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business-location-screening

Compare user-supplied candidate locations for a local business using nearby competitors, public access signals, review samples, and available city-level context. Use for an early screen, not a lease or viability recommendation.
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mise à jour2026-10-26 02:46:59

Business location screening

Compare a small set of user-supplied candidate sites or neighborhoods for a local business. This is an early evidence screen for competition, access, and public context, not a market-size estimate, commercial rent analysis, or site-viability recommendation.

Define the comparison

Confirm the business type, candidate addresses or coordinates, target customer, search radius, travel mode, and the factors the user cares about. Keep the candidate list fixed across sources. If the user has not provided candidate locations, ask for a small set or state a narrow search scope; do not invent a site shortlist from map ranking alone.

Collect matched location evidence

  1. Resolve each candidate location with Google Maps or Apple Maps and record the returned coordinates, address, provider ID, and region. Keep IDs from each provider separate. A search result is not proof a proposed lease address is available.
  2. Discover exact Google Maps business-category labels from the dataset facets before filtering. Use the same business definition, radius, country, and query plan for every candidate. Combine a bounded stored-dataset baseline with live Google/Apple/Yelp checks, but keep each source's coverage and retrieval time separate.
  3. For competitors, count unique businesses within the defined boundary and record category, address, status, rating, review count, and source IDs. Deduplicate by provider ID first; across providers require a consistent address or other identifying evidence. Unknown status and coordinates stay unknown rather than being dropped silently.
  4. If access matters, use Apple Maps walking/driving directions or ETA between each candidate and user-named anchors such as transit stops or destination areas. These are route/time snapshots, not footfall, commute, or customer traffic measurements. Transit departures are live service data, not a long-term frequency guarantee.
  5. Read only a small, disclosed sample of nearby competitor reviews when it helps characterize service gaps. Keep source, date, sample size, and relevant/irrelevant counts; a review sample is not neighborhood demand.
  6. Optional Numbeo city indices and Redfin housing-market records provide broad household-cost or residential-market context. They are city/metro/ZIP housing measures, not commercial lease rates, local incomes for every catchment, or site-level affordability.
  7. For zoning, licensing, and local tax checks, use user-provided sources or find official local-government pages with Bing. Confirm the page applies to the candidate's municipality, then scrape that public URL. Preserve the municipality, source URL, and date. A page is not confirmation that a specific property is permitted for the proposed use.

Interpret and deliver

Return a candidate-by-candidate evidence matrix with search areas, source, radius/route settings, retrieved counts, deduplication method, competitor categories, review sample, access observations, and any city-level context. State data windows and missing inputs beside each candidate.

Do not rank sites with a single score unless the user supplies the criteria and weights. Map counts, ratings, nearby anchors, and travel times do not establish demand, revenue, foot traffic, zoning approval, commercial rent, or lease availability. Identify the official demographic, traffic, property, licensing, and cost data that would still be needed before signing a lease.

scripts/crawlora.sh /datasets/google-map-businesses/facets facet=category q=coffee country=us
scripts/crawlora.sh /datasets/google-map-businesses/nearby lat=37.7768 lon=-122.3980 radius_m=1500 page=1 page_size=50
scripts/crawlora.sh -X POST /google/map/search '{"keyword":"coffee shop 700 Folsom St San Francisco","language":"en","country":"us"}'
scripts/crawlora.sh /yelp/search term='coffee shops' location='San Francisco, CA' limit=10
scripts/crawlora.sh /apple-maps/eta origin_latitude=37.7768 origin_longitude=-122.3980 destination_latitude=37.7894 destination_longitude=-122.4010 mode=walking
scripts/crawlora.sh /datasets/housing-markets/search q='San Francisco' region_type=city latest=true
scripts/crawlora.sh /datasets/numbeo-cities/search q='San Francisco' country='United States' page_size=5
scripts/crawlora.sh /bing/search q='San Francisco cafe zoning permit official' count=5 country=us lang=en-us