Geographic Information System (GIS) enables healthcare planners to identify underserved populations, optimize facility siting, and close critical gaps in patient access across India’s complex healthcare network. By combining spatial analysis with health data, GIS transforms how policymakers and providers address accessibility challenges in rural, tribal, and mountainous regions where distances and terrain create real barriers to care.
Why Healthcare Accessibility Matters
India operates one of the world’s largest public health networks, yet coverage remains deeply uneven. As of March 2023, the country maintained 1,69,615 Sub-Centres (SCs), 31,882 Primary Health Centres (PHCs), and 6,359 Community Health Centres (CHCs). By late 2025, this base had grown further, with nearly 1.8 lakh Ayushman Arogya Mandirs upgraded under Ayushman Bharat from existing SCs and PHCs. Despite this scale, large rural belts, tribal areas, and mountainous regions still face accessibility gaps that leave millions without timely access to essential care.
This is the core challenge GIS addresses: instead of relying on assumptions about where healthcare is needed, GIS uses real spatial data to reveal the distance, terrain, and population distribution patterns that shape actual patient access.
What Is Spatial Accessibility in Healthcare?
Spatial accessibility is the geographic availability of healthcare services and the ease with which a population can reach them. It accounts for distance, travel time, terrain, transportation networks, and facility capacity. GIS transforms this concept from abstract policy language into actionable maps and numbers.
The most rigorous approach uses the two-step floating catchment area (2SFCA) method. It balances supply (available beds, doctors, services) and demand (population size, disease burden, age structure) to produce a single accessibility index. This differs fundamentally from simple distance analysis, since it accounts for whether a facility is overwhelmed or underutilized and how patients actually flow between competing options.
The supply-ratio step calculates healthcare supply relative to population within a service radius. The demand-allocation step then identifies which populations can access each facility, weighted by that supply-to-demand ratio. Unlike simpler methods, 2SFCA captures real competition: a PHC in a dense urban area has much less catchment capacity than one serving sparse rural villages.
Understanding India’s Healthcare Hierarchy
India’s public health system operates in tiers, each with specific coverage norms and roles. Sub-Centres (SCs) serve 5,000 people in plains (3,000 in hilly and tribal areas) and provide maternal and child health services, immunizations, and disease surveillance through Accredited Social Health Activist (ASHA) workers. Primary Health Centres (PHCs) cover 30,000 people (20,000 in hilly areas) and handle maternal deliveries, emergency obstetric care, and case management. Community Health Centres (CHCs) cover 1,20,000 people and offer inpatient care, surgical services, and blood bank facilities.
Beyond this hierarchy, Ayushman Bharat’s focus on strengthening peripheral points through Ayushman Arogya Mandirs has created a new spatial layer. The challenge isn’t just that norms exist on paper. It’s that implementation varies wildly. A PHC may sit 2 km from one village but 25 km from another across hilly terrain, and GIS makes these gaps visible.
Methods for Mapping Healthcare Access
Mapping patient access involves several overlapping spatial analyses, each answering a different planning question.
Isochrone or Travel-Time Analysis plots the area reachable from a facility within a specific time threshold. Indian geospatial health-access studies commonly use 30- and 60-minute bands for walking access, and 30-, 60-, 90-, and 120-minute bands for motorized access, depending on facility tier. Unlike straight-line distance, travel-time analysis follows actual road networks and accounts for terrain, speed limits, and seasonal blockages, which matters most in the Northeast and Himalayan regions. Planners who overlay isochrones with population density immediately spot underserved pockets.
Gravity or Allocation Models account for facility capacity and competition. A 50-bed CHC absorbs more patient volume than a 20-bed facility, and its geographic pull weakens with distance. Gravity modelling shows how realistic catchments differ from administrative boundaries and reveals which populations must travel to access adequate capacity.
