We extract venue details, user tips, tastes, ratings, and location metadata from Foursquare City Guide. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Venue Data objects from foursquare.com. All fields typed and schema-versioned.
"venue_id": "4b4606f2f964a520c11426e3", "name": "Blue Bottle Coffee", "primary_category": "Coffee Shop", "latitude": 37.7763, "longitude": -122.4233, "rating": 9.1, "price_tier": 2, "city": "San Francisco"
| # | venue_id | name | primary_category | sub_categories | latitude | longitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Tips and Reviews objects from foursquare.com. All fields typed and schema-versioned.
"tip_id": "5a2b3c4d5e6f7a8b9c0d1e2f", "venue_id": "4b4606f2f964a520c11426e3", "user_name": "Jane Doe", "text": "The New Orleans Iced Coffee is incredible.", "created_at": "2026-03-12T14:22:00Z", "upvotes": 42, "language": "en"
| # | tip_id | venue_id | user_id | user_name | text | created_at |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Attributes and Tastes objects from foursquare.com. All fields typed and schema-versioned.
"venue_id": "4b4606f2f964a520c11426e3", "tastes": "['cold brew', 'pastries', 'avocado toast']", "outdoor_seating": true, "credit_cards": true, "wifi": "Free", "wheelchair_accessible": true
| # | venue_id | tastes | outdoor_seating | credit_cards | parking | wifi |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Footfall Signals objects from foursquare.com. All fields typed and schema-versioned.
"venue_id": "4b4606f2f964a520c11426e3", "total_checkins": 125430, "total_users": 45120, "total_tips": 842, "total_visits": 310500, "trending_status": false, "related_venues": "['4c5d6e7f8g9h0i1j']"
| # | venue_id | total_checkins | total_users | total_tips | total_visits | popular_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Photos objects from foursquare.com. All fields typed and schema-versioned.
"photo_id": "6b7c8d9e0f1a2b3c4d5e6f7a", "venue_id": "4b4606f2f964a520c11426e3", "url": "https://fastly.4sqi.net/img/general/original/123456.jpg", "width": 1920, "height": 1080, "created_at": "2026-01-15T09:30:00Z"
| # | photo_id | venue_id | user_id | url | width | height |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Foursquare scraper handles the complexity of spatial grid pagination, category hierarchies, and rate limits, delivering clean POI data ready for spatial analysis.
Extract comprehensive venue data including coordinates, precise addresses, and verified status across any city or geographic bounding box.
Map venues to Foursquare's detailed category taxonomy, capturing primary and secondary classifications for accurate filtering.
Capture the proprietary Foursquare rating out of 10, alongside user tips, upvotes, and historical sentiment indicators.
Extract nuanced venue features like wifi availability, parking situations, outdoor seating, and user-generated taste tags.
Track historical check-in counts, total unique users, and visit metrics to estimate location popularity and foot traffic.
Parse complex operating hours, including split shifts, holiday exceptions, and popular times data.
Map spatial relationships by extracting 'People also liked' and related venue graphs for competitive analysis.
Extract high-resolution image URLs, dimensions, and upload timestamps for visual verification of POIs.
Run continuous pipelines to detect new venue openings, permanent closures, and rating fluctuations over time.
Brief in. Clean data out.
Provide target cities, coordinate bounding boxes, or category lists. We design the extraction schema together.
We configure spatial grid crawlers, proxy rotation, and session management for foursquare.com.
Schema validation, null-rate checks, and coordinate accuracy verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting global POI data requires complex spatial pagination and rate limit management. Here is how we maintain reliable pipelines.
Foursquare limits search results per geographic area. We use H3 spatial indexing to generate dynamic bounding boxes, ensuring comprehensive extraction without missing dense urban areas or wasting requests on empty regions.
Foursquare aggressively rate-limits high-volume requests. We distribute requests across a global pool of residential ISP proxies, maintaining strict concurrency limits and randomised delays to avoid IP bans.
Venue attributes and hours are often unstructured. Our pipeline parses string representations of operating hours into standard ISO formats and maps raw attribute tags into boolean fields.
For POI monitoring, we maintain a hash index of last-seen values. Subsequent runs only push diffs, highlighting new openings, closures, and significant rating changes without full re-dumps.
Every run emits structured logs. We alert on null-rate spikes in critical fields like coordinates, category drift, and coverage drops, responding before data quality degrades.
City planners and GIS analysts use POI density, category distribution, and footfall signals to model urban development.
Retailers analyse competitor locations, complementary businesses, and popular hours to identify optimal locations for new stores.
Aggregators enrich their own listings with Foursquare ratings, user tips, and taste attributes to improve recommendations.
Quantitative funds track check-in trends and store closures across retail chains to predict quarterly performance.
Marketing agencies track venue visibility, rating changes, and review velocity for client locations across different cities.
Last-mile delivery platforms verify exact coordinates, operating hours, and entrance details for restaurants and retailers.
"Foursquare maintains one of the most accurate independent POI databases globally, but extracting global venue data requires navigating complex geo-spatial pagination."
Most teams fail at location scraping because they rely on naive grid searches. We use spatial indexing, residential proxies, and dynamic coordinate bounding to extract Foursquare venue data without missing edge cases or triggering rate limits. DataFlirt handles the infrastructure so you can focus on spatial analysis.
Everything supported by our foursquare.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and spatial grid iteration. Playwright handles JavaScript rendering for complex venue pages and interactive maps.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request to bypass rate limits and geographic restrictions.
Pipelines run on AWS Lambda and ECS. Airflow handles spatial job chunking and dependency management. State stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About foursquare.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Foursquare is generally permissible. DataFlirt targets only public, non-authenticated venue data, ratings, and tips. We do not extract private user check-ins or violate GDPR. Clients should review Foursquare ToS and consult legal counsel for specific use cases.
We use H3 spatial indexing to generate overlapping coordinate bounding boxes for your target regions. This ensures we capture all venues without hitting pagination limits in dense urban areas.
Yes. We can filter extraction by Foursquare primary or secondary categories, such as restaurants, retail stores, or parks, reducing pipeline execution time and storage costs.
We configure pipeline cadence based on your requirements. Most clients opt for weekly or monthly refreshes to track new openings, closures, and rating changes across large geographic areas.
We extract the metadata and URLs for public photos uploaded to venues. We do not download or store the actual image files, but provide the direct links for your systems to process.
We use residential ISP proxies, strict concurrency controls, and randomised request timing. Our spatial crawlers distribute requests geographically to avoid triggering local rate limit thresholds.
Our minimum engagements typically start with a defined set of cities or specific category verticals across a country. Contact us with your target regions for a scoped quote.
Yes. We provide a sample run for a specific neighbourhood or small city bounding box during the scoping process, allowing you to validate coordinates, schema fit, and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off POI export for a specific city or a continuous monitoring feed across global categories, we scope, build, and operate the pipeline. Tell us what you need.
