POI Data
We provide POI Data, which helps power geographical information system (GIS) tools and provides data-driven insights across a wide range of use cases, from marketing to public planning and fraud detection.
Our POI Data connects people's movements to over 15M+ Raw POIs across 150 countries. These are aggregated and anonymized data that are only used to offer context for the volume and patterns of visits to certain locations. This data feed is compiled from different data sources around the world.
Reach
Our POI/Place/OOH level insights are calculated based on Factori’s Mobility & People Graph data aggregated from multiple data sources globally. To achieve the desired foot-traffic attribution, specific attributes are combined to bring forward the desired reach data.
For instance, in order to calculate the foot traffic for a specific location, a combination of location ID, day of the week, and part of the day can be combined to give specific location intelligence data. There can be a maximum of 56 data records possible for one POI based on the combination of these attributes.
Data Export Methodology
Since we collect data dynamically, we provide the most updated data and insights via a best-suited method at a suitable interval (daily/weekly/monthly).
Use Cases
Credit Scoring: Financial services can use alternative data to score an underbanked or unbanked customer by validating locations and persona.
Retail Analytics: Analyze footfall trends in various locations and gain an understanding of customer personas.
Market Intelligence: Study various market areas, the proximity of points or interests, and the competitive landscape
Urban Planning: Build cases for urban development, public infrastructure needs, and transit planning based on fresh population data.
Schema
Attribute Name | Sample Values |
---|---|
LocationID | 4d40963eae8799d827d0ee1507068dfd |
name | 7-Eleven |
website | 7-eleven.com |
BrandID | 1f12f79901b84906deffc0bff68eb89e |
claimed | Y |
phone | (310) 393-7330 |
Phone2 | |
streetAddress | 630 Wilshire Blvd |
city | Santa Monica |
state | CA |
country_code | US |
zip | 90411 |
zip+4 | 90411-1502 |
lat | 34.0208943 |
lng | -118.49549 |
poi_status | open |
geoHash8 | 9q59x964 |
plusCode11 | 85632GC3+9R2 |
category | Convenience store |
specialty | Convenience store |
Additional discovered Categories | |
workHours | [Monday Open 24 hours, Tuesday Open 24 hours, Wednesday Open 24 hours, Thursday Open 24 hours, Friday Open 24 hours, Saturday Open 24 hours, Sunday Open 24 hours] |
Year Founded | |
Age | |
reviewsCount | 12 |
rating | 5.0 |
numberOfPictures | 5 |
tier1_naics_code | 44 |
tier1_naics_category | |
tier2_naics_code | 445 |
tier2_naics_category | |
tier3_naics_code | 4451 |
tier3_naics_category | |
tier4_naics_code | 44512 |
tier4_naics_category | |
tier5_naics_code | 445120 |
tier5_naics_category | Convenience Stores |
stock_ticker | SVNDY |
address_components | [{"long_name": "Chilonzor district, Block 31","short_name": "Chilonzor district, Block 31","types": ["establishment","point_of_interest","transit_station"]},{"long_name": "Учтепа тумани","short_name": "Учтепа тумани","types": ["political","sublocality","sublocality_level_1"]},{"long_name": "Тоshkent","short_name": "Тоshkent","types": ["locality","political"]},{"long_name": "Toshkent","short_name": "Toshkent","types": ["administrative_area_level_1","political"]},{"long_name": "Uzbekistan","short_name": "UZ","types": ["country","political"]}] |
formatted_address | Chilonzor district, Block 31, Тоshkent, Toshkent, Uzbekistan |
geometry_bounds | {"northeast": { "lat": 41.2373644, "lng": 69.2144752 }, "southwest": { "lat": 41.2371556, "lng": 69.2142112 }} |
geometry_location | {"lat":34.0208933,"lng":-118.49541} |
geometry_location_type | ROOFTOP |
geometry_viewport | {"northeast":{"lat":34.02112,"lng":-118.4952},"southwest":{"lat":34.02111,"lng":-118.49519}} |
types | street_address |
building_id | way_559939957 |
building_type | commercial |
building_name | Dynasties Restaurant Wok |
shape_type | polygon |
shape_polygon | POLYGON ((-118.4955766 34.0207802, -118.4956121 34.0208106, -118.4956272 34.0207983, -118.4956612 34.0208274, -118.4956277 34.0208546, -118.4956421 34.0208669, -118.4955481 34.020943, -118.4955552 34.0209491, -118.4953606 34.0211067, -118.4953546 34.0211015, -118.4953531 34.0211028, -118.4953374 34.0210894, -118.4953161 34.0211067, -118.4952468 34.0210473, -118.4955766 34.0207802)) |
geometry_id | 559939957 |
Physical Neighborhood Name | |
Social Neighborhood Name | |
Nearby Neighborhoods | |
Social Media Profile Links |
Updated 2 months ago