proceeds to process the sibling nodes of the child node  104 - 1 , which are the child nodes  104 - 2  through  104 - 4 . Since the number of users in each of the child nodes  104 - 2  through  104 - 4  is less than the predetermined maximum number of users, the child nodes  104 - 2  through  104 - 4  are also identified as nodes for the finished quadtree data structure. 
     Once the history manager  56  has finished processing the child nodes  104 - 1  through  104 - 4 , the history manager  56  identifies the parent node of the child nodes  104 - 1  through  104 - 4 , which in this case is the child node  102 - 1 . The history manager  56  then processes the sibling nodes of the child node  102 - 1 , which are the child nodes  102 - 2  through  102 - 4 . In this example, the number of users in each of the child nodes  102 - 2  through  102 - 4  is less than the predetermined maximum number of users. As such, the child nodes  102 - 2  through  102 - 4  are identified as nodes for the finished quadtree data structure. 
     Once the history manager  56  has finished processing the child nodes  102 - 1  through  102 - 4 , the history manager  56  identifies the parent node of the child nodes  102 - 1  through  102 - 4 , which in this case is the child node  100 - 1 . The history manager  56  then processes the sibling nodes of the child node  100 - 1 , which are the child nodes  100 - 2  through  100 - 4 . More specifically, the history manager  56  determines that the child node  100 - 2  includes more than the predetermined maximum number of users and, as such, divides the child node  100 - 2  into four child nodes  106 - 1  through  106 - 4 , as illustrated in  FIG. 13E . Because the number of users in each of the child nodes  106 - 1  through  106 - 4  is not greater than the predetermined maximum number of users, the child nodes  106 - 1  through  106 - 4  are identified as nodes for the finished quadtree data structure. Then, the history manager  56  proceeds to process the child nodes  100 - 3  and  100 - 4 . Since the number of users in each of the child nodes  100 - 3  and  100 - 4  is not greater than the predetermined maximum number of users, the child nodes  100 - 3  and  100 - 4  are identified as nodes for the finished quadtree data structure. Thus, at completion, the quadtree data structure for the base quadtree region  98  includes the child nodes  104 - 1  through  104 - 4 , the child nodes  102 - 2  through  102 - 4 , the child nodes  106 - 1  through  106 - 4 , and the child nodes  100 - 3  and  100 - 4 , as illustrated in  FIG. 13E . 
     As discussed above, the history manager  56  stores a history object for each of the nodes in the quadtree data structure including at least one user. As such, in this example, the history manager  56  stores history objects for the child nodes  104 - 2  and  104 - 3 , the child nodes  102 - 2  and  102 - 4 , the child nodes  106 - 1  and  106 - 4 , and the child node  100 - 3 . However, no history objects are stored for the nodes that do not have any users (i.e., the child nodes  104 - 1  and  104 - 4 , the child node  102 - 3 , the child nodes  106 - 2  and  106 - 3 , and the child node  100 - 4 ). 
       FIG. 14  illustrates the operation of the system  10  of  FIG. 1  wherein a mobile device is enabled to request and receive historical data from the MAP server  12  according to one embodiment of the present disclosure. As illustrated, in this embodiment, the MAP application  32 - 1  of the mobile device  18 - 1  sends a historical request to the MAP client  30 - 1  of the mobile device  18 - 1  (step  1600 ). In one embodiment, the historical request identifies either a POI or an AOI and a time window. A POI is a geographic point whereas an AOI is a geographic area. In one embodiment, the historical request is for a POI and a time window, where the POI is a POI corresponding to the current location of the user  20 - 1 , a POI selected from a list of POIs defined by the user  20 - 1  of the mobile device  18 - 1 , a POI selected from a list of POIs defined by the MAP application  32 - 1  or the MAP server  12 , a POI selected by the user  20 - 1  from a map, a POI implicitly defined via a separate application (e.g., POI is implicitly defined as the location of the nearest Starbucks coffee house in response to the user  20 - 1  performing a Google search for “Starbucks”), or the like. If the POI is selected from a list of POIs, the list of POIs may include static POIs which may be defined by street addresses or latitude and longitude coordinates, dynamic POIs which may be defined as the current locations of one or more friends of the user  20 - 1 , or both. 
     In another embodiment, the historical request is for an AOI and a time window, where the AOI may be an AOI of a geographic area of a predefined shape and size centered at the current location of the user  20 - 1 , an AOI selected from a list of AOIs defined by the user  20 - 1 , an AOI selected from a list of AOIs defined by the MAP application  32 - 1  or the MAP server  12 , an AOI selected by the user  20 - 1  from a map, an AOI implicitly defined via a separate application (e.g., AOI is implicitly defined as an area of a predefined shape and size centered at the location of the nearest Starbucks coffee house in response to the user  20 - 1  performing a Google search for “Starbucks”), or the like. If the AOI is selected from a list of AOIs, the list of AOIs may include static AOIs, dynamic AOIs which may be defined as areas of a predefined shape and size centered at the current locations of one or more friends of the user  20 - 1 , or both. Note that the POI or AOI of the historical request may be selected by the user  20 - 1  via the MAP application  32 - 1 . In yet another embodiment, the MAP application  32 - 1  automatically uses the current location of the user  20 - 1  as the POI or as a center point for an AOI of a predefined shape and size. 
     The time window for the historical request may be relative to the current time. For example, the time window may be the last hour, the last day, the last week, the last month, or the like. Alternatively, the time window may be an arbitrary time window selected by the user  20 - 1  such as, for example, yesterday from 7 pm-9 pm, last Friday, last week, or the like. Note that while in this example the historical request includes a single POI or AOI and a single time window, the historical request may include multiple POIs or AOIs and/or multiple time windows. 
     In one embodiment, the historical request is made in response to user input from the user  20 - 1  of the mobile device  18 - 1 . For instance, in one embodiment, the user  20 - 1  selects either a POI or an AOI and a time window and then instructs the MAP application  32 - 1  to make the historical request by, for example, selecting a corresponding button on a graphical user interface. In another embodiment, the historical request is made automatically in response to some event such as, for example, opening the MAP application  32 - 1 . 
     Upon receiving the historical request from the MAP application  32 - 1 , the MAP client  30 - 1  forwards the historical request to the MAP server  12  (step  1602 ). Note that the MAP client  30 - 1  may, in some cases, process the historical request from the MAP application  32 - 1  before forwarding the historical request to the MAP server  12 . For example, if the historical request from the MAP application  32 - 1  is for multiple POIs/AOIs and/or for multiple time windows, the MAP client  30 - 1  may process the historical request from the MAP application  32 - 1  to produce multiple historical requests to be sent to the MAP server  12 . For instance, a separate historical request may be produced for each POI/AOI and time window combination. However, for this discussion, the historical request is for a single POI or AOI for a single time window. 
     Upon receiving the historical request from the MAP client  30 - 1 , the MAP server  12  processes the historical request (step  1604 ). More specifically, the historical request is processed by the history manager  56  of the MAP server  12 . First, the history manager  56  obtains history objects that are relevant to the historical request from the datastore  64  of the MAP server  12 . The relevant history objects are those recorded for locations relevant to the POI or AOI and the time window for the historical request. The history manager  56  then processes the relevant history objects to provide historical aggregate profile data for the POI or AOI in a time context and/or a geographic context. In this embodiment, the historical aggregate profile data is based on the user profiles of the anonymous user records in the relevant history objects as compared to the user profile of the user  20 - 1  or a select subset thereof. In another embodiment, the historical aggregate profile data is based on the user profiles of the anonymous user records in the relevant history objects as compared to a target user profile defined or otherwise specified by the user  20 - 1 . 
     As discussed below in detail, for the time context, the history manager  56  divides the time window for the historical request into a number of time bands. Each time band is a fragment of the time window. Then, for each time band, the history manager  56  identifies a subset of the relevant history objects that are relevant to the time band (i.e., history objects recorded for time periods within the time band or that overlap the time band) and generates an aggregate profile for each of those history objects based on the user profiles of the anonymous user records in the history objects and the user profile, or a select subset of the user profile, of the user  20 - 1 . Then, the history manager  56  averages or otherwise combines the aggregate profiles for the history objects relevant to the time band. The resulting data for the time bands forms historical aggregate profile data that is to be returned to the MAP client  30 - 1 , as discussed below. 
     For the geographic context, the history manager  56  generates an average aggregate profile for each of a number of grids surrounding the POI or within the AOI. More specifically, history objects relevant to the POI or the AOI and the time window of the historical request are obtained. Then, the user profiles of the anonymous users in the relevant history objects are used to generate average aggregate profiles for a number of grids, or geographic regions, at or surrounding the POI or the AOI. These average aggregate profiles for the grids form historical aggregate profile data that is to be returned to the MAP client  30 - 1 , as discussed below. 
     Once the MAP server  12  has processed the historical request, the MAP server  12  returns the resulting historical aggregate profile data to the MAP client  30 - 1  (step  1606 ). As discussed above, the historical aggregate profile data may be in a time context or a geographic context. In an alternative embodiment, the data returned to the MAP client  30 - 1  may be raw historical data. The raw historical data may be the relevant history objects or data from the relevant history objects such as, for example, the user records in the relevant history objects, the user profiles of the anonymous user records in the relevant history objects, or the like. 
     Upon receiving the historical aggregate profile data, the MAP client  30 - 1  passes the historical aggregate profile data to the MAP application  32 - 1  (step  1608 ). Note that in an alternative embodiment where the data returned by the MAP server  12  is raw historical data, the MAP client  30 - 1  may process the raw historical data to provide desired data. For example, the MAP client  30 - 1  may process the raw historical data in order to generate average aggregate profiles for time bands within the time window of the historical request and/or to generate average aggregate profiles for regions near the POI or within the AOI of the historical request in a manner similar to that described above. The MAP application  32 - 1  then presents the historical aggregate profile data to the user  20 - 1  (step  1610 ). 
       FIGS. 15A and 15B  illustrate a flow chart for a process for generating historical aggregate profile data in a time context according to one embodiment of the present disclosure. First, upon receiving a historical request, the history manager  56  establishes a bounding box for the historical request based on the POI or the AOI for the historical request (step  1700 ). Note that while a bounding box is used in this example, other geographic shapes may be used to define a bounding region for the historical request (e.g., a bounding circle). In this embodiment, the historical request is from a mobile device of a requesting user, which in this example is the user  20 - 1 . If the historical request is for a POI, the bounding box is a geographic region corresponding to or surrounding the POI. For example, the bounding box may be a square geographic region of a predefined size centered on the POI. If the historical request is for an AOI, the bounding box is the AOI. In addition to establishing the bounding box, the history manager  56  establishes a time window for the historical request (step  1702 ). For example, if the historical request is for the last week and the current date and time are Sep. 17, 2009 at 10:00 pm, the history manager  56  may generate the time window as Sep. 10, 2009 at 10:00 pm through Sep. 17, 2009 at 10:00 pm. 
     Next, the history manager  56  obtains history objects relevant to the bounding box and the time window for the historical request from the datastore  64  of the MAP server  12  (step  1704 ). The relevant history objects are history objects recorded for time periods within or intersecting the time window and for locations, or geographic areas, within or intersecting the bounding box for the historical request. The history manager  56  also determines an output time band size (step  1706 ). In one exemplary embodiment, the output time band size is 1/100 th  of the amount of time from the start of the time window to the end of the time window for the historical request. For example, if the amount of time in the time window for the historical request is one week, the output time band size may be set to 1/100 th  of a week, which is 1.68 hours or 1 hour and 41 minutes. 
     The history manager  56  then sorts the relevant history objects into the appropriate output time bands of the time window for the historical request. More specifically, in this embodiment, the history manager  56  creates an empty list for each of output time band of the time window (step  1708 ). Then, the history manager  56  gets the next history object from the history objects identified in step  1704  as being relevant to the historical request (step  1710 ) and adds that history object to the list(s) for the appropriate output time band(s) (step  1712 ). Note that if the history object is recorded for a time period that overlaps two or more of the output time bands, then the history object may be added to all of the output time bands to which the history object is relevant. The history manager  56  then determines whether there are more relevant history objects to sort into the output time bands (step  1714 ). If so, the process returns to step  1710  and is repeated until all of the relevant history objects have been sorted into the appropriate output time bands. 
     Once sorting is complete, the history manager  56  determines an equivalent depth of the bounding box (D BB ) within the quadtree data structures used to store the history objects (step  1716 ). More specifically, the area of the base quadtree region (e.g., the base quadtree region  98 ) is referred to as A BASE . Then, at each depth of the quadtree, the area of the corresponding quadtree nodes is (¼) D *A BASE . In other words, the area of a child node is ¼ th  of the area of the parent node of that child node. The history manager  56  determines the equivalent depth of the bounding box (D BB ) by determining a quadtree depth at which the area of the corresponding quadtree nodes most closely matches an area of the bounding box (A BB ). 
     Note that equivalent quadtree depth of the bounding box (D BB ) determined in step  1716  is used below in order to efficiently determine the ratios of the area of the bounding box (A BB ) to areas of the relevant history objects (A HO ). However, in an alternative embodiment, the ratios of the area of the bounding box (A BB ) to the areas of the relevant history objects (A HO ) may be otherwise computed, in which case step  1716  would not be needed. 
     At this point, the process proceeds to  FIG. 15B  where the history manager  56  gets the list for the next output time band of the time window for the historical request (step  1718 ). The history manager  56  then gets the next history object in the list for the output time band (step  1720 ). Next, the history manager  56  sets a relevancy weight for the history object, where the relevancy weight is indicative of a relevancy of the history object to the bounding box (step  1722 ). For instance, a history object includes anonymized user profile data for a corresponding geographic area. If that geographic area is within or significantly overlaps the bounding box, then the history object will have a high relevancy weight. However, if the geographic area only overlaps the bounding box slightly, then the history object will have a low relevancy weight. In this embodiment, the relevancy weight for the history object is set to an approximate ratio of the area of the bounding box (A BB ) to an area of the history object (A HO ) computed based on a difference between the quadtree depth of the history object (D HO ) and the equivalent quadtree depth of the bounding box (D EQ ). The quadtree depth of the history object (D HO ) is stored in the history object. More specifically, in one embodiment, the relevancy weight of the history object is set according to the following: 
     
       
         
           
             
               relevancy 
               = 
               
                 
                   
                     A 
                     
                       B 
                        
                       B 
                     
                   
                   
                     A 
                     
                       H 
                        
                       O 
                     
                   
                 
                 ≅ 
                 
                   
                     ( 
                     
                       1 
                       4 
                     
                     ) 
                   
                   
                     
                       D 
                       
                         H 
                          
                         O 
                       
                     
                     - 
                     
                       D 
                       
                         B 
                          
                         B 
                       
                     
                   
                 
               
             
             , 
           
         
       
     
     for D HO &gt;D BB , and 
     relevancy=1, for D HO  D BB . 
     Next, the history manager  56  generates an aggregate profile for the history object using the user profile of the requesting user, which for this example is the user  20 - 1 , or a select subset thereof (step  1724 ). Note that the requesting user  20 - 1  may be enabled to select a subset of his user profile to be compared to the user profiles of the anonymous user records in the history objects by, for example, selecting one or more desired profile categories. In order to generate the aggregate profile for the history object, the history manager  56  compares the user profile of the user  20 - 1 , or the select subset thereof, to the user profiles of the anonymous user records stored in the history object. The resulting aggregate profile for the history object includes a number of user matches and a total number of users. In the embodiment where user profiles include lists of keywords for a number of profile categories, the number of user matches is the number of anonymous user records in the history object having user profiles that include at least one keyword that matches at least one keyword in the user profile of the user  20 - 1  or at least one keyword in the select subset of the user profile of the user  20 - 1 . The total number of users is the total number of anonymous user records in the history object. In addition or alternatively, the aggregate profile for the history object may include a list of keywords from the user profile of the user  20 - 1  or the select subset of the user profile of the user  20 - 1  having at least one user match. Still further, the aggregate profile for the history object may include the number of user matches for each of the keywords from the user profile of the user  20 - 1  or the select subset of the user profile of the user  20 - 1  having at least one user match. 
     The history manager  56  then determines whether there are more history objects in the list for the output time band (step  1726 ). If so, the process returns to step  1720  and is repeated until all of the history objects in the list for the output time band have been processed. Once all of the history objects in the list for the output time band have been processed, the history manager  56  combines the aggregate profiles of the history objects in the output time band to provide a combined aggregate profile for the output time band. More specifically, in this embodiment, the history manager  56  computes a weighted average of the aggregate profiles for the history objects in the output time band using the relevancy weights of the history objects (step  1728 ). In one embodiment, the aggregate profile of each of the history objects includes the number of user matches for the history object and the total number of users for the history object. In this embodiment, the weighted average of the aggregate profiles of the history objects in the output time band (i.e., the average aggregate profile for the output time band) includes the weighted average of the number of user matches for all of the history objects in the output time band, which may be computed as: 
     
       
         
           
             
               
                 user_matches 
                 AVG 
               
               = 
               
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                    
                   
                     ( 
                     
                       
                         
                           relevancy 
                           i 
                         
                         · 
                         number_of 
                       
                        
                       _user 
                        
                       
                         _matches 
                         i 
                       
                     
                     ) 
                   
                 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                    
                   
                     relevancy 
                     i 
                   
                 
               
             
             , 
           
         
       
     
     where relevancy i  is the relevancy weight computed in step  1722  for the i-th history object, number_of_user_matches i  is the number of user matches from the aggregate profile of the i-th history object, and n is the number of history objects in the list for the output time band. In a similar manner, in this embodiment, the average aggregate profile for the output time band includes the weighted average of the total number of users for all of the history objects in the output time band, which may be computed as: 
     
       
         
           
             
               
                 total_users 
                 AVG 
               
               = 
               
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                    
                   
                     ( 
                     
                       
                         relevancy 
                         i 
                       
                       · 
                       
                         total_users 
                         i 
                       
                     
                     ) 
                   
                 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                    
                   
                     relevancy 
                     i 
                   
                 
               
             
             , 
           
         
       
     
     where relevancy i  is the relevancy weight computed in step  1722  for the i-th history object, total_users i  is the total number of users from the aggregate profile of the i-th history object, and n is the number of history objects in the list for the output time band. In addition or alternatively, the average aggregate profile for the output time band may include the weighted average of the ratio of user matches to total users for all of the history objects in the output time band, which may be computed as: 
     
       
         
           
             
               
                 user_matches 
                 
                   total_users 
                   AVG 
                 
               
               = 
               
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                    
                   
                     ( 
                     
                       
                         relevancy 
                         i 
                       
                       · 
                       
                         
                           number_of 
                            
                           _user 
                            
                           
                             _matches 
                             i 
                           
                         
                         
                           total_users 
                           i 
                         
                       
                     
                     ) 
                   
                 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                    
                   
                     relevancy 
                     i 
                   
                 
               
             
             , 
           
         
       
     
     where relevancy i  is the relevancy weight computed in step  1722  for the i-th history object, number_of_user_matches i  is the number of user matches from the aggregate profile of the i-th history object, total_users i  is the total number of users from the aggregate profile of the i-th history object, and n is the number of history objects in the list for the output time band. 
     In addition or alternatively, if the aggregate profiles for the history objects in the output time band include the number of user matches for each keyword in the user profile of the user  20 - 1 , or the select subset thereof, having at least one user match, the average aggregate profile for the output time band may include a weighted average of the number of user matches for each of those keywords, which may be computed as: 
     
       
         
           
             
               
                 user_matches 
                 
                   
                     KEYWORD 
                      
                     _ 
                      
                     j 
                   
                   , 
                   AVG 
                 
               
               = 
               
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                    
                   
                     ( 
                     
                       
                         
                           relevancy 
                           i 
                         
                         · 
                         number_of 
                       
                        
                       _user 
                        
                       
                         _matches 
                         
                           
                             KEYWORD 
                              
                             _ 
                              
                             j 
                           
                           , 
                           i 
                         
                       
                     
                     ) 
                   
                 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                    
                   
                     relevancy 
                     i 
                   
                 
               
             
             , 
           
         
       
     
     where relevancy i  is the relevancy weight computed in step  1722  for the i-th history object, number_of_user_matches KEYWORD_j,i  is the number of user matches for the j-th keyword for the i-th history object, and n is the number of history objects in the list for the output time band. In addition or alternatively, the average aggregate profile for the output time band may include the weighted average of the ratio of the user matches to total users for each keyword, which may be computed as: 
     
       
         
           
             
               
                 user_matches 
                 
                   total_users 
                   
                     
                       KEYWORD 
                        
                       _ 
                        
                       j 
                     
                     , 
                     AVG 
                   
                 
               
               = 
               
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                    
                   
                     ( 
                     
                       
                         relevancy 
                         i 
                       
                       · 
                       
                         
                           number_of 
                            
                           _user 
                            
                           
                             _matches 
                             
                               
                                 KEYWORD 
                                  
                                 _ 
                                  
                                 j 
                               
                               , 
                               i 
                             
                           
                         
                         
                           total_users 
                           i 
                         
                       
                     
                     ) 
                   
                 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                    
                   
                     relevancy 
                     i 
                   
                 
               
             
             , 
           
         
       
