Method, System, and Computer Program Product for Generating a Classified Map

A computer-implemented method for generating a classified map on a computing device includes: receiving statistical data associated with each zone of a plurality of zones; generating based on the statistical data at least one classification score for each zone of the plurality of zones by performing a latent factor analysis on the statistical data to generate at least one latent factor score; causing to be displayed a map of a geographic region having the plurality of zones on a display of a computing device; based at least partially on the at least one classification score, causing at least one classification tag to be overlayed over each zone of the plurality of zones on the map to generate the classified map. A system and computer program product for generating a classified map on a computing device are also disclosed.

BACKGROUND

Field

This disclosure relates to computer-implemented classification of geographic regions and, in one non-limiting embodiment or aspect, to a method, system, and computer program product for generating a classified map on a computing device.

Description of Related Art

Individuals, groups of individuals, and organizations often make locational decisions based on perceived and/or amorphous classifications of various geographic regions. Non-limiting examples of locational decisions can include where to vacation, where to live, or where to locate a business.

The classifications associated with various geographic regions are often subjective and rooted in unsubstantiated and/or unquantified analysis. For example, supporting a conclusion that a geographic region is affluent, affordable, or youthful (example classifications) is a seemingly subjective endeavor. Thus, existing systems of classifying geographic regions often cause individuals, groups of individuals, and organizations making locational decisions to make incompletely informed decisions by relying on the subjective data or opinions.

Therefore, it would be desirable to more objectively classify geographic regions based on available statistical data and to generate an easily perceivable means for individuals, groups of individuals, and organizations to interpret the objectively classified geographic regions, in order to make a decision regarding location.

SUMMARY

Accordingly, and generally, provided is an improved method, system, and computer program product for generating a classified map on a computing device.

According to a non-limiting embodiment or aspect, a computer-implemented method for generating a classified map on a computing device includes: receiving, with at least one processor, statistical data associated with each zone of a plurality of zones; generating, with at least one processor, based on the statistical data at least one classification score for each zone of the plurality of zones by performing a latent factor analysis on the statistical data to generate the at least one classification score; causing to be displayed, with at least one processor, a map of a geographic region having the plurality of zones on a display of a computing device; based at least partially on the at least one classification score, causing to be overlayed, with at least one processor, at least one classification tag over each zone of the plurality of zones on the map to generate the classified map.

In one non-limiting embodiment or aspect, the statistical data may include transaction data associated with transactions initiated in each zone of the plurality of zones and merchant category codes associated with the transactions. The classification score for each zone may be generated based at least partially on at least one latent factor score. The statistical data may include socioeconomic data. The statistical data may include a count of transactions initiated in each zone of the plurality of zones sorted by merchant category codes associated with the transactions; where generating the at least one classification score includes performing, with at least one processor, the latent factor analysis on the transaction data to generate at least one latent factor score associated with each merchant category code; where generating the at least one classification score further includes associating at least one classification tag with each merchant category code based at least partially on the at least one latent factor score; and where the at least one classification score is based at least partially on the at least one classification tag associated with each merchant category code. Associating the at least one classification tag with each merchant category code may include performing a machine learning clustering technique. Generating the at least one classification score may include performing, with at least one processor, the latent factor analysis on the transaction data to generate a first latent factor score associated with each merchant category code and a second latent factor score associated with each merchant category code, where generating the at least one classification score further includes plotting a graph of the first latent factor score against the second latent factor score for each merchant category code and associating the at least on classification tag with each merchant category code based on clustering of the merchant category codes on the graph.

According to a non-limiting embodiment or aspect, a system for generating a classified map on a computing device includes at least one processor programmed or configured to: receive statistical data associated with each zone of a plurality of zones; generate, based on the statistical data, at least one classification score for each zone of the plurality of zones by performing a latent factor analysis on the statistical data to generate the at least one classification score; cause to be displayed a map of a geographic region having the plurality of zones on a display of a computing device; and based at least partially on the at least one classification score, cause to be overlayed at least one classification tag over each zone of the plurality of zones on the map to generate the classified map.

