Site Selection for Development: GIS Analysis with OpenStreetMap

Site selection for development is a multi-million dollar decision. A mistake costs years and budgets. Traditionally, developers rely on intuition, site visits, and scattered data. GIS analysis based on OpenStreetMap enables digitalizing this process: from initial screening to final site comparison.

Why OpenStreetMap for Development

Commercial GIS data (HERE, TomTom, Google) costs tens of thousands of dollars for single-region coverage. OpenStreetMap:

• Is free — no licensing fees
• Is global — 180+ countries, unified data format
• Is detailed — buildings, roads, POI, green areas, transit network
• Is current — updated daily by the community (in developed countries)
• Is exportable — to any GIS format for analysis

Stage 1: Initial Territory Screening

The first stage filters out obviously unsuitable sites. Rejection criteria:

Transport Accessibility

A site without roads has no potential. We analyze:

• Distance to nearest highway (highway=trunk, primary). Over 3 km is a minus for residential development.
• Public transit availability. Metro station or bus stop within 1 km. Data: railway=station, highway=bus_stop.
• Road network density. Connectivity Score shows how easily the site can be reached.

PostGIS SQL query:

-- Nearest metro station
SELECT name, ST_Distance(way::geography, ST_SetSRID(ST_MakePoint(37.62, 55.75), 4326)::geography) AS dist_m
FROM planet_osm_point
WHERE railway = 'station' AND station = 'subway'
ORDER BY way <-> ST_SetSRID(ST_MakePoint(37.62, 55.75), 4326)
LIMIT 5;

Engineering Infrastructure

OpenStreetMap contains utility network data (partially):

• power=line — power lines
• man_made=pipeline — pipelines
• man_made=water_tower — water towers

Proximity to existing networks reduces connection costs.

Environmental Constraints

A site in a water protection zone or near industrial facilities is a risk. We check:

• natural=wetland — wetlands
• landuse=industrial — nearby industrial zones
• natural=water — water bodies (protection zones)
• leisure=nature_reserve — nature reserves

Stage 2: Scoring Analysis

After initial screening, 5-10 candidates remain. For each, we calculate scoring metrics.

Metrics for Residential Development

Metrics for Commercial Real Estate

Calculation Example

Three sites in Moscow:

Khamovniki scores best but has the highest land cost. The final decision accounts for price too.

Stage 3: Detailed GIS Analysis

For finalists, we conduct deep analysis.

Walkability Map (Isochrone)

We build a zone accessible on foot within 15 minutes from the site. Inside, we count:

• POI by category
• Housing stock (potential buyers)
• Competitors (existing residential complexes)

Competitive Environment Analysis

OSM query for developments under construction:

-- Buildings under construction within 2 km
SELECT name, building, "addr:street", "addr:housenumber",
       ST_Area(way::geography) AS area_m2
FROM planet_osm_polygon
WHERE building IN ('construction', 'proposed')
  AND ST_DWithin(way::geography, center::geography, 2000)
ORDER BY area_m2 DESC;

Building Density

Analyzing existing development around the site:

• Building height (building:levels)
• Building type (building=apartments, building=commercial)
• Built-up area percentage

Noise and Environmental Factors

• Distance to highways (noise)
• Distance to airport (noise, height restrictions)
• Proximity to industrial zones and landfills
• Wind rose relative to industrial objects

Stage 4: Visualization and Reporting

Data Export

For presenting analysis results:

1. PDF — site map with scoring metrics, legend, and scale bar. Suitable for executives and investors.
2. Shapefile / GeoJSON — data for further analysis in ArcGIS, QGIS. Layers: infrastructure, accessibility zones, competitors.
3. DXF / DWG — base map for architectural design.

On osm2cdr.ru, you can export a site map in any of these formats with the desired style and scale.

Report Structure

A typical site selection report:

1. Overview — regional map with marked candidates
2. Scoring comparison — table and radar charts
3. Detailed maps — each site with isochrones and POI
4. SWOT per site — strengths/weaknesses based on data
5. Recommendation — site ranking with justification

Practical Workflow

Step 1: Define Search Area

City or district. Export a general map for orientation.

Step 2: Initial Screening in QGIS

Load OSM data (GeoPackage), overlay layers: transport, education, healthcare. Visually filter out unsuitable zones.

Step 3: Scoring via API

# Composite score for a location
curl "https://osm2cdr.ru/api/v2/score/livability?lat=55.73&lon=37.58"

The response carries the overall score, a verbal grade, and a components breakdown — the contribution of walkability, transit, green space, safety, education, health, commerce, culture, and connectivity. Each of those is also available at its own address: /api/v2/score/walkability, /api/v2/score/transit, /api/v2/score/green and so on, with the same lat and lon parameters.

Step 4: Isochrone Analysis

Isochrones are computed by the geo-analysis tool endpoint — via POST, with the computation frame in bbox and tool parameters in params. The tool is free and needs no API key: up to 10 runs per day without signing in, then free registration.

# 5/10/15-minute walking accessibility zones
curl -X POST "https://osm2cdr.ru/api/geo-analysis/isochrone" \
  -H "Content-Type: application/json" \
  -d '{
    "bbox": [37.55, 55.70, 37.62, 55.76],
    "params": {"lon": 37.58, "lat": 55.73, "minutes": [5, 10, 15], "mode": "walk"}
  }'

Step 5: Export and Report

Export final maps to PDF for presentation and Shapefile for archival.

Method Limitations

• OSM doesn't contain land prices. Site costs come from external sources (government registries, real estate platforms).
• Cadastral data is limited. OSM doesn't replace a cadastral plan (permitted use, encumbrances, protection zones).
• Data is incomplete in some regions. For small towns, POI coverage may be insufficient.
• Scoring isn't final. A low current Score doesn't mean the area lacks potential. A new metro station can change Transit Score by 30+ points.

Case Studies

Residential Complex, Moscow

A developer compared 4 sites in New Moscow. GIS analysis showed that the site with the best Transit Score (closer to a metro station) had 40% more POI in the walking accessibility zone, though it cost 15% more. Forecast: higher sales velocity would offset the land price difference.

Shopping Center, St. Petersburg

Analysis of Connectivity Score and pedestrian traffic (road density with highway=pedestrian) helped select the site with maximum footfall. The competitive environment's Commerce Score showed low competition within 1 km.

GIS analysis with OpenStreetMap doesn't replace expertise — it enhances it. Data enables faster, more objective decisions with less risk. And the tools — from QGIS to APIs — make this analysis accessible to any developer.