     
     where relevancy i  is the relevancy weight computed in step  1722  for the i-th history object, number_of_user_matches KEYWORD_j,i  is the number of user matches for the j-th keyword for the i-th history object, total_users i  is the total number of users from the aggregate profile of the i-th history object, and n is the number of history objects in the list for the output time band. 
     Next, the history manager  56  determines whether there are more output time bands to process (step  1730 ). If so, the process returns to step  1718  and is repeated until the lists for all output time bands have been processed. Once all of the output time bands have been processed, the history manager  56  outputs the combined aggregate profiles for the output time bands. More specifically, in this embodiment, the history manager  56  outputs the weighted average aggregate profiles computed in step  1728  for the output time bands as the historical aggregate profile data to be returned to the mobile device  18 - 1  (step  1732 ). 
       FIG. 16  is an exemplary Graphical User Interface (GUI)  108  that may be provided by the MAP application  32 - 1  of the mobile device  18 - 1  ( FIG. 1 ) in order to present historical aggregate profile data in a time context according to one embodiment of this disclosure. In operation, the MAP application  32 - 1  issues a historical request for a POI  110  in the manner described above. In response, the MAP server  12  uses the process of  FIGS. 15A and 15B  to generate historical aggregate profile data in response to the historical request in the time context. More specifically, the historical aggregate profile data includes an average aggregate profile for each of a number of output time bands within a time window established for the historical request. In this example, the time window is a four week period extending from the week of July 5 th  to the week of July 26 th . 
     Using the average aggregate profiles for the output time bands included in the historical aggregate profile data, the MAP application  32 - 1  generates a timeline  112  for the time window of the historical request. The timeline  112  is a graphical illustration of the average aggregate profiles for the output time bands. For example, if the average aggregate profile for each of the output time bands includes a weighted average of the number of user matches and a weighted average of the number of total users for the output time band, the timeline  112  may be indicative of the ratio of the weighted average of user matches to the weighted average of total users for each of the output time bands. In this example, the output time bands having a ratio of weighted average of user matches to weighted average of total users that is less than 0.25 are represented as having a low similarity, the output time bands having a ratio of weighted average of user matches to weighted average of total users that is in the range of 0.25-0.75 are represented as having varying degrees of intermediate similarity, and the output time bands having a ratio of weighted average of user matches to weighted average of total users that is greater than 0.75 are represented as having a high similarity. Note that output time bands for which there are no history objects may be grayed-out or otherwise indicated. 
     In addition, in this example, the GUI  108  also includes a second timeline  114  that zooms in on an area of the timeline  112  that includes the most activity or that includes the greatest number of output time bands having a high or medium similarity. Lastly, in this example, the GUI  108  includes an aggregate profile  116  for a crowd that is currently at the POI. Note that crowds and aggregate profiles for the crowds are discussed below in detail. 
       FIGS. 17A and 17B  illustrate a flow chart of a process for generating historical aggregate profile data in a geographic context according to one embodiment of the present disclosure. First, upon receiving a historical request, the history manager  56  establishes a bounding box for the historical request based on the POI or the AOI for the historical request (step  1800 ). Note that while a bounding box is used in this example, other geographic shapes may be used to define a bounding region for the historical request (e.g., a bounding circle). In this embodiment, the historical request is from a mobile device of a requesting user, which in this example is the user  20 - 1 . If the historical request is for a POI, the bounding box is a geographic region corresponding to or surrounding the POI. For example, the bounding box may be a square geographic region of a predefined size centered on the POI. If the historical request is for an AOI, the bounding box is the AOI. In addition to establishing the bounding box, the history manager  56  establishes a time window for the historical request (step  1802 ). For example, if the historical request is for the last week and the current date and time are Sep. 17, 2009 at 10:00 pm, the history manager  56  may generate the time window as Sep. 10, 2009 at 10:00 pm through Sep. 17, 2009 at 10:00 pm. 
     Next, the history manager  56  obtains history objects relevant to the bounding box and the time window of the historical request from the datastore  64  of the MAP server  12  (step  1804 ). The relevant history objects are history objects recorded for time periods within or intersecting the time window and for locations, or geographic areas, within or intersecting the bounding box for the historical request. The history manager  56  then sorts the relevant history objects into base quadtree regions. More specifically, in this embodiment, the history manager  56  creates an empty list for each relevant base quadtree region (step  1806 ). A relevant base quadtree region is a base quadtree region within which all or at least a portion of the bounding box is located. Therefore, for example, if a bounding box is located at the intersection of four base quadtree regions such that the bounding box overlaps a portion of each of the four base quadtree regions, then all four of the bounding boxes would be identified as relevant base quadtree regions. In contrast, if the bounding box is contained within a single base quadtree region, then that base quadtree region is the only relevant base quadtree region. 
     The history manager  56  then gets the next history object from the history objects identified in step  1804  as being relevant to the historical request (step  1808 ) and adds that history object to the list for the appropriate base quadtree region (step  1810 ). The history manager  56  then determines whether there are more relevant history objects to sort (step  1812 ). If so, the process returns to step  1808  and is repeated until all of the relevant history objects have been sorted into the appropriate base quadtree regions. 
     Once sorting is complete, the process proceeds to  FIG. 17B . The following steps generally operate to divide each base quadtree region into a grid, where a size of each grid location is set to a smallest history record size of all the history objects sorted into the list for that base quadtree region. Using the history objects in the base quadtree region, aggregate profiles are generated for each of the grid locations covered by the history object. Then, a combined aggregate profile is generated for each grid location based on the aggregate profiles generated using the corresponding history objects. 
     More specifically, the history manager  56  gets the list for the next base quadtree region (step  1814 ). The history manager  56  then gets the next history object in the list for the base quadtree region (step  1816 ). Next, the history manager  56  creates an aggregate profile for the history object using the user profile of the requesting user, which in this example is the user  20 - 1 , or a select subset of the user profile of the requesting user (step  1818 ). Note that the user  20 - 1  may be enabled to select a subset of his user profile to be used for aggregate profile creation by, for example, selecting one or more profile categories. In order to generate the aggregate profile for the history object, the history manager  56  compares the user profile of the user  20 - 1 , or the select subset thereof, to the user profiles of the anonymous user records stored in the history object. The resulting aggregate profile for the history object includes a number of user matches and a total number of users. In the embodiment where user profiles include lists of keywords for a number of profile categories, the number of user matches is the number of anonymous user records in the history object having user profiles that include at least one keyword that matches at least one keyword in the user profile of the user  20 - 1  or at least one keyword in the select subset of the user profile of the user  20 - 1 . The total number of users is the total number of anonymous user records in the history object. 
     Next, the history manager  56  determines whether a size of the history object is greater than the smallest history object size in the list of history objects for the base quadtree region (step  1820 ). If not, the aggregate profile for the history object is added to an output list for the corresponding grid location for the base quadtree region (step  1822 ) and the process proceeds to step  1830 . If the size of the history object is greater than the smallest history object size, the history manager  56  splits the geographic area, or location, of the history object into a number of grid locations each of the smallest history object size of all the history objects in the list for the base quadtree region (step  1824 ). The history manager  56  then divides the aggregate profile of the history object evenly over the grid locations for the history object (step  1826 ) and adds resulting aggregate profiles for the grid locations to output lists for those grid locations (step  1828 ). For example, if the geographic area of the history object is split into four grid locations and the aggregate profile for the history object includes eight user matches and sixteen total users, then the aggregate profile is divided evenly over the four grid locations such that each of the four grid locations is given an aggregate profile of two user matches and four total users. 
     The history manager  56  then determines whether there are more history objects to process for the base quadtree region (step  1830 ). If so, the process returns to step  1816  and is repeated until all of the history objects for the base quadtree region are processed. At that point, for each grid location in the base quadtree region having at least one aggregate profile in its output list, the history manager  56  combines the aggregate profiles in the output list for the grid location to provide a combined aggregate profile for the grid location. More specifically, in this embodiment, the history manager  56  computes average aggregate profiles for the grid locations for the base quadtree region (step  1832 ). In one embodiment, for each grid location, the average aggregate profile for the grid location includes an average number of user matches and an average total number of users for all of the aggregate profiles in the output list for that grid location. 
     Next, the history manager  56  determines whether there are more relevant base quadtree regions to process (step  1834 ). If so, the process returns to step  1814  and is repeated until all of the relevant base quadtree regions have been processed. At that point, the history manager  56  outputs the grid locations and the average aggregate profiles for the grid locations in each of the relevant base quadtree regions (step  1836 ). The grid locations and their corresponding average aggregate profiles form the historical aggregate profile data that is returned to the mobile device  18 - 1  of the user  20 - 1  in response to the historical request. 
       FIG. 18  illustrates an exemplary GUI  118  that may be provided by the MAP application  32 - 1  of the mobile device  18 - 1  ( FIG. 1 ) to present historical aggregate profile data in the geographic context to the user  20 - 1  in response to a historical request. As illustrated, the GUI  118  includes a map  120  including a grid  122 . The grid  122  provides graphical information indicative of aggregate profiles for grid locations returned by the MAP server  12  in response to a historical request. The GUI  118  also includes buttons  124  and  126  enabling the user  20 - 1  to zoom in or zoom out on the map  120 , buttons  128  and  130  enabling the user  20 - 1  to toggle between the traditional map view as shown or a satellite map view, buttons  132  and  134  enabling the user  20 - 1  to switch between historical mode and a current mode (i.e., a view of current crowd data as discussed below in detail), and buttons  136  and  138  enabling the user  20 - 1  to hide or show POIs on the map  120 . 
     It should be noted that while the aggregate profiles in  FIGS. 15A through 18  are generated based on the user profile of the user  20 - 1  or a select subset of the user profile of the user  20 - 1 , the aggregate profiles may alternatively be generated based on a target user profile defined or otherwise specified by the user  20 - 1 . For example, the user  20 - 1  may define a target profile for a type of person with which the user  20 - 1  would like to interact. Then, by making a historical request with the target profile, the user  20 - 1  can learn whether people matching the target profile are historically located at a POI or an AOI. 
       FIG. 19  illustrates the operation of the system  10  of  FIG. 1  wherein the subscriber device  22  is enabled to request and receive historical aggregate profile data from the MAP server  12  according to one embodiment of the present disclosure. Note that, in a similar manner, the third-party service  26  may send historical requests to the MAP server  12 . As illustrated, in this embodiment, the subscriber device  22  sends a historical request to the MAP server  12  (step  1900 ). The subscriber device  22  sends the historical request to the MAP server  12  via the web browser  38 . In one embodiment, the historical request identifies either a POI or an AOI and a time window. The historical request may be made in response to user input from the subscriber  24  of the subscriber device  22  or made automatically in response to an event such as, for example, navigation to a website associated with a POI (e.g., navigation to a website of a restaurant). 
     Upon receiving the historical request, the MAP server  12  processes the historical request (step  1902 ). More specifically, as discussed above, the historical request is processed by the history manager  56  of the MAP server  12 . First, the history manager  56  obtains history objects that are relevant to the historical request from the datastore  64  of the MAP server  12 . The relevant history objects are those relevant to the POI or the AOI and the time window for the historical request. The history manager  56  then processes the relevant history objects to provide historical aggregate profile data for the POI or the AOI in a time context and/or a geographic context. In this embodiment, the historical aggregate profile data is based on comparisons of the user profiles of the anonymous user records in the relevant history objects to one another. In another embodiment, the aggregate profile data is based on comparisons of the user profiles of the anonymous user records in the relevant history objects and a target user profile. 
     Once the MAP server  12  has processed the historical request, the MAP server  12  returns the resulting historical aggregate profile data to the subscriber device  22  (step  1904 ). The historical aggregate profile data may be in the time context or the geographic context. In this embodiment where the historical aggregate profile data is to be presented via the web browser  38  of the subscriber device  22 , the MAP server  12  formats the historical aggregate profile data in a suitable format before sending the historical aggregate profile data to the web browser  38  of the subscriber device  22 . Upon receiving the historical aggregate profile data, the web browser  38  of the subscriber device  22  presents the historical aggregate profile data to the user  20 - 1  (step  1906 ). 
       FIGS. 20A and 20B  illustrate a process for generating historical aggregate profile data in a time context in response to a historical request from the subscriber  24  at the subscriber device  22  according to one embodiment of the present disclosure. The process of  FIGS. 20A and 20B  is substantially the same as that described above with respect to  FIGS. 15A and 15B . More specifically, steps  2000  through  2022  are substantially the same as steps  1700  through  1722  of  FIGS. 15A and 15B . Likewise, steps  2026  through  2032  are substantially the same as steps  1726  through  1732  of  FIG. 15B . However, step  2024  of  FIG. 20B  is different from step  1724  of  FIG. 15B  with respect to the manner in which the aggregate profiles for the relevant history objects are computed. 
     More specifically, in this embodiment, since the historical request is from the subscriber  24 , the aggregate profile for the history object is generated by comparing the user profiles of the anonymous user records in the history object to one another. In this embodiment, the aggregate profile for the history object includes an aggregate list of keywords from the user profiles of the anonymous user records, the number of occurrences of each of those keywords in the user profiles of the anonymous user records, and the total number of anonymous user records in the history object. As such, in step  2028 , the weighted average of the aggregate profiles for the history objects in the output time band may provide an average aggregate profile including, for each keyword occurring in the aggregate profile of at least one of the history objects, a weighted average of the number of occurrences of the keyword. In addition, the average aggregate profile may include a weighted average of the total number of anonymous user records in the history objects. In addition or alternatively, the average aggregate profile may include, for each keyword, a weighted average of the number of occurrences of the keyword to the total number of anonymous user records. 
       FIGS. 21A and 21B  illustrate a process for generating historical aggregate profile data in a geographic context in response to a historical request from the subscriber  24  at the subscriber device  22  according to one embodiment of the present disclosure. The process of  FIGS. 21A and 21B  is substantially the same as that described above with respect to  FIGS. 17A and 17B . More specifically, steps  2100  through  2116  and  2120  through  2136  are substantially the same as steps  1800  through  1816  and  1820  through  1836  of  FIGS. 17A and 17B . However, step  2118  of  FIG. 21B  is different from step  1818  of  FIG. 17B  with respect to the manner in which the aggregate profiles for the history objects are computed. 
     More specifically, in this embodiment, since the historical request is from the subscriber  24 , the aggregate profile for the history object is generated by comparing the user profiles of the anonymous user records in the history object to one another. In this embodiment, the aggregate profile for the history object includes an aggregate list of keywords from the user profiles of the anonymous user records, the number of occurrences of each of those keywords in the user profiles of the anonymous user records, and the total number of anonymous user records in the history object. As such, in step  2132 , the weighted average of the aggregate profiles for the each of the grid locations may provide an average aggregate profile including, for each keyword, a weighted average of the number of occurrences of the keyword. In addition, the average aggregate profile for each grid location may include a weighted average of the total number of anonymous user records. In addition or alternatively, the average aggregate profile for each grid location may include, for each keyword, a weighted average of the number of occurrences of the keyword to the total number of anonymous user records. 
       FIG. 22  begins a discussion of the operation of the crowd analyzer  58  to form crowds of users according to one embodiment of the present disclosure. Specifically,  FIG. 22  is a flow chart for a spatial crowd formation process according to one embodiment of the present disclosure. Note that, in one embodiment, this process is performed in response to a request for crowd data for a POI or an AOI. In another embodiment, this process may be performed proactively by the crowd analyzer  58  as, for example, a background process. 
     First, the crowd analyzer  58  establishes a bounding box for the crowd formation process (step  2200 ). Note that while a bounding box is used in this example, other geographic shapes may be used to define a bounding region for the crowd formation process (e.g., a bounding circle). In one embodiment, if crowd formation is performed in response to a specific request, the bounding box is established based on the POI or the AOI of the request. If the request is for a POI, then the bounding box is a geographic area of a predetermined size centered at the POI. If the request is for an AOI, the bounding box is the AOI. Alternatively, if the crowd formation process is performed proactively, the bounding box is a bounding box of a predefined size. 
     The crowd analyzer  58  then creates a crowd for each individual user in the bounding box (step  2202 ). More specifically, the crowd analyzer  58  queries the datastore  64  of the MAP server  12  to identify users currently located within the bounding box. Then, a crowd of one user is created for each user currently located within the bounding box. Next, the crowd analyzer  58  determines the two closest crowds in the bounding box (step  2204 ) and determines a distance between the two crowds (step  2206 ). The distance between the two crowds is a distance between crowd centers of the two crowds. Note that the crowd center of a crowd of one is the current location of the user in the crowd. The crowd analyzer  58  then determines whether the distance between the two crowds is less than an optimal inclusion distance (step  2208 ). In this embodiment, the optimal inclusion distance is a predefined static distance. If the distance between the two crowds is less than the optimal inclusion distance, the crowd analyzer  58  combines the two crowds (step  2210 ) and computes a new crowd center for the resulting crowd (step  2212 ). The crowd center may be computed based on the current locations of the users in the crowd using a center of mass algorithm. At this point the process returns to step  2204  and is repeated until the distance between the two closest crowds is not less than the optimal inclusion distance. At that point, the crowd analyzer  58  discards any crowds with less than three users (step  2214 ). Note that throughout this disclosure crowds are only maintained if the crowds include three or more users. However, while three users is the preferred minimum number of users in a crowd, the present disclosure is not limited thereto. The minimum number of users in a crowd may be defined as any number greater than or equal to two users. 
       FIGS. 23A through 23D  graphically illustrate the crowd formation process of  FIG. 22  for an exemplary bounding box  139 . In  FIGS. 23A through 23D , crowds are noted by dashed circles, and the crowd centers are noted by cross-hairs (+). As illustrated in  FIG. 23A , initially, the crowd analyzer  58  creates crowds  140  through  148  for the users in the geographic area, where, at this point, each of the crowds  140  through  148  includes one user. The current locations of the users are the crowd centers of the crowds  140  through  148 . Next, the crowd analyzer  58  determines the two closest crowds and a distance between the two closest crowds. In this example, at this point, the two closest crowds are crowds  142  and  144 , and the distance between the two closest crowds  142  and  144  is less than the optimal inclusion distance. As such, the two closest crowds  142  and  144  are combined by merging crowd  144  into crowd  142 , and a new crowd center (+) is computed for the crowd  142 , as illustrated in  FIG. 23B . Next, the crowd analyzer  58  again determines the two closest crowds, which are now crowds  140  and  142 . The crowd analyzer  58  then determines a distance between the crowds  140  and  142 . Since the distance is less than the optimal inclusion distance, the crowd analyzer  58  combines the two crowds  140  and  142  by merging the crowd  140  into the crowd  142 , and a new crowd center (+) is computed for the crowd  142 , as illustrated in  FIG. 23C . At this point, there are no more crowds separated by less than the optimal inclusion distance. As such, the crowd analyzer  58  discards crowds having less than three users, which in this example are crowds  146  and  148 . As a result, at the end of the crowd formation process, the crowd  142  has been formed with three users, as illustrated in  FIG. 23D . 
       FIGS. 24A through 24D  illustrate a flow chart for a spatial crowd formation process according to another embodiment of the present disclosure. In this embodiment, the spatial crowd formation process is triggered in response to receiving a location update for one of the users  20 - 1  through  20 -N and is preferably repeated for each location update received for the users  20 - 1  through  20 -N. As such, first, the crowd analyzer  58  receives a location update, or a new location, for a user (step  2300 ). Assume that, for this example, the location update is received for the user  20 - 1 . In response, the crowd analyzer  58  retrieves an old location of the user  20 - 1 , if any (step  2302 ). The old location is the current location of the user  20 - 1  prior to receiving the new location. The crowd analyzer  58  then creates a new bounding box of a predetermined size centered at the new location of the user  20 - 1  (step  2304 ) and an old bounding box of a predetermined size centered at the old location of the user  20 - 1 , if any (step  2306 ). The predetermined size of the new and old bounding boxes may be any desired size. As one example, the predetermined size of the new and old bounding boxes is 40 meters by 40 meters. Note that if the user  20 - 1  does not have an old location (i.e., the location received in step  2300  is the first location received for the user  20 - 1 ), then the old bounding box is essentially null. Also note that while bounding “boxes” are used in this example, the bounding areas may be of any desired shape. 
     Next, the crowd analyzer  58  determines whether the new and old bounding boxes overlap (step  2308 ). If so, the crowd analyzer  58  creates a bounding box encompassing the new and old bounding boxes (step  2310 ). For example, if the new and old bounding boxes are 40×40 meter regions and a 1×1 meter square at the northeast corner of the new bounding box overlaps a 1×1 meter square at the southwest corner of the old bounding box, the crowd analyzer  58  may create a 79×79 meter square bounding box encompassing both the new and old bounding boxes. 
     The crowd analyzer  58  then determines the individual users and crowds relevant to the bounding box created in step  2310  (step  2312 ). The crowds relevant to the bounding box are crowds that are within or overlap the bounding box (e.g., have at least one user located within the bounding box). The individual users relevant to the bounding box are users that are currently located within the bounding box and not already part of a crowd. Next, the crowd analyzer  58  computes an optimal inclusion distance for individual users based on user density within the bounding box (step  2314 ). More specifically, in one embodiment, the optimal inclusion distance for individuals, which is also referred to herein as an initial optimal inclusion distance, is set according to the following equation: 
     
       
         
           
             
               
                 initial_optimal 
                  
                 _inclusion 
                  
                 _dist 
               
               = 
               
                 a 
                 · 
                 
                   
                     
                       A 
                       BoundingBox 
                     
                     
                       number_of 
                        
                       _users 
                     
                   
                 
               
             
             , 
           
         
       
     
     where a is a number between 0 and 1, A BoundingBox  is an area of the bounding box, and number_of_users is the total number of users in the bounding box. The total number of users in the bounding box includes both individual users that are not already in a crowd and users that are already in a crowd. In one embodiment, a is ⅔. 
     The crowd analyzer  58  then creates a crowd for each individual user within the bounding box that is not already included in a crowd and sets the optimal inclusion distance for the crowds to the initial optimal inclusion distance (step  2316 ). At this point, the process proceeds to  FIG. 24B  where the crowd analyzer  58  analyzes the crowds relevant to the bounding box to determine whether any of the crowd members (i.e., users in the crowds) violate the optimal inclusion distance of their crowds (step  2318 ). Any crowd member that violates the optimal inclusion distance of his or her crowd is then removed from that crowd (step  2320 ). The crowd analyzer  58  then creates a crowd of one user for each of the users removed from their crowds in step  2320  and sets the optimal inclusion distance for the newly created crowds to the initial optimal inclusion distance (step  2322 ). 
     Next, the crowd analyzer  58  determines the two closest crowds for the bounding box (step  2324 ) and a distance between the two closest crowds (step  2326 ). The distance between the two closest crowds is the distance between the crowd centers of the two closest crowds. The crowd analyzer  58  then determines whether the distance between the two closest crowds is less than the optimal inclusion distance of a larger of the two closest crowds (step  2328 ). If the two closest crowds are of the same size (i.e., have the same number of users), then the optimal inclusion distance of either of the two closest crowds may be used. Alternatively, if the two closest crowds are of the same size, the optimal inclusion distances of both of the two closest crowds may be used such that the crowd analyzer  58  determines whether the distance between the two closest crowds is less than the optimal inclusion distances of both of the two closest crowds. As another alternative, if the two closest crowds are of the same size, the crowd analyzer  58  may compare the distance between the two closest crowds to an average of the optimal inclusion distances of the two closest crowds. 
     If the distance between the two closest crowds is less than the optimal inclusion distance, the two closest crowds are combined or merged (step  2330 ), and a new crowd center for the resulting crowd is computed (step  2332 ). Again, a center of mass algorithm may be used to compute the crowd center of a crowd. In addition, a new optimal inclusion distance for the resulting crowd is computed (step  2334 ). In one embodiment, the new optimal inclusion distance for the resulting crowd is computed as: 
     
       
         
           
             
               average 
               = 
               
                 
                   1 
                   
                     n 
                     + 
                     1 
                   
                 
                 · 
                 
                   ( 
                   
                     
                       initial_optimal 
                        
                       _inclusion 
                        
                       _dist 
                     
                     + 
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         n 
                       
                        
                       
                         d 
                         i 
                       
                     
                   
                   ) 
                 
               
             
             , 
             
               
 
             
              
             
               
                 optimal_inclusion 
                  
                 _dist 
               
               = 
               
                 average 
                 + 
                 
                   
                     ( 
                     
                       
                         1 
                         n 
                       
                       · 
                       
                         
                           ∑ 
                           
                             i 
                             = 
                             1 
                           
                           n 
                         
                          
                         
                           
                             ( 
                             
                               
                                 d 
                                 i 
                               
                               - 
                               average 
                             
                             ) 
                           
                           2 
                         
                       
                     
                     ) 
                   
                 
               
             
             , 
           
         
       
     
     where n is the number of users in the crowd and d i  is a distance between the ith user and the crowd center. In other words, the new optimal inclusion distance is computed as the average of the initial optimal inclusion distance and the distances between the users in the crowd and the crowd center plus one standard deviation. 
     At this point, the crowd analyzer  58  determines whether a maximum number of iterations have been performed (step  2336 ). The maximum number of iterations is a predefined number that ensures that the crowd formation process does not indefinitely loop over steps  2318  through  2334  or loop over steps  2318  through  2334  more than a desired maximum number of times. If the maximum number of iterations has not been reached, the process returns to step  2318  and is repeated until either the distance between the two closest crowds is not less than the optimal inclusion distance of the larger crowd or the maximum number of iterations has been reached. At that point, the crowd analyzer  58  discards crowds with less than three users, or members (step  2338 ) and the process ends. 
     Returning to step  2308  in  FIG. 24A , if the new and old bounding boxes do not overlap, the process proceeds to  FIG. 24C  and the bounding box to be processed is set to the old bounding box (step  2340 ). In general, the crowd analyzer  58  then processes the old bounding box in much the same manner as described above with respect to steps  2312  through  2338 . More specifically, the crowd analyzer  58  determines the individual users and crowds relevant to the bounding box (step  2342 ). The crowds relevant to the bounding box are crowds that are within or overlap the bounding box (e.g., have at least one user located within the bounding box). The individual users relevant to the bounding box are users that are currently located within the bounding box and not already part of a crowd. Next, the crowd analyzer  58  computes an optimal inclusion distance for individual users based on user density within the bounding box (step  2344 ). More specifically, in one embodiment, the optimal inclusion distance for individuals, which is also referred to herein as an initial optimal inclusion distance, is set according to the following equation: 
     
       
         
           
             
               
                 initial_optimal 
                  
                 _inclusion 
                  
                 _dist 
               
               = 
               
                 a 
                 · 
                 
                   
                     
                       A 
                       BoundingBox 
                     
                     
                       number_of 
                        
                       _users 
                     
                   
                 
               
             
             , 
           
         
       
     
     where a is a number between 0 and 1, A BoundingBox  is an area of the bounding box, and number_of_users is the total number of users in the bounding box. The total number of users in the bounding box includes both individual users that are not already in a crowd and users that are already in a crowd. In one embodiment, a is ⅔. 
     The crowd analyzer  58  then creates a crowd of one user for each individual user within the bounding box that is not already included in a crowd and sets the optimal inclusion distance for the crowds to the initial optimal inclusion distance (step  2346 ). At this point, the crowd analyzer  58  analyzes the crowds for the bounding box to determine whether any crowd members (i.e., users in the crowds) violate the optimal inclusion distance of their crowds (step  2348 ). Any crowd member that violates the optimal inclusion distance of his or her crowd is then removed from that crowd (step  2350 ). The crowd analyzer  58  then creates a crowd of one user for each of the users removed from their crowds in step  2350  and sets the optimal inclusion distance for the newly created crowds to the initial optimal inclusion distance (step  2352 ). 
     Next, the crowd analyzer  58  determines the two closest crowds in the bounding box (step  2354 ) and a distance between the two closest crowds (step  2356 ). The distance between the two closest crowds is the distance between the crowd centers of the two closest crowds. The crowd analyzer  58  then determines whether the distance between the two closest crowds is less than the optimal inclusion distance of a larger of the two closest crowds (step  2358 ). If the two closest crowds are of the same size (i.e., have the same number of users), then the optimal inclusion distance of either of the two closest crowds may be used. Alternatively, if the two closest crowds are of the same size, the optimal inclusion distances of both of the two closest crowds may be used such that the crowd analyzer  58  determines whether the distance between the two closest crowds is less than the optimal inclusion distances of both of the two closest crowds. As another alternative, if the two closest crowds are of the same size, the crowd analyzer  58  may compare the distance between the two closest crowds to an average of the optimal inclusion distances of the two closest crowds. 
     If the distance between the two closest crowds is less than the optimal inclusion distance, the two closest crowds are combined or merged (step  2360 ), and a new crowd center for the resulting crowd is computed (step  2362 ). Again, a center of mass algorithm may be used to compute the crowd center of a crowd. In addition, a new optimal inclusion distance for the resulting crowd is computed (step  2364 ). As discussed above, in one embodiment, the new optimal inclusion distance for the resulting crowd is computed as: 
     
       
         
           
             
               average 
               = 
               
                 
                   1 
                   
                     n 
                     + 
                     1 
                   
                 
                 · 
                 
                   ( 
                   
                     
                       initial_optimal 
                        
                       _inclusion 
                        
                       _dist 
                     
                     + 
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         n 
                       
                        
                       
                         d 
                         i 
                       
                     
                   
                   ) 
                 
               
             
             , 
             
               
 
             
              
             
               
                 optimal_inclusion 
                  
                 _dist 
               
               = 
               
                 average 
                 + 
                 
                   
                     ( 
                     
                       
                         1 
                         n 
                       
                       · 
                       
                         
                           ∑ 
                           
                             i 
                             = 
                             1 
                           
                           n 
                         
                          
                         
                           
                             ( 
                             
                               
                                 d 
                                 i 
                               
                               - 
                               average 
                             
                             ) 
                           
                           2 
                         
                       
                     
                     ) 
                   
                 
               
             
             , 
           
         
       
     
     where n is the number of users in the crowd and d i  is a distance between the ith user and the crowd center. In other words, the new optimal inclusion distance is computed as the average of the initial optimal inclusion distance and the distances between the users in the crowd and the crowd center plus one standard deviation. 
     At this point, the crowd analyzer  58  determines whether a maximum number of iterations have been performed (step  2366 ). If the maximum number of iterations has not been reached, the process returns to step  2348  and is repeated until either the distance between the two closest crowds is not less than the optimal inclusion distance of the larger crowd or the maximum number of iterations has been reached. At that point, the crowd analyzer  58  discards crowds with less than three users, or members (step  2368 ). The crowd analyzer  58  then determines whether the crowd formation process for the new and old bounding boxes is done (step  2370 ). In other words, the crowd analyzer  58  determines whether both the new and old bounding boxes have been processed. If not, the bounding box is set to the new bounding box (step  2372 ), and the process returns to step  2342  and is repeated for the new bounding box. Once both the new and old bounding box have been processed, the crowd formation process ends. 
       FIGS. 25A through 25D  graphically illustrate the crowd formation process of  FIGS. 24A through 24D  for a scenario where the crowd formation process is triggered by a location update for a user having no old location. In this scenario, the crowd analyzer  58  creates a new bounding box  150  for the new location of the user, and the new bounding box  150  is set as the bounding box to be processed for crowd formation. Then, as illustrated in  FIG. 25A , the crowd analyzer  58  identifies all individual users currently located within the bounding box  150  and all crowds located within or overlapping the bounding box. In this example, crowd  152  is an existing crowd relevant to the bounding box  150 . Crowds are indicated by dashed circles, crowd centers are indicated by cross-hairs (+), and users are indicated as dots. Next, as illustrated in  FIG. 25B , the crowd analyzer  58  creates crowds  154  through  158  of one user for the individual users, and the optional inclusion distances of the crowds  154  through  158  are set to the initial optimal inclusion distance. As discussed above, the initial optimal inclusion distance is computed by the crowd analyzer  58  based on a density of users within the bounding box  150 . 
     The crowd analyzer  58  then identifies the two closest crowds  154  and  156  in the bounding box  150  and determines a distance between the two closest crowds  154  and  156 . In this example, the distance between the two closest crowds  154  and  156  is less than the optimal inclusion distance. As such, the two closest crowds  154  and  156  are merged and a new crowd center and new optimal inclusion distance are computed, as illustrated in  FIG. 25C . The crowd analyzer  58  then repeats the process such that the two closest crowds  154  and  158  in the bounding box  150  are again merged, as illustrated in  FIG. 23D . At this point, the distance between the two closest crowds  152  and  154  is greater than the appropriate optimal inclusion distance. As such, the crowd formation process is complete. 
       FIGS. 26A through 26F  graphically illustrate the crowd formation process of  FIGS. 24A through 24D  for a scenario where the new and old bounding boxes overlap. As illustrated in  FIG. 26A , a user moves from an old location to a new location, as indicated by an arrow. The crowd analyzer  58  receives a location update for the user giving the new location of the user. In response, the crowd analyzer  58  creates an old bounding box  160  for the old location of the user and a new bounding box  162  for the new location of the user. Crowd  164  exists in the old bounding box  160 , and crowd  166  exists in the new bounding box  162 . 
     Since the old bounding box  160  and the new bounding box  162  overlap, the crowd analyzer  58  creates a bounding box  168  that encompasses both the old bounding box  160  and the new bounding box  162 , as illustrated in  FIG. 26B . In addition, the crowd analyzer  58  creates crowds  170  through  176  for individual users currently located within the bounding box  168 . The optimal inclusion distances of the crowds  170  through  176  are set to the initial optimal inclusion distance computed by the crowd analyzer  58  based on the density of users in the bounding box  168 . 
     Next, the crowd analyzer  58  analyzes the crowds  164 ,  166 , and  170  through  176  to determine whether any members of the crowds  164 ,  166 , and  170  through  176  violate the optimal inclusion distances of the crowds  164 ,  166 , and  170  through  176 . In this example, as a result of the user leaving the crowd  164  and moving to his new location, both of the remaining members of the crowd  164  violate the optimal inclusion distance of the crowd  164 . As such, the crowd analyzer  58  removes the remaining users from the crowd  164  and creates crowds  178  and  180  of one user each for those users, as illustrated in  FIG. 26C . 
     The crowd analyzer  58  then identifies the two closest crowds in the bounding box  168 , which in this example are the crowds  174  and  176 . Next, the crowd analyzer  58  computes a distance between the two crowds  174  and  176 . In this example, the distance between the two crowds  174  and  176  is less than the initial optimal inclusion distance and, as such, the two crowds  174  and  176  are combined. In this example, crowds are combined by merging the smaller crowd into the larger crowd. Since the two crowds  174  and  176  are of the same size, the crowd analyzer  58  merges the crowd  176  into the crowd  174 , as illustrated in  FIG. 26D . A new crowd center and new optimal inclusion distance are then computed for the crowd  174 . 
     At this point, the crowd analyzer  58  repeats the process and determines that the crowds  166  and  172  are now the two closest crowds. In this example, the distance between the two crowds  166  and  172  is less than the optimal inclusion distance of the larger of the two crowds  166  and  172 , which is the crowd  166 . As such, the crowd  172  is merged into the crowd  166  and a new crowd center and optimal inclusion distance are computed for the crowd  166 , as illustrated in  FIG. 26E . At this point, there are no two crowds closer than the optimal inclusion distance of the larger of the two crowds. As such, the crowd analyzer  58  discards any crowds having less than three members, as illustrated in  FIG. 26F . In this example, the crowds  170 ,  174 ,  178 , and  180  have less than three members and are therefore removed. The crowd  166  has three or more members and, as such, is not removed. At this point, the crowd formation process is complete. 
       FIGS. 27A through 27E  graphically illustrate the crowd formation process of  FIGS. 24A through 24D  in a scenario where the new and old bounding boxes do not overlap. As illustrated in  FIG. 27A , in this example, the user moves from an old location to a new location. The crowd analyzer  58  creates an old bounding box  182  for the old location of the user and a new bounding box  184  for the new location of the user. Crowds  186  and  188  exist in the old bounding box  182 , and crowd  190  exists in the new bounding box  184 . In this example, since the old and new bounding boxes  182  and  184  do not overlap, the crowd analyzer  58  processes the old and new bounding boxes  182  and  184  separately. 
     More specifically, as illustrated in  FIG. 27B , as a result of the movement of the user from the old location to the new location, the remaining users in the crowd  186  no longer satisfy the optimal inclusion distance for the crowd  186 . As such, the remaining users in the crowd  186  are removed from the crowd  186 , and crowds  192  and  194  of one user each are created for the removed users as shown in  FIG. 26C . In this example, no two crowds in the old bounding box  182  are close enough to be combined. As such, processing of the old bounding box  182  is complete, and the crowd analyzer  58  proceeds to process the new bounding box  184 . 
     As illustrated in  FIG. 27D , processing of the new bounding box  184  begins by the crowd analyzer  58  creating a crowd  196  of one user for the user. The crowd analyzer  58  then identifies the crowds  190  and  196  as the two closest crowds in the new bounding box  184  and determines a distance between the two crowds  190  and  196 . In this example, the distance between the two crowds  190  and  196  is less than the optimal inclusion distance of the larger crowd, which is the crowd  190 . As such, the crowd analyzer  58  combines the crowds  190  and  196  by merging the crowd  196  into the crowd  190 , as illustrated in  FIG. 27E . A new crowd center and new optimal inclusion distance are then computed for the crowd  190 . At this point, the crowd formation process is complete. 
     Before proceeding, a variation of the spatial formation process discussed above with respect to  FIGS. 24A through 24D, 25A through 25D, 26A through 26F, and 27A through 27E  will be described. In this alternative embodiment, a location accuracy of the location update from the user received in step  2300  is considered. More specifically, in step  2300 , the location update received by the MAP server  12  includes the updated location of the user  20 - 1  as well as a location accuracy for the location of the user  20 - 1 , which may be expressed as, for example, a radius in meters from the location of the user  20 - 1 . In the embodiment where the location of the user  20 - 1  is obtained from a GPS receiver of the mobile device  18 - 1 , the location accuracy of the location of the user  20 - 1  may be provided by the GPS receiver or derived from data from the GPS receiver as well be appreciated by one having ordinary skill in the art. 
     Then, in steps  2302  and  2304 , sizes of the new and old bounding boxes centered at the new and old locations of the user  20 - 1  are set as a function of the location accuracy of the new and old locations of the user  20 - 1 . If the new location of the user  20 - 1  is inaccurate, then the new bounding box will be large. If the new location of the user  20 - 1  is accurate, then the new bounding box will be small. For example, the length and width of the new bounding box may be set to M times the location accuracy of the new location of the user  20 - 1 , where the location accuracy is expressed as a radius in meters from the new location of the user  20 - 1 . The number M may be any desired number. For example, the number M may be 5. In a similar manner, the location accuracy of the old location of the user  20 - 1  may be used to set the length and width of the old bounding box. 
     In addition, the location accuracy may be considered when computing the initial optimal inclusion distances used for crowds of one user in steps  2314  and  2344 . As discussed above, the initial optimal inclusion distance is computed based on the following equation: 
     
