Patent Publication Number: US-2023153858-A1

Title: Systems and Methods for Statistical Dynamic Geofencing

Description:
CLAIM OF PRIORITY 
     This application is a continuation of and claims priority to U.S. patent application Ser. No. 16/506,488, filed Jul. 9, 2019, which in turn is a continuation of and claims priority to U.S. patent application Ser. No. 13/707,316, filed Dec. 6, 2012, which is now U.S. Pat. No. 10,380,636, issued Aug. 13, 2019, the entire disclosure of which is hereby incorporated by reference herein in its entirety. 
    
    
     TECHNICAL FIELD 
     This application relates generally to data processing within a network-based system operating over a distributed network or data processing on a mobile device, and more specifically to systems and methods for implementing statistical dynamic geofencing. 
     BACKGROUND 
     The ever increasing use of smart phones, such as the iPhone® (from Apple, Inc. of Cupertino, Calif.), with data connections and location determination capabilities is slowly changing the way people interact, shop for products and services, and even manage accounts. Smart phones can provide users with nearly instant information regarding a wide range of information, such as product availability, friend locations, or pricing. For example, applications such as RedLaser™ (from eBay, Inc. of San Jose, Calif.) allow a smart phone user to scan a bar code and instantly check prices across online and local retail outlets. Smart phones also commonly include mechanisms, such as global positioning system (GPS) receivers, that allow the devices to constantly update location information. These technology changes are also driving changes in the way merchants and brand advertisers target and deliver advertising, particularly mobile advertising. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Some embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings in which: 
         FIG.  1    is a block diagram depicting a system for using statistical dynamic geofences to assist in targeted publication distribution, according to an example embodiment. 
         FIG.  2    is a block diagram illustrating an environment for operating a mobile device, according to an example embodiment. 
         FIG.  3    is a block diagram illustrating a mobile device, according to an example embodiment. 
         FIG.  4    is a block diagram illustrating a network-based system for using statistical dynamic geofences to assist in targeted publication distribution, according to an example embodiment. 
         FIG.  5    is a block diagram illustrating geofencing modules, according to an example embodiment. 
         FIG.  6    is a diagram illustrating geofence updating via statistical analysis, according to an example embodiment. 
         FIG.  7    is a flowchart illustrating a method of generating and using statistical dynamic geofences to assist in targeted publication distribution, according to an example embodiment. 
         FIG.  8    is a flowchart illustrating a method of updating statistical dynamic geofences to refine targeted publication distribution, according to an example embodiment. 
         FIG.  9    is a diagrammatic representation of a machine in the example form of a computer system within which a set of instructions for causing the machine to perform any one or more of the methodologies discussed herein may be executed. 
     
    
    
     DEFINITIONS 
     Location—For the purposes of this specification and the associated claims, the term “location” is used to refer to a geographic location, such as a longitude/latitude combination or a street address. The term location is also used within this specification in reference to a physical location associated with a merchant, an event, or other similar physical destination. 
     Point of Interest (POI)—For the purposes of this specification and the associated claims, the term POI is used in a manner similar to location, and refers to or identifies a geographic (physical) location. For example, a POI may be a retail store, such as Starbucks, and may identify that retail store by name, address, GPS coordinates, or any other known method of identifying a unique physical location. 
     Real-time—For the purposes of this specification and the associated claims, the term “real-time” is used to refer to calculations or operations performed on the fly as events occur or input is received by the operable system. However, the use of the term “real-time” is not intended to preclude operations that cause some latency between input and response, so long as the latency is an unintended consequence induced by the performance characteristics of the machine. 
     Context—For the purposes of this specification and the associated claims, the term “context” is used to refer to environmental inputs, such as location, time, and weather conditions, among others. The context generally refers to conditions describing an individual&#39;s (e.g., user&#39;s) environment and/or activities. For example, context information can include a user&#39;s location, direction of movement, current activity (e.g., working, driving, playing golf, shopping, etc.), current weather conditions, time of day, and time of year (e.g., season), among other things. In certain examples, context information about a user can also include past events, purchase history, or other historical data about the user. 