Accessibility Indices and Suitability Modelling combine travel time, facility capacity, and population density into a single score per location, letting planners rank areas by priority for intervention. Suitability modelling for facility siting overlays land availability, proximity to road networks, power and water access, and population demand within a service radius, so planners can test scenarios such as how much accessibility would improve if a new CHC were sited elsewhere.
ArcGIS Network Analyst and ArcGIS Pro provide the computational backbone for these analyses, while Indo ArcGIS Living Atlas offers base datasets, including road networks, terrain, and population grids, that feed into the models.
Key Use Cases for Healthcare Providers
AB-HWC Siting Optimization
Ayushman Bharat-Health and Wellness Centres (AB-HWCs), now rebranded as Ayushman Arogya Mandirs, aim to bring primary care to doorsteps, yet siting decisions often reflect administrative convenience rather than geographic need. GIS enables data-driven siting by overlaying population data, terrain, road networks, and ASHA worker coverage to identify underserved blocks. This workflow is particularly critical in tribal Schedule V areas and the Northeast.
Aspirational Districts and Health Convergence
NITI Aayog’s 112 Aspirational Districts include health metrics in their ranking frameworks. District administrators use GIS dashboards to identify ward-level access gaps, enabling targeted deployment of resources. This ward-level visibility is absent from national reporting but essential for local action.
Hill, Tribal, and Northeast Accessibility
Terrain-aware accessibility analysis is non-negotiable in Jammu & Kashmir, Himachal Pradesh, Uttarakhand, the Northeast, Andaman & Nicobar Islands, and Bastar. Straight-line distance is misleading here: villages that appear close on a map can sit well over an hour away by boat, foot, or mountain road once terrain and seasonal conditions are factored in. Walking-time and road-time analysis using terrain and seasonal accessibility data transforms how health planners approach these regions.
Data Requirements and Workflow
For meaningful GIS analysis, planners need baseline spatial datasets: health facility coordinates and attributes (bed count, services offered, staffing), population distribution at granular geographies, road networks with travel speeds and seasonal access, terrain and elevation data, and administrative boundaries at district and block levels. The Health Facility Registry under the Ayushman Bharat Digital Mission (ABDM) is improving standardization, but data completeness remains uneven across states.
The workflow typically progresses from data assembly through spatial analysis to visualization and stakeholder communication. ArcGIS Online and ArcGIS Hub enable health departments to publish findings as interactive dashboards and webmaps that non-technical stakeholders can use for decision-making.
Explore how GIS for Health helps departments turn facility and population data into dashboards their teams can act on.
Connecting to National Policy Frameworks
Government health agencies at national, state, and district levels use GIS tools to operationalize Ayushman Bharat. They geo-code and visualize the Health Facility Registry (HFR) and Healthcare Professionals Registry (HPR) under the ABDM, turning compliance databases into interactive spatial planning tools. They can also apply GIS to optimize eSanjeevani telemedicine spoke siting: the platform had already facilitated over 43 crore teleconsultations nationwide as of late 2025, and GIS can help identify which facilities would generate the highest clinical impact based on specialist scarcity, internet availability, and disease prevalence.
They also geo-code and visualize the Health Facility Registry (HFR) and Healthcare Professionals Registry (HPR) under the ABDM, transforming compliance databases into actionable planning fabrics.
Private Healthcare Expansion in Tier-2 and Tier-3 Cities
Private hospital networks increasingly look to Tier-2 and Tier-3 cities for expansion, and GIS-based spatial analysis is central to that strategy. Hospital groups can inform siting decisions for diagnostic chains, cath labs, oncology centers, and dialysis units by overlaying disease burden maps, insurance and PM-JAY empanelment density, and existing facility presence. This capability lets them identify where genuine service gaps exist, rather than expanding based on assumption or convenience, and lets them evaluate a location’s viability before committing capital.
This matters because Tier-2 and Tier-3 markets don’t behave like metros. Population density is lower, district-level road quality shapes travel patterns more than city transit does, and a single new facility can shift accessibility for an entire cluster of towns. Spatial suitability scoring, which layers land cost, footfall catchments, and competitor presence, gives hospital groups a repeatable way to compare candidate cities before committing capital, rather than relying on anecdotal market knowledge from a single region.
