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API directory · Google Maps Platform

Places Insights: aggregated place data for site selection and market analysis

Places Insights, the Places Aggregate API, delivers aggregated counts and data from Google's places database for a defined area, filterable by place type, operating status, price level and ratings. It is delivered via BigQuery and covers 250 million+ places. For retail, proptech and enterprise teams in India, it is the data layer behind site selection, trade-area analysis and competitive intelligence. Lepton builds on it and integrates it.

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What the API does

Aggregated place counts for an area, delivered into BigQuery

Places Insights, formerly the Places Aggregate API, returns aggregated counts of places within a defined geographic area: a circle, a named region or a custom polygon. Results are filterable by place type, operating status, price level and ratings. When the place count in the area is 100 or fewer, individual Place IDs are also returned. REST and gRPC are supported.

The data is delivered into BigQuery for analysis at scale and covers 250 million+ places. It is GA. The product doc slug is "places-insights" and redirects to the Places Aggregate API product page.

Google Maps Platform · Places Insights
POST v1:computeInsights · how many rated bookshops trade inside a 750 m circle
host areainsights.googleapis.com · REST and gRPC · the same data also ships as a BigQuery data exchange listing
200 OK
count 7

Request headers and JSON body

POST https://areainsights.googleapis.com/v1:computeInsights
Content-Type: application/json
X-Goog-Api-Key: <your key>

{
  "insights": ["INSIGHT_COUNT", "INSIGHT_PLACES"],
  "filter": {
    "locationFilter": {
      "circle": {
        "latLng": { "latitude": 12.9784, "longitude": 77.6408 },
        "radius": 750
      }
    },
    "typeFilter": {
      "includedPrimaryTypes": ["book_store"]
    },
    "operatingStatus": ["OPERATING_STATUS_OPERATIONAL"],
    "ratingFilter": { "minRating": 4.0 }
  }
}
This is the shape of a site-selection question, not a search. You do not get names, addresses, ratings or photos back: you get a number, and Place IDs only when the area is small enough. Widen the circle to a city and placeInsights drops away and count is all that returns. count is an int64, so it arrives as a string. The trade area can also be a named region or a custom polygon rather than a circle.

Response application/json

{
  "count": "7",
  "placeInsights": [
    { "place": "places/ChIJXXXXXXXXXXXXXXXXXXXXXX1" },
    { "place": "places/ChIJXXXXXXXXXXXXXXXXXXXXXX2" },
    { "place": "places/ChIJXXXXXXXXXXXXXXXXXXXXXX3" },
    { "place": "places/ChIJXXXXXXXXXXXXXXXXXXXXXX4" },
    { "place": "places/ChIJXXXXXXXXXXXXXXXXXXXXXX5" },
    { "place": "places/ChIJXXXXXXXXXXXXXXXXXXXXXX6" },
    { "place": "places/ChIJXXXXXXXXXXXXXXXXXXXXXX7" }
  ]
}
count7, and 7 objects in placeInsights, one per matching placeINSIGHT_PLACESIDs come back only while the area holds 100 places or fewerfiltersoperational only, primary type book_store, rated 4.0 and above
Fig. · One computeInsights call, trimmed for the page. The endpoint, the filter structure, the enum values and both response fields are the documented Places Insights surface. The circle, the count and the Place IDs are illustrative and describe no real trade area, site or business.

Use cases

Where teams use Places Insights for market and location decisions

  1. 01

    Retail · Site selection and trade-area competition analysis

    Retail expansion teams use Places Insights to count competitor outlets, complementary services and footfall-generating venues within a candidate site's trade area, so new location decisions are backed by place density data rather than manual surveys.

  2. 02

    Proptech · Neighbourhood amenity scoring for property listings

    Property platforms use Places Insights counts within a radius of each listing to compute neighbourhood amenity scores: school density, hospital proximity, retail variety and dining options, surfaced as structured data rather than a plain map.

  3. 03

    FMCG · Distribution coverage and white-space mapping

    FMCG and distribution teams use Places Insights to count relevant outlet types by region, identifying coverage gaps and white-space markets before planning a distribution push or a new product launch.

  4. 04

    Finance · Branch and ATM coverage optimization

    Banks and financial services use Places Insights to model their own branch and ATM density against population and competitor density across a city or district, identifying under-served zones for new placement.

Where Lepton comes in

An integration partner that also builds on Places Insights

Lepton's own SmartMarket product runs on Places Insights for site selection and trade-area analysis. This is not just an API we integrate. It is a surface we have built a production product on, so the pipeline, the polygon queries and the failure modes are ones we have already hit ourselves.

  1. 01

    SmartMarket · Lepton's own site-selection product on Places Insights

    SmartMarket integrates Places Insights as the places data layer for site selection and trade-area analysis. Teams looking to build their own location analytics product can work with Lepton on integration, and can evaluate SmartMarket as an off-the-shelf alternative.

  2. 02

    Integration · BigQuery pipeline setup and polygon query architecture

    We set up the Places Insights to BigQuery pipeline, design the polygon and filter query architecture for your analysis use case, and validate the data model against your site selection or competitive intelligence workflow.

  3. 03

    GCP · GCP project setup and API enablement

    Service accounts, API enablement, billing configuration and BigQuery dataset provisioning so Places Insights is callable, quota-managed and flowing into your analytics environment from day one.

Get started

Tell us the site selection or market analysis problem. We will show you the integration.

Bring the retail expansion or trade-area use case. We walk through how Places Insights and SmartMarket work together and what the integration looks like.

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