We combine spatial data, analytics and AI to help companies understand places, enrich their own workflows, and quantify the economic potential of candidate locations.
What we do
Use Space Intel to strengthen your own analytical stack, or to underwrite candidate locations with transparent ranges for revenue, profitability and ROI.
Spatial intelligence for your own stack
Structured spatial datasets, enrichment and derived features that feed directly into your existing models, analytics and AI workflows.
POIs · addresses · demographics · mobility · accessibility · catchments · competition
Flagship offering
Candidate locations → spatial and business data → AI-assisted forecasting → revenue, profitability and ROI → investment decision.
Quantify uncertainty with confidence ranges and downside, base and upside scenarios.
Hyper-local trade-area modeling, candidate-site scoring, and network-level coverage analysis tied directly to unit economics — every recommendation underwritten by real demand geography and defensible contribution margin per location, with sensitivities your finance team can stand behind.
Site-level sales, footfall, and demand forecasts that fuse mobility, demographics, competition, and your own performance data — delivered with confidence intervals you can plan against.
Place-based segmentation that connects who your customers are with where they are — so every campaign lands closer to revenue.
Optimize warehouses, dark stores, and last-mile nodes against real demand geography — including emerging drone and aerial delivery layers planned around airspace, demand density, and operational constraints.
Monitor competitor footprints, openings, and catchment overlap in near real time to defend share and identify under-served white space.
Location analytics, spatial data infrastructure, and planning support for municipalities and NGOs — from pedestrian-priority street studies to district-wide GIS systems.
How it works
Start with the level you need. We can supply enriched spatial inputs, analyse markets, or quantify the economic potential and uncertainty of a candidate location.
Use structured spatial data, enrichment and derived features in your existing models, analytics and AI workflows.
Turn place-based signals into market, catchment, competition and network intelligence your teams can act on.
Estimate revenue, profitability and ROI across downside, base and upside scenarios before committing capital.
Illustrative forecast · not a customer result
A decision range, not a promise: expected economics are shown with the drivers and uncertainties that can move the result.
€1.05M
Expected revenue
€230K
Expected EBITDA
23%
Expected ROI
€780K–€1.28M
Revenue range
78%
Probability ROI >15%
Key drivers
Catchment demand, accessibility, nearby competition and operating assumptions.
Major uncertainties
Ramp-up speed, local conversion, competitor response and input-data coverage.
Scenarios
Case studies
Four place-based projects showing how spatial data becomes intelligence, a measurable outcome and a clearer decision. Every figure below comes from the existing project evidence.
ClickClickPlay is a Berlin managed digital-signage operator — it installs street-facing and in-venue screens in Spätis, cafés, gyms and retail chains, then runs them end to end: hardware, content, remote monitoring, one monthly fee. Because placement quality directly drives their unit economics, growth depended on one question their field team could never answer before knocking on a door: is this venue actually worth a screen? We built the model that answers it — and it scales to any city and any venue category.
Business problem
Placement was guesswork. Venues that looked right on a walk-through often underperformed once installed, and there was no way to rank corridors or vet a location before the sales visit — so every pitch was a gamble on foot traffic, install feasibility and owner appetite.
Approach
A two-stage engine. First, score the Kiez: feeder-street density, business momentum and rent trajectory rank every neighbourhood corridor. Then drill to venue level — footfall, installability and revenue gap per address. Where open data had no usable address, we reconstructed one from the road network and neighbouring businesses, lifting coverage from 70% to ~99%.
Spatial intelligence
12,000+ businesses across Berlin and Brandenburg classified into 13 sectors and 89 categories, with every street scored for advertising value and rankings anchored on measured pedestrian counts rather than assumptions. The Prenzlauer Berg pilot put the Schönhauser Allee corridor first at 77.8/100 — feeder-street density 100/100, ~19,000 pedestrians a day.
Built to scale
The engine is city-agnostic by design: it runs on open street, business and footfall data, so a new market is a data load rather than a new project. The same 13-sector / 89-category taxonomy re-scores any venue type — cafés, gyms, retail franchises, hotels, medical practices — and the two-stage logic (rank the corridor, then the address) holds whether the target is 20 screens in one district or a national rollout. Address reconstruction is what makes it portable: coverage stays near-complete even where open data is patchy.
A working workflow for the sales team: pick an area, narrow to a category, export a ranked venue list — know it before knocking. Best fit is multi-location operators, where the value compounds with every additional site.
Usable address coverage (%)
+25%
Coffee sales uplift (3 weeks)
12,000+
Businesses analysed
ClickClickPlay treats screens as infrastructure. The screen network is the product; the scoring engine is what tells it where to grow next.
Project visuals
Vego is a vegan and vegetarian food-delivery startup operating an electric courier fleet on the Asian side of Istanbul. To scale sustainably, they needed to know exactly which neighborhoods to enter next — and in what order — to unlock the most profitable growth.
