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    "name": "google-maps-popular-times-analytics",
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    "description": "Collect Google Maps Popular Times foot-traffic curves in bulk: hour-by-hour busyness for every day of the week, plus live occupancy where available, for whole categories of venue in an area (every gym or cafe in a city). Wide or long format, JSON/CSV/Excel/NDJSON. No API key.",
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    "exampleRunInput": {
      "body": "{ \"helloWorld\": 123 }",
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    "categories": [],
    "isDeprecated": false,
    "title": "Google Maps Popular Times & Traffic Analytics",
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    "readmeSummary": "## Google Maps Popular Times & Traffic Analytics\n\nCollects Google Maps Popular Times foot-traffic curves in bulk for categories or lists of venues across an area, returning hour-by-hour weekly occupancy curves and live occupancy readings where available. The Actor scrapes Google Maps without requiring a login, browser automation, or API key, performing one request per venue and producing venue-level identity (name, category, address), geocoordinates and timezone, time-series occupancy (hourly curves for every weekday), current live-occupancy indicators and a flag denoting true live measurement versus a projection, precomputed weekly peak/busiest times and average occupancy metrics, user-rating and review-count metadata as proxies for venue scale, and stable place identifiers for tracking. Outputs can be emitted in a wide shape (one row per venue with the nested weekly curve) or a long, analysis-ready shape (one row per venue × day × hour) suitable for spreadsheet or programmatic aggregation; the Actor supports area-based searches (centre+radius or tiled bounding boxes) and explicit venue lists/identifiers. Operational behaviors include per-venue request pacing to avoid IP degradation and the need for residential-proxy routing to obtain the full popular-times data block from Google; live occupancy is provided only when Google supplies a live measurement and is signaled by a dedicated presence flag.\n\n## Use cases\n\n- Urban research and analysis of pedestrian or visitor patterns across a city  \n- Retail site selection and catchment-area foot-traffic comparison  \n- Investment analysis using venue-level weekly peak and average occupancy metrics  \n- Competitive operations research and benchmarking across chains or categories  \n- Bulk collection of category-specific foot-traffic curves for city-scale studies  \n- Monitoring and time-series analysis of specific venues over time (place-level tracking)",
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