Score neighbourhoods at scale

The problem

Demographic data tells you who lives in a neighbourhood. It doesn\'t tell you whether the neighbourhood is commercially alive, trending up, saturated, or quietly dying. Foot-traffic data tools (Placer.ai, Safegraph) give you that but cost $30k+/year and lock you into their dashboard.

Google Maps is a free, public source of "what\'s actually happening at street level": which businesses opened recently, which categories are dense, which areas are getting the reviews. Aggregating that into a per-zip or per-block score is just a SQL query away once you have the raw data — which is what Livescraper provides.

How it works in Livescraper

1
Pull all businesses across the target city
Set the region (city or county) and leave categories broad ("restaurant", "retail store", "salon", etc.) or include a target category. For a mid-size city, expect 30k–80k listings.
2
Bucket by geography
Each row has lat/lng plus a postal_code. Bucket by zip / postcode / your custom polygon set. SQL group-bys are enough — no GIS expertise needed.
3
Define your score formula
Common signals: business density per km², category mix (Herfindahl index of category diversity), average rating, review-velocity in last 90 days, % of listings claimed by owner (engagement proxy).
4
Validate against ground truth
Pick 5–10 zips you know well. Eyeball your score against your intuition. Tune the weights — usually density and review-velocity dominate; rating averages converge across zips.
5
Refresh quarterly
Neighbourhoods drift slowly. Quarterly pulls catch openings, closures and rating drift without overspending. Most teams keep the previous quarter as a baseline and look at deltas.

Worked example

A QSR chain evaluating Austin for a 12-location expansion pulled all restaurant + coffee shop + café listings across the Austin metro — 38,400 rows in under an hour. They scored each zip on (review_velocity × 0.4) + (claimed_pct × 0.3) + (avg_rating × 0.2) + (1 / category_HHI × 0.1). The top 8 zips by score included two they hadn\'t been considering — both had real estate availability the broker hadn\'t surfaced. Total Livescraper spend: ~$77. Versus the $14k Placer.ai quote, that\'s 180× cheaper.

What you get back

One row per business — the raw rows from Google Maps Data Scraper. You aggregate to zip / block / polygon yourself with a SQL group-by. Fields available:

✓Location keys: latitude, longitude, postal_code, city, borough, state, country, plus_code
✓Volume signals: average_rating, total_reviews, reviews_per_score_1..5, photos_count
✓Category mix: type, sub_types, category — feeds a Herfindahl-style diversity index per zip
✓Engagement signals: is_verified, owner_title, owner_link, working_hours
✓Lifecycle: business_status — flags permanently closed / temporarily closed
✓Price level: price_range ($–$$$$ where Google has it) — proxy for the neighbourhood income mix

"Review velocity" and "score" columns shown in the table below are your computed aggregates from these raw rows — Livescraper gives you the input data; the scoring formula is yours to design.

postal_code	business_count	avg(rating)	sum(total_reviews)	pct(is_verified)	your_score
78704	482	4.5	142,310	78%	0.89
78745	321	4.3	64,840	62%	0.71
78758	274	4.2	41,210	54%	0.58

Sample rows · not a real query result · your data will be richer

Best for / Not for

Best for

Retail / QSR / hospitality chains underwriting new locations
Real estate investors comparing commercial blocks
Urbanists / municipal planners studying neighbourhood economic health
Market-entry teams ranking cities or zips for expansion

Not for

Pure-residential demographics (use Census data instead)
Foot-traffic / dwell-time analysis — Maps is presence-data, not visit-data
Sub-block (street-level) precision — postcodes are the smallest practical bucket

FAQ

How does this compare to Placer.ai / Safegraph?

Placer.ai and Safegraph give you foot-traffic data (people-counts moving through locations) sourced from mobile-device tracking. Livescraper gives you business-presence data (what businesses ARE there + how active they are). Different question. We're much cheaper and complementary; you'd use both if you needed both views.

Can I get business open/close dates?

Not directly — Google doesn't expose "opened on date X" reliably. But we surface review-history start, which is a strong proxy: a business with no reviews older than 6 months is almost certainly newly opened.

How do I integrate with my mapping / BI tool?

Output is just CSV/JSON with lat/lng columns. Drag into Mapbox, Kepler.gl, Tableau, Looker, or PostGIS. Most teams join on postal_code into their existing data warehouse.

How big a region can I pull at once?

A mid-size US city: 30k–80k rows in 30–60 min. A whole US state: hundreds of thousands of rows over a few hours. For a country-wide pull, we recommend splitting by state/region for control over cost.