Business problem
A growing operation with no spatial visibility into where loyal versus churning customers clustered, and no data-driven way to choose new service areas for its restaurant network.
Approach
We standardized internal order data alongside open demographic and competitor datasets, then ran spatial RFM analyses on order frequency, basket size and total spend to segment customers by location.
Spatial intelligence
Two parallel growth scenarios — one weighted toward residents, one toward daytime workers — were correlated to surface priority expansion zones and forecast service-area profitability for restaurants in the network.
A prioritized expansion roadmap, restaurant-level service areas and a supply-chain plan — now powering Vego's next investment round and growth strategy.
Order volume uplift in priority zones
25–35%
Avg. delivery distance (km)
−26%
Cost per order reduction
Project visuals
As one of Istanbul's most historically significant and spatially complex districts, Eyüpsultan requires an integrated, data-driven urban management approach capable of supporting large-scale planning, infrastructure and governance decisions. To strengthen the municipality's decision-making capacity, a comprehensive GIS database was developed by integrating datasets produced by municipal departments, public institutions and open-data sources into a single spatial platform.
Business problem
Urban datasets related to planning, infrastructure, demographics, transportation and land use existed across disconnected systems and in multiple formats, limiting the municipality's ability to compare, analyze and manage information collectively. The absence of an integrated spatial data infrastructure made it difficult to produce coordinated, evidence-based planning and service decisions at the neighborhood and district scales.
Approach
Datasets were collected from municipal departments, public institutions and open-data sources through institutional coordination and official data requests. Raster, CAD, textual and GIS datasets were standardized, georeferenced and spatialized within a unified coordinate system.
Spatial intelligence
The resulting 50 GB geodatabase was organized under 15 main thematic categories — administrative boundaries, socio-economic structure, election data, land use, transportation, infrastructure, geological structure, natural assets, conservation areas, zoning plans and urban transformation — with 400+ subcategories, layers and thematic datasets enabling integrated visualization, querying, classification and spatial analysis.
A municipality-wide spatial decision-support system serving Eyüpsultan — enabling evidence-based planning at parcel, building and network scale.
50 GB
Unified data
400+
Thematic category layers
Data cycle (days)
Project visuals
A spatial decision-support model was developed to identify which streets within Istanbul's Historic Peninsula should be pedestrianized or redesigned as pedestrian-priority corridors. The study integrated multiple spatial variables — slope, cultural heritage density, commercial activity, public transportation accessibility, pedestrian mobility and user perception — into a unified analytical framework.
Business problem
Pedestrianization decisions within the Historic Peninsula were often evaluated through fragmented approaches focused primarily on physical street conditions. The district's complex urban structure, tourism intensity, historical layers and pedestrian dynamics required an integrated methodology capable of identifying streets with the highest pedestrian-oriented transformation potential.
Approach
Multiple spatial datasets were produced, standardized and integrated into a road-network database. Variables such as street slope, cultural inventory density, non-residential land-use intensity, public transportation mobility and survey-based mobility perception were weighted to generate a pedestrianization / walkability score for each street segment.
Spatial intelligence
Using spatial interpolation, network analysis and multi-criteria evaluation (AHP), all datasets were analyzed through street centerline geometries — enabling a comparative assessment of pedestrian-oriented transformation potential across the Historic Peninsula.
A data-driven pedestrianization prioritization model for the Historic Peninsula — a spatial decision-support framework for public-space design, mobility planning and sustainable urban transportation strategies.
Walkable public space expansion
22–23%
Tax revenue from corridor commercial units (indexed)
1,000+
Street segments scored
Project visuals
Founders
Space Intel is led by two urban planners who have spent their careers turning location data into operating decisions — across global quick commerce and the planning of one of the world's most complex cities.
Co-founder · Group Senior Manager, Footprint Strategy at Delivery Hero
Connect on LinkedInPreviously · Delivery Hero · Getir · KEYM · ITU · YTU
Berlin-based urban planner with 10 years of deep GIS expertise and global location-strategy leadership across retail, quick commerce and urban development. Studied City and Regional Planning at Yıldız Technical University; MSc from Istanbul Technical University in spatial analytics, location strategy and real-estate development. Recognition & languages: Winner of the 2024 Yukselish (Ascension) National Competition in strategic project management out of 16,000 participants. Speaks English, German, Turkish, Azerbaijani and Russian.
Previously · Historic Peninsula Administration · İlke Planning · Mese Urban Lab · YTU
Istanbul-born urbanist (1992) with a BSc in City and Regional Planning and an MSc in Urban Design from Yıldız Technical University. More than a decade of experience across urban planning, urban design and spatial strategy.
Contact us
Ask for a location forecast, spatial dataset or market analysis. Our Berlin or Istanbul team will respond within one business day.