In one non-limiting embodiment or aspect, the statistical data may include transaction data associated with transactions initiated in each zone of the plurality of zones and merchant category codes associated with the transactions. The classification score for each zone may be generated based at least partially on at least one latent factor score. The statistical data may include socioeconomic data. The statistical data may include a count of transactions initiated in each zone of the plurality of zones sorted by merchant category codes associated with the transactions; where generating the at least one classification score includes the at least one processor performing the latent factor analysis on the transaction data to generate at least one latent factor score associated with each merchant category code; where generating the at least one classification score includes the at least one processor associating at least one classification tag with each merchant category code based at least partially on the at least one latent factor score; and where the at least one classification score is based at least partially on the at least one classification tag associated with each merchant category code. Associating the at least one classification tag with each merchant category code may include the at least one processor performing a machine learning clustering technique. Generating the at least one classification score may include the at least one processor performing the latent factor analysis on the transaction data to generate a first latent factor score associated with each merchant category code and a second latent factor score associated with each merchant category code, where generating the at least one classification score further includes the at least one processor plotting a graph of the first latent factor score against the second latent factor score for each merchant category code and associating the at least on classification tag with each merchant category code based on clustering of the merchant category codes on the graph.

According to a non-limiting embodiment or aspect, a computer program product for generating a classified map on a computing device includes at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to: receive statistical data associated with each zone of a plurality of zones; generate, based on the statistical data, at least one classification score for each zone of the plurality of zones by performing a latent factor analysis on the statistical data to generate the at least one classification score; cause to be displayed a map of a geographic region having the plurality of zones on a display of a computing device; and based at least partially on the at least one classification score, cause to be overlayed at least one classification tag over each zone of the plurality of zones on the map to generate the classified map.

In one non-limiting embodiment or aspect, the statistical data may include transaction data associated with transactions initiated in each zone of the plurality of zones and merchant category codes associated with the transactions. The classification score for each zone may be generated based at least partially on at least one latent factor score. The statistical data may include socioeconomic data. The statistical data may include a count of transactions initiated in each zone of the plurality of zones sorted by merchant category codes associated with the transactions; where generating the at least one classification score includes the at least one processor performing the latent factor analysis on the transaction data to generate at least one latent factor score associated with each merchant category code; where generating the at least one classification score includes the at least one processor associating at least one classification tag with each merchant category code based at least partially on the at least one latent factor score; and where the at least one classification score is based at least partially on the at least one classification tag associated with each merchant category code. Associating the at least one classification tag with each merchant category code may include the at least one processor performing a machine learning clustering technique. Generating the at least one classification score may include the at least one processor performing the latent factor analysis on the transaction data to generate a first latent factor score associated with each merchant category code and a second latent factor score associated with each merchant category code, where generating the at least one classification score further includes the at least one processor plotting a graph of the first latent factor score against the second latent factor score for each merchant category code and associating the at least on classification tag with each merchant category code based on clustering of the merchant category codes on the graph.

Further embodiments or aspects are set forth in the following numbered clauses:

Clause 1: A computer-implemented method for generating a classified map on a computing device comprising: receiving, with at least one processor, statistical data associated with each zone of a plurality of zones; generating, with at least one processor, based on the statistical data at least one classification score for each zone of the plurality of zones by performing a latent factor analysis on the statistical data to generate the at least one classification score; causing to be displayed, with at least one processor, a map of a geographic region having the plurality of zones on a display of a computing device; based at least partially on the at least one classification score, causing to be overlayed, with at least one processor, at least one classification tag over each zone of the plurality of zones on the map to generate the classified map.

Clause 2: The method of clause 1, wherein the statistical data comprises transaction data associated with transactions initiated in each zone of the plurality of zones and merchant category codes associated with the transactions.

Clause 3: The method of clause 1 or 2, wherein the classification score for each zone is generated based at least partially on at least one latent factor score.

Clause 4: The method of any of clauses 1-3, wherein the statistical data comprises socioeconomic data.

Clause 5: The method of any of clauses 1-4, wherein the statistical data comprises a count of transactions initiated in each zone of the plurality of zones sorted by merchant category codes associated with the transactions; wherein generating the at least one classification score comprises performing, with at least one processor, the latent factor analysis on the transaction data to generate at least one latent factor score associated with each merchant category code; wherein generating the at least one classification score further comprises associating at least one classification tag with each merchant category code based at least partially on the at least one latent factor score; and wherein the at least one classification score is based at least partially on the at least one classification tag associated with each merchant category code.

Clause 6: The method of any of clauses 1-5, wherein associating the at least one classification tag with each merchant category code comprises performing a machine learning clustering technique.

