Patent Publication Number: US-2019197640-A1

Title: Flock-based crowd movement analysis for predictive conflict identification in a safety response system

Description:
TECHNICAL FIELD 
     The present invention relates generally to predictive conflict identification in a safety response system and, more specifically, to a safety response system that determines a potential conflict between two or more groups intersecting at a physical location and evaluates a risk of conflict between the groups based on attributes of the groups. 
     BACKGROUND 
     When groups of people with different interests or preferences crowd in or around public places, such as sports stadiums, entertainment or recreation venues, parks, or street corners, tensions can run high between these groups. In some instances, these meetings can devolve into aggression, violence, or other situations dangerous to members of the groups, as well as to bystanders. Such dangerous situations can escalate quickly, drawing many members of both groups into a conflict or other undesirable or dangerous incident. Although peace officers, such as public or private security officers, attempt to diffuse dangerous situations and maintain civil peace, the peace officers can become quickly overwhelmed if a dangerous situation escalates beyond their ability to respond. Peace officers may find that they are spread too thinly for an effective response or that they simply arrived too late at a scene, after the situation has already escalated. Although peace officer organizations attempt to deploy officers to areas where altercations are thought most likely to occur or to areas where the officers will have the best vantage points to monitor a crowd, peace officers often have difficulty predicting whether a situation might escalate and responding in time to prevent this from occurring. 
     SUMMARY 
     Approaches presented herein enable predictive conflict identification in a safety response system. More specifically, a likely intersection location of two or more flocks of people is determined based on predicted travel vectors of the flocks. Information about attributes of the people in each of the flocks is obtained to determine whether a potential conflict exists between the intersecting flocks. These flock attributes are analyzed, with attributes indicative of a potential conflict between the flocks being assigned a greater weight than attributes not indicative of a potential conflict. Based on the analysis, a conflict factor score is assigned to the intersection of the flocks. A security authority is then selectively informed by a generated notification, in the case that it is determined that the conflict factor meets or exceeds a predetermined security threshold, of the predicted intersection of the flocks. 
     One aspect of the present invention includes a method for predictive conflict identification in a safety response system, comprising: determining, based on a predicted travel vector of a first flock and a predicted travel vector of a second flock, an intersection location of the first flock and the second flock; obtaining attribute information associated with people of the first flock and people of the second flock; analyzing the obtained attribute information to determine a conflict factor between the first flock and the second flock; determining whether the conflict factor exceeds a predetermined security threshold; and selectively generating, responsive to a determination that the conflict factor exceeds the predetermined security threshold, a notification. 
     Another aspect of the present invention includes a safety response computer system for predictive conflict identification, the computer system comprising: a memory medium comprising program instructions; a bus coupled to the memory medium; and a processor, for executing the program instructions, coupled to a predictive security response engine via the bus that when executing the program instructions causes the system to: determine, based on a predicted travel vector of a first flock and a predicted travel vector of a second flock, an intersection location of the first flock and the second flock; obtain attribute information associated with people of the first flock and people of the second flock; analyze the obtained attribute information to determine a conflict factor between the first flock and the second flock; determine whether the conflict factor exceeds a predetermined security threshold; and selectively generate, responsive to a determination that the conflict factor exceeds the predetermined security threshold, a notification. 
     Yet another aspect of the present invention includes a computer program product for predictive conflict identification in a safety response system, the computer program product comprising a computer readable hardware storage device, and program instructions stored on the computer readable hardware storage device, to: determine, based on a predicted travel vector of a first flock and a predictive travel vector of a second block, an intersection location of the first flock and the second flock; obtain attribute information associated with people of the first flock and people of the second flock; analyze the obtained attribute information to determine a conflict factor between the first flock and the second flock; determine whether the conflict factor exceeds a predetermined security threshold; and selectively generate, responsive to a determination that the conflict factor exceeds the predetermined security threshold, a notification. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       These and other features of this invention will be more readily understood from the following detailed description of the various aspects of the invention taken in conjunction with the accompanying drawings in which: 
         FIG. 1  shows an architecture in which the invention may be implemented according to illustrative embodiments; 
         FIG. 2  shows a more detailed system architecture for predictive conflict identification in a safety response system according to illustrative embodiments; 
         FIG. 3  shows an illustrative example of predictive conflict identification in a safety response system according to illustrative embodiments; 
         FIG. 4  shows determination of a conflict factor between two flocks that are likely to intersect according to illustrative embodiments; and 
         FIG. 5  shows a process flowchart for predictive conflict identification in a safety response system according to illustrative embodiments. 
     