Challenges and the Road Ahead
Data Quality and Completeness
GIS is only as good as its underlying data. Hospital coordinates, bed counts, and service offerings remain incomplete or outdated in many public health databases. The Health Facility Registry under ABDM is improving this, but gaps persist, particularly for private facilities, AYUSH providers, and rural diagnostic centers.
Integrating Real-World Constraints
Models assume rational behaviour and uniform travel speeds, yet reality is messier. Patients often bypass nearer facilities for perceived quality or trust, and monsoon rains make Northeast road networks impassable for weeks. Walking times also vary with age, health status, and terrain familiarity. Capturing these realities in spatial analysis requires qualitative research alongside GIS.
Skill Gaps in Health Administration
GIS remains a specialized discipline. Most health department planners lack training in spatial analysis, network modelling, and scenario testing. Building capacity within state and district health offices, not just central agencies, is essential for mainstreaming GIS in healthcare planning.
Balancing Local and National Frameworks
National norms, one SC per 5,000 people, one CHC per 1,20,000, often don’t fit local geographies. GIS enables evidence-based departure from norms, but this requires clear protocols for when and how planners can override standards based on terrain, demographic shifts, or disease burden.
Bridging Public and Private Data Silos
Private hospitals, diagnostic chains, and AYUSH providers rarely feed facility data into the ABDM Health Facility Registry, leaving planners with an incomplete picture of total capacity in a region. This gap matters most in Tier-2 and Tier-3 cities, where private providers are expanding fastest and public dashboards undercount real accessibility. Data-sharing agreements between public health departments and private networks are essential for GIS models to reflect ground conditions rather than partial, government-only data.
GIS is fundamentally a tool for transparency. By making invisible access gaps visible, it gives health planners, policymakers, and citizens evidence to demand equitable investment. India’s healthcare challenge isn’t a lack of ambition. It’s a challenge of precision: getting the right resources to the right places at the right time.
As Ayushman Bharat scales and private sector investment accelerates in underserved regions, GIS will become not optional but foundational to health planning. The agencies that master spatial analysis today will be the ones best positioned to close India’s healthcare access gaps tomorrow.
FAQs
1.What is spatial accessibility in healthcare?
Spatial accessibility is the geographic availability of healthcare services and the ease with which populations can reach them. GIS measures this by combining travel time, facility capacity, and population distribution to reveal who can access what care and where service gaps exist.
2.How does GIS help reduce healthcare service gaps?
GIS identifies underserved populations through isochrone analysis (travel-time mapping), gravity modelling (facility capacity), and accessibility indices. These tools show planners where to site new facilities, redeploy resources, or deploy telemedicine to maximize coverage efficiently.
3.What is Ayushman Bharat and how does GIS support it?
Ayushman Bharat is India’s flagship health scheme combining PM-JAY (insurance), Ayushman Arogya Mandirs (primary care), ABDM (digital health), and eSanjeevani (telemedicine). GIS supports it by optimizing facility siting, mapping PM-JAY facility access, geo-coding the Health Facility Registry, and identifying high-impact locations for telemedicine deployment.
4.What datasets do health departments need for GIS analysis?
Essential datasets include health facility locations and attributes (beds, services, staffing), population distribution at block or village level, road networks with travel speeds, terrain and elevation data, and administrative boundaries. The ABDM Health Facility Registry is improving data standardization.
5.How can GIS improve emergency ambulance response?
GIS supports India’s “golden hour” emergency framework, the doctrine behind the 108 and 102 ambulance networks, which aims to get patients to the nearest hospital within the first hour. Dynamic fleet-positioning models built on road networks and population density help identify where ambulance coverage gaps are widest, especially in rural and hilly areas.
Written by
Esri India Marketing