Clause 7: The method of any of clauses 1-6, wherein generating the at least one classification score comprises performing, with at least one processor, the latent factor analysis on the transaction data to generate a first latent factor score associated with each merchant category code and a second latent factor score associated with each merchant category code, wherein generating the at least one classification score further comprises plotting a graph of the first latent factor score against the second latent factor score for each merchant category code and associating the at least on classification tag with each merchant category code based on clustering of the merchant category codes on the graph.

Clause 8: A system for generating a classified map on a computing device comprising at least one processor programmed or configured to: receive statistical data associated with each zone of a plurality of zones; generate, based on the statistical data, at least one classification score for each zone of the plurality of zones by performing a latent factor analysis on the statistical data to generate the at least one classification score; cause to be displayed a map of a geographic region having the plurality of zones on a display of a computing device; and based at least partially on the at least one classification score, cause to be overlayed at least one classification tag over each zone of the plurality of zones on the map to generate the classified map.

Clause 9: The system of clause 8, wherein the statistical data comprises transaction data associated with transactions initiated in each zone of the plurality of zones and merchant category codes associated with the transactions.

Clause 10: The system of clause 8 or 9, wherein the classification score for each zone is generated based at least partially on at least one latent factor score.

Clause 11: The system of any of clauses 8-10, wherein the statistical data comprises socioeconomic data.

Clause 12: The system of any of clauses 8-11, wherein the statistical data comprises a count of transactions initiated in each zone of the plurality of zones sorted by merchant category codes associated with the transactions; wherein generating the at least one classification score comprises the at least one processor performing the latent factor analysis on the transaction data to generate at least one latent factor score associated with each merchant category code; wherein generating the at least one classification score comprises the at least one processor associating at least one classification tag with each merchant category code based at least partially on the at least one latent factor score; and wherein the at least one classification score is based at least partially on the at least one classification tag associated with each merchant category code.

Clause 13: The system of any of clauses 8-12, wherein associating the at least one classification tag with each merchant category code comprises the at least one processor performing a machine learning clustering technique.

Clause 14: The system of any of clauses 8-13, wherein generating the at least one classification score comprises the at least one processor performing the latent factor analysis on the transaction data to generate a first latent factor score associated with each merchant category code and a second latent factor score associated with each merchant category code, wherein generating the at least one classification score further comprises the at least one processor plotting a graph of the first latent factor score against the second latent factor score for each merchant category code and associating the at least on classification tag with each merchant category code based on clustering of the merchant category codes on the graph.

Clause 15: A computer program product for generating a classified map on a computing device, the computer program product comprising at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to: receive statistical data associated with each zone of a plurality of zones; generate, based on the statistical data, at least one classification score for each zone of the plurality of zones by performing a latent factor analysis on the statistical data to generate the at least one classification score; cause to be displayed a map of a geographic region having the plurality of zones on a display of a computing device; and based at least partially on the at least one classification score, cause to be overlayed at least one classification tag over each zone of the plurality of zones on the map to generate the classified map.

Clause 16: The computer program product of clause 15, wherein the statistical data comprises transaction data associated with transactions initiated in each zone of the plurality of zones and merchant category codes associated with the transactions.

Clause 17: The computer program product of clause 15 or 16, wherein the classification score for each zone is generated based at least partially on at least one latent factor score.

Clause 18: The computer program product of any of clauses 15-17, wherein the statistical data comprises socioeconomic data.

Clause 19: The computer program product of any of clauses 15-18, wherein the statistical data comprises a count of transactions initiated in each zone of the plurality of zones sorted by merchant category codes associated with the transactions; wherein generating the at least one classification score comprises the at least one processor performing the latent factor analysis on the transaction data to generate at least one latent factor score associated with each merchant category code; wherein generating the at least one classification score comprises the at least one processor associating at least one classification tag with each merchant category code based at least partially on the at least one latent factor score; and wherein the at least one classification score is based at least partially on the at least one classification tag associated with each merchant category code.

Clause 20: The computer program product of any of clauses 15-19, wherein associating the at least one classification tag with each merchant category code comprises the at least one processor performing a machine learning clustering technique.

Clause 21: The computer program product of any of clauses 15-20, wherein generating the at least one classification score comprises the at least one processor performing the latent factor analysis on the transaction data to generate a first latent factor score associated with each merchant category code and a second latent factor score associated with each merchant category code, wherein generating the at least one classification score further comprises the at least one processor plotting a graph of the first latent factor score against the second latent factor score for each merchant category code and associating the at least on classification tag with each merchant category code based on clustering of the merchant category codes on the graph.