       
         
           
             
               
                 initial_optimal 
                  
                 _inclusion 
                  
                 _dist 
               
               = 
               
                 a 
                 · 
                 
                   
                     
                       A 
                       BoundingBox 
                     
                     
                       number_of 
                        
                       _users 
                     
                   
                 
               
             
             , 
           
         
       
     
     where a is a number between 0 and 1, A BoundingBox  is an area of the bounding box, and number of users is the total number of users in the bounding box. The total number of users in the bounding box includes both individual users that are not already in a crowd and users that are already in a crowd. In one embodiment, a is ⅔. However, if the computed initial optimal inclusion distance is less than the location accuracy of the current location of the individual user in a crowd, then the location accuracy, rather than the computed value, is used for the initial optimal inclusion distance for that crowd. As such, as location accuracy decreases, crowds become larger and more inclusive. In contrast, as location accuracy increases, crowds become smaller and less inclusive. In other words, the granularity with which crowds are formed is a function of the location accuracy. 
     Likewise, when new optimal inclusion distances for crowds are recomputed in steps  2334  and  2364 , location accuracy may also be considered. As discussed above, the new optimal inclusion distance may first be computed based on the following equation: 
     
       
         
           
             
               average 
               = 
               
                 
                   1 
                   
                     n 
                     + 
                     1 
                   
                 
                 · 
                 
                   ( 
                   
                     
                       initial_optimal 
                        
                       _inclusion 
                        
                       _dist 
                     
                     + 
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         n 
                       
                        
                       
                         d 
                         i 
                       
                     
                   
                   ) 
                 
               
             
             , 
             
               
 
             
              
             
               
                 optimal_inclusion 
                  
                 _dist 
               
               = 
               
                 average 
                 + 
                 
                   
                     ( 
                     
                       
                         1 
                         n 
                       
                       · 
                       
                         
                           ∑ 
                           
                             i 
                             = 
                             1 
                           
                           n 
                         
                          
                         
                           
                             ( 
                             
                               
                                 d 
                                 i 
                               
                               - 
                               average 
                             
                             ) 
                           
                           2 
                         
                       
                     
                     ) 
                   
                 
               
             
             , 
           
         
       
     
     where n is the number of users in the crowd and d i  is a distance between the ith user and the crowd center. In other words, the new optimal inclusion distance is computed as the average of the initial optimal inclusion distance and the distances between the users in the crowd and the crowd center plus one standard deviation. However, if the computed value for the new optimal inclusion distance is less than an average location accuracy of the users in the crowd, the average location accuracy of the users in the crowd, rather than the computed value, is used as the new optimal inclusion distance. 
       FIG. 28  illustrates the operation the system  10  of  FIG. 1  to enable the mobile devices  18 - 1  through  18 -N to request crowd data for currently formed crowds according to one embodiment of the present disclosure. Note that while in this example the request is initiated by the MAP application  32 - 1  of the mobile device  18 - 1 , this discussion is equally applicable to the MAP applications  32 - 2  through  32 -N of the other mobile devices  18 - 2  through  18 -N. In addition, in a similar manner, requests may be received from the third-party applications  34 - 1  through  34 -N. 
     First, the MAP application  32 - 1  sends a crowd request to the MAP client  30 - 1  (step  2400 ). The crowd request is a request for crowd data for crowds currently formed near a specified POI or within a specified AOI. The crowd request may be initiated by the user  20 - 1  of the mobile device  18 - 1  via the MAP application  32 - 1  or may be initiated automatically by the MAP application  32 - 1  in response to an event such as, for example, start-up of the MAP application  32 - 1 , movement of the user  20 - 1 , or the like. In one embodiment, the crowd request is for a POI, where the POI is a POI corresponding to the current location of the user  20 - 1 , a POI selected from a list of POIs defined by the user  20 - 1 , a POI selected from a list of POIs defined by the MAP application  32 - 1  or the MAP server  12 , a POI selected by the user  20 - 1  from a map, a POI implicitly defined via a separate application (e.g., POI is implicitly defined as the location of the nearest Starbucks coffee house in response to the user  20 - 1  performing a Google search for “Starbucks”), or the like. If the POI is selected from a list of POIs, the list of POIs may include static POIs which may be defined by street addresses or latitude and longitude coordinates, dynamic POIs which may be defined as the current locations of one or more friends of the user  20 - 1 , or both. Note that in some embodiments, the user  20 - 1  may be enabled to define a POI by selecting a crowd center of a crowd as a POI, where the POI would thereafter remain static at that point and would not follow the crowd. 
     In another embodiment, the crowd request is for an AOI, where the AOI may be an AOI of a predefined shape and size centered at the current location of the user  20 - 1 , an AOI selected from a list of AOIs defined by the user  20 - 1 , an AOI selected from a list of AOIs defined by the MAP application  32 - 1  or the MAP server  12 , an AOI selected by the user  20 - 1  from a map, an AOI implicitly defined via a separate application (e.g., AOI is implicitly defined as an area of a predefined shape and size centered at the location of the nearest Starbucks coffee house in response to the user  20 - 1  performing a Google search for “Starbucks”), or the like. If the AOI is selected from a list of AOIs, the list of AOIs may include static AOIs, dynamic AOIs which may be defined as areas of a predefined shape and size centered at the current locations of one or more friends of the user  20 - 1 , or both. Note that in some embodiments, the user  20 - 1  may be enabled to define an AOI by selecting a crowd such that an AOI is created of a predefined shape and size centered at the crowd center of the selected crowd. The AOI would thereafter remain static and would not follow the crowd. The POI or the AOI of the crowd request may be selected by the user  20 - 1  via the MAP application  32 - 1 . In yet another embodiment, the MAP application  32 - 1  automatically uses the current location of the user  20 - 1  as the POI or as a center point for an AOI of a predefined shape and size. 
     Upon receiving the crowd request, the MAP client  30 - 1  forwards the crowd request to the MAP server  12  (step  2402 ). Note that in some embodiments, the MAP client  30 - 1  may process the crowd request before forwarding the crowd request to the MAP server  12 . For example, in some embodiments, the crowd request may include more than one POI or more than one AOI. As such, the MAP client  30 - 1  may generate a separate crowd request for each POI or each AOI. 
     In response to receiving the crowd request from the MAP client  30 - 1 , the MAP server  12  identifies one or more crowds relevant to the crowd request (step  2404 ). More specifically, in one embodiment, the crowd analyzer  58  performs a crowd formation process such as that described above in  FIG. 22  to form one or more crowds relevant to the POI or the AOI of the crowd request. In another embodiment, the crowd analyzer  58  proactively forms crowds using a process such as that described above in  FIGS. 24A through 24D  and stores corresponding crowd records in the datastore  64  of the MAP server  12 . Then, rather than forming the relevant crowds in response to the crowd request, the crowd analyzer  58  queries the datastore  64  to identify the crowds that are relevant to the crowd request. The crowds relevant to the crowd request may be those crowds within or intersecting a bounding region, such as a bounding box, for the crowd request. If the crowd request is for a POI, the bounding region is a geographic region of a predefined shape and size centered at the POI. If the crowd request is for an AOI, the bounding region is the AOI. 
     Once the crowd analyzer  58  has identified the crowds relevant to the crowd request, the MAP server  12  generates crowd data for the identified crowds (step  2406 ). As discussed below in detail, the crowd data for the identified crowds may include aggregate profiles for the crowds, information characterizing the crowds, or both. In addition, the crowd data may include spatial information defining the locations of the crowds, the number of users in the crowds, the amount of time the crowds have been located at or near the POI or within the AOI of the crowd request, or the like. The MAP server  12  then returns the crowd data to the MAP client  30 - 1  (step  2408 ). 
     Upon receiving the crowd data, the MAP client  30 - 1  forwards the crowd data to the MAP application  32 - 1  (step  2410 ). Note that in some embodiments the MAP client  30 - 1  may process the crowd data before sending the crowd data to the MAP application  32 - 1 . The MAP application  32 - 1  then presents the crowd data to the user  20 - 1  (step  2412 ). The manner in which the crowd data is presented depends on the particular implementation of the MAP application  32 - 1 . In one embodiment, the crowd data is overlaid upon a map. For example, the crowds may be represented by corresponding indicators overlaid on a map. The user  20 - 1  may then select a crowd in order to view additional crowd data regarding that crowd such as, for example, the aggregate profile of that crowd, characteristics of that crowd, or the like. 
     Note that in one embodiment, the MAP application  32 - 1  may operate to roll-up the aggregate profiles for multiple crowds into a rolled-up aggregate profile for those crowds. The rolled-up aggregate profile may be the average of the aggregate profiles of the crowds. For example, the MAP application  32 - 1  may roll-up the aggregate profiles for multiple crowds at a POI and present the rolled-up aggregate profile for the multiple crowds at the POI to the user  20 - 1 . In a similar manner, the MAP application  32 - 1  may provide a rolled-up aggregate profile for an AOI. In another embodiment, the MAP server  12  may roll-up crowds for a POI or an AOI and provide the rolled-up aggregate profile in addition to or as an alternative to the aggregate profiles for the individual crowds. 
       FIG. 29A  is a flow chart illustrating step  2406  of  FIG. 28  in more detail according to one embodiment of the present disclosure. In this embodiment, the crowd data returned by the MAP server  12  includes aggregate profiles for the crowds identified for the POI or the AOI. In this embodiment, upon receiving the crowd request, the MAP server  12  triggers the crowd analyzer  58  to identify crowds relevant to the current request, and then passes the identified crowds to the aggregation engine  60  in order to generate aggregate profiles for the identified crowds. 
     More specifically, after the crowd analyzer  58  has identified the crowds relevant to the current request, the identified crowds are passed to the aggregation engine  60 . The aggregation engine  60  selects a next crowd to process, which for the first iteration is the first crowd (step  2500 -A). The aggregation engine  60  then selects the next user in the crowd (step  2502 -A). Next, the aggregation engine  60  compares the user profile of the user in the crowd to the user profile of the requesting user, which for this example is the user  20 - 1  of the mobile device  18 - 1 , or a select subset of the user profile of the requesting user (step  2504 -A). In some embodiments, the user  20 - 1  may be enabled to select a subset of his user profile to be used for generation of the aggregate profile. For example, in the embodiment where user profiles are expressed as keywords in a number of profile categories, the user  20 - 1  may select one or more of the profile categories to be used for aggregate profile generation. When comparing the user profile of the user in the crowd to the user profile of the user  20 - 1 , the aggregation engine  60  identifies matches between the user profile of the user in the crowd and the user profile of the user  20 - 1  or the select subset of the user profile of the user  20 - 1 . In one embodiment, the user profiles are expressed as keywords in a number of profile categories. The aggregation engine  60  may then make a list of keywords from the user profile of the user in the crowd that match keywords in user profile of the user  20 - 1  or the select subset of the user profile of the user  20 - 1 . 
     Next, the aggregation engine  60  determines whether there are more users in the crowd (step  2506 -A). If so, the process returns to step  2502 -A and is repeated for the next user in the crowd. Once all of the users in the crowd have been processed, the aggregation engine  60  generates an aggregate profile for the crowd based on data resulting from the comparisons of the user profiles of the users in the crowd to the user profile of the user  20 - 1  or the select subset of the user profile of the user  20 - 1  (step  2508 -A). In an alternative embodiment, the aggregation engine  60  generates an aggregate profile for the crowd based on data resulting from the comparisons of the user profiles of the users in the crowd to a target user profile defined or otherwise specified by the user  20 - 1 . In one embodiment, the data resulting from the comparisons is a list of matching keywords for each of the users in the crowd. The aggregate profile may then include a number of user matches over all keywords and/or a ratio of the number of user matches over all keywords to the number of users in the crowd. The number of user matches over all keywords is a number of users in the crowd having at least one keyword in their user profile that matches a keyword in the user profile of the user  20 - 1  or the select subset of the user profile of the user  20 - 1 . The aggregate profile may additionally or alternatively include, for each keyword in the user profile of the user  20 - 1  or the select subset of the user profile of the user  20 - 1 , a number of user matches for the keyword or a ratio of the number of user matches for the keyword to the number of users in the crowd. Note that keywords in the user profile of the user  20 - 1  or the select subset of the user profile of the user  20 - 1  that have no user matches may be excluded from the aggregate profile. In addition, the aggregate profile for the crowd may include a total number of users in the crowd. 
     The aggregate profile for the crowd may additionally or alternatively include a match strength that is indicative of a degree of similarity between the user profiles of the users in the crowd and the user profile of the user  20 - 1 . The match strength may be computed as a ratio of the number of user matches to the total number of users in the crowd. Alternatively, the match strength may be computed as a function of the number of user matches per keyword and keyword weights assigned to the keywords. The keyword weights may be assigned by the user  20 - 1 . 
     Once the aggregate profile of the crowd is generated, the aggregation engine  60  determines whether there are more crowds to process (step  2510 -A). If so, the process returns to step  2500 -A and is repeated for the next crowd. Once aggregate profiles have been generated for all of the crowds relevant to the current request, the aggregate profiles for the crowds are returned (step  2512 -A). More specifically, the aggregate profiles are included in the crowd data returned to the MAP client  30 - 1  in response to the current request. 
     Note that in some embodiments the user  20 - 1  is enabled to activate a “nearby POIs” feature. If this feature is enabled, the crowds identified by the crowd analyzer  58  and processed by the aggregation engine  60  to produce corresponding aggregate profiles may also include crowds located at or near any nearby POIs. The nearby POIs may be POIs predefined by the user  20 - 1 , the MAP application  32 - 1 , and/or the MAP server  12  that are within a predefined distance from the POI or the AOI of the current request. 
       FIG. 29B  is a flow chart illustrating step  2406  of  FIG. 28  in more detail according to another embodiment of the present disclosure. In this embodiment, the crowd data returned by the MAP server  12  includes aggregate profiles for the crowds identified for the POI or the AOI. In this embodiment, upon receiving the crowd request, the MAP server  12  triggers the crowd analyzer  58  to identify crowds relevant to the current request, and then passes the identified crowds to the aggregation engine  60  in order to generate aggregate profiles for the identified crowds. 
     More specifically, after the crowd analyzer  58  has identified the crowds relevant to the current request, the identified crowds are passed to the aggregation engine  60 . The aggregation engine  60  selects a next crowd to process, which for the first iteration is the first crowd (step  2500 -B). The aggregation engine  60  then selects the next user in the crowd (step  2502 -B). Next, the aggregation engine  60  compares the user profile of the user in the crowd to the user profile of the requesting user, which for this example is the user  20 - 1  of the mobile device  18 - 1 , or a select subset of the user profile of the requesting user (step  2504 -B). In some embodiments, the user  20 - 1  may be enabled to select a subset of his user profile to be used for generation of the aggregate profile. For example, in the embodiment where user profiles are expressed as keywords in a number of profile categories, the user  20 - 1  may select one or more of the profile categories to be used for aggregate profile generation. When comparing the user profile of the user in the crowd to the user profile of the user  20 - 1 , the aggregation engine  60  identifies matches between the user profile of the user in the crowd and the user profile of the user  20 - 1  or the select subset of the user profile of the user  20 - 1 . In this embodiment, the user profiles are expressed as keywords in a number of profile categories. The aggregation engine  60  may then make a list of keywords from the user profile of the user in the crowd that match keywords in user profile of the user  20 - 1  or the select subset of the user profile of the user  20 - 1 . 
     Next, the aggregation engine  60  determines whether there are more users in the crowd (step  2506 -B). If so, the process returns to step  2502 -B and is repeated for the next user in the crowd. Once all of the users in the crowd have been processed, the aggregation engine  60  generates an aggregate profile for the crowd based on data resulting from the comparisons of the user profiles of the users in the crowd to the user profile of the user  20 - 1  or the select subset of the user profile of the user  20 - 1  (step  2508 -B). In an alternative embodiment, the aggregation engine  60  generates an aggregate profile for the crowd based on data resulting from the comparisons of the user profiles of the users in the crowd to a target user profile defined or otherwise specified by the user  20 - 1 . In this embodiment, the data resulting from the comparisons is a list of matching keywords for each of the users in the crowd. The aggregate profile may then include a number of user matches over all keywords and/or a ratio of the number of user matches over all keywords to the number of users in the crowd. The number of user matches over all keywords is a number of users in the crowd having at least one keyword in their user profile that matches a keyword in the user profile of the user  20 - 1  or the select subset of the user profile of the user  20 - 1 . The aggregate profile may additionally or alternatively include, for each keyword in the user profile of the user  20 - 1  or the select subset of the user profile of the user  20 - 1 , a number of user matches for the keyword or a ratio of the number of user matches for the keyword to the number of users in the crowd. Note that keywords in the user profile of the user  20 - 1  or the select subset of the user profile of the user  20 - 1  that have no user matches may be excluded from the aggregate profile. In addition, the aggregate profile for the crowd may include a total number of users in the crowd. 
     The aggregate profile for the crowd may additionally or alternatively include a match strength that is indicative of a degree of similarity between the user profiles of the users in the crowd and the user profile of the user  20 - 1 . The match strength may be computed as a ratio of the number of user matches to the total number of users in the crowd. Alternatively, the match strength may be computed as a function of the number of user matches per keyword and keyword weights assigned to the keywords. The keyword weights may be assigned by the user  20 - 1 . 
     Once the aggregate profile of the crowd is generated, in this embodiment, the aggregation engine  60  compares the user profiles of the users in the crowd to one another to determine N keywords having the highest number of user matches among the users in the crowd (step  2510 -B). Here, N may be, for example, five. The aggregation engine  60  then adds any of the N keywords that are not already in the aggregate profile to the aggregate profile and flags those keywords as non-matching keywords (step  2512 -B). These keywords are flagged as non-matching because they do not match any of the keywords in the user profile, or select subset thereof, of the user  20 - 1 . The non-matching keywords are preferably differentiated from the matching keywords in the aggregate profile when presented to the user  20 - 1 . The non-matching keywords are particularly beneficial where there are few or no matching keywords between the user profile of the user  20 - 1  and the user profiles of the users in the crowd. In this situation, the non-matching keywords would allow the user  20 - 1  to gain some understanding of the interests of the users in the crowd. 
     Next, the aggregation engine  60  determines whether there are more crowds to process (step  2514 -B). If so, the process returns to step  2500 -B and is repeated for the next crowd. Once aggregate profiles have been generated for all of the crowds relevant to the current request, the aggregate profiles for the crowds are returned (step  2516 -B). More specifically, the aggregate profiles are included in the crowd data returned to the MAP client  30 - 1  in response to the current request. 
     Note that in some embodiments the user  20 - 1  is enabled to activate a “nearby POIs” feature. If this feature is enabled, the crowds identified by the crowd analyzer  58  and processed by the aggregation engine  60  to produce corresponding aggregate profiles may also include crowds located at or near any nearby POIs. The nearby POIs may be POIs predefined by the user  20 - 1 , the MAP application  32 - 1 , and/or the MAP server  12  that are within a predefined distance from the POI or the AOI of the current request. 
       FIG. 30  illustrates the operation of the system  10  of  FIG. 1  to enable the subscriber device  22  to request information regarding current crowds according to one embodiment of the present disclosure. First, subscriber device  22  sends a crowd request to the MAP client  30 - 1  (step  2600 ). The crowd request is a request for current crowds at a specified POI or AOI. The crowd request may be initiated by the subscriber  24  at the subscriber device  22  via the web browser  38  or a custom application enabled to access the MAP server  12 . Preferably, the subscriber  24  is enabled to identify the POI or the AOI for the crowd request by, for example, selecting the POI or the AOI on a map, selecting a crowd center of an existing crowd as a POI, selecting a crowd location of an existing crowd as a center of an AOI, selecting the POI or the AOI from a predefined list of POIs and/or AOIs, or the like. The predefined list of POIs and/or AOIs may be defined by, for example, the subscriber  24  and/or the MAP server  12 . 
     In response to receiving the crowd request from the subscriber device  22 , the MAP server  12  identifies one or more crowds relevant to the crowd request (step  2602 ). More specifically, in one embodiment, the crowd analyzer  58  performs a crowd formation process such as that described above in  FIG. 22  to form one or more crowds relevant to the POI or the AOI of the crowd request. In another embodiment, the crowd analyzer  58  proactively forms crowds using a process such as that described above in  FIGS. 24A through 24C  and stores corresponding crowd records in the datastore  64  of the MAP server  12 . Then, rather than forming the relevant crowds in response to the crowd request, the crowd analyzer  58  queries the datastore  64  to identify the crowds that are relevant to the crowd request. The crowds relevant to the crowd request may be those crowds within or overlapping a bounding region, such as a bounding box, for the crowd request. If the crowd request is for a POI, the bounding region is a geographic region of a predefined shape and size centered at the POI. If the crowd request is for an AOI, the bounding region is the AOI. 
     Once the crowd analyzer  58  has identified the crowds relevant to the crowd request, the MAP server  12  generates crowd data for the identified crowds (step  2604 ). The crowd data for the identified crowds may include aggregate profiles for the crowds, information characterizing the crowds, or both. In addition, the crowd data may include the locations of the crowds, the number of users in the crowds, the amount of time the crowds have been located at or near the POI or within the AOI, or the like. The MAP server  12  then returns the crowd data to the MAP client  30 - 1  (step  2606 ). In the embodiment where the subscriber  24  accesses the MAP server  12  via the web browser  38  at the subscriber device  22 , the MAP server  12  formats the crowd data into a suitable web format before sending the crowd data to the subscriber device  22 . The manner in which the crowd data is formatted depends on the particular implementation. In one embodiment, the crowd data is overlaid upon a map. For example, in one embodiment, the MAP server  12  may provide the crowd data to the subscriber device  22  via one or more web pages. Using the one or more web pages, crowd indicators representative of the locations of the crowds may be overlaid on a map. The subscriber  24  may then select a crowd in order to view additional crowd data regarding that crowd such as, for example, the aggregate profile of that crowd, characteristics of that crowd, or the like. Upon receiving the crowd data, the subscriber device  22  presents the crowd data to the subscriber  24  (step  2608 ). Note that in one embodiment, the MAP server  12  may roll-up the aggregate profiles for multiple crowds at a POI or in an AOI to provide a rolled-up aggregate profile that may be returned in addition to or as an alternative to the aggregate profiles of the individual crowds. 
     It should be noted that in some embodiments, the subscriber  24  may be enabled to specify filtering criteria via the web browser  38  or a custom application for interacting with the MAP server  12 . For example, the subscriber  24  may specify filtering criteria regarding types of crowds in which the subscriber  24  is or is not interested. For instance, the crowd data may be presented to the subscriber  24  via one or more web pages that enable the subscriber  24  to select a filtering feature. In response, a list of keywords appearing in the user profiles of the crowds identified as being relevant to the current request may be presented to the subscriber  24 . The subscriber  24  may then specify one or more keywords from the list such that crowds having users with user profiles that do not include any of the specified keywords are filtered, or removed, and are therefore not considered when generating the crowd data in response to a crowd request. 
       FIG. 31  is a flow chart illustrating step  2604  of  FIG. 30  in more detail according to one embodiment of the present disclosure. In this embodiment, the crowd data returned by the MAP server  12  includes aggregate profiles for the crowds identified for the POI or the AOI. In this embodiment, upon receiving the crowd request, the MAP server  12  triggers the crowd analyzer  58  to identify crowds relevant to the crowd request, and then passes the identified crowds to the aggregation engine  60  in order to generate aggregate profiles for the identified crowds. 
     More specifically, after the crowd analyzer  58  has identified the crowds relevant to the crowd request, the identified crowds are passed to the aggregation engine  60 . The aggregation engine  60  selects a next crowd to process, which for the first iteration is the first crowd (step  2700 ). The aggregation engine  60  then generates an aggregate profile for the crowd based on a comparison of the user profiles of the users in the crowd to one another (step  2702 ). Note that in an alternative embodiment, the aggregation engine  60  then generates an aggregate profile for the crowd based on a comparison of the user profiles of the users in the crowd to a target user profile defined by the subscriber  24 . 
     In one embodiment, in order to generate the aggregate profile for the crowd, the user profiles are expressed as keywords for each of a number of profile categories. Then, the aggregation engine  60  may determine an aggregate list of keywords for the crowd. The aggregate list of keywords is a list of all keywords appearing in the user profiles of the users in the crowd. The aggregate profile for the crowd may then include a number of user matches for each keyword in the aggregate list of keywords for the crowd. The number of user matches for a keyword is the number of users in the crowd having a user profile that includes that keyword. The aggregate profile may include the number of user matches for all keywords in the aggregate list of keywords for the crowd or the number of user matches for keywords in the aggregate list of keywords for the crowd having more than a predefined number of user matches (e.g., more than 1 user match). The aggregate profile may also include the number of users in the crowd. In addition or alternatively, the aggregate profile may include, for each keyword in the aggregate list or each keyword in the aggregate list having more than a predefined number of user matches, a ratio of the number of user matches for the keyword to the number of users in the crowd. 
     Once the aggregate profile of the crowd is generated, the aggregation engine  60  determines whether there are more crowds to process (step  2704 ). If so, the process returns to step  2700  and is repeated for the next crowd. Once aggregate profiles have been generated for all of the crowds relevant to the crowd request, the aggregate profiles for the crowds are returned (step  2706 ). Note that in some embodiments the subscriber  24  is enabled to activate a “nearby POIs” feature. If this feature is enabled, the crowds identified by the crowd analyzer  58  and processed by the aggregation engine  60  to produce corresponding aggregate profiles may also include crowds located at or near any nearby POIs. The nearby POIs may be POIs predefined by the subscriber  24  and/or the MAP server  12  that are within a predefined distance from the POI or the AOI of the crowd request. 
       FIGS. 32A through 32E  illustrate a GUI  198  for an exemplary embodiment of the MAP application  32 - 1  of the mobile device  18 - 1  ( FIG. 1 ). As illustrated in  FIG. 32A , the GUI  198  includes a settings screen  198 - 1  that is presented in response to selection of a corresponding settings button  200  by the user  20 - 1 . A navigation button  202  may be selected to view a map and perform navigation functions such as obtaining directions to a desired location. A list button  204  enables the user  20 - 1  to view a list of friends, crowds, POIs, and AOIs, as discussed below. Regarding the settings displayed in the settings screen  198 - 1  of the GUI  198 , the user  20 - 1  is enabled to provide his Facebook® login information which, as described above, enables the user profile of the user  20 - 1  to be obtained from the Facebook® social networking service. In this example, the user  20 - 1  has already been logged in to Facebook. As such, the user  20 - 1  may logout of Facebook by selecting a logout button  206 . In addition, by selecting a profile setting  208 , the user  20 - 1  is enabled to view his profile and select one or more profile categories to be used for aggregate profile generation. 
     The settings screen  198 - 1  also enables the user  20 - 1  to configure a number of privacy settings. Namely, the settings screen  198 - 1  enables the user  20 - 1  to set a stealth mode switch  210  to either an on position or an off position. When the stealth mode switch  210  is in the on position, the location of the user  20 - 1  is not reported to the friends of the user  20 - 1 . However, the location of the user  20 - 1  is still reported for use by the MAP server  12 . The privacy settings also include a location refresh setting  212  that enables the user  20 - 1  to configure how often location updates are to be sent by the MAP application  32 - 1 . Lastly, the settings screen  198 - 1  includes an alerts setting  214  that enables the user  20 - 1  to configure one or more alerts. As discussed below, an alert can be tied to a particular POI or AOI such that the user  20 - 1  is alerted, or notified, when a crowd at the particular POI or AOI satisfies one or more specified criteria. Alternatively, an alert can be tied to a particular crowd such that the user  20 - 1  is alerted, or notified, when the crowd satisfies one or more specified criteria. 
     Returning to the profile setting  208 , if the user  20 - 1  selects the profile setting  208 , a user profile screen  198 - 2  is presented to the user  20 - 1  via the GUI  198 , as illustrated in  FIG. 32B . The user profile screen  198 - 2  shows a number of profile categories  216 A through  216 E and corresponding lists of keywords  218 A through  218 E, which form the user profile of the user  20 - 1 . The user  20 - 1  is enabled to select one or more of the profile categories  216 A through  216 E to be used for aggregate profile generation (i.e., comparison to user profiles for history objects and crowds to create corresponding aggregate profiles for the user  20 - 1 ). In this example, the user  20 - 1  has selected his “My Interests” profile category  216 C, where the corresponding list of keywords  218 C define general interests of the user  20 - 1 . In the user profile screen  198 - 2 , the user  20 - 1  can return to the settings screen  198 - 1  by selecting a settings button  220 . 
       FIGS. 32C and 32D  illustrate a list screen  198 - 3  that is presented to the user  20 - 1  via the GUI  198  in response to selecting the list button  204 . The list screen  198 - 3  includes a friends button  222 , a crowds button  224 , a POI button  226 , an areas button  228 , and an all button  230 . The list screen  198 - 3  enables the user  20 - 1  to view a list of his friends by selecting the friends button  222 , a list of crowds at POIs or within AOIs of the user  20 - 1  by selecting the crowds button  224 , a list of POIs of the user  20 - 1  by selecting the POI button  226 , or a list of AOIs of the user  20 - 1  by selecting the areas button  228 . In addition, the list screen  198 - 3  enables the user  20 - 1  to view a list that includes the friends of the user, the crowds at POIs or within AOIs of the user  20 - 1 , the POIs of the user  20 - 1 , and the AOIs of the user  20 - 1  by selecting the all button  230 . 
     In this example, the user  20 - 1  has selected the all button  230 . As such, the list screen  198 - 3  presents an AOI list  232  that includes a number of AOIs previously defined by the user  20 - 1 . Note that each of the AOIs may be a static AOI defining a static geographic area or a dynamic AOI that is defined relative to a dynamic location such as a location of a friend of the user  20 - 1 . For instance, in this example, the “Near Jack Shephard” AOI is a geographic area of a defined shape and size that is centered at the current location of the user&#39;s friend Jack Shephard. Note that in one embodiment, persons whose current locations may be used for dynamic AOIs are limited to the friends of the user  20 - 1 . The user  20 - 1  may select an AOI from the AOI list  232  in order to view crowd data for the AOI. For example, by selecting the My Neighborhood AOI, the GUI  198  may present a map including the My Neighborhood AOI. Crowds relevant to the My Neighborhood AOI are presented on the map. The user  20 - 1  may then select a desired crowd in order to view detailed information regarding that crowd such as, for example, the aggregate profile of the crowd, characteristics of the crowd, or both. 
     The list screen  198 - 3  also presents a crowds list  234  that includes a number of crowds that are at the POIs or within the AOIs of the user  20 - 1 . In this example, there are twelve crowds. The GUI  198  enables the user  20 - 1  to select a crowd from the crowds list  234  in order to view additional information regarding the crowd. For example, by selecting the Crowd of 6, the user  20 - 1  may be presented with a map showing the current location of the Crowd of 6 and detailed information regarding the Crowd of 6 such as, for example, the aggregate profile of the Crowd of 6, characteristics of the Crowd of 6, or both. 
     The list screen  198 - 3  also includes a friends list  236 , as illustrated in  FIG. 32D . The user  20 - 1  may select a friend from the friends list  236  in order to view crowds nearby that friend. In other words, the current locations of the friends of the user  20 - 1  are treated as temporary or dynamic POIs such that crowd data for current locations of the friends of the user  20 - 1  is obtained from the MAP server  12 . In addition, the user  20 - 1  may choose to define an AOI centered at the current location of a friend to create a dynamic AOI, as discussed above. The friends list  236  also presents the current location of the friends of the user  20 - 1  relative to the current location of the user  20 - 1 . 
     The list screen  198 - 3  also includes a POI list  238  that includes a number of POIs of the user  20 - 1 . The user  20 - 1  may select a POI from the POI list  238  in order to view crowd data for the POI. For example, by selecting the Steve&#39;s house POI, the GUI  198  may present a map including the Steve&#39;s house POI. Crowds at or near the Steve&#39;s house POI are presented on the map. The user  20 - 1  may then select a desired crowd in order to view detailed information regarding that crowd such as, for example, the aggregate profile of the crowd, characteristics of the crowd, or both. Lastly, returning to  FIG. 32C , the list screen  198 - 3  includes a You item  240  that may be selected by the user  20 - 1  to access the user profile screen  198 - 2  ( FIG. 32B ). 
       FIG. 32E  is a crowd data display screen  198 - 4  presented by the GUI  198 . In this example, the user  20 - 1  has selected the Around You AOI from the AOI list  232  ( FIG. 32C ). As a result, the GUI  198  presents the crowd data display screen  198 - 4  for the Around You AOI. The crowd data display screen  198 - 4  includes a map area  242 . In this example, the current location of the user  20 - 1  is used as the center of the Around You AOI. The current location of the user  20 - 1  is represented in the map area  242  by a corresponding indicator  244 . Crowds in the Around You AOI are represented in the map area by crowd indicators  246  through  250 . In this embodiment, the crowd indictors  246  through  250  show the locations of the crowds as well as match strengths for the crowds. The locations of the crowds are included in the crowd data. The match strengths for the crowds may be included in the aggregate profiles for the crowds or may be determined based on the aggregate profiles for the crowds. In this embodiment, the match strength of a crowd is computed as a ratio of the number of user matches over all keywords to the number of users in the crowd. A ratio of one results in a highest match strength, and a ratio of zero results in a lowest match strength. 
     Using the GUI  198 , the user  20 - 1  is enabled to select a particular crowd in the map area  242  to view more detailed information for that crowd in a crowd detail area  252  of the crowd data display screen  198 - 4 . In this example, the user  20 - 1  has selected the crowd indicator  246 . As a result, more detailed information for the crowd represented by the crowd indicator  246  is presented in the crowd detail area  252 . The more detailed information for the crowd is from the crowd data for the crowd or derived from the crowd data for the crowd. In this example, the aggregate profile of the crowd is used to derive the match strength for the crowd, and the match strength is presented in the crowd detail area  252 . In addition, the crowd size and number of user matches over all keywords are obtained from the aggregate profile for the crowd and presented in the crowd detail area  252 . In this example, a quality factor for the crowd is also presented. As discussed below in detail, the quality factor of the crowd may be an average of a quality or confidence of the current locations of the users in the crowd. Still further, the crowd data display screen  198 - 4  includes a keyword matches area  254  for presenting keyword matches for the selected crowd. In this example, a font size of the keywords in the keyword matches area  254  reflects the number of user matches for that keyword. Therefore, in this example, the number of user matches for the keyword “technology” is greater than the number of user matches for the keyword “books.” 
       FIGS. 33A through 33C  illustrate an exemplary web interface  256  provided by the MAP server  12  and presented to the subscriber  24  at the subscriber device  22 . The web interface  256  includes a number of tabs  258  through  272 , namely, a home tab  258 , a realtime tab  260 , a historical tab  262 , a watch zones tab  264 , an alerts tab  266 , a filters tab  268 , a reports tab  270 , and an account tab  272 . The home tab  258  enables the subscriber  24  to view a home screen. The home screen may include any desired information such as, for example, a link to a Frequently Asked Question (FAQ) page, instructions on how to use the web interface  256 , or the like. The realtime tab  260  enables the subscriber to view realtime crowd data for POIs and/or AOIs of the subscriber  24 . The historical tab  262  enables the subscriber  24  to view historical data for a POI or an AOI in a time context and/or a geographic context in the manner described above. The watch zones tab  264  enables the subscriber  24  to select POIs and/or AOIs of interest to the subscriber  24 . The alerts tab  266  enables the subscriber  24  to configure one or more alerts. The filters tab  268  enables the subscriber  24  to configure filters and/or select filters to be applied to the crowd data in the realtime or historical view. The reports tab  270  enables the subscriber  24  to access reports previously generated for crowds of interest, POIs, and/or AOIs. Lastly, the account tab  272  enables the subscriber  24  to manage the subscriber&#39;s account. 
     More specifically,  FIG. 33A  illustrates the web interface  256  when the realtime tab  260  has been selected by the subscriber  24 . When the realtime tab  260  is selected, the web interface  256  presents a map area  274  that shows an AOI  276  and a number of crowds  278  through  282  currently located within the AOI  276 . In addition, in this exemplary embodiment, crowds  284  and  286  that are outside the AOI  276  are also illustrated. The crowds  284  and  286  are crowds located at other POIs or within other AOIs of the subscriber  24  that are not currently being viewed by the subscriber  24 . The subscriber  24  may view another POI or AOI by selecting the desired POI or AOI from a list presented in response to selection of a button  288 . In this example, POIs and AOIs are generically referred to as watch zones. 
     In this example, the subscriber  24  selects the crowd  278 . In response, the web interface  256  presents an aggregate profile window  290  to the subscriber  24 , as illustrated in  FIG. 33B . The aggregate profile window  290  presents an aggregate profile of the crowd  278 , where in this embodiment the aggregate profile is in the form of an interest histogram showing the number of user matches in the crowd  278  for each of a number of keywords. The subscriber  24  may be enabled to create an alert for the crowd  278  by selecting a create an alert button  292 . In response, the subscriber  24  may be enabled to utilize the keywords in the aggregate profile window  290  to create an alert. For example, the subscriber  24  may create an alert such that the subscriber  24  is notified when the number of user matches for the keyword “Sushi” in the crowd  278  reaches one hundred. The subscriber  24  may also be enabled to create a report for the crowd  278  by selecting a create a report button  294 . The report may, for example, include details about the crowd  278  such as, for example, the location of the crowd  278 , the size of the crowd  278 , the aggregate profile of the crowd  278 , the current time and date, or the like, where the report may be saved or printed by the subscriber  24 . 
     In addition, the subscriber  24  may be enabled to create a filter by selecting a create a filter button  296 . In response to selecting the create a filter button  296 , a new filter screen  298  is presented to the subscriber  24 , as illustrated in  FIG. 33C . The subscriber  24  may then select keywords from the interest histogram for the crowd  278  to be used for the filter. In addition, the subscriber  24  may be enabled to add new keywords to the filter by selecting an add keywords button  300 . Once the subscriber  24  has configured the filter, the subscriber  24  is enabled to create the filter by selecting a create button  302 . Once the filter is created, the filter may be used to filter crowds for any AOI or POI of the subscriber  24 . 
       FIGS. 34 through 45  describe the operation of the crowd analyzer  58  of the MAP server  12  to characterize crowds according to another embodiment of the present disclosure. More specifically, the crowd analyzer  58  may determine a degree-of-fragmentation, best and worst case average DOS, and/or a degree of bidirectionality for crowds. This information may then be included in crowd data for those crowds returned to the mobile devices  18 - 1  through  18 -N and/or the subscriber device  22 . In addition or alternatively, the data characterizing crowds may be used to filter crowds. For example, a filter may be applied such that crowds having a worst-case average DOS greater than a defined threshold are not presented to a user/subscriber. The filtering may be performed by the MAP server  12  before returning crowd data to the requesting device (i.e., one of the mobile devices  18 - 1  through  18 -N, the subscriber device  22 , or a device hosting the third-party service  26 ). Alternatively, the filtering may be performed by the mobile devices  18 - 1  through  18 -N, the subscriber device  22 , or a device hosting the third-party service  26 . 
       FIG. 34  is a flow chart illustrating a spatial crowd fragmentation process according to one embodiment of the present disclosure. This process is similar to the spatial crowd formation process discussed above with respect to  FIG. 22 . First, the crowd analyzer  58  creates a crowd fragment of one user for each user in a crowd (step  2800 ). Note that this spatial crowd fragmentation process may be performed reactively in response to a current request for crowd data for a POI or an AOI or performed proactively. Next, the crowd analyzer  58  determines the two closest crowd fragments in the crowd (step  2802 ) and a distance between the two closest crowd fragments (step  2804 ). The distance between the two closest crowd fragments is the distance between the crowd fragment centers of the two closest crowd fragments. The crowd fragment center for a crowd fragment having only one user is the current location of that one user. 
     The crowd analyzer  58  then determines whether the distance between the two closest crowd fragments is less than an optimal inclusion distance for a crowd fragment (step  2806 ). In one embodiment, the optimal inclusion distance for a crowd fragment is a predefined static value. In another embodiment, the optimal inclusion distance of the crowd may vary. For example, if the spatial crowd formation process of  FIGS. 24A through 24D  is used for proactive crowd formation, then the optimal inclusion distance for the crowd may vary. As such, the optimal inclusion distance for a crowd fragment within the crowd may be defined as a fraction of the optimal inclusion distance of the crowd such that the optimal inclusion distance for a crowd fragment within the crowd varies along with the optimal inclusion distance for the crowd itself. 
     If the distance between the two closest crowd fragments is less than the optimal inclusion distance for a crowd fragment, then the two closest crowd fragments are combined (step  2808 ) and a new crowd fragment center is computed for the resulting crowd fragment (step  2810 ). The crowd fragment center may be computed using, for example, a center of mass algorithm. At this point the process returns to step  2802  and is repeated. Once the two closest crowd fragments in the crowd are separated by more than the optimal inclusion distance for a crowd fragment, the process ends. At this point, the crowd analyzer  58  has created the crowd fragments or defined the crowd fragments for the crowd. The crowd analyzer  58  may then represent the degree of fragmentation of the crowd based on the number of crowd fragments in the crowd and, optionally, an average number of users per crowd fragment. The degree of fragmentation of the crowd may be included in the crowd data returned to the requesting device in response to a crowd request for a POI or an AOI to which the crowd is relevant. 
       FIGS. 35A and 35B  graphically illustrate the spatial crowd fragmentation process of  FIG. 34  for an exemplary crowd  304  having bounding box  305 .  FIG. 35A  illustrates the crowd  304  before spatial crowd fragmentation.  FIG. 35B  illustrates the crowd  304  after spatial crowd fragmentation. As illustrated, after spatial crowd fragmentation, the crowd  304  includes a number of crowd fragments  306  through  314 . As such, the crowd  304  has a degree of fragmentation of five crowd fragments with an average of approximately 2 users per crowd fragment. Thus, the crowd  304  has a moderately high degree of fragmentation. The highest degree of fragmentation for the crowd  304  would be to have eleven crowd fragments with an average of one user per crowd fragment. The lowest degree of fragmentation for the crowd  304  would be to have one crowd fragment with an average of eleven users per crowd fragment. 
       FIG. 36  illustrates a connectivity-based crowd fragmentation process according to one embodiment of the present disclosure. First, the crowd analyzer  58  creates a crowd fragment for each user in the crowd (step  2900 ). Note that this connectivity-based crowd fragmentation process may be performed reactively in response to a current request for crowd data for a POI or an AOI or performed proactively. Next, the crowd analyzer  58  selects a next pair of crowd fragments in the crowd (step  2902 ) and then selects one user from each of those crowd fragments (step  2904 ). The crowd analyzer  58  then determines a DOS between the users from the pair of crowd fragments (step  2906 ). More specifically, as will be appreciated by one of ordinary skill in the art, DOS is a measure of the degree to which the two users are related in a social network (e.g., the Facebook® social network, the MySpace® social network, or the LinkedIN® social network). The two users have a DOS of one if one of the users is a friend of the other user, a DOS of two if one of the users is a friend of a friend of the other user, a DOS of three if one of the users is a friend of a friend of a friend of the other user, etc. If the two users are not related in a social network or have an unknown DOS, the DOS for the two users is set to a value equal to or greater than the maximum DOS for a crowd fragment. 
     The crowd analyzer  58  then determines whether the DOS between the two users is less than a predefined maximum DOS for a crowd fragment (step  2908 ). For example, the predefined maximum DOS may be three. However, other maximum DOS values may be used to achieve the desired crowd fragmentation. If the DOS between the two users is not less than the predefined maximum DOS, the process proceeds to step  2916 . If the DOS between the two users is less than the predefined maximum DOS, the crowd analyzer  58  determines whether a bidirectionality requirement is satisfied (step  2910 ). The bidirectionality requirement specifies whether the relationship between the two users must be bidirectional (i.e., the first user must directly or indirectly know the second user and the second user must directly or indirectly know the first user). Bidirectionality may or may not be required depending on the particular embodiment. If the two users satisfy the bidirectionality requirement, the crowd analyzer  58  combines the pair of crowd fragments (step  2912 ) and computes a new crowd fragment center for the resulting crowd fragment (step  2914 ). The process then returns to step  2902  and is repeated for a next pair of crowd fragments. If the two users do not satisfy the bidirectionality requirement, the process proceeds to step  2916 . 
     At this point, whether proceeding from step  2908  or step  2910 , the crowd analyzer  58  determines whether all user pairs from the two crowd fragments have been processed (step  2916 ). If not, the process returns to step  2904  and is repeated for a new pair of users from the two crowd fragments. If all user pairs from the two crowd fragments have been processed, the crowd analyzer  58  then determines whether all crowd fragments have been processed (step  2918 ). If not, the process returns to step  2902  and is repeated until all crowd fragments have been processed. Once this process is complete, the crowd analyzer  58  has determined the number of crowd fragments in the crowd. The degree of fragmentation of the crowd may then be provided as the number of crowd fragments and the average number of users per crowd fragment. 
       FIGS. 37A and 37B  graphically illustrate the connectivity-based crowd fragmentation process of  FIG. 36 .  FIG. 37A  illustrates a crowd  316  having a number of users and a bounding box  317 .  FIG. 37B  illustrates the crowd  316  after the connectivity-based crowd fragmentation process has been performed. As illustrated, there are three crowd fragments resulting from the connectivity-based crowd fragmentation process. Namely, crowd fragment A has four users marked as “A,” crowd fragment B has five users marked as “B,” and crowd fragment C has three users marked as “C.” As illustrated, the users in a particular crowd fragment may not be close to one another spatially since, in this embodiment, there is no spatial requirement for users of the crowd fragment other than that the users of the crowd fragment are in the same crowd. 
       FIG. 38  is a flow chart illustrating a recursive crowd fragmentation process that uses both spatial crowd fragmentation and connectivity-based crowd fragmentation according to one embodiment of the present disclosure. First, the crowd analyzer  58  performs a spatial crowd fragmentation process to create a number of crowd fragments for a crowd (step  3000 ). The spatial crowd fragmentation process may be the spatial crowd fragmentation process of  FIG. 34 . The crowd analyzer  58  then selects a next crowd fragment of the crowd fragments created for the crowd (step  3002 ). Next, the crowd analyzer  58  performs a connectivity-based crowd fragmentation process to create a number of sub-fragments for the crowd fragment of the crowd (step  3004 ). The connectivity-based crowd fragmentation process may be the connectivity-based crowd fragmentation process of  FIG. 36 . The crowd analyzer  58  then determines whether the last crowd fragment of the crowd has been processed (step  3006 ). If not, the process returns to step  3002  and is repeated until the last crowd fragment of the crowd has been processed. At that point, the process is complete. The degree of fragmentation for the crowd may then include the number of sub-fragments and average number of users per sub-fragment for each crowd fragment. 
       FIG. 39  is a flow chart illustrating a recursive crowd fragmentation process that uses both spatial crowd fragmentation and connectivity-based crowd fragmentation according to another embodiment of the present disclosure. First, the crowd analyzer  58  performs a connectivity-based crowd fragmentation process to create a number of crowd fragments for a crowd (step  3100 ). The connectivity-based crowd fragmentation process may be the connectivity-based crowd fragmentation process of  FIG. 36 . The crowd analyzer  58  then selects a next crowd fragment of the crowd fragments created for the crowd (step  3102 ). Next, the crowd analyzer  58  performs a spatial crowd fragmentation process to create a number of sub-fragments for the crowd fragment of the crowd (step  3104 ). The spatial crowd fragmentation process may be the spatial crowd fragmentation process of  FIG. 34 . The crowd analyzer  58  then determines whether the last crowd fragment of the crowd has been processed (step  3106 ). If not, the process returns to step  3102  and is repeated until the last crowd fragment of the crowd has been processed. At that point, the process is complete. The degree of fragmentation for the crowd may then include the number of sub-fragments and average number of users per sub-fragment for each crowd fragment. 
       FIGS. 40A and 40B  illustrate an exemplary graphical representation of the degree of fragmentation for a crowd. This exemplary graphical representation may be presented by the MAP application  32 - 1  based on corresponding crowd data provided by the MAP server  12  in response to a crowd request or presented by the MAP server  12  to the subscriber  24  via the web browser  38  of the subscriber device  22 .  FIG. 40A  illustrates a graphical representation of the degree of fragmentation for a crowd having two crowd fragments with an average of twenty-five users per crowd fragment.  FIG. 40B  illustrates a graphical representation of the degree of fragmentation for a crowd having twenty-five crowd fragments with an average of two users per crowd fragment. 
       FIG. 41  is a flow chart for a process for determining a best-case and worst-case average DOS for a crowd fragment of a crowd according to one embodiment of the present disclosure. The crowd analyzer  58  counts the number of 1 DOS, 2 DOS, . . . , M DOS relationships in a crowd fragment (step  3200 ) and the number of user pairs in the crowd fragment for which explicit relationships are not defined or known (step  3202 ). More specifically, for each pair of users in the crowd fragment, the crowd analyzer  58  determines the DOS between the pair of users if the DOS between the pair of user is known or determines that the DOS between the pair of users is not defined or known if the DOS between the pair of users is in fact not defined or known. Based on these determinations, the crowd analyzer  58  counts the number of user pairs having a DOS of 1, the number of user pairs having a DOS of 2, etc. In addition, the crowd analyzer  58  counts the number of user pairs for which no relationship is defined or known. 
     The crowd analyzer  58  then computes a best-case average DOS for the crowd fragment using a best-case DOS for the user pairs in the crowd fragment for which explicit relationships are not defined (step  3204 ). In this embodiment, the best-case average DOS is 1. The best-case average DOS may computed as: 
     