     Geofence—For the purposes of this specification and the associated claims, the term “geofence” is used to refer to various regions or boundaries of interest that include a geographic area within a distance or travel time to a point of interest. However, a geofence need not be limited to any geometric shape or an arbitrary boundary drawn on a map. A geofence can be used to determine a geographical area of interest for calculation of demographics, advertising, or similar purposes. Geofences can be used in conjunction with the advertisement generation and delivery concepts discussed herein. For example, a geofence can be used to assist in determining whether a user (or mobile device associated with the user) is within a geographic area of interest to a particular advertiser (e.g., a local merchant) or capable of traveling to the particular advertiser in a specified period of time. If the user is within a geofence established by the merchant, the systems discussed herein can use that information to generate a dynamic advertisement from the advertiser and deliver the offer to the user (e.g., via a mobile device associated with the user). 
     Additional detail regarding providing and receiving location-based services, including geo-location and geofence concepts, can be found in U.S. Pat. No. 7,848,765, titled “Location-Based Services,” granted to Phillips et al., which is hereby incorporated by reference. 
     DETAILED DESCRIPTION 
     Example systems and methods are described for using statistical dynamic geofencing for targeting publication delivery to mobile devices, among other things. Also described are systems and methods for generating, updating, and utilizing statistical dynamic geofences. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of example embodiments. It will be evident, however, to one skilled in the art, that the present invention may be practiced without these specific details. It will also be evident that statistical dynamic geofencing is not limited to the examples provided and may include other scenarios not specifically discussed. 
     Geofences can be used within a location-aware publication system to target publications for distribution within limited geographical areas. Geofences can be defined in terms of GPS coordinates (e.g., latitude and longitude) combined with a radius measured in meters or feet, for example. Alternatively, geofences can also be defined according to a series of GPS coordinates defining a bounding box. In yet other examples, a geofence can be any geometric shape defined by a mathematical formula and anchored by a GPS coordinate. Other methods of defining, maintaining, and using geofences can be used without limitation with the systems and methods discussed herein. 
     One challenge identified by the inventors in effective use of geofences for targeting publication distribution can include accurately predicting whether the geofence is likely to include target consumers who are likely to respond to the publication. A solution to this challenge can include the use of statistical analysis of demographic data, such as census data, in geographic areas of interest to determine optimal geofence size, shape, and even placement. Targeting publications, such as advertisements, for distribution within limited geographical areas can allow merchants and other advertisers to selectively target publications based on statistical analysis of data, such as demographic data. 
     With the increased popularity of mobile devices, such as the iPhone®, with location-aware capabilities, the usefulness of location-aware publication systems has increased. Location-aware publication systems can receive location data on mobile devices directly from individual mobile devices or from a carrier, such as AT&amp;T or Verizon. In some examples, the location data may also include, or be able to be correlated with, demographic data associated with the users of the mobile devices. In such examples, a location-aware publication system can analyze historical trends in location and demographic data to generate geofences to target locations around POIs. 
     In an example, a coffee franchise may want to target certain demographic characteristics (target demographic parameters) of potential customers around each franchise location. A location-aware publication system can utilize general census-type demographic data to initially generate a set of geofences around (or associated with) each of the target POIs. In an example, the demographic characteristic might be average income, and the publication system can analyze demographic data in geographic areas surrounding a POI to determine the size and/or shape required for a geofence to encompass a certain predicted number or density of individuals with the target demographic (e.g., average income over $80,000/year). In certain examples, the geofence may not be centered on a particular POI, but rather allowed to float within a defined geographic area in order to best capture the target demographic. In these examples, the defined float geography can be centered or otherwise tied to one or more POIs, in order to ensure that the targeted audience is within a certain distance of an advertiser&#39;s physical locations. 
     In these examples, the target demographics can include population density, average income, age ranges, percentage of male or females, average education level, active mobile device usage, or any other similar demographic characteristic. Geofences can also be generated based on other data susceptible to statistical analysis, such as competitors (locations or density) or WiFi hotspots, among other things. Some additional example target demographics can include: household income, marital status, sex, age, ethnicity, race, profession, average number of children, no children, median age, and male/female median age, among other things. Data sources for demographic data can include United States government collected census data, such as is available within a ZIP code database (from ZIP-CODES.COM, www.zip-codes.com/zip-code-database.asp (last visited Dec. 5, 2012)). 
     In certain examples, statistical dynamic geofences can be generated and subsequently updated based on one or more target parameters. Referring back to the coffee franchise example, once an initial geofence has been generated and used to target distribution of a mobile advertisement, response results to the advertisement can be monitored and analyzed to update subsequent geofences for targeting future advertisement distribution. For example, the publication system can monitor click-throughs (or similar indications of interest in the distributed advertisement) and correlate the click-throughs with demographic data to determine whether the advertisement is reaching the target audience. Based on analysis of the click-through-related demographic data, the size or shape of the geofence can be updated in an attempt to increase the predicted number of target recipients matching a certain characteristic. 