Clause 22: A system for generating a classified map on a computing device comprising: an electronic payment processing network configured to process payment transactions between users and merchants using transaction data associated with the transactions; a database configured to store at least a portion of the transaction data associated with the transactions; and a classification system in communication with the electronic payment processing network and the database, the classification system configured to receive transaction data associated with each zone of a plurality of zones; generate, based on the transaction data, at least one classification score for each zone of the plurality of zones by performing a latent factor analysis on the transaction data to generate at least one latent factor score; cause to be displayed a map of a geographic region having the plurality of zones on a display of a computing device; and based at least partially on the at least one classification score, cause to be overlayed at least one classification tag over each zone of the plurality of zones on the map to generate the classified map.

DETAILED DESCRIPTION

As used herein, the term “transaction service provider” may refer to an entity that receives transaction authorization requests from merchants or other entities and provides guarantees of payment, in some cases through an agreement between the transaction service provider and an issuer institution. For example, a transaction service provider may include a payment network such as Visa® or any other entity that processes transactions. The term “transaction processing system” may refer to one or more computer systems operated by or on behalf of a transaction service provider, such as a transaction processing server executing one or more software applications. A transaction processing system may include one or more processors and, in some non-limiting embodiments, may be operated by or on behalf of a transaction service provider.

As used herein, the term “issuer institution” or “issuer” may refer to one or more entities, such as a bank, that provide accounts to customers for conducting transactions (e.g., payment transactions), such as initiating credit and/or debit payments. For example, an issuer institution may provide an account identifier, such as a personal account number (PAN), to a customer that uniquely identifies one or more accounts associated with that customer. The account identifier may be embodied on a portable financial device, such as a physical financial instrument, e.g., a payment card, and/or may be electronic and used for electronic payments. The term “issuer system” refers to one or more computer systems operated by or on behalf of an issuer institution, such as a server computer executing one or more software applications. For example, an issuer system may include one or more authorization servers for authorizing a transaction.

As used herein, the term “merchant” may refer to an individual or entity that provides goods and/or services, or access to goods and/or services, to customers based on a transaction, such as a payment transaction. The term “merchant” or “merchant system” may also refer to one or more computer systems operated by or on behalf of a merchant, such as a server computer executing one or more software applications. A “point-of-sale (POS) system,” as used herein, may refer to one or more computers and/or peripheral devices used by a merchant to engage in payment transactions with customers, including one or more card readers, near-field communication (NFC) receivers, RFID receivers, and/or other contactless transceivers or receivers, contact-based receivers, payment terminals, computers, servers, input devices, and/or other like devices that can be used to initiate a payment transaction.

As used herein, the term “portable financial device” may refer to a payment card (e.g., a credit or debit card), a gift card, a smartcard, smart media, a payroll card, a healthcare card, a wrist band, a machine-readable medium containing account information, a keychain device or fob, an RFID transponder, a retailer discount or loyalty card, a cellular phone, an electronic wallet mobile application, a personal digital assistant (PDA), a pager, a security card, a computer, an access card, a wireless terminal, a transponder, and/or the like. In some non-limiting embodiments, the portable financial device may include volatile or non-volatile memory to store information (e.g., an account identifier, a name of the account holder, and/or the like).

As used herein, the term “computing device” may refer to one or more electronic devices that are configured to directly or indirectly communicate with or over one or more networks. The computing device may be a mobile device. As an example, a mobile device may include a cellular phone (e.g., a smartphone or standard cellular phone), a portable computer, a wearable device (e.g., watches, glasses, lenses, clothing, and/or the like), a personal digital assistant (PDA), and/or other like devices. In other non-limiting embodiments, the computing device may be a desktop computer or other non-mobile computer. Furthermore, the term “computer” may refer to any computing device that includes the necessary components to receive, process, and output data, and normally includes a display, a processor, a memory, an input device, and a network interface. An “application” or “application program interface” (API) refers to computer code or other data sorted on a computer-readable medium that may be executed by a processor to facilitate the interaction between software components, such as a client-side front-end and/or server-side back-end for receiving data from the client. An “interface” refers to a generated display, such as one or more graphical user interfaces (GUIs) with which a user may interact, either directly or indirectly (e.g., through a keyboard, mouse, etc.).