       
         
           
             
               
                 AverageDOS 
                 BestCase 
               
               = 
               
                 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         1 
                       
                       M 
                     
                      
                     
                       ( 
                       
                         i 
                         · 
                         
                           DOS_count 
                           i 
                         
                       
                       ) 
                     
                   
                   + 
                   
                     
                       DOS 
                       BestCase 
                     
                     · 
                     Num_Unknown 
                   
                 
                 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         1 
                       
                       M 
                     
                      
                     
                       ( 
                       
                         DOS_count 
                         i 
                       
                       ) 
                     
                   
                   + 
                   Num_Unknown 
                 
               
             
             , 
           
         
       
     
     where AverageDOS BestCase  is the best-case average DOS for the crowd fragment, DOS count, is the number of user pairs for the ith DOS, DOS BestCase  is the best-case DOS, and Num_Unknown is the number of user pairs for which a relationship is not defined or is unknown. 
     The crowd analyzer  58  also computes the worst-case average DOS for the crowd fragment using a worst-case DOS for the user pairs in the crowd fragment for which explicit relationships are not defined (step  3206 ). In this embodiment, the worst-case DOS is a greatest possible DOS that the crowd analyzer  58  considers, which may be, for example, a DOS of greater than or equal to 7. For instance, the worst-case DOS may be 10. However, other values for the worst-case DOS may be used. The worst-case average DOS may computed as: 
     
       
         
           
             
               
                 AverageDOS 
                 WorstCase 
               
               = 
               
                 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         1 
                       
                       M 
                     
                      
                     
                       ( 
                       
                         i 
                         · 
                         
                           DOS_count 
                           i 
                         
                       
                       ) 
                     
                   
                   + 
                   
                     
                       DOS 
                       WorstCase 
                     
                     · 
                     Num_Unknown 
                   
                 
                 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         1 
                       
                       M 
                     
                      
                     
                       ( 
                       
                         DOS_count 
                         i 
                       
                       ) 
                     
                   
                   + 
                   Num_Unknown 
                 
               
             
             , 
           
         
       
     
     where AverageDOS WorstCase  is the worst-case average DOS for the crowd fragment, DOS count, is the number of user pairs for the ith DOS, DOS WorstCase  is the worst-case DOS, and Num_Unknown is the number of user pairs for which a relationship is not defined or is unknown. 
       FIG. 42  is a more detailed flow chart illustrating the process for determining a best-case and worst-case average DOS for a crowd fragment according to one embodiment of the present disclosure. First, the crowd analyzer  58  selects the next user in the crowd fragment, which for the first iteration is the first user in the crowd fragment (step  3300 ), and clears a found member list (step  3302 ). The crowd analyzer  58  then sets a current DOS to one (step  3304 ). Next, the crowd analyzer  58  selects a next friend of the user (step  3306 ). Note that, in one embodiment, information identifying the friends of the user are obtained from the one or more profile servers  14  along with the user profile of the user. The crowd analyzer  58  then determines whether the friend of the user is also a member of the crowd fragment (step  3308 ). If not, the process proceeds to step  3314 . If the friend is also a member of the crowd fragment, the crowd analyzer  58  determines whether the friend is already in the found member list (step  3310 ). If so, the process proceeds to step  3314 . If the friend is also a member of the crowd fragment and is not already in the found member list, the crowd analyzer  58  increments a found count for the current DOS and adds the friend to the found member list (step  3312 ). At this point, whether proceeding from step  3308  or step  3310 , the crowd analyzer  58  then determines whether the user has more friends to process (step  3314 ). If so, the process returns to step  3306  and is repeated for the next friend of the user. 
     Once all of the friends of the user have been processed, the crowd analyzer  58  performs steps  3306  through  3314  recursively for each newly found friend, incrementing the current DOS for each recursion, up to a maximum number of recursions (step  3316 ). Newly found friends are friends added to the found member list in the iteration or recursion of steps  3306  through  3314  just completed. In more general terms, steps  3306  through  3316  operate to find friends of the user selected in step  3300  that are also members of the crowd fragment and increment the found count for a DOS of 1 for each of the found friends of the user. Then, for each friend of the user that was found to also be a member of the crowd fragment, the crowd analyzer  58  finds friends of that friend of the user that are also members of the crowd fragment and increments the found count for a DOS of 2 for each of the found friends of the friend of the user. The process continues in this manner to count the number of user relationships between the user selected in step  3300  and other members in the crowd fragment up to the Mth DOS. 
     Next, the crowd analyzer  58  determines a count of users in the crowd fragment that were not found as being directly or indirectly related to the user selected in step  3300  (step  3318 ). More specifically, by looking at the found member list and the total number of users in the crowd fragment, the crowd analyzer  58  is enabled to determine the count of users in the crowd fragment that were not found as being directly or indirectly related to the user. 
     At this point, the crowd analyzer  58  determines whether there are more users in the crowd fragment to process (step  3320 ). If so, the process returns to step  3300  and is repeated for the next user in the crowd fragment. Once all of the users in the crowd fragment have been processed, the crowd analyzer  58  computes a best-case average DOS for the crowd fragment (step  3322 ). Again, in one embodiment, the best-case average DOS for the crowd fragment is computed as: 
     