    
    
     The drawings are not necessarily to scale. The drawings are merely representations, not intended to portray specific parameters of the invention. The drawings are intended to depict only typical embodiments of the invention, and therefore should not be considered as limiting in scope. In the drawings, like numbering represents like elements. 
     DETAILED DESCRIPTION 
     Illustrative embodiments will now be described more fully herein with reference to the accompanying drawings, in which illustrative embodiments are shown. It will be appreciated that this disclosure may be embodied in many different forms and should not be construed as limited to the illustrative embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope of this disclosure to those skilled in the art. 
     Furthermore, the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, the use of the terms “a”, “an”, etc., do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items. Furthermore, similar elements in different figures may be assigned similar element numbers. It will be further understood that the terms “comprises” and/or “comprising”, or “includes” and/or “including”, when used in this specification, specify the presence of stated features, regions, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, regions, integers, steps, operations, elements, components, and/or groups thereof. 
     Unless specifically stated otherwise, it may be appreciated that terms such as “processing,” “detecting,” “determining,” “evaluating,” “receiving,” or the like, refer to the action and/or processes of a computer or computing system, or similar electronic data center device, that manipulates and/or transforms data represented as physical quantities (e.g., electronic) within the computing system&#39;s registers and/or memories into other data similarly represented as physical quantities within the computing system&#39;s memories, registers or other such information storage, transmission or viewing devices. The embodiments are not limited in this context. 
     As stated above, embodiments described herein provide for predictive conflict identification in a safety response system. More specifically, a likely intersection location of two or more flocks of people is determined based on predicted travel vectors of the flocks. Information about attributes of the people in each of the flocks is obtained to determine whether a potential conflict exists between the intersecting flocks. These flock attributes are analyzed, with attributes indicative of a potential conflict between the flocks being assigned a greater weight than attributes not indicative of a potential conflict. Based on the analysis, a conflict factor score is assigned to the intersection of the flocks. A security authority is then selectively informed by a generated notification, in the case that it is determined that the conflict factor meets or exceeds a predetermined security threshold, of the predicted intersection of the flocks. 
     Referring now to  FIG. 1 , a computerized implementation  10  of an embodiment of the present invention will be shown and described. Computerized implementation  10  is only one example of a suitable implementation and is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the invention described herein. Regardless, computerized implementation  10  is capable of being implemented and/or performing any of the functionality set forth hereinabove. 
     In computerized implementation  10 , there is a computer system/server  12 , which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server  12  include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and/or the like. 
     This is intended to demonstrate, among other things, that the present invention could be implemented within a network environment (e.g., the Internet, a wide area network (WAN), a local area network (LAN), a virtual private network (VPN), etc.), a cloud computing environment, a cellular network, or on a stand-alone computer system. Communication throughout the network can occur via any combination of various types of communication links. For example, the communication links can comprise addressable connections that may utilize any combination of wired and/or wireless transmission methods. Where communications occur via the Internet, connectivity could be provided by conventional TCP/IP sockets-based protocol, and an Internet service provider could be used to establish connectivity to the Internet. Still yet, computer system/server  12  is intended to demonstrate that some or all of the components of implementation  10  could be deployed, managed, serviced, etc., by a service provider who offers to implement, deploy, and/or perform the functions of the present invention for others. 
     Computer system/server  12  is intended to represent any type of computer system that may be implemented in deploying/realizing the teachings recited herein. Computer system/server  12  may be described in the general context of computer system/server executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on, that perform particular tasks or implement particular abstract data types. In this particular example, computer system/server  12  represents an illustrative system for carrying out embodiments of the present invention. It should be understood that any other computers implemented under the present invention may have different components/software, but can perform similar functions. 
     Computer system/server  12  in computerized implementation  10  is shown in the form of a general-purpose computing device. The components of computer system/server  12  may include, but are not limited to, one or more processors or processing units  16 , a system memory  28 , and a bus  18  that couples various system components including system memory  28  to processing unit  16 . 
     Bus  18  represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnects (PCI) bus. 
     Processing unit  16  refers, generally, to any apparatus that performs logic operations, computational tasks, control functions, etc. A processor may include one or more subsystems, components, and/or other processors. A processor will typically include various logic components that operate using a clock signal to latch data, advance logic states, synchronize computations and logic operations, and/or provide other timing functions. During operation, processing unit  16  collects and routes signals representing inputs and outputs between external devices  14  and input devices (not shown). The signals can be transmitted over a LAN and/or a WAN (e.g., T1, T3, 56 kb, X.25), broadband connections (ISDN, Frame Relay, ATM), wireless links (802.11, Bluetooth, etc.), and so on. In some embodiments, the signals may be encrypted using, for example, trusted key-pair encryption. Different systems may transmit information using different communication pathways, such as Ethernet or wireless networks, direct serial or parallel connections, USB, Firewire®, Bluetooth®, or other proprietary interfaces. (Firewire is a registered trademark of Apple Computer, Inc. Bluetooth is a registered trademark of Bluetooth Special Interest Group (SIG)). 
     In general, processing unit  16  executes computer program code, such as program code for carrying out embodiments of the present invention, which is stored in memory  28 , storage system  34 , and/or program/utility  40 . While executing computer program code, processing unit  16  can read and/or write data to/from memory  28 , storage system  34 , and program/utility  40 . 
     Computer system/server  12  typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server  12 , and it includes both volatile and non-volatile media, removable and non-removable media. 