As used herein, the term “server” may refer to or include one or more processors or computers, storage devices, or similar computer arrangements that are operated by or facilitate communication and processing for multiple parties in a network environment, such as the internet, although it will be appreciated that communication may be facilitated over one or more public or private network environments and that various other arrangements are possible. Further, multiple computers, e.g., servers, or other computerized devices, e.g., point-of-sale devices, directly or indirectly communicating in the network environment may constitute a “system,” such as a merchant's point-of-sale system. Reference to “a server” or “a processor,” as used herein, may refer to a previously-recited server and/or processor that is recited as performing a previous step or function, a different server and/or processor, and/or a combination of servers and/or processors. For example, as used in the specification and the claims, a first server and/or a first processor that is recited as performing a first step or function may refer to the same or different server and/or a processor recited as performing a second step or function.

Non-limiting embodiments or aspects of the present disclosure are directed to a method, system, and computer program product for generating a classified map on a computing device. Non-limiting embodiments allows for otherwise subjective classifications of geographic regions to be classified using a more objective analysis, which quantitatively scores each geographic region using relevant statistical data. Non-limiting embodiments allows the classification system to analyze the statistical data to generate classification scores and to overlay classification tags over a map to form a classified map based on the generated classification scores. This classified map may allow users to visualize classifications associated with various geographic regions, in order to make informed decisions based on objective statistical data. Non-limiting embodiments place the classification system in communication with the electronic payment processing network so as to utilize transaction data from an electronic payment processing network as the statistical data for generating the classification scores and the classified map. This transaction data provides a statistically significant sample of data, in that each transaction initiated using a portable financial device may contribute to the dataset, such that a latent factor analysis may be performed to determine factors that objectively indicate the classification associated with a geographic region. In this way, non-limiting embodiments allow data associated with consumer transactions to be provided in such a way to be able to illustrate for users classifications associated with geographic regions, which are displayed via a classified map. This may allow for quicker and more accurate decision making based on the classification associated with a geographic region.

Referring toFIG. 1, a non-limiting embodiment or aspect of a system10for generating a classified map on a computing device is shown. In the system10, a user11(e.g., a consumer) may initiate a payment transaction using a portable financial device12issued to the user11. The payment transaction may be processed over an electronic payment processing network14including: a merchant system16operated by or on behalf of a merchant, a transaction processing system (TPS)18operated by or on behalf of a transaction service provider, and an issuer system20operated by or on behalf of an issuer. The TPS database22may be a part of the TPS18or may be a separate database.

With continued reference toFIG. 1, the user11may initiate a payment transaction using the portable financial device12by communicating the account information associated with the portable financial device to the merchant system16(e.g., by swiping the portable financial device at a merchant POS system or by entering the account information into a secure online checkout website during an online transaction). The payment transaction may be processed over the electronic payment processing network14by the merchant system16communicating a transaction message to the TPS18. In response to receiving the transaction message, the TPS18may communicate an authorization request to the issuer system20to cause the issuer system to make an authorization decision regarding the payment transaction. The authorization decision may include to approve or decline the payment transaction. The issuer system20may communicate an authorization response to the TPS18, the authorization response including the authorization decision. The TPS18may communicate the authorization decision to the merchant system16via a response message. Each payment transaction initiated by a user using a portable financial device associated with the transaction service provider associated with the TPS18may be processed in this manner over the electronic payment processing network14.

As payments are processed over the electronic payment processing network14involving the TPS18, the TPS18may collect certain statistical data associated with the transactions being processed. This statistical data collected by the TPS18may be stored in a TPS database22or may be communicated by the TPS18directly to a classification system24, which will be described in more detail hereinafter.

The statistical data may include transaction data associated with transactions processed over the electronic payment processing network14. For example, the transaction data may include the data elements defined by ISO 8583, which is an international standard for financial transaction card originated interchange messaging. Non-limiting examples of transaction data include primary account number (PAN), expiration date, CVV code, transaction amount, transaction date, transaction time, merchant identifier, merchant category code, identifier associated with goods and/or service purchased, whether each transaction was approved or declined, zone in which transaction was initiated (e.g., zip code), user name, user residential address, and the like. The transaction data may include any information communicated over the electronic payment processing network14in the course of processing a payment transaction.

The statistical data may include socioeconomic data. The socioeconomic data may include socioeconomic data associated with the user initiating the payment transaction, such as gender, age, ethnicity, race, occupation, household income, marital status, and the like.

