Map a category across a region

The problem

Market research firms sell you a "competitor density report" for $2,000 — six weeks of work and the data is already four months old when it lands. Internal teams trying to do the same with manual Google Maps searches max out around 200 listings before the work gets sloppy. Neither path gives you a defensible number for a board deck.

Livescraper turns "how many independent cafés operate in Lisbon" or "every car wash in the Greater Toronto Area" into a structured CSV in under an hour, with the rating and review-count data you need to filter real businesses from ghosts.

How it works in Livescraper

1
Define the region precisely
Pick a single city, multiple cities, a state/province, or a country. Livescraper handles the geocoding — you don't need lat/lng boxes. For dense urban areas, use neighbourhoods to keep result sets focused.
2
Set categories — be specific
"Restaurant" is too broad; "Italian restaurant" + "pizzeria" + "trattoria" returns cleaner data. You can include multiple categories in one task. Use Custom Categories to match exact Google category strings if you need ISO precision.
3
Add filters that matter for analysis
Min rating, min review count, has-website, has-phone. These trim out closed and ghost listings — typically 10–20% of raw Maps results that you don't want in a market analysis.
4
Run and open in your tool of choice
XLSX opens directly in Excel pivot tables. CSV is best for Python/R analysis. JSON gives you the nested category arrays and hour data without parsing.
5
Optional: layer reviews or emails on top
Same task, two checkboxes. Add Review enrichment for sentiment analysis at a regional level, or Email enrichment if you want to message the operators for a partnership or acquisition pipeline.

Worked example

A coffee chain considering a Manchester expansion ran café + coffee shop + tea house across Greater Manchester. 1,612 listings returned in 18 minutes, with full ratings, review counts, hours, price level, and "claimed by owner" flags. They filtered to rating ≥ 4.3 and ≥ 100 reviews (487 rows passed), then bucketed by postcode to find the lowest-density high-rated neighbourhoods. Total cost: ~$3.20. The exercise took one afternoon vs the 4-week external research quote they'd been considering.

What you get back

One row per business. From Google Maps Data Scraper:

✓Location: business_name, full_address, street, borough, city, state, postal_code, country, latitude, longitude, plus_code, timezone
✓Categorisation: type, sub_types, category, area_service
✓Ratings & reviews: average_rating, total_reviews, reviews_per_score_1..5, price_range
✓Operating data: working_hours, popular_time, business_status
✓Online presence: business_website, business_phone, is_verified, owner_title, logo_url, photos_count
✓Internal IDs: place_id, google_id, place_cid, google_place_url

business_name	postal_code	category	average_rating	total_reviews	price_range
Caffè Brera	M3 4LX	café	4.7	1243	$$
Northern Tea Power	M1 4DZ	tea house	4.8	892	$
Pollen Bakery	M4 6JG	café	4.6	2104	$$

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

Best for / Not for

Best for

Strategy / corp dev teams sizing a new market
Franchise expansion territory analysis
Investment due diligence (real estate, hospitality, services)
Academic research on local economies

Not for

Pulling categories with weak Google Maps coverage (some B2B services)
Historic / time-series analysis — Maps shows the current state only
Customer-level demographics — Maps is business-level data

FAQ

How exhaustive is the result? Will I get every business?

For categories that businesses actually use on Google Maps, coverage is essentially complete — typically 95%+ of what a manual search would find. Sparse industries (some B2B, some industrial) have weaker Maps coverage by nature; you'll see that reflected in the count.

Can I do this for the whole country in one task?

Yes, but be deliberate. A "every restaurant in Italy" run might return 800,000+ rows. We recommend splitting by region for control over cost and faster iteration.

What if the same business is listed under multiple categories?

You'll see it once per category that matched your query. The place_id stays the same, so de-duping is one column join.

Does the data include hours and price levels?

Yes — opening hours per day of week, special holiday hours where Google has them, and the $–$$$$ price band when Google has classified it. About 60–70% of restaurants and 20–30% of services have a price level.