Most Indian ULBs lack the spatial data infrastructure needed for evidence-based planning. This essay examines what a minimal viable spatial data stack looks like for a mid-size Indian city and how to build it.
Evidence-based urban planning requires spatial data — property boundaries, land use, infrastructure networks, demographic distributions, hazard zones. In most Indian cities, this data either does not exist in digital form, exists in silos across different departments, or is available but inaccessible due to institutional barriers.
The Data Gap
India's urban data landscape is fragmented. The Census produces ward-level demographic data decennially. The Survey of India produces topographic maps at controlled scales. State government revenue departments hold property records (Record of Rights, Pahani) that are digitized only partially. Municipal corporations have property tax databases that rarely interoperate with physical survey data.
The result is that most Indian ULBs make planning decisions — zoning, infrastructure investment, development permissions — without access to integrated spatial datasets.
A Minimal Viable Spatial Stack
For a mid-size Indian city (population 200,000–1,000,000), a minimal viable spatial data infrastructure includes:
Core layers: - Municipal boundary and ward boundaries - Road network with centerlines and attributes - Property polygons with linkage to tax database - Land use (existing and proposed/master plan) - Infrastructure networks (water, sewer, power lines)
Analysis-ready data: - Census data linked to ward polygons - Land parcel data with development status - Building footprints (from satellite imagery or survey)
Platforms: - QGIS for analysis (free, open-source) - PostGIS/PostgreSQL for spatial database - GeoServer for web map serving - OpenLayers or Leaflet for web visualization
Building the Stack
The National Urban Digital Mission (NUDM) and Smart Cities Mission have both recognized this gap and are funding GIS capacity in Indian ULBs. However, technology procurement alone is not sufficient. Sustainable GIS capacity requires:
- 1. In-house GIS staff with planning domain knowledge
- 2. Data governance protocols for data collection and updation
- 3. Interoperability standards for data exchange between departments
- 4. Open data policies to share non-sensitive spatial data with researchers and the public
Several cities — Pune, Surat, Ahmedabad — offer models of sustained municipal GIS capacity worth studying.