The statistical data (e.g., the transaction data and/or socioeconomic data) may include any of the previously discussed statistical data sorted by geographic zones. As one non-limiting example, the transaction data may include data associated with merchant category codes associated with processed payment transactions, and the data associated with the merchant category codes may be sorted by geographic region in which that payment transaction was initiated, such that a count of transactions initiated in each geographic zone by merchant category code is ascertained. The geographic zone may be any definable geographic region. In some non-limiting embodiments, the geographic zone is a neighborhood, school district, zip code, township, town, municipality, borough, city, district, county, parish, state, commonwealth, province, territory, colony, country, continent, hemisphere, or some collection or combination thereof.

With continued reference toFIG. 1, the system10may include a classification system24. The classification system24may refer to one or more computer systems operated by or on behalf of a transaction service provider, an issuer, a merchant, or other third-party entity. The classification system24may include one or more processors. The classification system24may be in communication with the TPS18and/or the TPS database22to receive the statistical data. Thus, the classification system24may be in communication with the electronic payment processing network14. The classification system24may also be in communication with a maps system26and/or a computing device28, as described hereinafter.

The classification system24may receive the previously-described statistical data, such as the transaction data associated with a plurality of zones. In response to receiving the statistical data, the classification system24may analyze the statistical data and generate, at least one classification score based on the statistical data. The classification score may be generated based at least partially on the at least one latent factor score. Example classification scores may include a numerical score (e.g., a score between 0 and 100), a level (e.g., low, medium, high), an alphabetical grade (e.g., A, B, C, D, F, etc.), or any other conceivable scoring system.

The classification score may be associated with at least one class. A “class” may refer to a number of persons or things (e.g., a zone) regarded as forming a group by reason of common attributes, characteristics, qualities, or traits. For example, the classification score may quantify the degree to which a zone represents a specific class. The classification score may specify (e.g., quantify) the degree to which a zone is, for example, affluent, affordable, youthful, educated, technophilic, physically active, politically active, outdoorsy, hipster, industrial, agricultural, health conscious, and other like classes.

The classification score may be generated for each zone of the plurality of zones. In one non-limiting example, the classification score for each zone may be generated by the classification system24performing a latent factor analysis on the statistical data to generate at least one latent factor score. Non-limiting examples of the classification system24generating the classification score for each zone will be detailed hereinafter.

With continued reference toFIG. 1, in response to the classification system24generating the classification score for each zone, the classification system may cause a map of a geographic region having the plurality of zones to be displayed on a display of the computing device28. In some non-limiting embodiments, the classification system24may communicate with the maps system26to receive a map of the geographic region having the zones and may communicate that map to the computing device28, such that the map is displayed on the computing device. In some non-limiting embodiments, the classification system may communicate with the maps system26to cause the maps system26to communicate a map of the geographic region having the zones to the computing device28, to be displayed thereon. The maps system26may be a part of the classification system24or may be a separate system. The maps system may be Google Maps or any other web mapping service. The maps system26may be a server for generation of an API, in order to communicate with Google Maps or other web mapping service.

With continued reference toFIG. 1, the classification system24may cause at least one classification tag to be overlayed over each zone of the plurality of zones on the map displayed on the computing device28to generate a classified map. The classification tags may be caused to be overlayed by the classification system24based on the generated classification scores. The classification tag may be a label over the zone to label the zone as being associated with that class. The classification tag may include a color or pattern that visually indicates to the viewer of the computing device28that the zone is associated with a particular classification tag. The classification tag may indicate the degree to which the zone is associated with that class (e.g., a lighter or darker shade of the color of the classification tag).

Referring toFIG. 2, a computer-implemented method30for generating a classified map on the computing device28is shown. A first step32may include receiving, with the classification system24, statistical data (seeFIGS. 3-8) associated with each zone of a plurality of zones. A second step34may include generating, with the classification system24, based on the statistical data at least one classification score (seeFIG. 9) for each zone of the plurality of zones by performing a latent factor analysis (seeFIGS. 6 and 7) on the statistical data to generate at least one latent factor score. A third step36may include causing to be displayed, with the classification system24, a map of a geographic region having the plurality of zones on a display of a computing device (seeFIG. 10). A fourth step38may include, based at least partially on the at least one classification score, causing to be overlayed, with the classification system24, at least one classification tag over each zone of the plurality of zones on the map to generate the classified map (seeFIGS. 11A-11B).