       
         
           
             
               
                 AverageDOS 
                 BestCase 
               
               = 
               
                 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         1 
                       
                       M 
                     
                      
                     
                       ( 
                       
                         i 
                         · 
                         
                           found_count 
                           DOSi 
                         
                       
                       ) 
                     
                   
                   + 
                   
                     
                       DOS 
                       BestCase 
                     
                     · 
                     Num_Unknown 
                   
                 
                 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         1 
                       
                       M 
                     
                      
                     
                       ( 
                       
                         found_count 
                         DOSi 
                       
                       ) 
                     
                   
                   + 
                   Num_Unknown 
                 
               
             
             , 
           
         
       
     
     where AverageDOS BestCase  is the best-case average DOS for the crowd fragment, found_count DOSi  is the found count for the ith DOS, DOS BestCase  is the best-case DOS which may be set to, for example, 1, and Num_Unknown is the total count of user pairs in the crowd fragment that were not found as being directly or indirectly related. 
     In addition, the crowd analyzer  58  computes a worst-case average DOS for the crowd fragment (step  3324 ). Again, in one embodiment, the worst-case average DOS for the crowd fragment is computed as: 
     
       
         
           
             
               
                 AverageDOS 
                 WorstCase 
               
               = 
               
                 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         1 
                       
                       M 
                     
                      
                     
                       ( 
                       
                         i 
                         · 
                         
                           found_count 
                           DOSi 
                         
                       
                       ) 
                     
                   
                   + 
                   
                     
                       DOS 
                       WorstCase 
                     
                     · 
                     Num_Unknown 
                   
                 
                 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         1 
                       
                       M 
                     
                      
                     
                       ( 
                       
                         found_count 
                         DOSi 
                       
                       ) 
                     
                   
                   + 
                   Num_Unknown 
                 
               
             
             , 
           
         
       
     
     where AverageDOS WorstCase  is the worst-case average DOS for the crowd fragment, found_count DOSi  is the found count for the ith DOS, DOS WorstCase  is the worst-case DOS which may be set to, for example, 10, and Num_Unknown is the total count of user pairs in the crowd fragment that were not found as being directly or indirectly related. At this point the process is complete and the best-case and worst-case average DOS for the crowd fragment may be returned as part of the crowd data for the corresponding crowd. It should be noted that while the processes of  FIGS. 41 and 42  were described above as being performed on a crowd fragment, the same processes may be performed on a crowd in order to determine a best-case and worst-case average DOS for the crowd. 
       FIGS. 43A through 43D  illustrate an exemplary graphical representation of the best-case and worst-case average DOS for a crowd fragment according to one embodiment of the present disclosure. Such graphical representations may be presented to the mobile users  20 - 1  through  20 -N by the MAP applications  32 - 1  through  32 -N or presented to the subscriber  24  by the MAP server  12  via the web browser  38  at the subscriber device  22  based on data included in the crowd data for corresponding crowds.  FIG. 43A  illustrates the graphical representation for a crowd fragment wherein all users in the crowd fragment are friends with one another. As such, both the best-case and worst-case average DOS for the crowd fragment are 1.  FIG. 43B  illustrates the graphical representation for a crowd fragment wherein the best-case average DOS is 2 and the worst-case average DOS is 3.  FIG. 43C  illustrates the graphical representation for a crowd fragment wherein the best-case average DOS is 4 and the worst-case average DOS is greater than 7. Lastly,  FIG. 43D  illustrates the graphical representation for a crowd fragment wherein the best-case average DOS is 6 and the worst-case average DOS is 7. Again, while in these examples the graphical representations are for the best-case and worst-case average DOS for a crowd fragment, best-case and worst-case average DOS for a crowd may additionally or alternatively be computed by the MAP server  12  and presented to the users  20 - 1  through  20 -N or the subscriber  24 . 
       FIG. 44  is a flow chart for a process of determining a degree of bidirectionality of relationships between users in a crowd fragment according to one embodiment of the present disclosure. Note, however, that this same process may be used to determine a degree of bidirectionality of relationships between users in a crowd. First, the crowd analyzer  58  selects the next user in a crowd fragment, which for the first iteration is the first user in the crowd fragment (step  3400 ). The crowd analyzer  58  then selects the next friend of the user (step  3402 ). Again, note that friends of the users  20 - 1  through  20 -N may have been previously been obtained from the one or more profile servers  14  along with the user profiles of the users  20 - 1  through  20 -N and provided to the MAP server  12 . The crowd analyzer  58  then determines whether the friend of the user is a member of the crowd fragment (step  3404 ). If not, the process proceeds to step  3412 . If the friend of the user is a member of the crowd fragment, the crowd analyzer  58  increments a connection count (step  3406 ). In addition, the crowd analyzer  58  determines whether the relationship between the user and the friend is bidirectional (step  3408 ). In other words, the crowd analyzer  58  determines whether the user is also a friend of that friend. If not, the process proceeds to step  3412 . If so, the crowd analyzer  58  increments a bidirectional count (step  3410 ). 
     At this point, whether proceeding from step  3404 , step  3408 , or step  3410 , the crowd analyzer  58  determines whether the user has more friends to process (step  3412 ). If so, the process returns to step  3402  and is repeated for the next friend of the user. Once all of the friends of the user have been processed, the crowd analyzer  58  determines whether there are more users in the crowd fragment (step  3414 ). If so, the process returns to step  3400  and is repeated for the next user in the crowd fragment. Once steps  3402  through  3412  have been performed for all of the users in the crowd fragment, the crowd analyzer  58  computes a ratio of the bidirectional count (i.e., the number of bidirectional friend relationships) over the connection count (i.e., the number of unidirectional and bidirectional friend relationships) for the crowd fragment (step  3416 ). At this point, the process ends. In this embodiment, the ratio of the bidirectionality count to the connection count reflects the degree of bidirectionality of friendship relationships for the crowd fragment and may be returned to the requesting user or subscriber in the crowd data for the corresponding crowd. 
       FIGS. 45A through 45C  illustrate an exemplary graphical representation of the degree of bidirectionality of friendship relationships for a crowd fragment according to one embodiment of the present disclosure. Note that this graphical representation may also be used to present the degree of bidirectionality of friendship relationships for a crowd.  FIG. 45A  illustrates the graphical representation for a crowd having a ratio of bidirectional friend relationships to total friend relationships of approximately 0.5.  FIG. 45B  illustrates the graphical representation for a crowd having a ratio of bidirectional friend relationships to total friend relationships of approximately 0.2.  FIG. 45C  illustrates the graphical representation for a crowd having a ratio of bidirectional friend relationships to total friend relationships of approximately 0.95. Graphical representations such as those in  FIGS. 45A through 45C  may be presented to the mobile users  20 - 1  through  20 -N by the MAP applications  32 - 1  through  32 -N or presented to the subscriber  24  by the MAP server  12  via the web browser  38  at the subscriber device  22  based on data included in the crowd data for corresponding crowds. 
       FIGS. 46 through 51  describe embodiments of the present disclosure where confidence levels for the current locations of users in a crowd are determined and utilized to provide a quality level for the aggregate profile for the crowd and/or confidence levels for individual keywords included in the aggregate profile for the crowd. In general, in many implementations, the current locations of the users  20 - 1  through  20 -N are not updated instantaneously or even substantially instantaneously. There are many reasons why the current locations of the users  20 - 1  through  20 -N are not and possibly cannot be updated instantaneously. For example, battery life and performance limitations, non-continuous network connectivity, platform limitations such as the inability to run applications in the background, and security architectures (e.g., J2ME MIDP2.0 security architecture) may all limit the ability of the mobile devices  18 - 1  through  18 -N to provide continuous location updates to the MAP server  12 . As a result, the users  20 - 1  through  20 -N may move from their current locations stored by the MAP server  12  well before corresponding location updates are received by the MAP server  12 . For instance, if the user  20 - 1  turns the mobile device  18 - 1  off, then the mobile device  18 - 1  is unable to send location updates for the user  20 - 1 . As such, the current location stored for the user  20 - 1  at the MAP server  12  will no longer be accurate if the user  20 - 1  moves to a new location while the mobile device  18 - 1  is off. 
       FIGS. 46 through 51  describe embodiments where the contribution of the user profiles of the users  20 - 1  through  20 -N to aggregate profiles of corresponding crowds is modified based on an amount of time that has expired since receiving location updates for the users  20 - 1  through  20 -N. More specifically,  FIG. 46  is a flow chart for a process for generating a quality level for an aggregate profile for a crowd according to one embodiment of the present disclosure. As discussed above, the crowd analyzer  58  of the MAP server  12  creates an aggregate profile for one or more crowds relevant to a POI or an AOI in response to a crowd request from a requestor (i.e., one of the users  20 - 1  through  20 -N, the subscriber  24 , or the third-party service  26 ). Depending on the particular embodiment, the aggregate profile may be generated based on comparisons of the user profiles of the users in the crowd to a user profile or a select subset of the user profile of a requesting user (e.g., one of the users  20 - 1  through  20 -N for which the aggregate profile is generated), comparisons of the user profiles of the users in the crowd to a target user profile, or comparisons of the user profiles of the users in the crowd to one another. Using the following process, the crowd analyzer  58  can generate a quality level for the aggregate profile for one or more such crowds. Note that the quality level for the aggregate profile of a crowd may also be viewed as a quality level for the crowd itself particularly where a spatial crowd formation process has been used to form the crowd. 
     First, the crowd analyzer  58  of the MAP server  12  computes confidence levels for the current locations of the users in the crowd (step  3500 ). In one embodiment, the confidence level for the current location of a user ranges from 0 to 1, where the confidence level is set to 1 when the current location is updated and then linearly decreases to 0 over some desired period of time. As such, the confidence level of the current location of a user may be computed based on the following equation: 
       CL LOCATION   =Δt ·DR+CL LOCATION,PREVIOUS ,
 