     In other examples, demographic data analysis may determine that the size or shape of a geofence should shift over the course of a day. For example, between the hours of 7:00 AM and 9:00 AM, the demographic data may indicate that target population density is high, allowing for a small radius geofence to be used in delivering targeted publications (e.g., advertisements or coupon offers). However, between 9:01 AM and 3:00 PM, the demographic data may indicate that a much larger radius needs to be considered to capture a similar potential audience. In this example, the dynamic geofence may change in size depending upon time of day of distribution by the publication system. 
     Example System 
       FIG.  1    is a block diagram depicting a system  100  for using statistical dynamic geofences to assist in targeted publication distribution, according to an example embodiment. In an example, system  100  can include users  110 A- 110 N (collectively referred to as either user  110  or users  110  depending upon context) and a network-based publication system  120 . In an example, the users  110 A- 110 N can connect to the network-based publication system  120  via mobile devices  115 A- 115 N (collectively referred to as mobile device  115 ). Users  110 A- 110 N can also connect to the network-based publication system  120  via clients  140 A- 140 N (collectively referred to as client  140  or clients  140 ). In certain examples, users  110  can receive publications, on mobile devices  115  or clients  140 , from the network-based publication system  120  transmitted over network  105 , but the users  110  may not otherwise make any sort of direct connection with the network-based publication system  120 . 
     In an example, the users  110  can configure an account on the network-based publication system  120 . The account can be accessed by a user, such as user  110 A, using mobile device  115 A or client  140 A, if user  110 A meets the specified access criteria or rules. In an example, the access rules can include user identification and/or mobile device identification. A user account on the network-based publication system  120  can allow the user to define specific POIs of interest or provide other user data that can be used by the network-based publication system  120  for targeting publications. In some examples, the network-based publication system  120  can monitor user behavior and create geofences based on past and predicted user behaviors. In certain examples, the network-based publication system  120  can be used by merchants as a location-based advertising platform, where users, such as users  110 , opt-in to location-based advertisements. For example, Best Buy (of Minneapolis, Minn.) may use the network-based publication system  120  to provide location-based advertising to users  110  via mobile devices  115 . In this example, the network-based publication system  120  can use statistical dynamic geofences, as discussed herein, to target a geographic area that is likely to include a segment of users  110  that meet a target demographic parameter. In this example, Best Buy would define an advertising campaign that includes a target demographic and a list of POIs relevant to the campaign. 
     Example Operating Environment 
       FIG.  2    is a block diagram illustrating an environment  200  for operating a mobile device  115 , according to an example embodiment. The environment  200  is an example environment within which methods for using statistical dynamic geofences can be implemented. The environment  200  can include a mobile device  115 , a communication connection  210 , a network  220 , servers  230 , a communication satellite  270 , a merchant server  280 , and a database  290 . The servers  230  can optionally include location based service application (LBS)  240 , location determination application  250 , publication application  260 , and geofence application  265 . The database  290  can optionally include demographic data  292 , user profiles  294 , and/or location history  296 . The mobile device  115  represents one example device that can be utilized by a user to receive publications. The mobile device  115  may be any of a variety of types of devices (for example, a cellular telephone, a personal digital assistant (PDA), a Personal Navigation Device (PND), a handheld computer, a tablet computer, a notebook computer, or other type of movable device). The mobile device  115  may interface via a connection  210  with a communication network  220 . Depending on the form of the mobile device  115 , any of a variety of types of connections  210  and communication networks  220  may be used. 
     For example, the connection  210  may be Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or other type of cellular connection. Such connection  210  may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1xRTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, or other data transfer technology (e.g., fourth generation wireless, 4G networks). When such technology is employed, the communication network  220  may include a cellular network that has a plurality of cell sites of overlapping geographic coverage, interconnected by cellular telephone exchanges. These cellular telephone exchanges may be coupled to a network backbone (for example, the public switched telephone network (PSTN), a packet-switched data network, or other types of networks). 