     System memory  28  can include computer system readable media in the form of volatile memory, such as random access memory (RAM)  30  and/or cache memory  32 . Computer system/server  12  may further include other removable/non-removable, volatile/non-volatile computer system storage media, (e.g., VCRs, DVRs, RAID arrays, USB hard drives, optical disk recorders, flash storage devices, and/or any other data processing and storage elements for storing and/or processing data). By way of example only, storage system  34  can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM, or other optical media can be provided. In such instances, each can be connected to bus  18  by one or more data media interfaces. As will be further depicted and described below, memory  28  may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention. 
     Program code embodied on a computer readable medium may be transmitted using any appropriate medium including, but not limited to, wireless, wireline, optical fiber cable, radio-frequency (RF), etc., or any suitable combination of the foregoing. 
     Program/utility  40 , having a set (at least one) of program modules  42 , may be stored in memory  28  by way of example, and not limitation. Memory  28  may also have an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules  42  generally carry out the functions and/or methodologies of embodiments of the invention as described herein. 
     Computer system/server  12  may also communicate with one or more external devices  14  such as a keyboard, a pointing device, a display  24 , etc.; one or more devices that enable a consumer to interact with computer system/server  12 ; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server  12  to communicate with one or more other computing devices. Such communication can occur via I/O interfaces  22 . Still yet, computer system/server  12  can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter  20 . As depicted, network adapter  20  communicates with the other components of computer system/server  12  via bus  18 . It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server  12 . Examples include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc. 
     The inventors of the present invention have found that peace officers or other security personnel may be dispatched too late to maintain civil peace when an intersection of two or more groups of people leads to an escalating confrontation. For instance, if a group of fans of a sports team meet a group of fans of an opponent sports team, there may be a verbal or physical altercation, particularly if the two teams have recently competed against one another or will do so in the near future. Unfortunately, peace officers and security personnel are sometimes not deployed to a scene until after it becomes evident that a conflict is taking place. Therefore, the peace officers or security personnel cannot begin to initiate a response until the conflict actually begins, needing to first observe a conflict indicator, such as, for example, the exchange of words or gestures. By this time, it may be too late for peace officers or security personnel to arrive at a scene, take position, and act to stop further escalation. In the time needed to observe the occurring conflict and respond to it, both active participants in the conflict as well as bystanders may be injured. 
     Accordingly, the inventors of the present invention have developed an improved safety response system that permits potential conflicts between groups of people to be predicted and, therefore, peaceably prevented. More specifically, the system developed by the inventors of the present invention uses people segmentation, location tracking, and travelling pattern analysis to detect people who share some attributes and group them as a flock. The system then predicts the movement of the flock and identifies when and where intersections of two or more flocks are likely to occur. The system further determines (e.g., based on parameters and thresholds) if there is a likelihood of conflict between the flocks and, if so, enhances existing safety response systems by notifying peace officers or other security personnel of the likely conflict intersection before it has occurred. 
     Furthermore, embodiments of the present invention offer several advantages over previous solutions for maintaining civil peace. The predictive feature of embodiments of the present invention enhances the effectiveness of current safety, security, and surveillance systems by enabling these systems to analyze a crowd of people and assess a risk of conflict involving that crowd. This permits current safety, security, and surveillance systems to issue safety and security alerts more promptly, providing responders with an increased response time window. Furthermore, this permits current safety, security, and surveillance systems to prioritize safety and security issues and allot resources to respond to those issues having the greatest priority. Further, such enhanced safety, security, and surveillance systems permit a dispatcher to deploy peace officers or security personnel to where they will be most imminently needed, as opposed to merely where they are currently needed. In another example, such enhanced systems permit a dispatcher to rank of which situations are most in need of the presence of peace officers or security personnel and therefore give priority to those situations. It should also be understood that the predictive response of embodiments of the present invention offers additional security and safety over previous solutions by preempting and thereby preventing confrontations and resulting injuries. 
     Referring now to  FIG. 2 , a more detailed system architecture for predictive conflict identification in a safety response system according to illustrative embodiments is shown. Computer system/server  12  ( FIG. 1 ) can include predictive conflict identification engine  200  (hereinafter system  200 ) for performing embodiments of the present invention. It should be understood that, although system  200  is presented in illustrative embodiments of the present invention discussed herein as residing on computer system/server  12 , in some embodiments, system  200  can reside on any type of device, computer system, server, virtual server, cloud computing virtual machine, etc., capable of carrying out embodiments of the present invention. 
     In some embodiments, system  200  can be program/utility  40  of  FIG. 1  and can contain a set of components (e.g., program modules  42  of  FIG. 1 ) for carrying out embodiments of the present invention. These components can include, but are not limited to, information processing component  202 , crowd analysis component  204 , flock identification component  206 , intersection prediction component  208 , conflict researching component  210 , conflict analysis component  212 , and notification component  214 . 
     Through computer system/server  12 , system  200  can be in communication with people tracking information sources  220 , such as CCTV feed  222 , public camera images  224 , and/or mobile device network information  226 . Through computer system/server  12 , system  200  can further be in communication with remote information  230 . Remote information  230  can include publicly available information  232 , such as information that may be found on the Internet, digital public records, digital public calendars (e.g., game day schedules, parade schedules or routes), social media, and/or the like. Additionally, or in the alternative, remote information  230  can include news stories and other recent event information  234 , such as updates from a news website or information about an upcoming or recent sporting event. Additionally, or in the alternative, remote information  230  can include other information, such as from databases including historic records  236  and/or official records  238 . 