     where CL LOCATION  is the confidence level of the current location of the user, Δt is an amount of time that has elapsed since the confidence level of the current location of the user was last computed, DR is a predefined decrease rate or rate at which the confidence level is to decrease over time, and CL LOCATION,PREVIOUS  is the previous confidence level of the current location of the user. The decrease rate (DR) is preferably selected such that the confidence level (CL) of the current location of the user will decrease from 1 to 0 over a desired amount of time. Note that the decrease rate (DR) may be defined separately for each user or may be the same for all users. If defined separately, the decrease rate (DR) for a user may be defined once and re-used or defined on a case-by-case basis based on the user&#39;s current and past locations, profile, history, or the like. The desired amount of time may be any desired amount of time such as, but not limited to, a desired number of hours. As an example, the desired amount of time may be 12 hours, and the corresponding decrease rate (DR) is 1/12 if time is measured in hours and 1/(12×60×60×1000) if time is measures in milliseconds. Note that the MAP server  12  stores the confidence level (CL) of the user, a timestamp indicating when the confidence level (CL) was computed, and optionally a timestamp indicating when the current location of the user was last updated. This information may be stored in the user record for the user. Alternatively, only the timestamp of the last location update is stored in the user record for the user. If the initial confidence level (CL) varies per user, the initial confidence level (CL) is also stored in the user record. The current confidence level (CL) is determined whenever it is needed by retrieving the last location update timestamp from the user record, determining an amount of elapsed time between the current time and the time of the last location update, and calculating the new confidence level based on the decrease rate (DR) and the initial confidence level (CL). Also note that while the confidence levels of the current locations of the users in the crowd are computed using a linear algorithm in the exemplary embodiment described above, nonlinear algorithms may alternatively be used. 
     When computing the confidence levels for the current locations of the users in the crowds, the crowd analyzer  58  may also consider location confidence events. Note that timestamps of such location confidence events and the location confidence events themselves may also be stored to enable correct calculation of the confidence levels. The location confidence events may include negative location confidence events such as, but not limited to, the passing of a known closing time of a business (e.g., restaurant, bar, shopping mall, etc.) at which a user is located or movement of a crowd with which a user has a high affinity. The location confidence events may additionally or alternatively include positive location confidence events such as, but not limited to, frequent interaction with the corresponding MAP application by the user. Frequent interaction with the MAP application by the user may be indicated by reception of frequent location updates for the user. Note that, in addition to or as an alternative to using location confidence events, other information such as location profiles, event information (e.g., live music event, open-mic night, etc.), current as past crowd histories, or the like may be used when computing the confidence levels for the current locations of the users in the crowds. 
     The manner in which the crowd analyzer  58  handles positive and/or negative location confidence events when computing the confidence levels of the users in the crowd may vary. In one embodiment, in response to detecting a negative location confidence event with respect to a user, the crowd analyzer  58  may increase the decrease rate (DR) used to compute the confidence level (CL) of the current location of the user. Similarly, in response to detecting a positive location confidence event with respect to a user, the crowd analyzer  58  may decrease the decrease rate (DR) used to compute the confidence level (CL) of the current location of the user or replace the decrease rate (DR) with an increase rate such that the confidence level of the user increases in response to the location confidence event or while the location confidence event continues (e.g., increase while the user frequently interacts with the MAP application). 
     In another embodiment, in response to detecting a negative location confidence event with respect to a user, the crowd analyzer  58  may decrease the confidence level (CL) of the current location of the user by a predefined amount. For example, if the negative location event is the passing of a closing time of a business at which the user is located, the crowd analyzer  58  may decrease the confidence level (CL) of the user to zero. Similarly, in response to detecting a positive location confidence event with respect to a user, the crowd analyzer  58  may increase the confidence level (CL) of the current location of the user by a predefined amount. For example, in response to detecting that the user is frequently interacting with the MAP application at his mobile device, the crowd analyzer  58  may increase the confidence level (CL) of the current location of the user by 0.1. 
     Once the confidence levels of the current locations of the users in the crowd are computed, the crowd analyzer  58  determines a quality level for the aggregate profile of the crowd (step  3502 ). In one embodiment, the quality level for the crowd is computed as an average of the confidence levels of the current locations of the users in the crowd. The quality level of the aggregate profile may then be provided along with the aggregate profile in the crowd data for the crowd returned to the requestor. 
       FIG. 47  illustrates an exemplary GUI  318  for presenting an aggregate profile  320  for a crowd and a quality level  322  of the aggregate profile  320  generated using the process of  FIG. 46  according to one embodiment of the present disclosure.  FIG. 48  illustrates another exemplary GUI  324  for presenting an aggregate profile  326  for a crowd and a quality level  328  of the aggregate profile  326  generated using the process of  FIG. 46  according to one embodiment of the present disclosure. However, in the GUI  324 , the aggregate profile  326  also indicates a relative number of user matches for each of a number of keywords in the aggregate profile  326 . More specifically, in a keyword area  330  of the GUI  324 , the sizes of the keywords indicate the relative number of user matches for the keywords. Therefore, in this example, the keyword “books” has a larger number of user matches that the keyword “politics,” as indicated by the size, or font size, of the two keywords in the keyword area  330  of the GUI  324 . 
       FIG. 49  illustrates a flow chart for a process for generating confidence factors for keywords included in an aggregate profile for a crowd based on confidence levels for current locations of users in the crowd according to one embodiment of the present disclosure. As discussed above, the crowd analyzer  58  creates an aggregate profile for one or more crowds relevant to a POI or an AOI in response to a crowd request from a requestor (i.e., one of the users  20 - 1  through  20 -N, the subscriber  24 , or the third-party service  26 ). Depending on the particular embodiment, the aggregate profile may be generated based on comparisons of the user profiles of the users in the crowd to a user profile or a select subset of the user profile of a requesting user (e.g., one of the users  20 - 1  through  20 -N for which the aggregate profile is generated), comparisons of the user profiles of the users in the crowd to a target user profile, or comparisons of the user profiles of the users in the crowd to one another. As also discussed above, in one embodiment, the aggregate profile for a crowd includes a number of user matches for each of a number of keywords and/or a ratio of the number of user matches to the total number of users in the crowd for each of a number of keywords. 
     In order to generate confidence factors for each keyword in an aggregate profile for a crowd, the crowd analyzer  58  of the MAP server  12  computes confidence levels for the current locations of the users in the crowd (step  3600 ). The confidence levels for the current locations of the users may be computed as discussed above with respect to step  3500  of  FIG. 46 . In general, the confidence levels for the current locations of the users may be computed based on an amount of time since the current location of the user was last updated, location confidence events, or both. Once the confidence levels of the current locations of the users in the crowd are computed, the crowd analyzer  58  determines a confidence level for each keyword in the aggregate profile of the crowd based on the confidence levels for the current locations of the corresponding users (step  3602 ). In one embodiment, for each keyword, the confidence level for the keyword is computed as an average of the confidence levels of the current locations of the users in the crowd having user profiles including the keyword. In other words, for each keyword, there are a number of user matches. The confidence levels of the current locations of the users corresponding to the user matches for the keyword are averaged to provide the confidence level for the keyword. 
       FIG. 50  illustrates an exemplary GUI  332  for presenting an aggregate profile  334  for a crowd including an indication of a confidence level for each of a number of keywords in the aggregate profile  334  according to one embodiment of the present disclosure. More specifically, in this embodiment, the aggregate profile  334  includes a quality level  336  of the aggregate profile  334  generated using the process of  FIG. 46 . However, the quality level  336  of the aggregate profile  334  is optional. The GUI  332  includes a keyword area  338  that graphically illustrates the keywords in the aggregate profile  334  and the confidence levels of the keywords. In this embodiment, the confidence levels of the keywords are graphically indicated via opacity of the keywords in the keyword area  338 . The lighter the text of the keyword, the lesser the confidence level of the keyword. Conversely, the darker the text of the keyword, the greater the confidence level of the keyword. Thus, in this example, the confidence level for the keyword “books” is greater than the confidence level of the keyword “politics,” and the confidence level of the keyword “photography” is greater than the confidence levels of the keywords “books” and “politics.” In addition, in this embodiment, the size of the keywords in the keyword area  338  is indicative of the number of user matches for the keywords, as discussed above with respect to  FIG. 48 . Note that in an alternative embodiment, the size of the keywords in the keyword area  338  may be indicative of the confidence levels of the keywords rather than the number of user matches for the keywords. 
       FIG. 51  graphically illustrates modification of the confidence level of the current location of a user according to one embodiment of the present disclosure. As illustrated, at time  0 , a location update for the user is received by the MAP server  12  and, as such, the confidence level of the current location of the user is set to 1. At time  1 , a positive location confidence event is detected. This positive location confidence event may be detected when, for example, the crowd analyzer  58  is generating an aggregate profile for a crowd in which the user is included and the user has been frequently interacting with the MAP application of his mobile device. As a result of the positive location confidence event, in this embodiment, the confidence level for the current location of the user at time  1  is computed using an increase rate (i.e., a positive rate of change) rather than a decrease rate (DR). As such, the confidence level of the current location of the user increases from time  0  to time  1  as shown. Alternatively, in response to the positive location confidence event, the confidence level for the current location of the user at time  1  may be increased by a predefined amount such as, for example, 0.1 points. Next, at time  2 , another positive location confidence event is detected. As a result of this second positive location confidence event, in this embodiment, the increase rate is further increased, and the confidence level for the current location of the user at time  2  is computed using the new increase rate. As such, the confidence level of the current location of the user further increases from time  1  to time  2 . Alternatively, in response to the positive location confidence event, the confidence level for the current location of the user at time  2  may be further increased by the predefined amount such as, for example, 0.1 points. 
     At time  3 , the confidence level of the current location of the user is updated. The confidence level of the current location of the user may be updated by the crowd analyzer  58  before generating an aggregate profile for a crowd in which the user is included. In this example, since a location confidence event is not detected at time  3 , the confidence level for the current location of the user is computed based on the previous confidence level computed at time  3  and a predefined decrease rate. As such, the confidence level for the current location of the user at time  3  is less than the confidence level for the current location of the user at time  2 . 
     At time  4 , a negative location confidence event is detected. As a result, in this example, the decrease rate is increased, and the confidence level for the current location of the user at time  4  is computed based on the new decrease rate. As such, the confidence level for the current location of the user at time  4  is less than the confidence level for the current location of the user at time  3 . Based on the new decrease rate, the confidence level for the current location of the user continues to decrease until reaching 0 at approximately 4.5 hours after time  0 . Alternatively, in response to the negative location confidence event, the confidence level for the current location of the user at time  4  may be decreased by a predefined amount in addition to or as an alternative to decreasing the confidence level by an amount determined by the amount of time that has elapsed between time  3  and time  4  and the decrease rate. 
       FIG. 52  illustrates the operation of the system  10  of  FIG. 1  to perform a process for efficiently handling requests for crowd data for large geographic areas according to one embodiment of the present disclosure. As illustrated, the MAP application  32 - 1  of the mobile device  18 - 1  sends a crowd request to the MAP client  30 - 1  (step  3700 ). Next, the MAP client  30 - 1  sends the crowd request to the MAP server  12  (step  3702 ). In response to receiving the crowd request, the MAP server  12  establishes a bounding box for the request (step  3704 ). Note that while a bounding box is referred to herein, a bounding region of any desired shape may be used. In one embodiment, the crowd request is a request for crowd data for a POI such that the bounding box is a geographic region of a predefined size centered at the POI. In another embodiment, the crowd request is a request for crowd data for an AOI such that the bounding box is a geographic region corresponding to the AOI. 
     The MAP server  12 , and more specifically the crowd analyzer  58 , then determines whether a size of the bounding box is greater than a predefined maximum size (step  3706 ). While not illustrated, if the size of the bounding box is not greater than the predefined maximum size, the crowd analyzer  58  identifies crowds relevant to the bounding box, obtains crowd data for the crowds, and returns the crowd data to the MAP client  30 - 1  in the manner described above. However, in this embodiment, the size of the bounding box is greater than the predefined maximum size. As such, the crowd analyzer  58  identifies one or more hotspots within the bounding box (step  3708 ). More specifically, the MAP server  12  maintains a list of hotspots, and the one or more hotspots within the bounding box are selected from the list of hotspots. In general, a hotspot is a geographic point (e.g., latitude and longitude coordinates, a physical address, or the like) where a significant number of crowds have historically been located and/or where a significant number of crowds are currently located. 
     In one embodiment, the MAP server  12 , and more specifically the crowd analyzer  58 , monitors crowds over time and identifies geographic points near which a significant number of crowds are typically located as hotspots. In another embodiment, hotspots may be defined by the users  20 - 1  through  20 -N in a collaborative process. For example, the users  20 - 1  through  20 -N may be enabled to nominate geographic points (e.g., POIs, latitude and longitude coordinates, a street address, or the like) as hotspots. Once a geographic point, or substantially the same geographic point, receives a predefined minimum number of nominations, the geographic point is defined as a hotspot. The geographic point may remain a hotspot permanently. Alternatively, the geographic point may be removed as a hotspot if one or more removal criteria are satisfied such as, for example, receiving a predefined threshold number of nominations for removal as a hotspot over a defined amount of time. In yet another embodiment, persons or entities may pay a fee to have desired geographic points listed as hotspots. For example, a business owner may pay a fee to have the MAP server  12  list the physical location of his or her business as a hotspot. 
     Once the hotspots within the bounding box for the request are identified, the crowd analyzer  58  obtains crowd data for the hotspots (step  3710 ). More specifically, in one embodiment, the crowd analyzer  58  establishes initial request regions of a predefined shape and size centered at the hotspots. The initial request regions are preferably an optimal shape and size. Using the initial request regions centered at the hotspots, the crowd analyzer  58  identifies crowds relevant to the initial request regions centered at the hotspots. As discussed above, the crowd analyzer  58  may identify the crowds by performing a spatial crowd formation process in response to the request. Alternatively, the crowds may be formed proactively and corresponding crowd records may be stored in the datastore  64  of the MAP server  12 . In this case, the crowd analyzer  58  identifies the crowds relevant to the initial request regions centered at the hotspots by querying the datastore  64  of the MAP server  12 . The crowd analyzer  58  then obtains crowd data for the identified crowds. As discussed above, the identified crowds may be passed to the aggregation engine  60 , which may then generate aggregate profiles for the crowds. In addition or alternatively, the crowd analyzer  58  may determine characteristics of the crowds such as, for example, degree of fragmentation, best-case and worst-case average DOS, degree of bidirectionality, or the like. 
     In addition, the crowd analyzer  58  determines a needed number of follow-up requests to be performed by the MAP client  30 - 1  in order to obtain crowd data for the rest of the bounding box established for the crowd request (step  3712 ). In one embodiment, follow-up requests are used to obtain crowd data for a series of one or more outwardly radiating, concentric request regions around each of the hotspots. Each request region is a geographic region. Each follow-up request is for a corresponding one of the series of outwardly radiating, concentric request regions around the hotspots. The number of needed follow-up requests depends on the number of hotspots in the bounding box, the size of the outwardly radiating, concentric request regions for the follow-up requests, and the size of the bounding box. The crowd analyzer  58  of the MAP server  12  then sends the crowd data for the hotspots and the needed number of follow-up requests to the MAP client  30 - 1  (step  3714 ). The MAP client  30 - 1  then sends the crowd data for the hotspots to the MAP application  32 - 1  (step  3716 ), and the MAP application  32 - 1  presents the crowd data for the hotspots to the user  20 - 1  (step  3718 ). 
     In addition to providing the crowd data for the hotspots to the MAP application  32 - 1 , the MAP client  30 - 1  sends a follow-up request to the MAP server  12  (step  3720 ). In response, the crowd analyzer  58  of the MAP server  12  obtains crowd data for the follow-up request (step  3722 ). More specifically, the crowd analyzer  58  identifies the request regions for the follow-up request. The crowd analyzer  58  then identifies crowds relevant to the request regions for the follow-up request and obtains crowd data for the identified crowds. Note that any redundant crowd data may be eliminated by carefully structuring the request regions to prevent overlapping of bounding regions from the same follow-up request. Alternatively, either the crowds or the resulting crowd data may be filtered at the MAP server  12  or the MAP client  30 - 1  to remove redundant crowds or crowd data. The crowd analyzer  58  of the MAP server  12  then sends the crowd data for the follow-up request to the MAP client  30 - 1  (step  3724 ). The MAP client  30 - 1  then sends the crowd data for the follow-up request to the MAP application  32 - 1  (step  3726 ), and the MAP application  32 - 1  presents the crowd data to the user  20 - 1  (step  3728 ). 
     In this embodiment, the needed number of follow-up requests is greater than one. As such, the MAP client  30 - 1  sends a second follow-up request to the MAP server  12  (step  3730 ). In response, the crowd analyzer  58  of the MAP server  12  obtains crowd data for the second follow-up request (step  3732 ). More specifically, the crowd analyzer  58  identifies the request regions for the follow-up request. The crowd analyzer  58  then identifies crowds that are relevant to the request regions for the follow-up request and obtains crowd data for the identified crowds. Again, note that any redundant crowd data may be eliminated by carefully structuring the request regions to prevent overlapping of request regions from the same follow-up request or previous follow-up requests. Alternatively, either the crowds or the resulting crowd data may be filtered at the MAP server  12  or the MAP client  30 - 1  to remove redundant crowds or crowd data. The crowd analyzer  58  of the MAP server  12  then sends the crowd data for the follow-up request to the MAP client  30 - 1  (step  3734 ). The MAP client  30 - 1  then sends the crowd data for the follow-up request to the MAP application  32 - 1  (step  3736 ), and the MAP application  32 - 1  presents the crowd data to the user  20 - 1  (step  3738 ). This process continues until crowd data for all of the follow-up requests has been obtained or until the process is otherwise terminated. For example, the process may be otherwise terminated if the user  20 - 1  initiates a crowd request for a different POI or AOI, if the user  20 - 1  deactivates the MAP application  32 - 1 , or the like. 
       FIGS. 53A through 53E  illustrate an exemplary series of outwardly radiating, concentric geographic regions for a number of hotspots HS 1  through HS 3  identified for a bounding box  340  established by the MAP server  12  in response to a crowd request.  FIG. 53A  illustrates the bounding box  340  established for the request and the hotspots HS 1  through HS 3  identified within the bounding box  340 .  FIG. 53B  illustrates initial request regions R 0   1  through R 0   3  established for the hotspots HS 1  through HS 3 , respectively. As discussed above, the crowd analyzer  58  identifies crowds relevant to the initial request regions R 0   1  through R 0   3 , obtains crowd data for the identified crowds, and returns the crowd data to the MAP client  30 - 1 . In addition, the crowd analyzer  58  determines the needed number of follow-up requests for the bounding box  340 . The needed number of follow-up requests for the bounding box  340  may vary depending on the size of the bounding box  340 , the size of the initial and follow-up request regions, and the number of hotspots in the bounding box  340 . In this example, the needed number of follow-up requests is nine. 
       FIG. 53C  illustrates first request regions R 1   1  through R 1   3  for the hotspots HS 1  through HS 3 , respectively, for a first follow-up request. Note that, in this exemplary example, a portion of the first request region R 1   3  for the third hotspot HS 3  overlaps the first request region R 1   2  for the second hotspot HS 2 . Thus, in order to prevent duplicate, or redundant, crowds and crowd data, filtering may be applied. Alternatively, the first request region R 1   3  of the third hotspot HS 3  may be modified to exclude the portion that overlaps the first request region R 1   2  of the second hotspot HS 2 . This redundancy may alternatively be addressed by querying the datastore  64  with a mathematical union of the first request regions R 1   1  through R 1   3 . When the crowd analyzer  58  receives the first follow-up request, the crowd analyzer  58  identifies crowds relevant to the first request regions R 1   1  through R 1   3 , obtains crowd data for the identified crowds, and returns the crowd data to the MAP client  30 - 1 . 
       FIG. 53D  illustrates second request regions R 2   1  through R 2   3  for the hotspots HS 1  through HS 3 , respectively, for a second follow-up request. Note that, in this exemplary embodiment, the second request region R 2   2  of the second hotspot HS 2  overlaps the second request region R 2   1  of the first hotspot HS 1  and the second request region R 2   3  of the third hotspot HS 3  overlaps the initial, first, and second request regions R 0   2 , R 1   2 , and R 2   2  of the second hotspot HS 2 . Thus, in order to prevent duplicate, or redundant, crowds and crowd data, filtering may be applied. Alternatively, the second request region R 2   2  of the second hotspot HS 2  may be modified to exclude the portion that overlaps the second request region R 2   1  of the first hotspot HS 1 . Likewise, the second request region R 2   3  of the third hotspot HS 3  may be modified to exclude the portion that overlaps the initial, first, and second request regions R 0   2 , R 1   2 , and R 2   2  of the second hotspot HS 2 . This redundancy may alternatively be addressed by querying the datastore  64  with a mathematical union of the second request regions R 2   1  through R 2   3  and then filtering to remove crowds that are duplicates from previous queries made to the datastore  64  for the initial and first request regions R 0   1  through R 0   3  and R 1   1  through R 1   3 . When the crowd analyzer  58  receives the second follow-up request, the crowd analyzer  58  identifies crowds relevant to the second request regions R 2   1  through R 2   3 , obtains crowd data for the identified crowds, and returns the crowd data to the MAP client  30 - 1 . This process continues for a number of additional follow-up requests until the crowd data is returned for all of the bounding box  340 , as illustrated in  FIG. 53E . Note that in  FIG. 53E , dashed lines for overlapping request regions have been omitted for clarity. 
       FIG. 54  graphically illustrates one exemplary variation to the follow-up request regions illustrated in  FIGS. 53A through 53E . In this embodiment, once there is a substantial amount of overlap between the follow-up regions for the hotspots HS 1  through HS 3 , the crowd analyzer  58  may establish follow-up regions that are no longer outwardly radiating, concentric regions around the hotspots HS 1  through HS 3 . More specifically, in this example, the second request regions R 2   1  through R 2   3  for the second follow-up request have a substantial amount of overlap. As such, request regions R 3  through R 6  for subsequent follow-up requests are provided such that the remaining portion of the bounding box  340  is quickly and efficiently filled-in. Sizes of the request regions R 3  through R 6  are preferably an optimal size or approximately an optimal size for querying the datastore  64  of the MAP server  12  for relevant crowds. 
     The discussion above with respect to  FIGS. 52 through 54  provides a process for handling a request for crowd data for a large geographic area by first focusing on hotspots within the large geographic area and then progressing outwardly from those hotspots to progressively provide crowd data for the large geographic area to the requestor. In another embodiment, a request for a large geographic area may be handled by first focusing on locations within the large geographic area such as locations of friends of the requestor and/or POIs previously defined or selected by the requestor and then progressing outwardly from those locations until crowd data for the large geographic area is returned to the requestor. These other locations may be used in addition to or as an alternative to hotspots. These other locations may be used in the same manner described above with respect to hotspots in order to divide a request for a large geographic area into an initial request and a number of follow-up requests. 
     In yet another embodiment, when a request for crowd data for a large geographic area is received by the MAP server  12 , crowds within the large geographic area may be identified and corresponding crowd data is obtained. The MAP server  12  may then first return the crowd data for crowds satisfying predefined criteria. For example, the MAP server  12  may return the crowd data for the crowds according to match strength between the user profiles of the users in the crowd and the user profile of the requesting user, a select portion of the user profile of the requesting user, or a target profile defined or otherwise specified by the requesting user. In this manner, the most relevant crowd data may be returned to the requesting user first. 
     It should be noted that while the process described above with respect to  FIGS. 52 through 54  focuses on a request from one of the mobile devices  18 - 1  through  18 -N, a similar process may be used internally at the MAP server  12  to process requests for crowd data for large geographic areas from the subscriber device  22  and/or the third-party service  26 . For example, upon receiving a request for crowd data for a large geographic area from the subscriber device  22  via the web browser  38 , the MAP server  12  may first obtain and return crowd data for one or more hotspots within the large geographic area and then progressively return crowd data for outwardly radiating, concentric areas around the hotspots. 
       FIGS. 55 through 61  describe aspects of an embodiment of the present disclosure wherein the crowd analyzer  58  of the MAP server  12  provides a crowd tracking feature. In general, over time, the crowd analyzer  58  creates a number of crowd snapshots for each crowd. In addition, in order to accurately track the crowds, the crowd analyzer  58  captures crowd mergers, captures crowd splits, and re-establishes crowds, as discussed below in detail. 
       FIG. 55  illustrates exemplary data records that may be used to represent crowds, users, crowd snapshots, and anonymous users according to one embodiment of the present disclosure. As illustrated, for each crowd created by the crowd analyzer  58  of the MAP server  12  (i.e., each crowd created that has three or more users), a corresponding crowd record  342  is created and stored in the datastore  64  of the MAP server  12 . The crowd record  342  for a crowd includes a users field, a North-East (NE) corner field, a South-West (SW) corner field, a center field, a crowd snapshots field, a split from field, and a combined into field. The users field stores a set or list of user records  344  corresponding to a subset of the users  20 - 1  through  20 -N that are currently in the crowd. The NE corner field stores a location corresponding to a NE corner of a bounding box for the crowd. The NE corner may be defined by latitude and longitude coordinates and optionally an altitude. Similarly, the SW corner field stores a location of a SW corner of the bounding box for the crowd. Like the NE corner, the SW corner may be defined by latitude and longitude coordinates and optionally an altitude. Together, the NE corner and the SW corner define a bounding box for the crowd, where the edges of the bounding box pass through the current locations of the outermost users in the crowd. The center field stores a location corresponding to a center of the crowd. The center of the crowd may be defined by latitude and longitude coordinates and optionally an altitude. Together, the NE corner, the SW corner, and the center of the crowd form spatial information defining the location of the crowd. Note, however, that the spatial information defining the location of the crowd may include additional or alternative information depending on the particular implementation. The crowd snapshots field stores a list of crowd snapshot records  346  corresponding to crowd snapshots for the crowd. As discussed below in detail, the split from field may be used to store a reference to a crowd record corresponding to another crowd from which the crowd split, and the combined into field may be used to store a reference to a crowd record corresponding to another crowd into which the crowd has been merged. 
     Each of the user records  344  includes an ID field, a location field, a profile field, a crowd field, and a previous crowd field. The ID field stores a unique ID for one of the users  20 - 1  through  20 -N for which the user record  344  is stored. The location field stores the current location of the user, which may be defined by latitude and longitude coordinates and optionally an altitude. The profile field stores the user profile of the user, which may be defined as a list of keywords for one or more profile categories. The crowd field is used to store a reference to a crowd record of a crowd of which the user is currently a member. The previous crowd field may be used to store a reference to a crowd record of a crowd of which the user was previously a member. 
     Each of the crowd snapshot records  346  includes an anonymous users field, a NE corner field, a SW corner field, a center field, a sample time field, and a vertices field. The anonymous users field stores a set or list of anonymous user records  348 , which are anonymized versions of user records for the users that are in the crowd at a time the crowd snapshot was created. The NE corner field stores a location corresponding to a NE corner of a bounding box for the crowd at the time the crowd snapshot was created. The NE corner may be defined by latitude and longitude coordinates and optionally an altitude. Similarly, the SW corner field stores a location of a SW corner of the bounding box for the crowd at the time the crowd snapshot was created. Like the NE corner, the SW corner may be defined by latitude and longitude coordinates and optionally an altitude. The center field stores a location corresponding to a center of the crowd at the time the crowd snapshot was created. The center of the crowd may be defined by latitude and longitude coordinates and optionally an altitude. Together, the NE corner, the SW corner, and the center of the crowd form spatial information defining the location of the crowd at the time the crowd snapshot was created. Note, however, that the spatial information defining the location of the crowd at the time the crowd snapshot was created may include additional or alternative information depending on the particular implementation. The sample time field stores a timestamp indicating a time at which the crowd snapshot was created. The timestamp preferably includes a date and a time of day at which the crowd snapshot was created. The vertices field stores locations of users in the crowd at the time the crowd snapshot was created that define an actual outer boundary of the crowd (e.g., as a polygon) at the time the crowd snapshot was created. Note that the actual outer boundary of a crowd may be used to show the location of the crowd when displayed to a user. 
     Each of the anonymous user records  348  includes an anonymous ID field and a profile field. The anonymous ID field stores an anonymous user ID, which is preferably a unique user ID that is not tied, or linked, back to any of the users  20 - 1  through  20 -N and particularly not tied back to the user or the user record for which the anonymous user record  348  has been created. In one embodiment, the anonymous user records  348  for a crowd snapshot record  346  are anonymized versions of the user records  344  of the users in the crowd at the time the crowd snapshot was created. The manner in which the user records  344  are anonymized to create the anonymous user records  348  may be the same as that described above with respect to maintaining a historical record of anonymized user profile data according to location. The profile field stores the anonymized user profile of the anonymous user, which may be defined as a list of keywords for one or more profile categories. 
       FIGS. 56A through 56D  illustrate one embodiment of a spatial crowd formation process that may be used to enable the crowd tracking feature. This spatial crowd formation process is similar to that described above with respect to  FIGS. 24A through 24D . In this embodiment, the spatial crowd formation process is triggered in response to receiving a location update for one of the users  20 - 1  through  20 -N and is preferably repeated for each location update received for the users  20 - 1  through  20 -N. As such, first, the crowd analyzer  58  receives a location update, or a new location, for a user (step  3800 ). In response, the crowd analyzer  58  retrieves an old location of the user, if any (step  3802 ). The old location is the current location of the user prior to receiving the new location of the user. The crowd analyzer  58  then creates a new bounding box of a predetermined size centered at the new location of the user (step  3804 ) and an old bounding box of a predetermined size centered at the old location of the user, if any (step  3806 ). The predetermined size of the new and old bounding boxes may be any desired size. As one example, the predetermined size of the new and old bounding boxes is 40 meters by 40 meters. Note that if the user does not have an old location (i.e., the location received in step  3800  is the first location received for the user), then the old bounding box is essentially null. Also note that while bounding “boxes” are used in this example, the bounding regions may be of any desired shape. 
     Next, the crowd analyzer  58  determines whether the new and old bounding boxes overlap (step  3808 ). If so, the crowd analyzer  58  creates a bounding box encompassing the new and old bounding boxes (step  3810 ). For example, if the new and old bounding boxes are 40×40 meter regions and a 1×1 meter square at the northeast corner of the new bounding box overlaps a 1×1 meter square at the southwest corner of the old bounding box, the crowd analyzer  58  may create a 79×79 meter square bounding box encompassing both the new and old bounding boxes. 
     The crowd analyzer  58  then determines the individual users and crowds relevant to the bounding box created in step  3810  (step  3812 ). Note that the crowds relevant to the bounding box are pre-existing crowds resulting from previous iterations of the spatial crowd formation process. In this embodiment, the crowds relevant to the bounding box are crowds having crowd bounding boxes that are within or overlap the bounding box established in step  3810 . In order to determine the relevant crowds, the crowd analyzer  58  queries the datastore  64  of the MAP server  12  to obtain crowd records for crowds that are within or overlap the bounding box established in step  3810 . The individual users relevant to the bounding box are users that are currently located within the bounding box and are not already members of a crowd. In order to identify the relevant individual users, the crowd analyzer  58  queries the datastore  64  of the MAP server  12  for user records of users that are currently located in the bounding box created in step  3810  and are not already members of a crowd. Next, the crowd analyzer  58  computes an optimal inclusion distance for individual users based on user density within the bounding box (step  3814 ). The optimal inclusion distance may be computed as described above with respect to step  2314  of  FIG. 24A . 
     The crowd analyzer  58  then creates a crowd of one user for each individual user within the bounding box established in step  3810  that is not already included in a crowd and sets the optimal inclusion distance for those crowds to the initial optimal inclusion distance (step  3816 ). The crowds created for the individual users are temporary crowds created for purposes of performing the crowd formation process. At this point, the process proceeds to  FIG. 56B  where the crowd analyzer  58  analyzes the crowds in the bounding box established in step  3810  to determine whether any of the crowd members (i.e., users in the crowds) violate the optimal inclusion distance of their crowds (step  3818 ). Any crowd member that violates the optimal inclusion distance of his or her crowd is then removed from that crowd and the previous crowd fields in the corresponding user records are set (step  3820 ). More specifically, in this embodiment, a member is removed from a crowd by removing the user record of the member from the set or list of user records in the crowd record of the crowd and setting the previous crowd stored in the user record of the user to the crowd from which the member has been removed. The crowd analyzer  58  then creates a crowd of one user for each of the users removed from their crowds in step  3820  and sets the optimal inclusion distance for the newly created crowds to the initial optimal inclusion distance (step  3822 ). 
     Next, the crowd analyzer  58  determines the two closest crowds in the bounding box (step  3824 ) and a distance between the two closest crowds (step  3826 ). The distance between the two closest crowds is the distance between the crowd centers of the two closest crowds, which are stored in the crowd records for the two closest crowds. The crowd analyzer  58  then determines whether the distance between the two closest crowds is less than the optimal inclusion distance of a larger of the two closest crowds (step  3828 ). If the two closest crowds are of the same size (i.e., have the same number of users), then the optimal inclusion distance of either of the two closest crowds may be used. Alternatively, if the two closest crowds are of the same size, the optimal inclusion distances of both of the two closest crowds may be used such that the crowd analyzer  58  determines whether the distance between the two closest crowds is less than the optimal inclusion distances of both of the crowds. As another alternative, if the two closest crowds are of the same size, the crowd analyzer  58  may compare the distance between the two closest crowds to an average of the optimal inclusion distances of the two crowds. 
     If the distance between the two closest crowds is greater than the optimal inclusion distance, the process proceeds to step  3840 . However, if the distance between the two closest crowds is less than the optimal inclusion distance, the two crowds are merged (step  3830 ). The manner in which the two crowds are merged differs depending on whether the two crowds are pre-existing crowds or temporary crowds created for the spatial crowd formation process. If both crowds are pre-existing crowds, one of the two crowds is selected as a non-surviving crowd and the other is selected as a surviving crowd. If one crowd is larger than the other, the smaller crowd is selected as the non-surviving crowd and the larger crowd is selected as a surviving crowd. If the two crowds are of the same size, one of the crowds is selected as the surviving crowd and the other crowd is selected as the non-surviving crowd using any desired technique. The non-surviving crowd is then merged into the surviving crowd by adding the set or list of user records for the non-surviving crowd to the set or list of user records for the surviving crowd and setting the merged into field of the non-surviving crowd to a reference to the crowd record of the surviving crowd. In addition, the crowd analyzer  58  sets the previous crowd fields of the user records in the set or list of user records from the non-surviving crowd to a reference to the crowd record of the non-surviving crowd. 
     If one of the crowds is a temporary crowd and the other crowd is a pre-existing crowd, the temporary crowd is selected as the non-surviving crowd, and the pre-existing crowd is selected as the surviving crowd. The non-surviving crowd is then merged into the surviving crowd by adding the set or list of user records from the crowd record of the non-surviving crowd to the set or list of user records in the crowd record of the surviving crowd. However, since the non-surviving crowd is a temporary crowd, the previous crowd field(s) of the user record(s) of the user(s) in the non-surviving crowd are not set to a reference to the crowd record of the non-surviving crowd. Similarly, the crowd record of the temporary record may not have a merged into field, but, if it does, the merged into field is not set to a reference to the surviving crowd. 
     If both the crowds are temporary crowds, one of the two crowds is selected as a non-surviving crowd and the other is selected as a surviving crowd. If one crowd is larger than the other, the smaller crowd is selected as the non-surviving crowd and the larger crowd is selected as a surviving crowd. If the two crowds are of the same size, one of the crowds is selected as the surviving crowd and the other crowd is selected as the non-surviving crowd using any desired technique. The non-surviving crowd is then merged into the surviving crowd by adding the set or list of user records for the non-surviving crowd to the set or list of user records for the surviving crowd. However, since the non-surviving crowd is a temporary crowd, the previous crowd field(s) of the user record(s) of the user(s) in the non-surviving crowd are not set to a reference to the crowd record of the non-surviving crowd. Similarly, the crowd record of the temporary record may not have a merged into field, but, if it does, the merged into field is not set to a reference to the surviving crowd. 
     Next, the crowd analyzer  58  removes the non-surviving crowd (step  3832 ). In this embodiment, the manner in which the non-surviving crowd is removed depends on whether the non-surviving crowd is a pre-existing crowd or a temporary crowd. If the non-surviving crowd is a pre-existing crowd, the removal process is performed by removing or nulling the users field, the NE corner field, the SW corner field, and the center field of the crowd record of the non-surviving crowd. In this manner, the spatial information for the non-surviving crowd is removed from the corresponding crowd record such that the non-surviving or removed crowd will no longer be found in response to spatial-based queries on the datastore  64 . However, the crowd snapshots for the non-surviving crowd are still available via the crowd record for the non-surviving crowd. In contrast, if the non-surviving crowd is a temporary crowd, the crowd analyzer  58  may remove the crowd by deleting the corresponding crowd record. 
     The crowd analyzer  58  also computes a new crowd center for the surviving crowd (step  3834 ). Again, a center of mass algorithm may be used to compute the crowd center of a crowd. In addition, a new optimal inclusion distance for the surviving crowd is computed (step  3836 ). In one embodiment, the new optimal inclusion distance for the surviving crowd is computed in the manner described above with respect to step  2334  of  FIG. 24B . 
     At this point, the crowd analyzer  58  determines whether a maximum number of iterations have been performed (step  3838 ). The maximum number of iterations is a predefined number that ensures that the crowd formation process does not indefinitely loop over steps  3818  through  3836  or loop over steps  3818  through  3836  more than a desired maximum number of times. If the maximum number of iterations has not been reached, the process returns to step  3818  and is repeated until either the distance between the two closest crowds is not less than the optimal inclusion distance of the larger crowd or the maximum number of iterations has been reached. At that point, the crowd analyzer  58  removes crowds with less than three users, or members (step  3840 ) and the process ends. As discussed above, in this embodiment, the manner in which a crowd is removed depends on whether the crowd is a pre-existing crowd or a temporary crowd. If the crowd is a pre-existing crowd, a removal process is performed by removing or nulling the users field, the NE corner field, the SW corner field, and the center field of the crowd record of the crowd. In this manner, the spatial information for the crowd is removed from the corresponding crowd record such that the crowd will no longer be found in response to spatial-based queries on the datastore  64 . However, the crowd snapshots for the crowd are still available via the crowd record for the crowd. In contrast, if the crowd is a temporary crowd, the crowd analyzer  58  may remove the crowd by deleting the corresponding crowd record. In this manner, crowds having less than three members are removed in order to maintain privacy of individuals as well as groups of two users (e.g., a couple). 
     Returning to step  3808  in  FIG. 56A , if the new and old bounding boxes do not overlap, the process proceeds to  FIG. 56C  and the bounding box to be processed is set to the old bounding box (step  3842 ). In general, the crowd analyzer  58  then processes the old bounding box in much that same manner as described above with respect to steps  3812  through  3840 . More specifically, the crowd analyzer  58  determines the individual users and crowds relevant to the bounding box (step  3844 ). Again, note that the crowds relevant to the bounding box are pre-existing crowds resulting from previous iterations of the spatial crowd formation process. In this embodiment, the crowds relevant to the bounding box are crowds having crowd bounding boxes that are within or overlap the bounding box. The individual users relevant to the bounding box are users that are currently located within the bounding box and are not already members of a crowd. Next, the crowd analyzer  58  computes an optimal inclusion distance for individual users based on user density within the bounding box (step  3846 ). The optimal inclusion distance may be computed as described above with respect to step  2344  of  FIG. 24C . 
     The crowd analyzer  58  then creates a crowd of one user for each individual user within the bounding box that is not already included in a crowd and sets the optimal inclusion distance for the crowds to the initial optimal inclusion distance (step  3848 ). The crowds created for the individual users are temporary crowds created for purposes of performing the crowd formation process. At this point, the crowd analyzer  58  analyzes the crowds in the bounding box to determine whether any crowd members (i.e., users in the crowds) violate the optimal inclusion distance of their crowds (step  3850 ). Any crowd member that violates the optimal inclusion distance of his or her crowd is then removed from that crowd and the previous crowd fields in the corresponding user records are set (step  3852 ). More specifically, in this embodiment, a member is removed from a crowd by removing the user record of the member from the set or list of user records in the crowd record of the crowd and setting the previous crowd stored in the user record of the user to the crowd from which the member has been removed. The crowd analyzer  58  then creates a crowd for each of the users removed from their crowds in step  3852  and sets the optimal inclusion distance for the newly created crowds to the initial optimal inclusion distance (step  3854 ). 
     Next, the crowd analyzer  58  determines the two closest crowds in the bounding box (step  3856 ) and a distance between the two closest crowds (step  3858 ). The distance between the two closest crowds is the distance between the crowd centers of the two closest crowds. The crowd analyzer  58  then determines whether the distance between the two closest crowds is less than the optimal inclusion distance of a larger of the two closest crowds (step  3860 ). If the two closest crowds are of the same size (i.e., have the same number of users), then the optimal inclusion distance of either of the two closest crowds may be used. Alternatively, if the two closest crowds are of the same size, the optimal inclusion distances of both of the two closest crowds may be used such that the crowd analyzer  58  determines whether the distance between the two closest crowds is less than the optimal inclusion distances of both of the two closest crowds. As another alternative, if the two closest crowds are of the same size, the crowd analyzer  58  may compare the distance between the two closest crowds to an average of the optimal inclusion distances of the two closest crowds. 
     If the distance between the two closest crowds is greater than the optimal inclusion distance, the process proceeds to step  3872 . However, if the distance between the two closest crowds is less than the optimal inclusion distance, the two crowds are merged (step  3862 ). The manner in which the two crowds are merged differs depending on whether the two crowds are pre-existing crowds or temporary crowds created for the spatial crowd formation process. If both crowds are pre-existing crowds, one of the two crowds is selected as a non-surviving crowd and the other is selected as a surviving crowd. If one crowd is larger than the other, the smaller crowd is selected as the non-surviving crowd and the larger crowd is selected as a surviving crowd. If the two crowds are of the same size, one of the crowds is selected as the surviving crowd and the other crowd is selected as the non-surviving crowd using any desired technique. The non-surviving crowd is then merged into the surviving crowd by adding the set or list of user records for the non-surviving crowd to the set or list of user records for the surviving crowd and setting the merged into field of the non-surviving crowd to a reference to the crowd record of the surviving crowd. In addition, the crowd analyzer  58  sets the previous crowd fields of the set or list of user records from the non-surviving crowd to a reference to the crowd record of the non-surviving crowd. 
     If one of the crowds is a temporary crowd and the other crowd is a pre-existing crowd, the temporary crowd is selected as the non-surviving crowd, and the pre-existing crowd is selected as the surviving crowd. The non-surviving crowd is then merged into the surviving crowd by adding the user records from the set or list of user records from the crowd record of the non-surviving crowd to the set or list of user records in the crowd record of the surviving crowd. However, since the non-surviving crowd is a temporary crowd, the previous crowd field(s) of the user record(s) of the user(s) in the non-surviving crowd are not set to a reference to the crowd record of the non-surviving crowd. Similarly, the crowd record of the temporary record may not have a merged into field, but, if it does, the merged into field is not set to a reference to the surviving crowd. 
     If both the crowds are temporary crowds, one of the two crowds is selected as a non-surviving crowd and the other is selected as a surviving crowd. If one crowd is larger than the other, the smaller crowd is selected as the non-surviving crowd and the larger crowd is selected as a surviving crowd. If the two crowds are of the same size, one of the crowds is selected as the surviving crowd and the other crowd is selected as the non-surviving crowd using any desired technique. The non-surviving crowd is then merged into the surviving crowd by adding the set or list of user records for the non-surviving crowd to the set or list of user records for the surviving crowd. However, since the non-surviving crowd is a temporary crowd, the previous crowd field(s) of the user record(s) of the user(s) in the non-surviving crowd are not set to a reference to the crowd record of the non-surviving crowd. Similarly, the crowd record of the temporary record may not have a merged into field, but, if it does, the merged into field is not set to a reference to the surviving crowd. 
     Next, the crowd analyzer  58  removes the non-surviving crowd (step  3864 ). In this embodiment, the manner in which the non-surviving crowd is removed depends on whether the non-surviving crowd is a pre-existing crowd or a temporary crowd. If the non-surviving crowd is a pre-existing crowd, the removal process is performed by removing or nulling the users field, the NE corner field, the SW corner field, and the center field of the crowd record of the non-surviving crowd. In this manner, the spatial information for the non-surviving crowd is removed from the corresponding crowd record such that the non-surviving or removed crowd will no longer be found in response to spatial-based queries on the datastore  64 . However, the crowd snapshots for the non-surviving crowd are still available via the crowd record for the non-surviving crowd. In contrast, if the non-surviving crowd is a temporary crowd, the crowd analyzer  58  may remove the crowd by deleting the corresponding crowd record. 
     The crowd analyzer  58  also computes a new crowd center for the surviving crowd (step  3866 ). Again, a center of mass algorithm may be used to compute the crowd center of a crowd. In addition, a new optimal inclusion distance for the surviving crowd is computed (step  3868 ). In one embodiment, the new optimal inclusion distance for the surviving crowd is computed in the manner described above with respect to step  2364  of  FIG. 24D . 
     At this point, the crowd analyzer  58  determines whether a maximum number of iterations have been performed (step  3870 ). If the maximum number of iterations has not been reached, the process returns to step  3850  and is repeated until either the distance between the two closest crowds is not less than the optimal inclusion distance of the larger crowd or the maximum number of iterations has been reached. At that point, the crowd analyzer  58  removes crowds with less than three users, or members (step  3872 ). As discussed above, in this embodiment, the manner in which a crowd is removed depends on whether the crowd is a pre-existing crowd or a temporary crowd. If the crowd is a pre-existing crowd, a removal process is performed by removing or nulling the users field, the NE corner field, the SW corner field, and the center field of the crowd record of the crowd. In this manner, the spatial information for the crowd is removed from the corresponding crowd record such that the crowd will no longer be found in response to spatial-based queries on the datastore  64 . However, the crowd snapshots for the crowd are still available via the crowd record for the crowd. In contrast, if the crowd is a temporary crowd, the crowd analyzer  58  may remove the crowd by deleting the corresponding crowd record. In this manner, crowds having less than three members are removed in order to maintain privacy of individuals as well as groups of two users (e.g., a couple). 