     In another example, the connection  210  may be a Wi-Fi or IEEE 802.11x type connection, a Worldwide Interoperability for Microwave Access (WiMAX) connection, or another type of wireless data connection. In such an embodiment, the communication network  220  may include one or more wireless access points coupled to a local area network (LAN), a wide area network (WAN), the Internet, or other packet-switched data network. 
     In yet another example, the connection  210  may be a wired connection, for example an Ethernet link, and the communication network may be a LAN, a WAN, the Internet, or other packet-switched data network. Accordingly, a variety of different configurations are expressly contemplated. 
     A plurality of servers  230  may be coupled via interfaces to the communication network  220 , for example, via wired or wireless interfaces. These servers  230  may be configured to provide various types of services to the mobile device  115 . For example, one or more servers  230  may execute LBS applications  240 , which interoperate with software executing on the mobile device  115 , to provide LBSs to a user. LBSs can use knowledge of the device&#39;s location, and/or the location of other devices, to provide location-specific information, recommendations, notifications, interactive capabilities, and/or other functionality to a user. For example, an LBS application  240  can provide location data to a network-based publication system  120 , which can then be used to provide location-aware publications from the network-based publication system  120 , where servers  230  can be operating with the network-based publication system  120 . Knowledge of the device&#39;s location, and/or the location of other devices, may be obtained through interoperation of the mobile device  115  with a location determination application  250  executing on one or more of the servers  230 . Location information may also be provided by the mobile device  115 , without use of a location determination application, such as application  250 . In certain examples, the mobile device  115  may have some limited location determination capabilities that are augmented by the location determination application  250 . In some examples, the servers  230  can also include publication application  260  for providing location-aware publication of data such as advertisements or offers. In certain examples, location data can be provided to the publication application  260  by the location determination application  250 . In some examples, the location data provided by the location determination application  250  can include merchant information (e.g., identification of a retail location). In certain examples, the location determination application  250  can receive signals via the network  220  to further identify a location. For example, a merchant may broadcast a specific IEEE 802.11 service set identifier (SSID) that can be interpreted by the location determination application  250  to identify a particular retail location. In another example, the merchant may broadcast an identification signal via radio-frequency identification (RFID), near-field communication (NFC), or a similar protocol that can be used by the location determination application  250 . In addition to examples using these various mechanisms to identify a particular location, these mechanisms (e.g., SSIDs, RFIDs, NFC, and so forth) can be used as secondary authentication factors, which are discussed in more detail below. 
     In certain examples, the geofence application  265  can leverage the LBS application  240  or the location determination application  250  to assist in generating and/or updating geofences based on current or historical statistics. 
     Example Mobile Device 
       FIG.  3    is a block diagram illustrating the mobile device  115 , according to an example embodiment. The mobile device  115  may include a processor  310 . The processor  310  may be any of a variety of different types of commercially available processors suitable for mobile devices (for example, an XScale architecture microprocessor, a Microprocessor without Interlocked Pipeline Stages (MIPS) architecture processor, or another type of processor). A memory  320 , such as a Random Access Memory (RAM), a Flash memory, or other type of memory, is typically accessible to the processor. The memory  320  may be adapted to store an operating system (OS)  330 , as well as application programs  340 , such as a mobile location enabled application that may provide LBSs to a user. The processor  310  may be coupled, either directly or via appropriate intermediary hardware, to a display  350  and to one or more input/output (I/O) devices  360 , such as a keypad, a touch panel sensor, a microphone, and the like. Similarly, in some embodiments, the processor  310  may be coupled to a transceiver  370  that interfaces with an antenna  390 . The transceiver  370  may be configured to both transmit and receive cellular network signals, wireless data signals, or other types of signals via the antenna  390 , depending on the nature of the mobile device  115 . In this manner, the connection  210  with the communication network  220  may be established. Further, in some configurations, a GPS receiver  380  may also make use of the antenna  390  to receive GPS signals. 
     Example Platform Architecture 
       FIG.  4    is a block diagram illustrating a network-based system  400  for using statistical dynamic geofences to assist in targeted publication distribution, according to an example embodiment. The block diagram depicts a network-based system  400  (in the exemplary form of a client-server system), within which an example embodiment can be deployed. A networked system  402  is shown, in the example form of a network-based location-aware publication or payment system, that provides server-side functionality, via a network  404  (e.g., the Internet or WAN) to one or more client machines  410 ,  412 .  FIG.  4    illustrates, for example, a web client  406  (e.g., a browser, such as the Internet Explorer browser developed by Microsoft Corporation of Redmond, Wash.) and a programmatic client  408  (e.g., PAYPAL payments smart phone application from PayPal, Inc. of San Jose, Calif.) executing on respective client machines  410  and  412 . In an example, the client machines  410  and  412  can be in the form of a mobile device, such as mobile device  115 . In yet another example, the programmatic client  408  can be the RedLaser mobile shopping application from eBay, Inc. of San Jose, Calif. 