     Referring now to  FIG. 3  in connection with  FIG. 2 , an illustrative example of predictive conflict identification in a safety response system is shown. Information processing component  202  of system  200 , as executed by computer system/server  12 , can obtain information from people tracking information sources  220 , such as security camera  352  in view of a crowd. Information processing component  202  organizes the obtained information for use by system  200 . As briefly discussed above, this obtained information can include camera feeds and images from public security cameras. In some embodiments, information processing component  202  can correlate incoming information to determine a location and travel path of one or more persons or a crowd. Information processing component  202  may further contain image recognition capabilities to assist in determining a location and travel path of one or more persons  360 A-N or crowds  362 A-N. A travel path of crowd  362 N can, in some embodiments, be determined by averaging a location and/or travel path of several persons  360 A-N in crowd  362 N. Further, image recognition, speech recognition, and/or text recognition abilities may be used to identify attributes of a crowd, such as several people wearing a certain sports team&#39;s memorabilia or carrying indicators, such as posters and signs, of affiliation with or support for a certain team. 
     Crowd analysis component  204  of system  200 , as executed by computer system/server  12 , can analyze a composition of a plurality of crowds of people using segmentation to determine shared attributes of a plurality of people in each crowd. To accomplish this, crowd analysis component can apply flock determination algorithms such as people segmentation (e.g., several people with a shared interest), location tracking (e.g., several people staying in the same area or close to one another), crowd density (e.g., people concentrated in a certain area), and travelling pattern analysis (e.g., several people moving in the same direction) to the information obtained and processed by information processing component  202 . Additional information may be pulled from remote information  230  to further determine whether several people  360 A-N in crowd  362 A (or in crowd  362 B or crowd  362 N) share the same or similar interests. For example, crowd analysis component  204  may use facial recognition to identify a person in crowd  362 N. Crowd analysis component  204  can use this identification to cross-reference the identified person in crowd  362 A against remote information  230 , such as publicly available information  232  corresponding to the person including, for example, a social media account or official records  238 . From this information, crowd analysis component  204  can find people who share some attributes or interests and who may also, possibly, be part of a flock. For instance, because crowd  362 A has 20 people walking in the same direction at a similar pace with a relatively high concentration of people per area, crowd analysis component  204  can analyze the composition of crowd  362 A using segmentation to categorize members of crowd  362 A into groups based on shared features. For instance, in this example, crowd analysis component  204  may determine the following attributes of crowd  362 A: 75% of the people are fans of a specific sports team (e.g., The “Big State Triangles”), as indicated on social media; and 90% of them are male and 70% are in the 20-30 age group, as indicated by official records. 
     Flock identification component  206  of system  200 , as executed by computer system/server  12 , can identify, based on the shared attributes determined by crowd analysis component  204 , a first flock and a second flock, each formed by a crowd of people, where the first flock primarily consists of people having a first shared attribute and the second flock primarily consists of people having a second shared attribute. Flock determination may, in some embodiments, be based on a segment of crowd  362 A (or several closely related and/or overlapping segments of crowd  362 A) reaching a predetermined threshold of a percent/fraction of the whole crowd  362 A. For instance, continuing the example above, if a flock threshold is set at two-thirds shared interests, then, since over two-thirds of the people in crowd  362 A are “Big State Triangles” fans, they are categorized as flock  364 A. Likewise, because more people than the requisite threshold in crowd  362 B are “North State Circles” fans, crowd  362 B is identified as flock  364 B. In some embodiments, this threshold may change based on a combination of features. For instance, a group of young people mostly in the 20-30 age group may have a lower requisite threshold than a group of older people mostly in the 70-80 age group. 
     It should be noted that a flock may be formed even if most of its participants do not know each other or at least do not appear to know one another. Furthermore, a flock can be any size, from two people to thousands. Therefore, even if there is little to no social media interconnection (e.g., shared friends) between members of crowd  362 B, the crowd can still be designated as flock  364 B. It may be the case that members of a flock may recognize they have shared interests (e.g., by wearing memorabilia from the same team), even if they have never met before or have no online social connections or other publicly known connections. It should also be understood that the shared interests used for flock identification can be based on shared features other than a team affiliation. Such shared features may include, for example, similar ages or age group, organizational affiliations, and/or any other interest or trait that two or more people share in common. In some instances, two or more of these factors may be combined to form a segment, such as young people who are fans of a specific sports team. It should also be understood that not all crowds are flocks. For example, persons  360 E-N of crowd  362 N do not have sufficient similarities across a wide enough segment of crowd  362 N to be a flock. Instead, persons  360 E-N have dissimilar interests and are too widely dispersed and intermixed within crowd  362 N for any portion of the crowd to be a flock of similar interests. 
     Intersection prediction component  208  of system  200 , as executed by computer system/server  12 , can predict a travel vector of a first flock and of a second flock and determine, based on the predicted travel vectors, an intersection location of the first flock and the second flock. Intersection prediction component  208  uses the movement information gathered by information processing component  202  to project a location, a travel path, a speed, or a combination of the three—in other words, travel vectors  366 A and  366 B—of identified flocks  364 A and  364 B, respectively. For example, intersection prediction component  208  may determine that flock  364 A is moving northeast at 2.5 miles per hour (mph), while flock  364 B is moving northwest at 2.0 mph. In some embodiments, travel vectors  366 A and  366 B can be displayed on a map or other visual display for viewing by a user (e.g., a peace officer dispatcher) of system  200 . Travel vectors  366 A and  366 B can be determined any of several ways, such as, but not limited to, averaging the location, direction/travel path, and speed of all members of flocks  364 A and  364 B, respectively. In other embodiments, travel vectors  366 A and  366 B can be measured based on the location, direction/travel path, and speed of one or more members of flocks  364 A and  364 B who are at the leading edge of flocks  364 A and  364 B. In this embodiment, the movements of the leading edge of flocks  364 A and  364 B, and the time at which or the time until those leading edges are predicted to collide, would be the focus of intersection prediction component  208 . 