     The crowd analyzer  58  then determines whether the crowd formation process for the new and old bounding boxes is done (step  3874 ). In other words, the crowd analyzer  58  determines whether both the new and old bounding boxes have been processed. If not, the bounding box is set to the new bounding box (step  3876 ), and the process returns to step  3844  and is repeated for the new bounding box. Once both the new and old bounding boxes have been processed, the crowd formation process ends. 
       FIG. 57  illustrates a process for creating crowd snapshots according to one embodiment of the present disclosure. In this embodiment, after the spatial crowd formation process of  FIGS. 56A through 56D  is performed in response to a location update for a user, the crowd analyzer  58  detects crowd change events, if any, for the relevant crowds (step  3900 ). The relevant crowds are pre-existing crowds that are within the bounding region(s) processed during the spatial crowd formation process in response to the location update for the user. The crowd analyzer  58  may detect crowd change events by comparing the crowd records of the relevant crowds before and after performing the spatial crowd formation process in response to the location update for the user. The crowd change events may be a change in the users in the crowd, a change to a location of one of the users within the crowd, or a change in the spatial information for the crowd (e.g., the NE corner, the SW corner, or the crowd center). Note that if multiple crowd change events are detected for a single crowd, then those crowd change events are preferably consolidated into a single crowd change event. 
     Next, the crowd analyzer  58  determines whether there are any crowd change events (step  3902 ). If not, the process ends. Otherwise, the crowd analyzer  58  gets the next crowd change event (step  3904 ) and generates a crowd snapshot for a corresponding crowd (step  3906 ). More specifically, the crowd change event identifies a crowd record stored for a crowd for which the crowd change event was detected. A crowd snapshot is then created for that crowd by creating a new crowd snapshot record for the crowd and adding the new crowd snapshot to the list of crowd snapshots stored in the crowd record for the crowd. The crowd snapshot record includes a set or list of anonymized user records, which are an anonymized version of the user records for the users in the crowd at the current time. In addition, the crowd snapshot record includes the NE corner, the SW corner, and the center of the crowd at the current time as well as a timestamp defining the current time as the sample time at which the crowd snapshot record was created. Lastly, locations of users in the crowd that define the outer boundary of the crowd at the current time are stored in the crowd snapshot record as the vertices of the crowd. After creating the crowd snapshot, the crowd analyzer  58  determines whether there are any more crowd change events (step  3908 ). If so, the process returns to step  3904  and is repeated for the next crowd change event. Once all of the crowd change events are processed, the process ends. 
       FIG. 58  illustrates a process that may be used to re-establish crowds and detect crowd splits according to one embodiment of the present disclosure. In general, in order to accurately track a crowd, it is preferable to enable crowds that have been removed to be re-established in the future. For example, a crowd may be removed as a result of users in the crowd deactivating their MAP applications (or powering down their mobile devices). If those users then move together to a different location and then reactivate their MAP applications (or power on their mobile devices), it is preferable for the resulting crowd to be identified as the same crowd that was previously removed. In other words, it is desirable to re-establish the crowd. In addition, in order to accurately track a crowd, it is desirable to capture when the crowd splits into two or more crowds. 
     Accordingly, in this embodiment, the spatial crowd formation process of  FIGS. 56A through 56D  is performed in response to a location update for a user. The crowd analyzer  58  then gets a next relevant crowd (step  4000 ). The relevant crowds are pre-existing and new crowds that are within the bounding region(s) processed during the spatial crowd formation process in response to the location update for the user. Note that, for the first iteration, the next relevant crowd is the first relevant crowd. The crowd analyzer  58  then determines a maximum number of users in the crowd from a common previous crowd (step  4002 ). More specifically, the crowd analyzer  58  examines the previous crowd fields of the user records of all of the users in the crowd to identify users from a common previous crowd. For each previous crowd found in the user records of the users in the crowd, the crowd analyzer  58  counts the number of users in the crowd that are from that previous crowd. The crowd analyzer  58  then selects the previous crowd having the highest number of users, and determines that the number of users counted for the selected previous crowd is the maximum number of users in the crowd from a common previous crowd. 
     The crowd analyzer  58  then determines whether the maximum number of users in the crowd from a common previous crowd is greater than a predefined threshold number of users (step  4004 ). In an alternative embodiment, rather than determining the maximum number of users from a common previous crowd and comparing that number to a predefined threshold number of users, a maximum percentage of users in the crowd from a common previous crowd may be determined and compared to a predefined threshold percentage. If the maximum number of users in the crowd from a common previous crowd is not greater than the predefined threshold number of users, the process proceeds to step  4010 . Otherwise, the crowd analyzer  58  determines whether the common previous crowd has been removed (step  4006 ). If so, then the crowd is re-established as the common previous crowd (step  4008 ). More specifically, in this embodiment, the crowd is re-established as the common previous crowd by storing the set or list of user records, the NE corner, the SW corner, and the center from the crowd record of the crowd in the crowd record of the common previous crowd. The crowd record for the crowd may then deleted. In addition, the previous crowd fields of the users from the common previous crowd may be set to null or otherwise cleared. Once the common previous crowd is re-established, the crowd analyzer  58  determines whether there are more relevant crowds to process (step  4010 ). If so, the process returns to step  4000  and is repeated until all relevant crowds are processed. 
     Returning to step  4006 , if the common previous crowd has not been removed, the crowd analyzer  58  identifies the crowd as being split from the common previous crowd (step  4012 ). More specifically, in this embodiment, the crowd analyzer  58  stores a reference to the crowd record of the common previous crowd in the split from field of the crowd record of the crowd. At this point, the crowd analyzer  58  then determines whether there are more relevant crowds to process (step  4010 ). If so, the process returns to step  4000  and is repeated until all relevant crowds are processed, at which time the process ends. 
       FIG. 59  graphically illustrates the process of re-establishing a crowd for an exemplary crowd according to one embodiment of the present disclosure. As illustrated, at TIME  1 , CROWD A has been formed and a corresponding crowd record has been created and stored. Between TIME  1  and TIME  2 , three users from CROWD A have moved, thereby resulting in the removal of those three users from CROWD A as well as the removal of CROWD A. Again, CROWD A has been removed by removing the set or list of user records and spatial information from the crowd record for CROWD A. At TIME  2 , a new crowd, CROWD B, has been formed for the three users that were previously in CROWD A. As such, the previous crowd fields for the three users now in CROWD B indicate that the three users are from CROWD A. Using the process of  FIG. 58 , the crowd analyzer  58  determines that the three users in CROWD B have a common previous crowd, namely, CROWD A. As a result, the crowd analyzer  58  re-establishes CROWD B as CROWD A, as shown at TIME  2 ’. 
       FIG. 60  graphically illustrates the process for capturing a crowd split for an exemplary crowd according to one embodiment of the present disclosure. As illustrated, at TIME  1 , CROWD A has been formed and a corresponding crowd record has been created and stored. Between TIME  1  and TIME  2 , four users from CROWD A have separated from the other three users of CROWD A. As a result, a new crowd, CROWD B, has been formed at TIME  2  for the four users from CROWD A. Using the process of  FIG. 58 , the crowd analyzer  58  determines that the four users in CROWD B are all from CROWD A and therefore identifies CROWD B as being split from CROWD A. 
       FIG. 61  graphically illustrates the merging of two exemplary pre-existing crowds according to one embodiment of the present disclosure. As discussed above, the merger of crowds is performed during the spatial crowd formation process of  FIGS. 56A through 56D . As illustrated, at TIME  1 , CROWD A and CROWD B have been formed and corresponding crowd records have been created and stored. Between TIME  1  and TIME  2 , CROWD A and CROWD B move close to one another such that the distance between CROWD A and CROWD B is less than the optimal inclusion distance(s) at TIME  2 . As such, the crowd analyzer  58  merges CROWD A into CROWD B at TIME  2 ′. As part of the merger, CROWD A is removed, and the merged into field of the crowd record for CROWD A is set to a reference to the crowd record for CROWD B. In addition, the previous crowd fields in the user records of the user from CROWD A are set to a reference to the crowd record of CROWD A. 
       FIG. 62  illustrates the operation of the MAP server  12  of  FIG. 1  to serve a request for crowd tracking data for a crowd according to one embodiment of the present disclosure. First, the subscriber device  22  sends a crowd tracking data request for a crowd to the MAP server  12  (step  4100 ). Note that access to crowd tracking data is preferably a subscription service only available to subscribers, such as the subscriber  24  at the subscriber device  22 , for a subscription fee. The crowd tracking data request identifies a particular crowd. For example, in one embodiment, the crowd data for a number of crowds near a POI or within an AOI is presented to the subscriber  24  at the subscriber device  22  in the manner described above. The subscriber  24  may then select one of those crowds and initiate a request for crowd tracking data for the selected crowd. In response, the subscriber device  22  sends the crowd tracking data request for the selected crowd to the MAP server  12 . 
     In response to receiving the crowd tracking data request, the MAP server  12 , and more specifically the crowd analyzer  58 , obtains relevant crowd snapshots for the crowd (step  4102 ). In one embodiment, the crowd tracking data request is a general crowd tracking data request for the crowd. As such, the relevant crowd snapshots are all crowd snapshots for the crowd. In another embodiment, the crowd tracking data request may include one or more criteria to be used to identify the relevant crowd snapshots. The one or more criteria may include time-based criteria such that only those crowd snapshots for the crowd that satisfy the time-based criteria are identified as the relevant crowd snapshots. For example, the time-based criteria may define a range of dates such as Oct. 1, 2009 through Oct. 8, 2009 or define a range of times within a particular day such as 5 pm through 9 pm on Oct. 1, 2009. The one or more criteria may additionally or alternatively include user-based criteria such that only those crowd snapshots including anonymous users satisfying the user-based criteria are identified as the relevant crowd snapshots. For example, the user-based criteria may include one or more interests and a minimum number or percentage of users such that only those crowd snapshots including at least the minimum number or percentage of anonymous users having the one or more interests are identified as the relevant crowd snapshots. Note that by using user-based criteria, the subscriber  24  is enabled to track sub-crowds within a crowd. 
     Next, the crowd analyzer  58  of the MAP server  12  generates crowd tracking data for the crowd based on the relevant crowd snapshots (step  4104 ). The crowd tracking data includes data indicative of the location of the crowd over time, which can be determined based on the spatial information and sample times from the relevant crowd snapshots. In addition, the crowd tracking data may include an aggregate profile for the crowd for each of the relevant crowd snapshots or at least some of the relevant crowd snapshots, an average aggregate profile for all of the relevant crowd snapshots, an average aggregate profile for a subset of the relevant crowd snapshots, or average aggregate profiles for a number of subsets of the relevant crowd snapshots. For example, the relevant crowd snapshots may be divided into a number of time bands such that at least some of the time bands include multiple relevant crowd snapshots. An average crowd snapshot may then be created for each of the time bands. The crowd analyzer  58  may utilize the aggregation engine  60  to obtain an aggregate profile for a crowd snapshot based on the interests of the anonymous users in the crowd snapshot. More specifically, in a manner similar to that described above, an aggregate profile for a crowd snapshot may be computed by comparing the interests of the anonymous users to one another or by comparing the interests of the anonymous users to a target profile. The crowd tracking data may also contain other information derived from the relevant crowd snapshots such as, for example, the number of users in the relevant crowd snapshots, crowd characteristics for the crowd for the relevant crowd snapshots, or the like. 
     The crowd analyzer  58  returns the crowd tracking data for the crowd to the subscriber device  22  (step  4106 ). Note that in the embodiment where the subscriber device  22  interacts with the MAP server  12  via the web browser  38 , the MAP server  12  returns the crowd tracking data to the subscriber device  22  in a format suitable for use by the web browser  38 . For example, the crowd tracking data may be returned via a web page including a map, wherein indicators of the location of the crowd over time as defined by the relevant crowd snapshots may be overlaid upon the map. The subscriber  24  may then be enabled to select one of those indicators to view additional information regarding the crowd at that time such as, for example, an aggregate profile of a corresponding crowd snapshot of the crowd. Once the crowd tracking data is received at the subscriber device  22 , the crowd tracking data is presented to the subscriber  24  (step  4108 ). 
     In addition to enabling an entity, such as the subscriber  24 , to track crowds, the crowd snapshots of crowds may also be used to provide additional metrics about the crowds. These metrics may be included in the crowd data generated for the crowds and returned to the users  20 - 1  through  20 -N, the subscriber  24 , or the third-party service  26 . For example, a quality factor for a crowd may be provided as a function of a duration of time that the crowd has existed. The duration of time that the crowd has existed can be determined from the crowd snapshots for the crowd. For instance, a crowd may have a high quality if the crowd has existed (not been removed) for a duration of two or more hours, a low quality if the crowd has existed for a duration of less than five minutes, or one of various intermediate degrees of quality if the crowd has existed for a duration of between five minutes and two hours. Note that when determining whether to remove a user from a crowd, the quality of the crowd may be used to relax or stretch the optimal inclusion distance for the crowd with respect to user removal. This relaxation or stretching of the optimal inclusion distance with respect to user removal may then retract to its original value after or over a desired period of time. The retracting period may also be a function of the quality of the crowd. In this manner, if a crowd has existed for a long period of time, the MAP server  12  will be more lenient when determining whether to remove a user from that crowd because the crowd is stable and the user will likely move back to within the optimal inclusion distance from the center of the crowd. 
     As another example, the crowd snapshots of a crowd may be used to compute a motility of the crowd based on how much area the crowd covers over time. For instance, the distance that the crowd has traveled over a period of time may be determined based on the crowd centers stored in the crowd snapshots for the crowd during that period of time. The total distance traveled over the period of time can be provided as the motility of the crowd. The motility of the crowd may additionally or alternatively consider a speed at which the crowd moves over a period of time. 
       FIG. 63  illustrates the operation of the MAP server  12  to enable alerts according to one embodiment of the present disclosure. In this embodiment, the MAP client  30 - 1  of the mobile device  18 - 1  interacts with the MAP server  12  via the MAP client  30 - 1  to configure an alert (steps  4200  and  4202 ). More specifically, in this embodiment, the user  20 - 1  interacts with the MAP application  32 - 1  to provide input defining a desired alert. In one embodiment, the alert may be configured such that the user  20 - 1  is alerted when a particular crowd meets one or more criteria specified by the user  20 - 1  or when one or more crowds relevant to a POI or an AOI meet one or more criteria specified by the user  20 - 1 . The one or more criteria may be based on the aggregate profile of the relevant crowd(s), the characteristics of the relevant crowd(s), the quality of the relevant crowd(s), or a combination thereof. For example, the one or more criteria specified for the alert may include an aggregate profile based criterion such that the user  20 - 1  is alerted when the aggregate profile of the crowd specified by the user  20 - 1  satisfies the aggregate profile based criterion or when the aggregate profile of one or more crowds relevant to the POI or the AOI specified by the user  20 - 1  satisfies the aggregate profile based criterion. The aggregate profile based criterion may be that a minimum match strength for the aggregate profile of the crowd as compared to the user profile of the user  20 - 1 . As another example, the one or more criteria for the alert may include one or more user-based criteria such as, for example, a criterion stating that the user  20 - 1  is to be alerted when a defined minimum number of users in the crowd have a specified interest. As yet another example, the one or more criterion for the alert may include a criterion stating that the user  20 - 1  is to be alerted when a crowd having at least a specified number of users is at or near a specified POI or AOI or when a specified crowd has at least a specified number of users. 
     Once the alert is configured, the MAP server  12  monitors the crowd specified for the alert or crowds relevant to the POI or the AOI specified for the alert to detect when the one or more criteria for the alert are satisfied (step  4204 ). Once the one or more criteria for the alert are satisfied, the alert is triggered such that the MAP server  12  sends the alert to the MAP client  30 - 1 , which in turn sends the alert to the MAP application  32 - 1  (steps  4206  and  4208 ). The MAP application  32 - 1  then presents the alert to the user  20 - 1  (step  4210 ). The alert may be presented as, for example, a visual alert or an audible alert. 
       FIG. 64  illustrates the MAP server  12  according to another embodiment of the present disclosure. This embodiment is similar to the embodiment of the MAP server  12  illustrated in  FIG. 2  but further includes a POI request processing function  350  and a POI filtering function  352 . The POI request processing function  350  and the POI filtering function  352  are implemented in software, hardware, or a combination thereof. In general, the POI request processing function  350  operates to process POI requests from the mobile devices  18 - 1  through  18 -N, the subscriber device  22 , and/or the third-party service  26 . In response to a POI request, the POI request processing function  350  obtains a list of POIs relevant to a bounding region for the POI request from either a local source or a remote third-party POI service such as, for example, Yelp, Google® Maps, or the like. The POI filtering function  352  then filters the list of POIs based on crowd data relevant to the list of POIs and returns a resulting filtered list of POIs to the requestor. As discussed below, the crowd data used to filter the list of POIs may be attributes of crowds located at or near the POIs in the list of POIs, aggregate profiles for crowds relevant to a bounding region encompassing the POIs in the list of POIs (i.e., a bounding region for the list of POIs), or both. 
       FIG. 65  illustrates the operation of the system  10  including the MAP server  12  of  FIG. 64  to process a POI request according to one embodiment of the present disclosure. In this example, the POI request originates from the mobile device  18 - 1 . However, the POI request may alternatively originate from the subscriber device  22  or the third-party service  26 . First, the POI request is sent from the MAP application  32 - 1  to the MAP client  30 - 1  within the mobile device  18 - 1  (step  4300 ). The POI request may be a general request for all POIs near the current location of the user  20 - 1  of the mobile device  18 - 1 , a general request for all POIs near a location specified by the POI request, a specific request for specific types of POIs near the current location of the user  20 - 1  of the mobile device  18 - 1 , or a specific request for specific types of POIs near a location specified by the POI request. Specific types of POIs may be, for example, restaurants, coffee houses, night clubs, shopping centers, gas stations, or the like. Preferably, the POI request is initiated by the user  20 - 1  of the mobile device  18 - 1 . Further, if the POI request is a specific request for specific types of POIs, the user  20 - 1  of the mobile device  18 - 1  preferably defines the specific types of POIs for the POI request. Still further, if the POI request is a POI request for a specified location other than the current location of the user  20 - 1 , the POI request includes information defining a location for the POI request such as, for example, a zip code, a city name, or the like. Alternatively, the location for the POI request may be an arbitrary geographic area selected by the user  20 - 1  using any suitable technique such as, for example, selection from a map. In response to receiving the POI request, the MAP client  30 - 1  sends the POI request to the MAP server  12  (step  4302 ). 
     In response to receiving the POI request from the MAP client  30 - 1  of the mobile device  18 - 1 , the POI request processing function  350  of the MAP server  12  obtains a list of POIs for the POI request (step  4304 ). In one embodiment, the POI requesting processing function  350  obtains the list of POIs from a local source such as the datastore  64 . For instance, a master list of POIs including all POIs known to the MAP server  12  may be stored in the datastore  64  of the MAP server  12 . The POI request processing function  350  may then obtain the list of POIs for the POI request from the master list of POIs in the datastore  64 . Specifically, for each POI in the master list of POIs, the master list of POIs includes information identifying the location of the POI and metadata describing the POI. Then, in order to obtain the list of POIs for the POI request, the POI request processing function  350  determines a bounding region for the POI request. The bounding region may be a geographic region of a predefined shape and size centered at the current location of the user  20 - 1  of the mobile device  18 - 1 . Alternatively, if a location is specified by the POI request and that location is a geographic point (e.g., a street address), then the bounding region may be a geographic region of a predefined shape and size centered at the location specified in the POI request. If the location specified by the POI request is a geographic region (e.g., a city), then the bounding region for the POI request may correspond to that geographic region. The POI request processing function  350  may then identify POIs in the master list of POIs that are located within the bounding region for the POI request and that satisfy any additional criteria of the POI request, if any (e.g., desired POI type). The identified POIs that satisfy the POI request form the list of POIs for the POI request. Note that while a master list of POIs is used for this exemplary embodiment, the POIs known to the MAP server  12  may otherwise be stored within the datastore  64 . 
     In another embodiment, the POI request processing function  350  obtains the list of POIs for the POI request from a third-party POI service. More specifically, in response to receiving the POI request, the POI request processing function  350  queries the third-party POI service for POIs that satisfy the POI request. The POIs that satisfy the POI request are POIs located within the bounding region for the POI request and any other criteria specified by the POI request (e.g., desired POI type). The POIs returned by the third-party POI service form the list of POIs for the POI request. 
     Next, the list of POIs is filtered by the POI filtering function  352  of the MAP server  12  based on crowd data that is relevant to the list of POIs (step  4306 ). As discussed below in detail, the crowd data that is relevant to the list of POIs and that is used to filter the list of POIs is either crowd attributes of crowds relevant to bounding regions of the POIs in the list of POIs or aggregate profiles of crowds relevant to a bounding region of the list of POIs as a whole. Once the filtered list of POIs is created, the MAP server  12  returns the filtered list of POIs to the MAP client  30 - 1  of the mobile device  18 - 1  (step  4308 ). The MAP client  30 - 1  then provides the filtered list of POIs to the MAP application  32 - 1  of the mobile device  18 - 1  (step  4310 ), and the MAP application  32 - 1  presents the filtered list of POIs to the user  20 - 1  (step  4312 ). The filtered list of POIs may be presented as, for example, markers on a map presented to the user  20 - 1 , where the markers indicate the locations of the POIs in the filtered list of POIs. 
       FIG. 66  illustrates the MAP server  12  according to another embodiment of the present disclosure. This embodiment is similar to the embodiment of the MAP server  12  illustrated in  FIG. 65 . However, in this embodiment, the list of POIs to be filtered is provided to the MAP server  12  as part of or in association with a POI filtering request. As such, in this embodiment, the MAP server  12  may not include the POI request processing function  350 . 
       FIG. 67  illustrates the operation of the system  10  including the MAP server  12  of  FIG. 66  to filter a list of POIs according to another embodiment of the present disclosure. In this example, the POI filtering request originates from the mobile device  18 - 1 . However, the POI request may alternatively originate from the subscriber device  22  or the third-party service  26 . First, the MAP application  32 - 1  of the mobile device  18 - 1  obtains a list of POIs (step  4400 ). The MAP application  32 - 1  may obtain the list of POIs using any suitable technique. For example, the MAP application  32 - 1  may obtain the list of POIs from a remote third-party POI source such as Yelp, Google® Maps, or the like. As another example, the MAP application  32 - 1  may obtain the list of POIs from an external portable navigation device connected to the mobile device  18 - 1  via a wired or wireless connection. The MAP application  32 - 1  then provides the list of POIs to the MAP client  30 - 1  within the mobile device  18 - 1  (step  4402 ). Preferably, the MAP application  32 - 1  provides the list of POIs to the MAP client  30 - 1  in response to initiation of the filtering process by the user  20 - 1  of the mobile device  18 - 1 . Alternatively, the MAP application  32 - 1  may provide the list of POIs to the MAP client  30 - 1  automatically. The MAP client  30 - 1  then sends a POI filtering request including the list of POIs to the MAP server  12  (step  4404 ). 
     In response to receiving the POI filtering request, the POI filtering function  352  of the MAP server  12  filters the list of POIs based on crowd data relevant to the list of POIs to provide a filtered list of POIs (step  4406 ). Again, as discussed below in detail, the crowd data that is relevant to the list of POIs and that is used to filter the list of POIs is either crowd attributes of crowds relevant to bounding regions of the POIs in the list of POIs or aggregate profiles of crowds relevant to a bounding region of the list of POIs as a whole. Once the filtered list of POIs is created, the MAP server  12  returns the filtered list of POIs to the MAP client  30 - 1  of the mobile device  18 - 1  (step  4408 ). The MAP client  30 - 1  then provides the filtered list of POIs to the MAP application  32 - 1  of the mobile device  18 - 1  (step  4410 ), and the MAP application  32 - 1  presents the filtered list of POIs to the user  20 - 1  (step  4412 ). The filtered list of POIs may be presented as, for example, markers on a map presented to the user  20 - 1 , where the markers indicate the locations of the POIs in the filtered list of POIs. 
       FIG. 68  is a flow chart illustrating the operation of the POI filtering function  352  to filter a list of POIs according to one embodiment of the present disclosure. Generally, in this embodiment, the POI filtering function  352  filters the list of POIs based on attributes of crowds located at the POIs in the list of POIs. First, the POI filtering function  352  initializes a filtered list of POIs (step  4500 ). At this point, the filtered list of POIs is empty. The POI filtering function  352  then gets a first POI in the list of POIs (step  4502 ). Next, the POI filtering function  352  determines a bounding region for the POI (step  4504 ). The bounding region for the POI is preferably a geographic region of a predefined shape and size centered at the POI. For example, the bounding region may be a 20 meter by 20 meter box-shaped bounding region centered at the POI. 
     Next, the POI filtering function  353  identifies any crowds relevant to the bounding region for the POI (step  4506 ). The crowds relevant to the bounding region are crowds that are within or overlap the bounding region of the POI. In one embodiment, the crowds that are relevant to the bounding region are identified by forming the crowds using a reactive crowd formation process such as that described above with respect to  FIG. 22 . In another embodiment, crowds are formed proactively using a proactive crowd formation process such as that described above with respect to  FIGS. 24A through 24D . The crowds relevant to the bounding region for the POI may then be identified by querying the datastore  64  of the MAP server  12  for crowds that are located within the bounding region of the POI or overlap the bounding region of the POI. 
     Once one or more crowds that are relevant to the POI are identified, the POI filtering function  352  determines whether to filter the POI based on a comparison of one or more attributes of the crowd and one or more crowd-based filtering criteria (step  4508 ). The one or more attributes of the crowd may include the size of the crowd (i.e., the number of users of the crowd), an aggregate profile of the crowd or a portion of the aggregate profile of the crowd, one or more characteristics of the crowd (e.g., degree of fragmentation, best-case average DOS, worst-case average DOS, or degree of bidirectional ity), or the like. The one or more crowd-based filtering criteria are preferably defined by the requestor for which the list of POIs is being filtered. For example, if the list of POIs is being filtered in response to a request from the mobile device  18 - 1 , then the one or more crowd-based filtering criteria are preferably defined by the user  20 - 1  of the mobile device  18 - 1  and provided within or in association with the POI request/POI filtering request. 
     The one or more crowd-based filtering criteria define undesired and/or desired crowd attributes. If the one or more crowd-based filtering criteria define undesired crowd attributes, then POIs having crowds with crowd attributes that match the undesired crowd attributes are filtered from the list of POIs. If the one or more crowd-based filtering criteria define desired crowd attributes, then POIs having crowds with crowd attributes that do not match the desired crowd attributes are filtered from the list of POIs. For example, the one or more crowd-based filtering criteria may include a criterion stating that POIs having crowds with crowd sizes greater than ten users are to be filtered from the list of POIs. As another example, the one or more crowd-based filtering criteria may include a criterion stating that POIs with crowds having aggregate profiles indicating that a majority of users in those crowds are interested in a specified topic are to be filtered from the list of POIs. For instance, if the user  20 - 1  dislikes the Dallas Cowboys, then the user  20 - 1  may define a crowd-based filtering criterion stating that POIs having crowds with aggregate profiles indicating that a majority of users in the crowds like the Dallas Cowboys are to be filtered. 
     If there are multiple crowds that are identified as being relevant to the POI, then the attributes of each of the crowds may be separately compared to the one or more crowd-based filtering criteria in order to determine whether to filter the POI from the list of POIs. Alternatively, the attributes of the multiple crowds relevant to the POI may be combined to provide combined crowd attributes for the POI. The combined crowd attributes for the POI may then be compared to the one or more crowd-based filtering criteria in order to determine whether to filter the POI from the list of POIs. 
     By comparing the one or more crowd-based filtering criteria to the attributes of the one or more crowds relevant to the POI, the POI filtering function  352  determines whether to filter the POI from the list of POIs. If the POI filtering function  352  determines that the POI is not to be filtered, the POI filtering function  352  adds the POI to the filtered list of POIs (step  4510 ). If the POI filtering function  352  determines that the POI is to be filtered, the process proceeds to step  4512 . At this point, whether proceeding from step  4508  or  4510 , the POI filtering function  352  determines whether the last POI in the list of POIs has been processed (step  4512 ). If not, the POI filtering function  352  gets the next POI in the list of POIs (step  4514 ) and returns to step  4504 . The process is repeated until all of the POIs in the list of POIs have been processed. Once all of the POIs in the list of POIs have been processed, the process ends. 
       FIG. 69  is a flow chart illustrating the operation of the POI filtering function  352  to filter a list of POIs according to another embodiment of the present disclosure. Generally, in this embodiment, the POI filtering function  352  filters the list of POIs based on data regarding crowds relevant to a bounding region for the list of POIs. First, a bounding region for the list of POIs to be filtered is determined (step  4600 ). In general, the bounding region for the list of POIs to be filtered is a geographic region encompassing the POIs in the list of POIs to be filtered. In the embodiment of  FIGS. 64 and 65 , the bounding region for the list of POIs is preferably the same as the bounding region for the POI request. In the embodiment of  FIGS. 64 and 65 , the bounding region for the list of POIs is preferably a geographic region for which the POIs were obtained (i.e., a geographic region in which the POIs in the list of POIs are located), and the bounding region for the list of POIs may be included in the POI filtering request. 
     Next, the POI filtering function  352  identifies any crowds that are relevant to the bounding region for the list of POIs (step  4602 ). The crowds relevant to the bounding region for the list of POIs are crowds that are within or overlap the bounding region for the list of POIs. In one embodiment, the crowds that are relevant to the bounding region are identified by forming the crowds using a reactive crowd formation process such as that described above with respect to  FIG. 22 . In another embodiment, crowds are formed proactively using a proactive crowd formation process such as that described above with respect to  FIGS. 24A through 24D . The crowds relevant to the bounding region for the list of POIs may then be identified by querying the datastore  64  of the MAP server  12  for crowds located within the bounding region of the list of POIs or overlap the bounding region of the list of POIs. 
     Once the crowds that are relevant to the bounding region of the list of POIs are identified, the POI filtering function  352  obtains an aggregate profile for each of the crowds (step  4604 ). For each of the crowds, the aggregate profile for the crowd may be obtained by generating the aggregate profile for the crowd using, for example, one of the processes described above in  FIGS. 29A and 29B  and  FIG. 31 . Next, the POI filtering function  352  combines the aggregate profiles of the crowds relevant to the bounding region of the list of POIs to provide a combined aggregate profile for the bounding region for the list of POIs (step  4606 ). Lastly, the POI filtering function  352  filters the list of POIs based on a comparison of metadata regarding the POIs in the list of POIs and the combined aggregate profile for the bounding region for the list of POIs (step  4608 ). 
     More specifically, in this embodiment, metadata describing the POIs in the list of POIs is obtained. The metadata may be provided during generation of the list of POIs. The metadata for the POIs in the list of POIs may be created using any suitable technique. For example, the metadata describing a POI may be created manually by a user, created automatically by analyzing a web page or website linked to the POIs, or the like. Then, the list of POIs is filtered based on a comparison of the metadata for the POIs in the list of POIs and the combined aggregate profile for the crowds relevant to the bounding region of the list of POIs. 
     In one embodiment, the combined aggregate profile includes combined interests of the crowds relevant to the bounding region of the list of POIs, and the combined aggregate profile is analyzed to determine whether there is a dominant interest. A dominant interest is an interest in the combined aggregate profile that has a substantially greater significance to the crowds relevant to the bounding region of the list of POIs than all of the other interests in the combined aggregate profile. For example, an interest in the combined aggregate profile may be identified as a dominant interest if the number of user matches for the interest is greater than the number of user matches for each of the other interests in the combined aggregate profile by a predefined threshold amount. This predefined threshold amount may be, for example, 100% such that the interest is identified as a dominant interest if the number of user matches is at least 100% greater than the number of user matches for each of the other interests in the combined aggregate profile. As another example, an interest in the combined aggregate profile may be identified as a dominant interest if the ratio of user matches to total number of users for the interest is at least a predefined threshold greater than the ratio of user matches to total number of users of each of the other interests in the combined aggregate profile. This predefined threshold may be, for example, 0.25 (i.e., 25 percentage points) such that the interest is identified as a dominant interest if the ratio of user matches to total number of users is at least 0.25 greater than the ratio of user matches to total number of users for each of the other interests in the combined aggregate profile. 
     Once the dominant interest is identified, each POI in the list of POIs is processed by comparing the metadata for the POI to the dominant interest. If the metadata does not include a descriptor (e.g., keyword) that matches or, optionally, is closely related to the dominant interest, then the corresponding POI is filtered from the list of POIs. As a result, a filtered list of POIs is provided that includes only those POIs from the list of POIs that match or, optionally, are closely related to the dominant interest of the crowds relevant to the bounding region of the list of POIs. 
     If a dominant interest is unable to be identified (i.e., there is no dominant interest), then the POI filtering function  352  may identify one or more most significant interests in the combined aggregate profile for the crowds relevant to the bounding region of the list of POIs. The one or more most significant interests are preferably one or more interests in the combined aggregate profile that have the highest number of user matches or highest ratio of user matches over total number of users. For example, the one or more most significant interests may be the interests from the combined aggregate profile having the two highest number of user matches or two highest ratios of user matches over total number of users. Alternatively, the one or more most significant interests may be one or more interests in the combined aggregate profile that have at least a predefined threshold number of user matches (e.g., ten user matches) or that have at least a predefined threshold ratio of user matches over total number of users (e.g., 50%). 
       FIG. 70  illustrates the MAP server  12  according to another embodiment of the present disclosure. This embodiment is similar to the embodiment of the MAP server  12  illustrated in  FIG. 2 . However, in this embodiment, the MAP server  12  also includes a crowd-sourced POI creation function  354 , which may be implemented in software, hardware, or a combination thereof. As discussed below in detail, the crowd-sourced POI creation function  354  operates to generate crowd-sourced POIs based on crowds. 
       FIG. 71  illustrates the operation of the system  10  including the MAP server  12  of  FIG. 70  to create crowd-sourced POIs according to one embodiment of the present disclosure. While this exemplary embodiment focuses on creation of crowd-sourced POIs in response to a request from the mobile device  18 - 1 , the MAP server  12  may additionally or alternatively operate to create crowd-sourced POIs in response requests from the subscriber device  22  and/or the third-party service  26 . First, the MAP application  32 - 1  sends a crowd-sourced POI request to the MAP client  30 - 1  within the mobile device  18 - 1  (step  4700 ). Preferably, the crowd-sourced POI request is initiated by the user  20 - 1  of the mobile device  18 - 1 . For example, the MAP application  32 - 1  may enable the user  20 - 1  to select a geographic area and then initiate a crowd-sourced POI request. The user  20 - 1  may be enabled to select a desired geographic area by, for example, selecting the geographic area on a map, entering a city name, entering a zip code, entering a street address and a maximum distance from that street address, or the like. Alternatively, the user  20 - 1  may initiate the crowd-sourced POI request for an area surrounding the current location of the user  20 - 1 . For example, the crowd-sourced POI request may be for a geographic area of a predefined shape and size centered at the current location of the user  20 - 1 . Upon receiving the crowd-sourced POI request, the MAP client  30 - 1  of the mobile device  18 - 1  sends the crowd-sourced POI request to the MAP server  12  (step  4702 ). 
     Upon receiving the crowd-sourced POI request, the crowd-sourced POI creation function  354  of the MAP server  12  creates one or more crowd-sourced POIs. More specifically, the crowd-sourced POI creation function  354  first determines a bounding region for the crowd-sourced POI request (step  4704 ). As discussed above, in one embodiment, the user  20 - 1  of the mobile device  18 - 1  selects a desired geographic area for the crowd-sourced POI request. In this case, information identifying the desired geographic area is provided within or in association with the crowd-sourced POI request, and the crowd-sourced POI creation function  354  sets the bounding region for the crowd-sourced POI request to the desired geographic area selected by the user  20 - 1 . In another embodiment, the crowd-sourced POI request is for a geographic area surrounding the current location of the user  20 - 1 . In this case, the crowd-sourced POI creation function  354  obtains the current location of the user  20 - 1  and sets the bounding region for the crowd-sourced POI request to a geographic area surrounding the current location of the user  20 - 1 . The current location of the user  20 - 1  may be obtained from the user record of the user  20 - 1  stored in the datastore  64  of the MAP server  12  or included in the crowd-sourced POI request received from the MAP client  30 - 1 . The geographic area surrounding the current location of the user  20 - 1  may be a predefined shape and size. Alternatively, the shape and/or size of the geographic area surrounding the current location of the user  20 - 1  may be selected by the user  20 - 1  and be included in the crowd-sourced POI request. 
     Next, the crowd-sourced POI creation function  354  identifies any crowds relevant to the bounding region for the crowd-sourced POI request (step  4706 ). The crowds relevant to the bounding region for the crowd-sourced POI request are crowds that are within or overlap the bounding region for the crowd-sourced POI request. In one embodiment, the crowds that are relevant to the bounding region are identified by forming the crowds using a reactive crowd formation process such as that described above with respect to  FIG. 22 . In another embodiment, crowds are formed proactively using a proactive crowd formation process such as that described above with respect to  FIGS. 24A through 24D . The crowds relevant to the bounding region for the crowd-sourced POI request may then be identified by querying the datastore  64  of the MAP server  12  for crowds located within the bounding region of the crowd-sourced POI request or overlap the bounding region of the crowd-sourced POI request. 
     The crowd-sourced POI creation function  354  then creates one or more crowd-sourced POIs based on the crowds identified as being relevant to the bounding region for the crowd-soured POI request (step  4708 ). In one embodiment, a separate crowd-sourced POI is created for each of the crowds. Optionally, crowd-sourced POIs that are within a predefined distance from one another (e.g., within 40 meters of one another) may be combined or merged into a single crowd-sourced POI. In another embodiment, the crowds relevant to the bounding region for the crowd-sourced POI request are processed to combine any crowds that are within a predefined distance from one another, thereby providing a processed set of crowds. The processed set of crowds includes any combined crowds resulting from the processing as well as any other relevant crowd that was not combined with another crowd. A separate crowd-sourced POI is created for each crowd in the processed set of crowds. 
     In addition, as part of the crowd-sourced POI creation process, metadata describing the crowd-sourced POIs may be generated and stored based on aggregate profiles of the crowds at the crowd-sourced POIs. More specifically, for a particular crowd-sourced POI resulting from a single crowd, an aggregate profile of the crowd at the crowd-sourced POI is determined and analyzed to determine whether there is a dominant interest for the crowd. A dominant interest is an interest in the aggregate profile having substantially greater significance to the crowd than all of the other interests in the aggregate profile. For example, an interest in the aggregate profile may be identified as a dominant interest if the number of user matches for the interest is greater than the number of user matches for each of the other interests in the aggregate profile by a predefined threshold amount. This predefined threshold amount may be, for example, 100% such that the interest is identified as a dominant interest if the number of user matches is at least 100% greater than the number of user matches for each of the other interests in the aggregate profile. As another example, an interest in the aggregate profile may be identified as a dominant interest if the ratio of user matches to total number of users for the interest is at least a predefined threshold greater than the ratio of user matches to total number of users of each of the other interests in the aggregate profile. This predefined threshold may be, for example, 0.25 (i.e., 25 percentage points) such that the interest is identified as a dominant interest if the ratio of user matches to total number of users is at least 0.25 greater than the ratio of user matches to total number of users for each of the other interests in the aggregate profile. If there is a dominant interest for the crowd, then the dominant interest is identified and stored as metadata describing the crowd-sourced POI. If there is no dominant interest, one or more most significant interests from the aggregate profile of the crowd (i.e., the interests having the highest number of user matches or highest ratio of user matches to total number of users in the crowd) may be identified and stored as metadata describing the crowd-sourced POI. 
     Once the one or more crowd-sourced POIs have been created, the crowd-sourced POIs are returned to the MAP client  30 - 1  of the mobile device  18 - 1  (step  4710 ). The MAP client  30 - 1  then returns the crowd-sourced POIs to the MAP application  32 - 1  (step  4712 ), and the MAP application  32 - 1  presents the crowd-sourced POIs to the user  20 - 1  (step  4714 ). The crowd-sourced POIs may be presented as, for example, markers on a map where the markers indicate the locations of the crowd-sourced POIs. Note that the crowd-sourced POIs, or alternatively one or more of the crowd-sourced POIs selected by the user  20 - 1  after the crowd-sourced POIs are presented in step  4714 , are stored within the user record of the user  20 - 1 . For example, these crowd-sourced POIs may be stored as “favorites” of the user  20 - 1 . These crowd-sourced POIs may thereafter be used by the user  20 - 1  for other purposes such as, for example, initiating a historical request for historical aggregate profile data or obtaining aggregate profile data for crowds located at the crowd-sourced POI at a particular point in time. Note that, once created, the location of a crowd-sourced POI and, optionally, the description of the crowd-sourced POI are static. 
       FIG. 72  is a flow chart illustrating a process performed by the crowd-sourced POI creation function  354  to promote a crowd-sourced POI to a permanent POI according to one embodiment of the present disclosure. This process may be triggered by an event such as, for example, a crowd-sourced POI request. More specifically, this process may be performed for each crowd-sourced POI created in response to a crowd-sourced POI request. As one exemplary alternative, the crowd-sourced POI creation function  354  may periodically process the crowd-sourced POIs stored in the user records of the users  20 - 1  through  20 -N. 
     First, the crowd-sourced POI creation function  354  identifies any previously created crowd-sourced POIs that are similar to a crowd-sourced POI being processed (step  4800 ). More specifically, the crowd-sourced POI creation function  354  analyzes previously created crowd-sourced POIs, which are preferably stored in the user records of the users  20 - 1  through  20 -N, to identify previously created crowd-sourced POIs that are similar to the crowd-sourced POI being processed. A previously created crowd-sourced POI is similar to the crowd-sourced POI being processed if the previously created crowd-sourced POI satisfies one or more predefined criteria with respect to the crowd-sourced POI being processed. The one or more predefined criteria includes at least one location-based criteria stating that the location of the previously created crowd-sourced POI must be equal to or essentially equal to the location of the crowd-sourced POI being processed. The location of the previously created crowd-sourced POI is essentially equal to the location of the crowd-sourced POI being processed if the two locations are within a predefined distance from one another. This predefined distance is preferably a small distance such as, for example, 20 meters. In addition, the one or more predefined criteria for determining whether the previously created crowd-sourced POI is similar to the crowd-sourced POI being processed may include a criterion stating that one or more of the metadata descriptors for the previously created crowd-sourced POI must match one or more metadata descriptors for the crowd-sourced POI being processed. 
     Next, the crowd-sourced POI creation function  354  determines whether the crowd-sourced POI being processed is to be promoted to a permanent POI based on the one or more previously created crowd-sourced POIs identified as being similar to the crowd-sourced POI being processed (step  4802 ). More specifically, in this embodiment, the crowd-sourced POIs include timestamps defining a date and time at which the crowd-sourced POIs were created. A determination that the crowd-sourced POI is to be promoted to a permanent POI may be made if the number of previously created crowd-sourced POIs that are similar to the crowd-sourced POI being processed is greater than a predefined threshold. In addition or alternatively, a determination that the crowd-sourced POI is to be promoted to a permanent POI may be made if the timestamps of the previously created crowd-sourced POIs and the timestamp of the crowd-sourced POI being processed indicate these crowd-sourced POIs have been located at the same location or essentially the same location over a predefined threshold amount of time (e.g., one month), or the like. 
     If the crowd-sourced POI being processed is not to be promoted, the process ends. If the crowd-sourced POI being processed is to be promoted, the crowd-sourced POI is promoted to a permanent POI (step  4804 ), and then the process ends. The crowd-sourced POI is promoted to a permanent POI by, for example, combining the crowd-sourced POI being processed and the similar previously created crowd-sourced POIs into a single POI and persisting that POI as a permanent POI. The POI may be persisted as a permanent POI by storing the POI in the datastore  64  of the MAP server  12  as a POI that may be utilized by the system  10 , by notifying a third-party POI source of the POI, or the like. The POI may then be used to, for example, serve subsequent POI requests from the mobile devices  18 - 1  through  18 -N, the subscriber device  22 , and/or the third-party service  26  in the manner described above with respect to  FIGS. 64 and 65 . 
       FIG. 73  illustrates a process performed by the MAP server  12  to augment metadata for a POI according to one embodiment of the present disclosure. As an example, this process may be performed by the crowd-sourced POI creation function  354  to augment metadata describing a newly created crowd-sourced POI. First, a bounding box is established for the POI (step  4900 ). The bounding box is a geographic region of a predefined shape and size centered at the POI. For example, the bounding box may be a 20 meter by 20 meter geographic region centered at the POI. Note that the bounding box is an exemplary bounding region for the POI. Bounding regions of other shapes and sizes may alternatively be used. Next, a time window is established (step  4902 ). The time window is preferably a predefined time window such as, for example, the past day, the past week, the past month, a particular day last week, a particular day last month, or the like. Note that while only one time window is used in this example, multiple time windows may alternatively be used (e.g., a separate time window for each Friday over the last six months). Next, history objects that are relevant to the bounding box and the time window are obtained from the datastore  64  of the MAP server  12  (step  4904 ). The relevant history objects are history objects recorded for time periods within or intersecting the time window and for locations, or geographic areas, within or intersecting the bounding box for the POI. 
     In this example, an equivalent depth of the bounding box (D BB ) within the quadtree data structures used to store the history objects is determined (step  4906 ). More specifically, the area of the base quadtree region (e.g., the base quadtree region  98 ) is referred to as A BASE . Then, at each depth of the quadtree, the area of the corresponding quadtree nodes is (¼) D *A BASE . In other words, the area of a child node is ¼ th  of the area of the parent node of that child node. The equivalent depth of the bounding box (D BB ) is determined by determining a quadtree depth at which the area of the corresponding quadtree nodes most closely matches an area of the bounding box (A BB ). Note that equivalent quadtree depth of the bounding box (D BB ) determined in step  4906  is used below in order to efficiently determine the ratios of the area of the bounding box (A BB ) to areas of the relevant history objects (A HO ). However, in an alternative embodiment, the ratios of the area of the bounding box (A BB ) to the areas of the relevant history objects (A HO ) may be otherwise computed, in which case step  4906  would not be needed. 
     At this point, the next history object is obtained from a list of the history objects obtained in step  4904  (step  4908 ). Next, a relevancy weight for the history object is set, where the relevancy weight is indicative of a relevancy of the history object to the bounding box (step  4910 ). For instance, a history object includes anonymized user profile data for a corresponding geographic area. If that geographic area is within or significantly overlaps the bounding box, then the history object will have a high relevancy weight. However, if the geographic area only overlaps the bounding box slightly, then the history object will have a low relevancy weight. In this embodiment, the relevancy weight for the history object is set to an approximate ratio of the area of the bounding box (A BB ) to an area of the history object (A HO ) computed based on a difference between the quadtree depth of the history object (D HO ) and the equivalent quadtree depth of the bounding box (D EQ ). The quadtree depth of the history object (D HO ) is stored in the history object. More specifically, in one embodiment, the relevancy weight of the history object is set according to the following: 
     
