13.3.5 — Public Health Systems — maturity: live

Access-to-Care Mapping

Using satellite imagery, population modelling and terrain analysis to map which communities can physically reach a health facility within an acceptable travel time.

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Governments cannot fix what they cannot see. In low- and middle-income countries, and in the rural periphery of wealthier ones, ministries of health routinely plan facility investment, staff deployment and mobile-clinic routes without reliable data on who lives where, what the road network actually looks like on the ground, or how seasonal flooding cuts communities off for months at a time. The result is chronic mismatch between where clinics exist and where they are needed, revealed only when disease burden data arrives years later.

Satellite-derived inputs close that gap in near real-time. Very-high-resolution optical imagery resolves settlement extents and informal housing density at sub-5m; SAR penetrates cloud cover to track road surface conditions and flood inundation through rainy seasons; and GNSS-calibrated elevation models generate accurate least-cost path travel-time surfaces across the full national territory. Fused with facility GPS coordinates and population grids, these layers produce an access-to-care index — the share of residents within 60 minutes of care by foot, motorbike or vehicle — that updates automatically as infrastructure changes.

The operational outcome is a living planning tool, not a static report. Health ministries can simulate the access gain from a proposed new clinic, optimise mobile-team schedules to maximise weekly coverage of underserved catchments, and generate evidence for capital budget submissions that external donors and finance ministries accept. During acute events — a flood, an epidemic corridor — the same platform re-routes community health worker deployments within hours rather than weeks.

What matters

Travel-time models built on outdated road surveys systematically undercount the un-served population; satellite-derived road extraction corrects this by detecting informal tracks and seasonal closures invisible to official maps.
A sovereign access-to-care index lets the ministry set the definition of acceptable travel time — 30, 60 or 90 minutes — rather than adopting a donor-defined threshold that may not reflect national terrain or policy.
Commercial access-mapping services aggregate national health facility data and population registers, creating a dependency where a foreign vendor holds the master dataset for a country's entire primary-care network.
Flood inundation events routinely sever rural access for 4–12 weeks per year; only a continuously refreshed satellite layer captures this seasonal deprivation in the official planning baseline.

Sovereignty score: 8/10 — A nation that cannot independently model its own population's physical access to healthcare cedes control of its facility investment strategy and health equity narrative to external actors.

Foreign commercial mapping vendors hosting national facility and population datasets are subject to their own governments' data-sharing agreements, subpoenas and export controls — none of which the health ministry can override.
Donor-funded access studies define the methodology, thresholds and geographic scope; a sovereign platform lets the ministry rerun analysis under its own assumptions when priorities shift or a new administration takes office.
During a disease outbreak or humanitarian emergency, access to near-real-time satellite tasking and analysis cannot be guaranteed through commercial SLA channels; a domestically operated pipeline has no contractual bottleneck at the moment it matters most.
Satellite-derived population and settlement data used in access models is increasingly treated as strategic infrastructure — control of the ground-truth layer is leverage in national statistical and development negotiations.

Reference architecture

Payload: Multispectral optical payload, 3–5m GSD, 20km swath for settlement and road extraction; secondary SAR payload, C-band, 5m stripmap for all-weather flood inundation and road-condition monitoring
Bus class: 16U cubesat or ESPA-class microsat, 80–120kg, 150W payload power; dual-payload configuration achievable on a single 120kg bus at 180W
Orbit: Sun-synchronous LEO at 500–550km; 12-satellite mixed optical/SAR walker constellation; 3–4 day full-country revisit for optical, 1–2 day for SAR under cloud cover
Ground segment: 2-station national network with X-band downlink and S-band TT&C; data processed at a sovereign cloud node co-located with the national mapping agency; SatNOGS-compatible UHF housekeeping backup
Software & data
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References

WHO – Primary Health Care and Access to Essential Services — WHO defines universal health coverage as requiring that all people have access to essential health services without financial hardship. Geographic inaccessibility is identified as a primary structural barrier in low-resource settings.
World Bank – Measuring Spatial Accessibility to Health Services — The World Bank's spatial health access methodology uses travel-time surfaces derived from satellite elevation, road and population data to identify underserved populations. It underlies health facility investment prioritisation in more than 40 countries.
ESA – Earth Observation for Sustainable Development Goals — ESA documents operational applications of Sentinel optical and SAR data for SDG monitoring, including road-network extraction, population settlement mapping and flood extent delineation relevant to healthcare access modelling.
USGS – Landsat Programme: Land Cover and Settlement Monitoring — Landsat's 30m multispectral archive provides the temporal baseline for tracking settlement expansion and land-cover change used in longitudinal access-to-care studies. Free data access underpins open-source health geography workflows worldwide.
UNICEF – Geographic Information Systems in Health Programme Planning — UNICEF's GIS for health programming guidance identifies satellite-derived catchment mapping as a core tool for immunisation equity analysis and facility placement, and notes that countries without internal GIS capacity are dependent on external analytical support.