We extract workspace locations, membership tiers, meeting room capacities, and real-time availability from Spaces. Delivered as clean JSON, CSV, or Parquet.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Locations objects from spaces.com. All fields typed and schema-versioned.
"location_id": "sp-lon-042", "name": "Spaces Oxford Street", "city": "London", "latitude": 51.5154, "longitude": -0.141, "timezone": "Europe/London", "status": "OPEN", "phone": "+44 20 3808 4200"
| # | location_id | name | country | city | address | latitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Memberships objects from spaces.com. All fields typed and schema-versioned.
"location_id": "sp-lon-042", "tier_name": "Coworking Membership", "price_monthly": 289.0, "currency": "GBP", "access_days": 30, "guest_passes": 2, "print_credits": 120, "setup_fee": 0.0
| # | location_id | tier_name | price_monthly | currency | access_days | home_club_access |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Meeting Rooms objects from spaces.com. All fields typed and schema-versioned.
"room_id": "mr-84921", "location_id": "sp-lon-042", "name": "The Boardroom", "capacity": 12, "price_per_hour": 65.0, "currency": "GBP", "screen_included": true, "whiteboard_included": true, "instant_book": true
| # | room_id | location_id | name | capacity | price_per_hour | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Dedicated Desks objects from spaces.com. All fields typed and schema-versioned.
"desk_id": "dd-304", "location_id": "sp-lon-042", "floor_level": 3, "price_monthly": 450.0, "currency": "GBP", "locker_included": true, "access_24_7": true, "availability_status": "AVAILABLE"
| # | desk_id | location_id | floor_level | price_monthly | currency | locker_included |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Virtual Offices objects from spaces.com. All fields typed and schema-versioned.
"location_id": "sp-lon-042", "package_name": "Virtual Office Plus", "price_monthly": 149.0, "currency": "GBP", "mail_handling": true, "business_address": true, "phone_answering": true, "meeting_room_credits": 5
| # | location_id | package_name | price_monthly | currency | mail_handling | business_address |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Spaces scraper captures the full hierarchy of the platform: global locations, dynamic pricing tiers, meeting room availability, and local amenities.
Extract coordinates, addresses, and contact details for every Spaces venue worldwide.
Track dynamic pricing for Coworking, Dedicated Desk, and Private Office tiers across regions.
Capture capacities, hourly rates, and AV equipment availability for individual meeting spaces.
Extract tier structures for mail forwarding, business address registration, and call answering.
Parse structured lists of location-specific features like barista coffee, parking, and showers.
Capture staff presence hours versus 24/7 member access availability per location.
Monitor which locations have immediate desk or private office availability versus waitlists.
Standardise pricing data across 40+ local currencies into a unified reporting schema.
Bypass location-gating to extract accurate pricing for regions outside the crawler IP.
Brief in. Clean data out.
Provide target regions, cities, or specific workspace types. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for spaces.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or Snowflake stage on agreed cadence.
Extracting from Spaces requires handling dynamic map interfaces and token-based booking flows. Here is our approach.
Spaces uses dynamic map clusters to load locations. We intercept the XHR endpoints to extract the raw JSON payloads directly, avoiding brittle DOM scraping.
Pricing changes based on the user IP. We route requests through region-specific residential proxies to capture accurate local rates and currencies.
Meeting room availability requires holding a session token. Our Playwright scripts maintain cookie state to query the availability calendar API.
Spaces frequently updates its frontend components. We rely on the underlying GraphQL and REST endpoints to ensure data stability.
Aggressive API scraping triggers IP bans. We distribute requests across a rotating pool of 12,000+ IPs with randomised delays.
Flex-space operators monitor Spaces pricing and promotional offers to optimise their own desk rates.
Analysts track location density and expansion patterns to identify emerging commercial hubs.
Enterprise teams aggregate coworking inventory to negotiate bulk passes for remote workforces.
Workspace marketplaces sync availability and pricing to provide unified booking interfaces.
Urban planners and real estate developers use venue density metrics to assess neighborhood commercial viability.
Revenue managers ingest competitor rates to train algorithmic pricing models for flexible office space.
"Spaces.com holds the blueprint to global flexible work trends, but extracting their dynamic, location-gated pricing requires dedicated infrastructure."
Scraping coworking platforms involves navigating map-based API endpoints, session-dependent booking calendars, and aggressive rate limiting. DataFlirt builds and maintains the extraction layer, delivering normalised inventory and pricing data directly to your warehouse. You focus on market analysis, we handle the infrastructure.
Everything supported by our spaces.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.
Bypassing the DOM to extract structured data directly from backend XHR responses.
Routing requests through city-level IPs to capture localised pricing and availability.
Running scheduled extraction jobs on AWS infrastructure with Airflow dependency management.
Data delivered to where your team already works — no new tooling required.
About spaces.com scraping, legality, and pipeline operations.
Ask us directly →We extract only public location, pricing, and availability data. We do not bypass authentication walls or extract user data.
We intercept the backend API calls that populate the map interface, extracting the raw coordinate and location metadata.
Yes. We use region-specific residential proxies to load the site exactly as a local user would, capturing native currency rates.
We can configure hourly or daily pipelines to query the booking calendar endpoints for specific locations.
Yes. As Spaces is part of the IWG network, we can extend the pipeline to cover Regus, HQ, and Signature locations.
We provide normalised, relational datasets linking locations, membership tiers, and meeting rooms via unique identifiers.
20-minute scoping call. Pilot dataset within the week. Production within two. Need a global extract of coworking inventory or continuous monitoring of flex-space pricing? We build and operate the pipeline. Tell us your requirements.