     An Application Programming Interface (API) server  414  and a web server  416  are coupled to, and provide programmatic and web interfaces respectively to, one or more application servers  418 . The application servers  418  host one or more publication modules  420  (in certain examples, these can also include commerce modules, advertising modules, and marketplace modules, to name a few), payment modules  422 , and geofencing modules  432 . The application servers  418  are, in turn, shown to be coupled to one or more database servers  424  that facilitate access to one or more databases  426 . In some examples, the application server  418  can access the databases  426  directly without the need for a database server  424 . 
     The publication modules  420  may provide a number of publication functions and services to users who access the networked system  402 . The payment modules  422  may likewise provide a number of payment services and functions to users. The payment modules  422  may allow users to accumulate value (e.g., in a commercial currency, such as the U.S. dollar, or a proprietary currency, such as “points”) in accounts, and then later to redeem the accumulated value for products (e.g., goods or services) that are advertised or made available via the various publication modules  420 , within retail locations, or within external online retail venues. The payment modules  422  can also be configured to facilitate payment processing based on geofence detection and work in conjunction with the geofence modules  432 . The geofencing modules  432  may provide generation and updating of statistical dynamic geofences, among other things. While the publication modules  420 , payment modules  422 , and geofencing modules  432  are shown in  FIG.  4    to all form part of the networked system  402 , it will be appreciated that, in alternative embodiments, the payment modules  422  may form part of a payment service that is separate and distinct from the networked system  402 . 
     Further, while the system  400  shown in  FIG.  4    employs client-server architecture, the present invention is of course not limited to such an architecture, and could equally well find application in a distributed, or peer-to-peer, architecture system, for example. The various publication modules  420 , payment modules  422 , and geofencing modules  432  could also be implemented as standalone systems or software programs, which do not necessarily have networking capabilities. 
     The web client  406  accesses the various publication modules  420 , payment modules  422 , and geofencing modules  432  via the web interface supported by the web server  416 . Similarly, the programmatic client  408  accesses the various services and functions provided by the publication modules  420 , payment modules  422 , and geofencing modules  432  via the programmatic interface provided by the API server  414 . The programmatic client  408  may, for example, be a smart phone application (e.g., the PAYPAL payments application) that enables users to process payments directly from their smart phones leveraging user profile data and current location information provided by the smart phone or accessed over the network  404 . 
       FIG.  4    also illustrates a third party application  428 , executing on a third party server machine  440 , as having programmatic access to the networked system  402  via the programmatic interface provided by the API server  414 . For example, the third party application  428  may, utilizing information retrieved from the networked system  402 , support one or more features or functions on a website hosted by the third party. The third party website may, for example, provide one or more promotional, marketplace or payment functions that are supported by the relevant applications of the networked system  402 . Additionally, the third party website may provide merchants with access to the geofencing modules  432  for advertising or marketing purposes (e.g., location-aware publication targeting). 
     Example Geofencing Modules 
       FIG.  5    is a block diagram illustrating geofencing modules  432 , according to an example embodiment. In this example, the geofencing modules  432  can include a rules engine  505 , a communication module  510 , a generation module  520 , a data collection module  530 , and a location module  540 . In an example, the geofencing modules  432  can access database  426  to store and/or retrieve generation rules, user profile data, location data, and demographic data, as well as other information, to enable statistical dynamic geofencing. 
     In an example, the rules engine  505  can be configured to manage and evaluate rules controlling geofence generation and updating. Rules can be provided by an advertiser for each campaign run on the networked system  402 . As discussed above, one of the “rules” can define the type of statistical analysis as well as the target demographic (or similar data set) to be used in generating or updating a geofence. For example, a geofence rule may correlate the geofence radius to population density of the target geographical area. In this example, the lower the population density, the larger the radius of the resulting geofence. Accordingly, for a franchisee with locations in different geographic areas, such a rule can allow for locations in low density areas and locations in high density areas to still fall within a single advertising campaign while maintaining a similar average number of impressions for distributed advertisements. 