     When two or more flocks are relatively near one another (e.g., in the same town/city, in the same neighborhood), intersection prediction component  208  can compare flock travel vectors  366 A and  366 B to determine whether flocks  364 A and  364 B are likely to intersect. This comparison may leverage historical travel pattern records  236 , such as those maintained by GPS mapping/navigation applications that monitor the locations of mobile devices of users of the GPS mapping/navigation application. Such information may be used to find travel paths and travel times most commonly taken by other historic GPS mapping/navigation application users having travel vectors similar to flock travel vectors  366 A and  366 B. In some embodiments, intersection prediction component  208  can use a calendar and known locations of events, for example retrieved from publically available information  232  or news and recent events  234 , to search for intersections of groups associated with an event and/or location on the calendar. For instance, intersection prediction component  208  may find two flocks that are currently traveling along parallel routes, but are both heading towards an arena around game time, where the flocks may intersect. If, based on flock travel vectors  366 A and  366 B, intersection is likely, intersection prediction component  208  determines at what time and at what location this intersection is likely to take place. 
     For example, based on flock travel vectors  366 A and  366 B indicating that flock  364 A is moving northeast at 4.5 mph and flock  364 B is moving northwest at 4.0 mph, respectively, intersection prediction component  208  determines that flocks  364 A and  364 B are highly likely to intersect at predicted intersection location  368  in five minutes time. In some embodiments, intersection prediction component  208  may determine that there are several locations and/or times where flocks  364 A and  364 B might intersect (e.g., if flock  364 A or  364 B has multiple leading edges, like an amoeba, and periodically moves in the direction of each of those leading edges, as opposed to unidirectionally). In such a case, intersection prediction component  208  can return a ranked list of predicted intersection locations  368  and/or times according to likelihood. 
     Referring now to  FIG. 4  (in conjunction with  FIGS. 2 and 3 ), determination of a conflict factor between two flocks that are likely to intersect is shown. In response to the identification of predicted intersection location  368  of two or more flocks  364 A-B, conflict researching component  210  of system  200 , as executed by computer system/server  12 , gathers attribute information associated with the people of the two or more flocks to determine whether there is a potential conflict between the two or more flocks. This gathered information may include the attributes of segments of flocks  364 A-B (i.e., crowds  362 A-B) earlier identified by crowd analysis component  204 . For example, conflict researching component  210  may note that 75% of the people in flock  364 A are part of the “Big State Triangles” fans segment of flock  364 A, while 89% of the people in flock  364 B are part of the “North State Circles” fans segment of flock  364 B. This gathered information may further include the location of predicted intersection location  368  relative to other places or geographic features in the same area. For example, conflict researching component  210  may note that predicted intersection location  368  is only a few yards from “North State Circles” Stadium. 
     Conflict researching component  210  can further search for information related to a potential conflict in remote information  230 . This information related to a potential conflict may include news and recent events  234 , such as current events related to the identified attributes of flocks  364 A-B. For example, conflict researching component  210  may find that the “Big State Triangles” are playing the “North State Circles,” of which segments of flocks  364 A and  364 B are fans, respectively, later that day at “North State Circles” Stadium according to a public sports events calendar. Conflict researching component  210  may further look up previous events and known history  236  related to the identified attributes of flocks  364 A-B. For example, conflict researching component  210  may discover that the “Big State Triangles” and the “North State Circles” have been competitors for years and that, on three occasions in the past decade, physical conflicts have broken out between “Big State Triangles” fans and “North State Circles” fans on game days near the stadiums in which the game in question was to be played. 
     In some embodiments, conflict researching component  210  may also use the facial recognition capabilities of crowd analysis component  204  to determine the identities of individuals in flock  364 A and/or  364 B. In other embodiments, conflict researching component  210  may use the identification obtained by crowd analysis component  204 . In any case, once the identity of an individual is determined, conflict researching component  210  can find additional information about the person in remote information  230 , such as publicly available information  232  on the person (e.g., social media  472  account). 
     In some embodiments, conflict researching component  210  can look for other aggravating factors that may increase the likelihood of an altercation between flocks  364 A and  364 B. Conflict researching component  210  can use the results of the image recognition performed by information processing component  202  to identify the presence of aggravating items or behaviors in flocks  364 A or  364 B. For instance, conflict researching component  210  can determine that several members of flock  364 B are carrying items or performing actions that increase the likelihood of an altercation, such as carrying or drinking out of beverage containers. 
     Conflict analysis component  212  of system  200 , as executed by computer system/server  12 , analyzes the attribute information obtained by conflict researching component  210  to determine a conflict factor between two or more flocks and determines whether the conflict factor exceeds a predetermined security threshold. To accomplish this, conflict analysis component  212  inputs the information gathered by conflict researching component  210  into comparison matrix  480 . In some embodiments, conflict analysis component  212  can organize the gathered information into sections or categories of characteristics/parameters within comparison matrix  480  for analysis. Each section or category of characteristics can be assigned characteristic severity weight  482  by conflict analysis component  212  depending on the severity of the information in that section/category. In some embodiments, conflict analysis component  212  can receive a set of thresholds, set by a user of system  200 , which controls the severity of weight assigned to the severity of characteristic information. In other embodiments, conflict analysis component  212  can learn, based on historical data, which characteristics at which severity are most indicative of an altercation requiring a security or peace officer presence. Conflict analysis component  212  can then set the weight thresholds accordingly. In any case, conflict analysis component  212  can aggregate these weighted characteristics to yield conflict factor  484 . Conflict factor  484  can be expressed several ways including, but not limited to, a hierarchical rating (e.g., high-medium-low) or a percentage (e.g., a scale between 0 and 100), etc. In some embodiments, conflict factor  484  is a probability of whether violence or an otherwise undesirable altercation will occur. This probability can be calculated, for example, by combining the weights of each characteristic to reach a total characteristics weight and then comparing this total characteristics weight to the maximum possible total weight (i.e., the combined weight of the maximum possible values/levels of all the characteristics) to get a fraction or percentage of total characteristics weight to total possible weight. 
     Continuing the illustrative example based on  FIG. 3 , conflict analysis component  212  may assign the following characteristics the following weights based on pre-set thresholds from a user of system  200 : 
     