In a further, non-limiting embodiment or aspect, a computer program product for generating a classified map on a computing device includes at least one non-transitory computer readable medium including program instructions that, when executed by at least one processor, cause the at least one processor to execute any of the methods described herein. The at least one processor may include the classification system24.

The following example is provided to illustrate an embodiment of the system, method, and computer program product for generating a classified map on a computing device, and is not meant to be limiting.

Referring back toFIG. 1, the TPS18may collect statistical data over the electronic payment processing network14in connection with payment transactions between users and merchants, as previously described.

Referring toFIG. 3, in this particular non-limiting example, a table40of the statistical data includes transaction data associated with transactions initiated over the electronic payment processing network14in each zone of a plurality of zones, the zones being zip codes. The statistical data includes a count of transactions initiated in each zip code sorted by merchant category code associated with the transactions. The rows of data in the table40ofFIG. 3correspond to zip codes, and each column corresponds to a merchant category code. Each cell in the table40corresponds to a count of the number of payment transactions associated with a particular merchant category code that were initiated in each zip code over a predetermined time period.

Referring toFIG. 4, a table42is shown in which the data from the table40inFIG. 3is normalized to create normalized statistical data. Either the statistical data from the table40inFIG. 3or the normalized statistical data from the table42inFIG. 4may be used. The normalized statistical data may bring all values in the table between 0 and 1 with 0 representing a transaction count of 0 and 1 representing a maximum transaction count for the data. Normalizing the data may eliminate some bias and may provide a scale for measurement.

Referring toFIGS. 5A-5B, the statistical data, such as the normalized statistical data from table42ofFIG. 4, may be analyzed using principal component analysis (PCA). By running a PCA on the normalized statistical data, a graph44of cumulative variance over principal components generated by the PCA can be generated, which is shown inFIG. 4, and the results of the PCA can be shown in the table46inFIG. 5B. The rows of the table46inFIG. 5Beach represent the merchant category codes (original variables), and each column represents a principal component (factor) generated by the PCA. The data in the table46inFIG. 5Brepresent factor loadings, which represent a correlation between the original variable and the factors. From the graph44inFIG. 5A, it can be seen that running a PCA on the normalized statistical data can be reduced to two columns (two principal components) while preserving approximately 80% of the information. This loss of 20% of the information may be justified based on the simplification of the data from 78 columns (one for each merchant category code) to 2 columns. It will be appreciated that these two principal components to which the data is reduced to do not in themselves represent a transaction count of merchant category code transactions in a zip code, but they represent a new characteristic that effectively summarizes the original 78 columns of normalized statistical data. This PCA helps to determine that the normalized statistical data may be reduced to two principal components while preserving approximately 80% of the data.

After it has been determined that the data can be reduced to two components while preserving an acceptable amount of data, a latent factor analysis (LFA) may be applied to the data. This LFA technique not only performs PCA as part of its initial processing but also generates a ‘latent’ or un-observed variable for all row elements (merchant category codes). Referring toFIG. 6, a table48of the output of the LFA is shown. Each row in the table48ofFIG. 6represents a merchant category code (original variables), and each column represents a factor (principal component) generated by the LFA. The data in the table48inFIG. 6represent factor loadings, which represent a correlation between the original variable and the factors. In the non-limiting example inFIG. 6, two factors (Factor1and Factor2) result from the LFA, and each merchant category code has a first latent factor score (loading) associated with Factor1and a second latent factor score (loading) associated with Factor2.

Referring toFIG. 7, a graph50may be generated that plots the second latent factor score against the first latent factor score for each merchant category code. As can be seen from the graph50ofFIG. 7a set of x and y coordinates may be associated with each merchant category code such that each merchant category code has a position on the graph50.

Referring toFIG. 8, a graph52associating classes54a-54cwith plotted merchant category codes is shown. Merchant category code may be grouped into classes54a-54cbased on their proximity to one another. The classes may be any of the previously described classes. For example, certain merchant category codes may be associated with a ‘youthful’ class54a,certain merchant category codes may be associated with an ‘affordable’ class54b,and certain merchant category codes may be associated with an ‘affluent’ class54c.In some non-limiting examples, a merchant category code belongs to only a single class. In some non-limiting examples, a merchant category code may belong to a plurality of different classes.

