Location Intelligence
We provide Location Intelligence 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 Location Intelligence Data connects people's movements to over 14M physical locations globally. These are aggregated and anonymised 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.
Location Intelligence Data Reach
Location Intelligence data brings the POI/Place/OOH level insights calculated on the basis of Factori’s Mobility & People Graph data aggregated from multiple data sources globally. In order 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 40 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 on 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 understanding of customer personas. - Market Intelligence
Study various market areas, 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 | Attribute Description | Sample |
|---|---|---|
| Location ID | STRING | Geographic Identifier (Quad Key 17, Geohash or Hex, POI Id, OOH Id) |
| n_visitors | INTEGER | Extrapolated count of distinct devices observed in the relevant time frame |
| day_of_week | STRING | Day of the week (eg : Monday, tuesday etc) |
| distance_from_home | DOUBLE | Distance from geoid to home in meters |
| do_date | DATE | Start date of month when we generated the data (Static date for the entire month) |
| month | STRING | Month to which the data pertains |
| part_of_day | STRING | Time of the Day : Will have values like Morning - 6AM to 12 PM, Afternoon 12 PM to 6 PM, Evening 6 PM to 10PM, Night 10 PM to 6 AM |
| travelled_countries | JSON | For Devices seen in specific Quad key in a given time frame - Proportion of travel to different countries |
| Visitor_country_origin | JSON | For Devices seen in specific Quad key in a given time frame - Proportion of home country |
| Visitor_home_origin | JSON | For Devices seen in specific Quad key in a given time frame - Proportion of quad keys with their home location |
| Visitor_work_origin | JSON | For Devices seen in specific Quad key in a given time frame - Proportion of quad keys with their work location |
| year | STRING | Year to which date pertains |
Updated 7 months ago