       
         
           
             
               relevancy 
               = 
               
                 
                   
                     A 
                     BB 
                   
                   
                     A 
                     HO 
                   
                 
                 ≅ 
                 
                   
                     ( 
                     
                       1 
                       4 
                     
                     ) 
                   
                   
                     
                       D 
                       HO 
                     
                     - 
                     
                       D 
                       BB 
                     
                   
                 
               
             
             , 
           
         
       
     
     for D HO &gt;D BB , and 
     relevancy=1, for D HO  D BB . 
     Next, an aggregate profile is generated for the history object (step  4912 ). In this embodiment, the aggregate profile for the history object is generated by comparing the anonymous user profiles of the anonymous user records in the history object to one another. However, in an alternative embodiment, the aggregate profile may be generated based on a comparison of the anonymous user profiles of the anonymous user records included in the history object and the user profile of the requesting user (i.e., the user  20  that initiated the crowd-sourced POI request), a select subset of the user profile of the requesting user, or a target user profile. A determination is then made as to whether there are more history objects in the list of history objects (step  4914 ). If so, the process returns to step  4908  and is repeated until all of the history objects in the list of history objects have been processed. 
     Once all of the history objects have been processed, the aggregate profiles of the history objects are combined to provide a combined aggregate profile. More specifically, in this embodiment, a weighted average of the aggregate profiles for the history objects is computed using the relevancy weights of the history objects (step  4916 ). In one embodiment, the aggregate profile of each of the history objects includes the number of user matches for the history object and the total number of users for the history object. In this embodiment, the aggregate profiles for the history objects include the number of user matches for each of a number of keywords. As such, the weighted average of the aggregate profiles of the history objects (i.e., the average aggregate profile) may include a weighted average of the number of user matches for each of those keywords, which may be computed as: 
     
       
         
           
             
               
                 user_matches 
                 
                   
                     KEYWORD 
                      
                     _ 
                      
                     j 
                   
                   , 
                   AVG 
                 
               
               = 
               
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                    
                   
                     ( 
                     
                       
                         
                           relevancy 
                           i 
                         
                         · 
                         number_of 
                       
                        
                       _user 
                        
                       
                         _matches 
                         
                           
                             KEYWORD 
                              
                             _ 
                              
                             j 
                           
                           , 
                           i 
                         
                       
                     
                     ) 
                   
                 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                    
                   
                     relevancy 
                     i 
                   
                 
               
             
             , 
           
         
       
     
     where relevancy, is the relevancy weight computed in step  4910  for the i-th history object, number_of_user_matches KEYWORD_j,i  is the number of user matches for the j-th keyword for the i-th history object, and n is the number of history objects. In addition or alternatively, the average aggregate profile may include the weighted average of the ratio of the user matches to total users for each keyword, which may be computed as: 
     
       
         
           
             
               
                 user_matches 
                  
                 
                   _ratio 
                   
                     
                       KEYWORD 
                        
                       _ 
                        
                       j 
                     
                     , 
                     AVG 
                   
                 
               
               = 
               
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                    
                   
                     ( 
                     
                       
                         relevancy 
                         i 
                       
                       · 
                       
                         
                           number_of 
                            
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                            
                           
                             _matches 
                             
                               
                                 KEYWORD 
                                  
                                 _ 
                                  
                                 j 
                               
                               , 
                               i 
                             
                           
                         
                         
                           total_users 
                           i 
                         
                       
                     
                     ) 
                   
                 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                    
                   
                     relevancy 
                     i 
                   
                 
               
             
             , 
           
         
       
     
     where relevancy, is the relevancy weight computed in step  4910  for the i-th history object, number_of_user_matches KEYWORD_j,i  is the number of user matches for the j-th keyword for the i-th history object, total users, is the total number of users from the aggregate profile of the i-th history object, and n is the number of history objects. 
     Next, the metadata describing the POI is augmented based on the weighted average of the aggregate profiles computed for the history objects (step  4918 ). More specifically, in one embodiment, a dominant interest may be identified using the weighted average of the number of user matches for each keyword, or interest, and/or the weighted average of the ratio of the number of user matches over the total number of users for each keyword, or interest. In this case, a dominant interest is an interest in the weighted average of the aggregate profiles that has a substantially greater significance than all of the other interests in the weighted average of the aggregate profiles. In addition, if no dominant interest can be identified, one or more most significant interests may be identified. For instance, one or more keywords having the highest weighted average of the number of user matches or the highest weighted ratio of the number of user matches over the total number of users may be identified as the one or more most significant interests. These most significant interests may be stored as additional metadata describing the POI. 
       FIG. 74  illustrates a process performed by the MAP server  12  to augment metadata for a POI according to another embodiment of the present disclosure. As an example, this process may be performed by the crowd-sourced POI creation function  354  to augment metadata describing a newly created crowd-sourced POI. First, a bounding box is established for the POI (step  5000 ). The bounding box is a geographic region of a predefined shape and size centered at the POI. For example, the bounding box may be a 20 meter by 20 meter geographic region centered at the POI. Note that the bounding box is an exemplary bounding region for the POI. Bounding regions of other shapes and sizes may alternatively be used. Next, a time window is established (step  5002 ). The time window is preferably a predefined time window such as, for example, the past day, the past week, the past month, a particular day last week, a particular day last month, or the like. Note that while only one time window is used in this example, multiple time windows may alternatively be used (e.g., a separate time window for each Friday over the last six months). Next, crowd snapshots that are relevant to the bounding box and the time window are obtained from the datastore  64  of the MAP server  12  (step  5004 ). The relevant crowd snapshots are crowd snapshots recorded for time periods within or intersecting the time window and for crowds located within or intersecting the bounding box for the POI. 
     Next, aggregate profiles for the relevant crowd snapshots are obtained (step  5006 ). More specifically, for each of the relevant crowd snapshots, the aggregate profile for the relevant crowd snapshot is preferably generated based on a comparison of the anonymous user profiles of the anonymous user records included in the relevant crowd snapshot to one another. However, the aggregate profile for the relevant crowd snapshot may alternatively be generated based on a comparison of the anonymous user profiles of the anonymous user records included in the relevant crowd snapshot to a user profile of a requesting user (e.g., a user profile of the user  20  of one of the mobile devices  18  that initiated the crowd-sourced POI request), a selected subset of the user profile of a requesting user, or a target user profile. 
     Lastly, the metadata for the POI is augmented based on the aggregate profiles for the relevant crowd snapshots (step  5008 ). More specifically, in one embodiment, the aggregate profiles for the relevant crowd snapshots are combined into a combined aggregate profile. For example, if user interests are expressed as keywords, the combined aggregate profile may include a combined number of user matches for each of a number of keywords over all of the aggregate profiles, a combined ratio of user matches to total number of users for each of a number of keywords over all of the aggregate profiles, or both. Using the combined aggregate profile, a dominant interest is identified. In this case, a dominant interest is an interest, or keyword, having substantially greater significance in the combined aggregate profile than each of the other interests in the combined aggregate profile. If a dominant interest is identified, then the dominant interest is stored as additional metadata describing the POI. In addition, if no dominant interest can be identified, one or more most significant interests in the combined aggregate profile may be identified. For instance, one or more keywords having the highest combined number of user matches or the highest radio of combined user matches to total users may be identified as the one or more most significant interests. The one or more most significant interests may be stored as additional metadata describing the POI. 
       FIG. 75  is a block diagram of the MAP server  12  according to one embodiment of the present disclosure. As illustrated, the MAP server  12  includes a controller  356  connected to memory  358 , one or more secondary storage devices  360 , and a communication interface  362  by a bus  364  or similar mechanism. The controller  356  is a microprocessor, digital Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), or the like. In this embodiment, the controller  356  is a microprocessor, and the application layer  40 , the business logic layer  42 , and the object mapping layer  62  are implemented in software and stored in the memory  358  for execution by the controller  356 . Further, the datastore  64  may be implemented in the one or more secondary storage devices  360 . The secondary storage devices  360  are digital data storage devices such as, for example, one or more hard disk drives. The communication interface  362  is a wired or wireless communication interface that communicatively couples the MAP server  12  to the network  28  ( FIG. 1 ). For example, the communication interface  362  may be an Ethernet interface, local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, or the like. 
       FIG. 76  is a block diagram of the mobile device  18 - 1  according to one embodiment of the present disclosure. This discussion is equally applicable to the other mobile devices  18 - 2  through  18 -N. As illustrated, the mobile device  18 - 1  includes a controller  366  connected to memory  368 , a communication interface  370 , one or more user interface components  372 , and the location function  36 - 1  by a bus  374  or similar mechanism. The controller  366  is a microprocessor, digital ASIC, FPGA, or the like. In this embodiment, the controller  366  is a microprocessor, and the MAP client  30 - 1 , the MAP application  32 - 1 , and the third-party applications  34 - 1  are implemented in software and stored in the memory  368  for execution by the controller  366 . In this embodiment, the location function  36 - 1  is a hardware component such as, for example, a GPS receiver. The communication interface  370  is a wireless communication interface that communicatively couples the mobile device  18 - 1  to the network  28  ( FIG. 1 ). For example, the communication interface  370  may be a local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, a mobile communications interface such as a cellular telecommunications interface, or the like. The one or more user interface components  372  include, for example, a touchscreen, a display, one or more user input components (e.g., a keypad), a speaker, or the like, or any combination thereof. 
       FIG. 77  is a block diagram of the subscriber device  22  according to one embodiment of the present disclosure. As illustrated, the subscriber device  22  includes a controller  376  connected to memory  378 , one or more secondary storage devices  380 , a communication interface  382 , and one or more user interface components  384  by a bus  386  or similar mechanism. The controller  376  is a microprocessor, digital ASIC, FPGA, or the like. In this embodiment, the controller  376  is a microprocessor, and the web browser  38  ( FIG. 1 ) is implemented in software and stored in the memory  378  for execution by the controller  376 . The one or more secondary storage devices  380  are digital storage devices such as, for example, one or more hard disk drives. The communication interface  382  is a wired or wireless communication interface that communicatively couples the subscriber device  22  to the network  28  ( FIG. 1 ). For example, the communication interface  382  may be an Ethernet interface, local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, a mobile communications interface such as a cellular telecommunications interface, or the like. The one or more user interface components  384  include, for example, a touchscreen, a display, one or more user input components (e.g., a keypad), a speaker, or the like, or any combination thereof. 
       FIG. 78  is a block diagram of a computing device  388  operating to host the third-party service  26  according to one embodiment of the present disclosure. The computing device  388  may be, for example, a physical server. As illustrated, the computing device  388  includes a controller  390  connected to memory  392 , one or more secondary storage devices  394 , a communication interface  396 , and one or more user interface components  398  by a bus  400  or similar mechanism. The controller  390  is a microprocessor, digital ASIC, FPGA, or the like. In this embodiment, the controller  390  is a microprocessor, and the third-party service  26  is implemented in software and stored in the memory  392  for execution by the controller  390 . The one or more secondary storage devices  394  are digital storage devices such as, for example, one or more hard disk drives. The communication interface  396  is a wired or wireless communication interface that communicatively couples the computing device  388  to the network  28  ( FIG. 1 ). For example, the communication interface  396  may be an Ethernet interface, local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, a mobile communications interface such as a cellular telecommunications interface, or the like. The one or more user interface components  398  include, for example, a touchscreen, a display, one or more user input components (e.g., a keypad), a speaker, or the like, or any combination thereof. 
     Those skilled in the art will recognize improvements and modifications to the preferred embodiments of the present invention. All such improvements and modifications are considered within the scope of the concepts disclosed herein and the claims that follow.