     In an example, the communication module  510  can be configured to manage communications between the geofencing modules  432  and a user, where the user is communicating via the mobile device  115  or the client  140 . The communication module  510  can also be configured to manage communications between the geofencing modules  432  and a merchant or advertiser. 
     In an example, the generation module  520  is configured to generate and update geofences according to information provided by modules, such as the data collection module  530 , the location module  540 , and the rules engine  505 . 
     In an example, the data collection module  530  is configured to collect data related to a publication or advertising campaign. In certain examples, the data collection module  530  can collect data detailing the results of an advertisement distribution, such as number of impressions and click-throughs. Impressions can indicate the number of times a mobile device displayed the advertisement, while click-throughs can indicate the number of users that interacted with an advertisement (e.g., clicked the ad). In certain examples, the data collection module  530  can aggregate results data and, in some cases, correlate demographic data related to the users with the advertising results. 
     In an example, the location module  540  is configured to receive location data from a mobile device, such as mobile device  115 , and determine from the location data a current physical location, which may include reference to landmarks or other sites of interest. In some examples, the location module  540  can receive GPS-type coordinates (e.g., longitude and latitude), which can be used to establish a current location associated with a mobile device (and, thus, a user of the mobile device). Using the longitude and latitude coordinates, the location module  540  can determine if the current location is within a geofence, for example. In another example, some merchants may broadcast specific wireless network signals that can be received by a mobile device, such as mobile device  115 . Once received, the mobile device  115  can include programming or circuitry to translate the signal into a specific location, or the mobile device  115  can simply retransmit the unique signal to the location module  540 . In an example, a merchant location can transmit a unique SSID, which the location module  540  can be programmed to interpret as identifying a specific merchant location. In another example, the merchant may broadcast a unique SSID within all of its locations and the location module  540  can be programmed to use a combination of the unique SSID and other location data (e.g., GPS coordinates or cell tower locations) to identify a specific location 
     Additional details regarding the functionality provided by the geofencing modules  432  are detailed in reference to  FIGS.  6 - 8   . 
     Example Dynamic Geofences 
       FIG.  6    is a diagram illustrating geofence updating via statistical analysis, according to an example embodiment. The map display  600  includes a number of geofences ( 610 - 630 ) and highlighted POIs (A-J). In this example, three geofences are illustrated including two dynamic geofences  610  ( 610 A,  610 B) and  620  ( 620 A,  620 B) and one static geofence  630 . All of the illustrated geofences can be used to target publications to recipients within a limited geographic area. Further, even geofence  630 , which is illustrated as a static or unchanging, can be updated to change its size or shape at some future point in time. 
     In an example, geofence  610 A can represent an initial state of geofence  610  generated around POI C. Geofence  610  represents a simple circular geofence centered on POI C. In this example, geofence  610  can include a geofence parameter defining the radius of the dynamic geofence. In an example, the geofence parameter for geofence  610  can be based on a target demographic, such as population density. In this example, the geofence  610  can shift between a first geofence parameter value resulting in geofence  610 A and a second geofence parameter value resulting in geofence  610 B due to shifts in the target demographic parameter. For example, if the target demographic parameter is population density, the shift may occur over the course of a day in the case of a downtown business district or over the course of the year for a vacation or retirement community. 
     In another example, geofence  620  represents a more complex geofence laid out to cover a particular geographic area, which may correlate to certain traffic patterns or local retail locations, among other things. Geofence  620  can be defined by a series of coordinates (latitude/longitude pairs), city block designations, or street intersections, among other things. Geofence  620  also illustrates the potential for a geofence to shift in shape due to changes in related demographics or other statistical data driving the particular shape. For example, an advertiser may want to avoid areas of a city that include competing locations, and such locations may change over time. In an example, a food truck operator may request that the network-based publication system  120  run a location-aware advertising campaign within the operating area for the food truck. Geofence  620  can illustrate two different operating areas  620 A and  620 B, which may represent areas targeted on different days of the week or a shift in areas based on changes in competitor locations. While Geofences  620 A and  620 B overlap, as illustrated in  FIG.  6   , there is no requirement for overlap between iterations of a dynamic geofence. 
     Example Methods 
       FIG.  7    is a flowchart illustrating a method  700  of generating and using statistical dynamic geofences to assist in targeted publication distribution, according to an example embodiment. In an example, the method  700  can include operations for: receiving a location-based campaign request at  710 , analyzing demographic data at  720 , determining a geofence parameter at  730 , generating a geofence at  740 , and distributing advertisements at  750 . 