With continued reference toFIG. 8, the classes54a-54cmay be associated with merchant category codes based on a human operator analyzing the data to suggest which class certain merchant category codes belong. However, in other examples, the classes54a-54cmay be associated with merchant category codes automatically, such as based on a machine learning clustering technique. Non-limiting examples of suitable machine learning clustering techniques include K-Means clustering or K-Nearest neighbors clustering.

Referring toFIG. 9, based on the classes54a-54cassociated with each merchant category code, at least one classification score may be generated for each zone. A table56shown inFIG. 9shows the classification scores by zone. The rows of the table56correspond to zip codes, and the columns D-K represent classes. The data associated with each row associated with columns D-K represent classification scores associated with each class. Each class may include several classification scores (e.g., Columns E-H are all affluent classification scores based on different algorithms). Each classification score is determined based on an algorithm based on the statistical data (e.g., the transaction count or normalized transaction count for each MCC after each MCC has been classified).

Column C from the table56inFIG. 9represents an overall score, which may include the sum of a plurality of classification scores to characterize the class of the zone overall. The overall score may represent the overall characterization of the particular zone. In some non-limiting examples, no overall score is determined, but only scores associated with each individual classification are determined. For example, for the classes54a-54cfromFIG. 8, an affluent score, an affordable score, and a youthful score may be generated for each zone. Also, an overall score that includes an algorithm representing some combination of the affluent score(s), the affordable score(s), and/or the youthful score(s) may also be determined to give the overall (“vibe”) score for each zone.

Referring toFIG. 10, a map58of the geographic region showing the plurality of zones is shown. This map58may be caused to be displayed by the classification system on the computing device of the user.

Referring toFIGS. 11A-11B, a classified map60of the geographic region showing the plurality of zones is shown. The classified may60may include the map58fromFIG. 10and at least one classification tag62a-62cover at least one of the zones. The classification tags62a-62cmay associate each of the zones with a particular class. For example, the zone labeled62ainFIG. 11Acorresponds to an affluent zone based on the affluent classification tag; the zone labeled62bcorresponds to a youthful zone based on the youthful classification tag; and the zone labeled62ccorresponds to an affordable zone based on the affordable classification tag.

It will be appreciated that other variations of classification tags may be used. For example, the classification tags may specify the degree to which a zone is associated with a specific class by displaying the classification score associated with that class or using a shade of a color associated with that class. For example, a zone labeled as a youthful zone may display a youthful classification score between 0-100, based on the degree to which that zone can be characterized as youthful, with 0 being the least youthful zone and 100 being the most youthful zone. In another example, blue may be a color of a classification tag associated with a youthful zone, and more youthful zones may receive a darker blue classification tag and less youthful zones may receive a lighter blue classification tag, such that the shade of the classification tag may indicate the relative degree to which that zone is associated with that class.

Each zone may receive a single classification tag, or each zone may include multiple classification tags. The user may interact with the classified map60so as to request the classification tags to be displayed. For example, the user may interact with the classified map60so as to see the degree to which each zone is associated with a certain class (e.g., how ‘youthful’ each zone is). In another example, the user may interact with the classified map60so that only the classification tag associated with the class each zone is most strongly associated with is shown.

In another non-limiting example as shown inFIG. 11B, the classified map60may include additional tagging64. The additional tagging64may, for example, indicate the merchant types (based on merchant category code) associated with each zone. The additional tagging64may include other relevant information, such as statistical data associated with transactions conducted in each zone.

Based on this non-limiting example, it is clear that a user may view the classified map60which is generated by a unique, unconventional arrangement of the electronic payment processing network14being in communication with the classification system24. The LFA of the statistical data from the electronic payment processing network14allows latent factors from the statistical data to be determined as they relate to specific characteristics (classes) associated with geographic zones. This allows a user to more readily understand geographic zones based on certain data received by certain entities (e.g., transaction service provider and/or issuers) in statistically significant amounts.

In the example shown inFIGS. 3-11A, the statistical data included count of transactions initiated during a specified time period sorted by merchant category code. However, it will be appreciated that other types of statistical data (e.g., transaction data associated with transactions) for each zone may be utilized in order to generate the classified map as disclosed herein. For example, any data associated with ISO8583or any other data collected by any system of any entity (e.g., merchant, transaction service provider, issuer) operating within the electronic payment processing network14sorted by zone may be utilized. Further other types analysis in addition to or in lieu of the PCA and LFA may be utilized to generate the classification score(s) associated with each zone.