       
         
           
               
               
               
             
               
                   
               
               
                 CHARACTERISTICS 
                 THRESHOLD 
                 WEIGHT 
               
               
                   
               
             
            
               
                 Intersection in 5 min. 
                 Low, &lt;5 min.; Medium, 5 to  
                 Medium 
               
               
                   
                 15 min.; High, &gt;15 min. 
                   
               
               
                 70% of flock 364A in  
                 Low, &lt;60% age 20-30; Medium,  
                 Medium 
               
               
                 20-30 age group 
                 60-80% age 20-30; High,  
                   
               
               
                   
                 &gt;80% age 20-30 
                   
               
               
                 Fan base is 75% segment  
                 Low, &lt;70%; Medium, 70-90%;  
                 Medium 
               
               
                 of flock 364A and 89%  
                 High, &gt;90% 
                   
               
               
                 segment of flock 364B 
                   
                   
               
               
                 Each flock contains  
                 Low, &lt;20 people; Medium,  
                 Medium 
               
               
                 between 20 and 30 people 
                 20-40 people; High, &gt;40 people 
                   
               
               
                 Intersection 20 yd. from  
                 Low, &gt;half mile from any  
                 High 
               
               
                 North State Circles  
                 hotspot; Medium, 200 yd. to half  
                   
               
               
                 Stadium 
                 mile from hotspot; High, within  
                   
               
               
                   
                 200 yd. of hotspot 
                   
               
               
                 History of interaction 
                 Low, amiable; Medium, neutral;  
                 High 
               
               
                   
                 High, unfriendly 
                   
               
               
                 In past 10 yrs, 3 physical  
                 Low, nonexistent; Medium,  
                 High 
               
               
                 conflicts near stadiums  
                 verbal altercations; High,  
                   
               
               
                 on game-day 
                 physical altercations 
                   
               
               
                 Circles vs. Triangles  
                 Low, &gt;a week; Medium, same  
                 High 
               
               
                 game in afternoon 
                 week; High, same day 
               
               
                   
               
            
           
         
       
     