     In an example, the method  700  can begin at  710  with the networked system  402  receiving a location-based campaign request from an advertiser. In this example, the location-based campaign request can include a list of POIs and a target demographic parameter. The target demographic parameter can control at least one aspect of a geofence to be used for targeting publications around a POI. 
     At  720 , the method  700  can continue with the networked system  402  analyzing demographic data related to the target demographic parameter. For example, if the target demographic parameter is average income, the networked system  402  can analyze demographic data from geographic areas around each POI in the list of POIs to determine the density of individuals within that geographic area that meet or exceed the target demographic parameter. A target demographic parameter can include population density, competitive POIs, income level, gender density, or age ranges, among other things. Further, in certain examples, multiple target demographic parameters may be defined and analyzed. 
     At  730 , the method  700  can continue with the networked system  402  determining a geofence parameter, such as a radius, based on the analysis of the demographic data. In the average income example, the geofence parameter can be tied to the determined (or predicted) density of individuals meeting or exceeding the target average income. In areas with a high density, the geofence parameter may be reduced (e.g., a smaller radius geofence may be centered on a POI in that geographic area); conversely, if the density is low, the geofence parameter may be increased. 
     At  740 , the method  700  can continue with the networked system  402  generating a geofence around at least one of the POIs in the list of POIs. As discussed above, the geofence generation can be based at least in part on the geofence parameter. In certain examples, the geofence parameter may include a list of coordinates, thereby allowing the geofence parameter to alter the size and shape of the generated geofence. 
     At  750 , the method  700  can conclude with the networked system  402  distributing location-aware advertisements (or similar publications) within the geofence generated around the POI. In an example, the advertisements can be distributed to a plurality of mobile devices, such as mobile devices  115 , located within the geofence area. The networked system  402  can use any of the methods discussed above, or known in the art, to determine the locations of the mobile devices  115 . 
     Though arranged serially in the example of  FIG.  7   , other examples may reorder the operations, omit one or more operations, and/or execute two or more operations in parallel using multiple processors or a single processor organized as two or more virtual machines or sub-processors. Moreover, still other examples can implement the operations as one or more specific interconnected hardware or integrated circuit modules with related control and data signals communicated between and through the modules. Thus, any process flow is applicable to software, firmware, hardware, and hybrid implementations. 
       FIG.  8    is a flowchart illustrating a method  800  of updating statistical dynamic geofences to refine targeted publication distribution, according to an example embodiment. As illustrated in  FIG.  8   , in this example, the process illustrated by method  800  can occur after the process of generating a geofence discussed with respect to  FIG.  7   . In this example, the method  800  can include operations such as: receiving campaign results data at  810 , analyzing the campaign results data at  820 , updating the geofence parameter at  830 , updating the geofence at  840 , and distributing advertisements within the updated geofence at  850 . 
     In an example, the method  800  can begin at  810  with the networked system  402  receiving campaign results data. The campaign results data can be collected based on the initial distribution of a publication (e.g., advertisement) within the initially generated geofence. At  820 , the method  800  can continue with the networked system  402  analyzing the campaign results data in reference to a target demographic parameter or other campaign driven parameter. In an example, the campaign results data can be correlated with demographic information related to the users targeted by the initial publication campaign. 
     At  830 , the method  800  can continue with the networked system  402  updating the geofence parameter based at least in part on the analysis of the campaign results data. In an example, the geofence parameter can be linked to the density of males over the age of 35, and analysis of the campaign result data may indicate that the projected density is different than the original demographic data analysis indicated. Accordingly, the networked system  402  can adjust to the detected change. 
     At  840 , the method  800  can continue with the networked system  402  updating the geofence around the POI based on the updated geofence parameter. At  850 , the method  800  can conclude with the networked system  402  distributing a second wave of advertising to mobile devices detected within the updated geofence area. 
     Though arranged serially in the example of  FIG.  8   , other examples may reorder the operations, omit one or more operations, and/or execute two or more operations in parallel using multiple processors or a single processor organized as two or more virtual machines or sub-processors. Moreover, still other examples can implement the operations as one or more specific interconnected hardware or integrated circuit modules with related control and data signals communicated between and through the modules. Thus, any process flow is applicable to software, firmware, hardware, and hybrid implementations. 
