Places Insights provides brand information for many categories of places. For example:
- For the category of "ATMs, Banks, and Credit Unions", the brands data contains an entry for each of the brands PNC, UBS, and Chase banks.
- For the category "Automotive Rentals", the data contains an entry for each of the brands Budget, Hertz, and Thrifty.
A typical use case for querying the brands dataset is to join it with a query on the place data to answer questions such as:
- What is the count of all stores by brand in an area?
- What is the count of my top three competitor brands in the area?
- What is the count of brands of a specific category, such as "Fitness" or "Gas Station", in the area?
About the brands dataset
The brands dataset for the US is named places_insights___us___sample.brands.
Brands dataset schema
The schema for the brands dataset defines three fields:
id: The brand ID.name: The brand name, such as "Hertz" or "Chase".category: The brand type, such as "Gas Station", "Food and Drink", or "Lodging". For a list of possible values, see Category values
Use brands dataset in a query
The places dataset schema defines the brand_ids field. If a place in the
places dataset is associated with a brand, then the brand_ids field for the
place contains the corresponding brand ID.
A typical query that references the brands dataset performs a JOIN with
the places dataset based on the brand_ids field.
For example, to find the count of the number of McDonald's restaurants within 2000 meters of the Empire State Building in New York City:
SELECT WITH AGGREGATION_THRESHOLD COUNT(*) FROM places_insights___us___sample.places_sample places, UNNEST(brand_ids) AS brand_id LEFT JOIN places_insights___us___sample.brands ON brand_id = brands.id WHERE ST_DWITHIN(ST_GEOGPOINT(-73.9857, 40.7484), point, 2000) AND brands.name = "McDonald's" AND business_status = "OPERATIONAL"
The next query returns the count of the number of cafes in New York City that belong to a brand, grouped by brand name:
SELECT WITH AGGREGATION_THRESHOLD brands.name, COUNT(*) AS store_count FROM places_insights___us___sample.places_sample places, UNNEST(brand_ids) AS brand_id LEFT JOIN places_insights___us___sample.brands ON brand_id = brands.id WHERE brands.category = "Food and Drink" AND "cafe" IN UNNEST(places.types) AND business_status = "OPERATIONAL" GROUP BY brands.name ORDER BY store_count DESC;
The following image shows the counts by brand:
Category values
The category field for a brand can contain the following values:
| Category type value |
|---|
ATMs, Banks and Credit Unions |
Automotive and Parts Dealers |
Automotive Rentals |
Automotive Services |
Dental |
Electric Vehicle Charging Stations |
Electronics Retailers |
Fitness |
Food and Drink |
Gas Station |
Grocery and Liquor |
Health and Personal Care Retailers |
Hospital |
Lodging |
Merchandise Retail |
Movie Theater |
Parking |
Telecommunications |