     It should be understood that the above table is provided for illustrative purposes only and is not intended to be limiting. Conflict analysis component  212  can consider any type and number of characteristics in conflict factor comparison matrix  480 . Such characteristics may include any item of information that may be pertinent to determining whether an intersection of a flock is likely to require a security response. Furthermore, although shown here as based on a three-tier hieratical rating, characteristic weights  482  may use any rating methodology or scale (e.g., numerical scale, a scale based on percentage, and/or the like). In any case, based on the above comparison, conflict analysis component  212  may determine that conflict factor  484  for intersection location  368  between flock  364 A and flock  364 B is high. By comparison, if flock  364 A were to alternatively intersect a different flock, which primarily includes neutral “South State Quad” fans, at a similar time and location, conflict factor  484  for that alternative intersection would be low to medium because the “Big State Triangles” fans of flock  364 A would not have the same potential conflict characteristics with a flock of fans of a neutral team. 
     Conflict analysis component  212  further determines if conflict factor  484  meets security issue threshold  486 . In some embodiments, security issue threshold  486  may be set by a user of system  200 . For example, conflict analysis component  212  can receive from the user security issue threshold  486  to be triggered in the event of a “high” conflict factor  484  or a conflict factor  484  above, for example, 75%. In still other embodiments, the setting of security issue threshold  486  may be automated based on historical data. For example, in some embodiments, conflict analysis component  212  can learn, based on historical data, at what conflict factor level a security or peace officer presence becomes needed. Conflict analysis component  212  can then set security issue threshold  486  accordingly. 
     If security issue threshold  486  is met or exceeded by conflict factor  484 , notification component  214  of system  200 , as executed by computer system/server  12 , can generate (e.g., send, or instruct to be sent) a notification  490  to a user. In an embodiment, this user may be a dispatcher for a set of peace officers or other security officers. Additionally or in the alternative, the user may be a set of security or peace officers, or anyone else responsible for maintaining civil peace at a location. The kinds of flock intersections that result in generation of a security warning or notification can be controlled by a user through notification component  214 , which can receive user-configured characteristic weights  482  and/or security issue threshold  486 . In some embodiments, notification component  214  can be configured by the user to issue different kinds of notifications  490  depending on the conflict factor of the flock intersection. For example, notification component  214  can be configured to issue a moderate warning  490  when conflict factor  484  is medium and a severe waning  490  when conflict factor  484  is high. 
     In some embodiments, once notification  490  is generated, users, such as a dispatcher, may decide how to respond to the impending intersection  368  of flocks  364 A and  364 B. In other embodiments, notification component  214  can offer a response recommendation, along with notification  490 , for best addressing intersection  368  of flocks  364 A and  364 B based on conflict factor  484  and other earlier identified characteristics of intersection  368 . For instance, for a medium conflict factor intersection, notification component  214  may recommend that responding officers first attempt to talk with the intersecting flocks. On the other hand, in the case of a high conflict factor intersection, notification component  214  may recommend a more robust security response. In still further embodiments, notification component  214  can determine which parties are notified based on the severity of conflict factor  484 . For example, notification component  214  may send a notification to a small private security team in response to a medium conflict factor  484 , but send a notification to a larger, public peace officer organization in response to a high conflict factor  484 . 
     In some embodiments, notification component  214  can provide a ranked list of notifications  490  for several different intersections between different flocks. This list may rank the different intersections according to a severity of the conflict factor associated with each intersection. Accordingly, the ranked list may serve as a recommended priority list for dispatching a limited pool of security or peace officers to the highest risk flock intersections first. Also, the ranked list may be used to ensure that a sufficient numbers of security or peace officers are dispatched to the intersections where they are most needed. In some embodiments, notification component  214  can specifically recommend where to dispatch officers and in what quantities based on the conflict factors. 
     In some embodiments, notification  490  may take any form practical to convey an alert, and information associated with the alert, to a user of system  200 . In one non-limiting example, notification  490  may include a map, such as that shown in  FIG. 3 , which displays a representation of flocks  364 A-B, flock travel paths  366 A-B, and flock intersections  368  in a visual display. In such an example, notification  490  can configure the map, when intersection  368  is clicked on by a user, to display information, such as the associated conflict factor, on the intersection, and/or can configure the map, when flock  364 A or  364 B is clicked on by a user, to display information, such as characteristics and attributes, on the flock. In other non-limiting examples, notification  490  may be tailored to seamlessly integrate into a pre-existing security response system, appearing among other items in an interface used by the dispatcher. In still other non-limiting examples, notification  490  can appear as a popup or other alert on a mobile device carried by individual security or peace officers. 