     Modules, Components and Logic 
     Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware modules. A hardware module is a tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client, or server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein. 
     In various embodiments, a hardware module may be implemented mechanically or electronically. For example, a hardware module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A hardware module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations. 
     Accordingly, the term “hardware module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired) or temporarily configured (e.g., programmed) to operate in a certain manner and/or to perform certain operations described herein. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where the hardware modules comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different hardware modules at different times. Software may accordingly configure a processor, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time. 
     Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple of such hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connects the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices and can operate on a resource (e.g., a collection of information). 
     The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processor-implemented modules. 
     Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented modules. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processors may be distributed across a number of locations. 
     The one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., APIs). 
     Electronic Apparatus and System 
     Example embodiments may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of these. Example embodiments may be implemented using a computer program product, for example, a computer program tangibly embodied in an information carrier, for example, in a machine-readable medium for execution by, or to control the operation of, data processing apparatus, for example, a programmable processor, a computer, or multiple computers. 
     A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network. 
     In example embodiments, operations may be performed by one or more programmable processors executing a computer program to perform functions by operating on input data and generating output. Method operations can also be performed by, and apparatus of example embodiments may be implemented as, special purpose logic circuitry (e.g., a FPGA or an ASIC). 
     The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In embodiments deploying a programmable computing system, it will be appreciated that both hardware and software architectures require consideration. Specifically, it will be appreciated that the choice of whether to implement certain functionality in permanently configured hardware (e.g., an ASIC), in temporarily configured hardware (e.g., a combination of software and a programmable processor), or a combination of permanently and temporarily configured hardware may be a design choice. Below are set out hardware (e.g., machine) and software architectures that may be deployed, in various example embodiments. 
     Example Machine Architecture and Machine-Readable Medium 
       FIG.  9    is a block diagram of a machine in the example form of a computer system  900  within which instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a PDA, a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. 
     The example computer system  900  includes a processor  902  (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory  904  and a static memory  906 , which communicate with each other via a bus  908 . The computer system  900  may further include a video display unit  910  (e.g., a liquid crystal displays (LCD) or a cathode ray tube (CRT)). The computer system  900  also includes an alpha-numeric input device  912  (e.g., a keyboard), a cursor control (user interface (UI) navigation) device  914  (e.g., a mouse), a disk drive unit  916 , a signal generation device  918  (e.g., a speaker) and a network interface device  920 . 
     Machine-Readable Medium 
     The disk drive unit  916  includes a machine-readable medium  922  on which is stored one or more sets of instructions and data structures (e.g., software)  924  embodying or used by any one or more of the methodologies or functions described herein. The instructions  924  may also reside, completely or at least partially, within the main memory  904 , static memory  906 , and/or within the processor  902  during execution thereof by the computer system  900 , with the main memory  904  and the processor  902  also constituting machine-readable media. 
     While the machine-readable medium  922  is shown in an example embodiment to be a single medium, the term “machine-readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more instructions or data structures. The term “machine-readable medium” shall also be taken to include any tangible medium that is capable of storing, encoding or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories and optical and magnetic media. Specific examples of machine-readable media include non-volatile memory, including by way of example, semiconductor memory devices (e.g., Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. 
     Transmission Medium 
     The instructions  924  may further be transmitted or received over a communications network  926  using a transmission medium. The instructions  924  may be transmitted using the network interface device  920  and any one of a number of well-known transfer protocols (e.g., HTTP). Examples of communication networks include a LAN, a WAN, the Internet, mobile telephone networks, Plain Old Telephone (POTS) networks, and wireless data networks (e.g., WiFi and WiMax networks). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such software. 
     Although an embodiment has been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the invention. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof, show by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled. 
     Such embodiments of the inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the above description. 
     All publications, patents, and patent documents referred to in this document are incorporated by reference herein in their entirety, as though individually incorporated by reference. In the event of inconsistent usages between this document and those documents so incorporated by reference, the usage in the incorporated reference(s) should be considered supplementary to that of this document; for irreconcilable inconsistencies, the usage in this document controls. 
     In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended; that is, a system, device, article, or process that includes elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,” “second,” and “third,” and so forth are used merely as labels, and are not intended to impose numerical requirements on their objects. 
     The Abstract of the Disclosure is provided to comply with 37 C.F.R. § 1.72(b), requiring an abstract that will allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment.