Discovering neighborhood clusters and uses therefor
Computer-based systems and methods for discovering neighborhood clusters in a geographic region, where the clusters have a mix of venues and are determined based on venue check-in data. The mix of venues for the clusters may be based on the social similarity between pairs of venues; or emblematic of certain neighborhood typologies; or emblematic of temporal check-in pattern types; or combinations thereof. The neighborhood clusters that are so discovered through venue-check in data could be used for many commercial and civic purposes.
1. A computer-based system comprising:
a plurality of electronic location sensors that capture time-stamped location data indicative of the location that venue visitors visit over time, wherein the plurality of electronic location sensors comprises electronic location sensors selected from the group consisting of:
mobile computing devices that each executes venue check-in software;
point-of-sale systems;
cameras;
biometric sensors;
vehicle sensors; and
presence sensors;
a computer database system that stores:
derived venue check-in data based on the time-stamped location data captured by the plurality of electronic location sensors, wherein the venue check-in data comprise venue check-in data from multiple venue visitors for multiple venues in a geographic region; and
venue category data for the multiple venues that indicate a venue category type for the multiple venues; and
a host computer system that comprises one or more processors that are in communication with the computer database system, wherein the one or more processors are programmed to identify two or more geographic clusters of venues in the geographic region, wherein each of the two or more geographic clusters of venues comprises a mix of one or more venues, using statistical inference from a probability distribution, based on patterns of venue category type in the venue category data emblematic of a neighborhood type, such that the mix of venues for each cluster is emblematic of a neighborhood type.
2. A computer-based system comprising:
a plurality of electronic location sensors that capture time-stamped location data indicative of the location that venue visitors visit over time;
a computer database system that stores:
derived venue check-in data based on the time-stamped location data captured by the plurality of electronic location sensors, wherein the venue check-in data comprise venue check-in data from multiple venue visitors for multiple venues in a geographic region; and
venue category data for the multiple venues that indicate a venue category type for the multiple venues;
a host computer system that comprises one or more processors that are in communication with the computer database system, wherein the one or more processors are programmed to identify two or more geographic clusters of venues in the geographic region, wherein each of the two or more geographic clusters of venues comprises a mix of one or more venues, using statistical inference from a probability distribution, based on patterns of venue category type in the venue category data emblematic of a neighborhood type, such that the mix of venues for each cluster is emblematic of a neighborhood type; and
an analytics server system in communication with the host computer system, wherein the analytics server system comprises one or more servers that are programmed to receive data about the two or more geographic clusters of venues in the geographic region determined by the host computer system and provide analytics using the two or more geographic clusters of venues in the geographic region determined by the host computer system.
3. The computer-based system of claim 2 , wherein the plurality of electronic location sensors comprises sensors selected from the group consisting of:
mobile computing devices that each executes venue check-in software;
point-of-sale systems;
cameras;
biometric sensors;
vehicle sensors; and
presence sensors.
4. The computer-based system of claim 1 , wherein the mix of venues is determined, by the one or more processors, using inference to compute a probabilistic distribution of venues for each cluster such that the mix of venues for each cluster is emblematic of a neighborhood type.
5. The computer-based system of claim 4 , wherein the mix of venues is determined, by the one or more processors, using statistical sampling.
6. The computer-based system of claim 5 , wherein the mix of venues is determined, by the one or more processors, using Gibbs sampling.
7. The computer-based system of claim 1 , wherein the two or more geographic clusters are identified using Gibbs sampling.
8. The computer-based system of claim 1 , wherein the one or more processors are further programmed to compare the two or more identified geographic clusters of venues in the geographic region based on a similarity of distributions of venue visitors that visit the two or more identified geographic clusters.
9. The computer-based system of claim 1 , wherein the geographic region comprises a city.
10. A computer-based system comprising:
a plurality of electronic location sensors that capture time-stamped location data indicative of the location that venue visitors visit over time, wherein the plurality of electronic location sensors comprises electronic location sensors selected from the group consisting of:
mobile computing devices that each executes venue check-in software;
point-of-sale systems;
cameras;
biometric sensors;
vehicle sensors; and
presence sensors;
a computer database system that stores derived venue check-in data based on the time-stamped location data captured by the plurality of electronic location sensors, wherein the venue check-in data comprise venue check-in data from multiple venue visitors for multiple venues in a geographic region, and wherein the check-in data from the venue visitors comprises check-in time data;
a host computer system that comprises one or more processors that are in communication with the computer database system, wherein the one or more processors are programmed to identify two or more geographic clusters of venues in the geographic region, wherein each of the two or more geographic clusters of venues comprises a mix of one or more venues, using statistical inference from a probability distribution, based on patterns of check-in time in the venue check-in data, such that the mix of venues for each cluster is emblematic of one of a predetermined number of temporal check-in pattern types.
11. The computer-based system of claim 10 , wherein the two or more geographic clusters are identified using Gibbs sampling.
12. The computer-based system of claim 10 , wherein the one or more processors are further programmed to compare the two or more identified geographic clusters of venues in the geographic region based on a similarity of distributions of venue visitors that visit the two or more identified geographic clusters.
13. The computer-based system of claim 10 , further comprising an analytics server system in communication with the host computer system, wherein the analytics server system comprises one or more servers that are programmed to receive data about the two or more geographic clusters of venues in the geographic region determined by the host computer system and provide analytics using the two or more geographic clusters of venues in the geographic region determined by the host computer system.
14. A computer-implemented method comprising:
storing, in a computer database system:
derived venue check-in data that is based on time-stamped location data captured by a plurality of electronic location sensors, wherein:
the time-stamped location data are indicative of the location that venue visitors visit over time, such that the derived venue check-in data comprises venue check-in data from multiple venue visitors for multiple venues in a geographic region; and
the plurality of electronic location sensors comprises electronic location sensors selected from the group consisting of:
mobile computing devices that each executes venue check-in software;
point-of-sale systems;
cameras;
biometric sensors;
vehicle sensors; and
presence sensors; and
venue category data for the multiple venues that indicate a venue category type for the multiple venues; and
identifying, by one or more processors of a host computer system that is in communication with the computer database system, two or more geographic clusters of venues in the geographic region, wherein each of the two or more geographic clusters of venues comprises a mix of one or more venues, using statistical inference from a probability distribution, based on patterns of venue category type in the venue category data emblematic of a neighborhood type, such that the mix of venues for each cluster is emblematic of a neighborhood type.
15. The method of claim 14 , further comprising transmitting, by the host computer system to an analytics server system that is in communication with the host computer system via an electronic data network, the two or more geographic clusters identified by the host computer system such that the analytics server system is capable of providing analytics using the two or more geographic clusters determined by the host computer system.
16. A computer-implemented method comprising:
storing, in a computer database system, derived venue check-in data based on the time-stamped location data captured by a plurality of electronic location sensors, wherein:
the venue check-in data comprise venue check-in data from multiple venue visitors for multiple venues in a geographic region;
the check-in data from the venue visitors comprises check-in time data; and
the plurality of electronic location sensors comprises electronic location sensors selected from the group consisting of:
mobile computing devices that each executes venue check-in software;
point-of-sale systems;
cameras;
biometric sensors;
vehicle sensors; and
presence sensors; and
identifying, by a host computer system that comprises one or more processors of a computer system that is in communication with the computer database system, two or more geographic clusters of venues in the geographic region, wherein each of the two or more geographic clusters of venues comprises a mix of one or more venues, using statistical inference from a probability distribution, based on patterns of check-in time in the venue check-in data, such that the mix of venues for each cluster is emblematic of one of a predetermined number of temporal check-in pattern types.
17. The method of claim 16 , further comprising transmitting, by the host computer system to an analytics server system that is in communication with the host computer system via an electronic data network, the two or more geographic clusters identified by the host computer system such that the analytics server system is capable of providing analytics using the two or more geographic clusters determined by the host computer system.
18. The computer-based system of claim 2 , wherein the plurality of electronic location sensors comprise mobile computing devices that each executes venue check-in software.
19. A computer-based system comprising:
a plurality of electronic location sensors that capture time-stamped location data indicative of the location that venue visitors visit over time;
a computer database system that stores derived venue check-in data based on the time-stamped location data captured by the plurality of electronic location sensors, wherein the venue check-in data comprise venue check-in data from multiple venue visitors for multiple venues in a geographic region, and wherein the check-in data from the venue visitors comprises check-in time data;
a host computer system that comprises one or more processors that are in communication with the computer database system, wherein the one or more processors are programmed to identify two or more geographic clusters of venues in the geographic region, wherein each of the two or more geographic clusters of venues comprises a mix of one or more venues, using statistical inference from a probability distribution, based on patterns of check-in time in the venue check-in data, such that the mix of venues for each cluster is emblematic of one of a predetermined number of temporal check-in pattern types; and
an analytics server system in communication with the host computer system, wherein the analytics server system comprises one or more servers that are programmed to receive data about the two or more geographic clusters of venues in the geographic region determined by the host computer system and provide analytics using the two or more geographic clusters of venues in the geographic region determined by the host computer system.
20. A computer-implemented method comprising:
storing, in a computer database system:
derived venue check-in data that is based on time-stamped location data captured by a plurality of electronic location sensors, wherein the time-stamped location data are indicative of the location that venue visitors visit over time, such that the derived venue check-in data comprises venue check-in data from multiple venue visitors for multiple venues in a geographic region; and
venue category data for the multiple venues that indicate a venue category type for the multiple venues;
identifying, by one or more processors of a host computer system that is in communication with the computer database system, two or more geographic clusters of venues in the geographic region, wherein each of the two or more geographic clusters of venues comprises a mix of one or more venues, using statistical inference from a probability distribution, based on patterns of venue category type in the venue category data emblematic of a neighborhood type, such that the mix of venues for each cluster is emblematic of a neighborhood type; and
transmitting, by the host computer system to an analytics server system that is in communication with the host computer system via an electronic data network, the two or more geographic clusters identified by the host computer system such that the analytics server system is capable of providing analytics using the two or more geographic clusters determined by the host computer system.
21. A computer-implemented method comprising:
storing, in a computer database system, derived venue check-in data based on time-stamped location data captured by a plurality of electronic location sensors, wherein the venue check-in data comprise venue check-in data from multiple venue visitors for multiple venues in a geographic region and wherein the check-in data from the venue visitors comprises check-in time data;
identifying, by a host computer system that comprises one or more processors of a computer system that is in communication with the computer database system, two or more geographic clusters of venues in the geographic region, wherein each of the two or more geographic clusters of venues comprises a mix of one or more venues, using statistical inference from a probability distribution, based on patterns of check-in time in the venue check-in data, such that the mix of venues for each cluster is emblematic of one of a predetermined number of temporal check-in pattern types; and
transmitting, by the host computer system to an analytics server system that is in communication with the host computer system via an electronic data network, the two or more geographic clusters identified by the host computer system such that the analytics server system is capable of providing analytics using the two or more geographic clusters determined by the host computer system.