     As depicted in  FIG. 5 , in one embodiment, a system (e.g., computer system/server  12 ) carries out the methodologies disclosed herein. Shown is a process flowchart  500  for predictive conflict identification in a safety response system. At  502 , intersection prediction component  208  determines, based on a predicted travel vector  366 A of a first flock  364 A and a predicted travel vector  366 B of a second flock  364 B, an intersection location  368  of the first flock  364 A and the second flock  364 B. At  504 , conflict researching component  210  obtains attribute information (e.g.,  232 ,  234 ,  236 , and  238 ) associated with people of the first flock  364 A and people of the second flock  364 B. At  506 , conflict analysis component  212  analyzes the obtained attribute information to determine a conflict factor  484  between the first flock  364 A and the second flock  364 B. At  508 , conflict analysis component  212  determines whether the conflict factor  484  exceeds a predetermined security threshold  486 . At  510 , notification component  214  selectively generates, responsive to a determination that the conflict factor  484  exceeds the predetermined security threshold  486 , a notification  490 . 
     Process flowchart  500  of  FIG. 5  illustrates the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     Some of the functional components described in this specification have been labeled as systems or units in order to more particularly emphasize their implementation independence. For example, a system or unit may be implemented as a hardware circuit comprising custom VLSI circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A system or unit may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, or the like. A system or unit may also be implemented in software for execution by various types of processors. A system or unit or component of executable code may, for instance, comprise one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified system or unit need not be physically located together, but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the system or unit and achieve the stated purpose for the system or unit. 
     Further, a system or unit of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be identified and illustrated herein within modules, and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices and disparate memory devices. 
     Furthermore, systems/units may also be implemented as a combination of software and one or more hardware devices. For instance, program/utility  40  may be embodied in the combination of a software executable code stored on a memory medium (e.g., memory storage device). In a further example, a system or unit may be the combination of a processor that operates on a set of operational data. 
     As noted above, some of the embodiments may be embodied in hardware. The hardware may be referenced as a hardware element. In general, a hardware element may refer to any hardware structures arranged to perform certain operations. In one embodiment, for example, the hardware elements may include any analog or digital electrical or electronic elements fabricated on a substrate. The fabrication may be performed using silicon-based integrated circuit (IC) techniques, such as complementary metal oxide semiconductor (CMOS), bipolar, and bipolar CMOS (BiCMOS) techniques, for example. Examples of hardware elements may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), logic gates, registers, semiconductor devices, chips, microchips, chip sets, and so forth. However, the embodiments are not limited in this context. 
     Any of the components provided herein can be deployed, managed, serviced, etc., by a service provider that offers to deploy or integrate computing infrastructure with respect to a process for predictive conflict identification in a safety response system. Thus, embodiments herein disclose a process for supporting computer infrastructure, comprising integrating, hosting, maintaining, and deploying computer-readable code into a computing system (e.g., computer system/server  12 ), wherein the code in combination with the computing system is capable of performing the functions described herein. 
     In another embodiment, the invention provides a method that performs the process steps of the invention on a subscription, advertising, and/or fee basis. That is, a service provider, such as a Solution Integrator, can offer to create, maintain, support, etc., a process for predictive conflict identification in a safety response system. In this case, the service provider can create, maintain, support, etc., a computer infrastructure that performs the process steps of the invention for one or more customers. In return, the service provider can receive payment from the customer(s) under a subscription and/or fee agreement, and/or the service provider can receive payment from the sale of advertising content to one or more third parties. 
     Also noted above, some embodiments may be embodied in software. The software may be referenced as a software element. In general, a software element may refer to any software structures arranged to perform certain operations. In one embodiment, for example, the software elements may include program instructions and/or data adapted for execution by a hardware element, such as a processor. Program instructions may include an organized list of commands comprising words, values, or symbols arranged in a predetermined syntax that, when executed, may cause a processor to perform a corresponding set of operations. 
     The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     It is apparent that there has been provided herein approaches for predictive conflict identification in a safety response system. While the invention has been particularly shown and described in conjunction with exemplary embodiments, it will be appreciated that variations and modifications will occur to those skilled in the art. Therefore, it is to be understood that the appended claims are intended to cover all such modifications and changes that fall within the true spirit of the invention.