Analytical methods for territorial planning policy

Infrastructure planning in Gabon rests on data that is scattered, heterogeneous and unevenly reliable. We present GIA, a geospatial decision-support platform that brings open layers (population, buildings, networks, environment, services, works in progress, event feeds) onto a common base, and derives from it reproducible indicators for three kinds of decision: diagnosis, prioritisation and vigilance. We formalise its analytical modules (dasymetric allocation of the 2026 census population, accessibility over the road network, facility siting by maximal covering, network fragility through the bridges of the road graph, section load through betweenness centrality, and alert rules) then apply them to access to healthcare.

The inventory used is the OpenStreetMap volunteered survey, 253 facilities. Its coverage is uneven, and we state that before any result: 218 of its 253 points lie in one province out of nine, and one whole province carries none. On that basis, 90.7% of the population lives within one hour of a centre and 327,232 people lie beyond; five well-placed centres would cover 46.1% of them, and 45.7% of the population depends on a single road link.

The first location the model designates falls in the province the inventory leaves empty. We publish it while naming it, because a declared artefact remains usable and a silent one does not. A geographic indicator measures first the map that carries it, and a decision-support platform must make that measurement visible before the one it is asked for.

Keywordsspatial decision support · geographic information system · infrastructure · healthcare accessibility · maximal covering · positional uncertainty · data provenance · open data · Gabon

Population. Provincial census counts are allocated to departments and then to a 0.025° grid in proportion to buildings.

Accessibility. One multi-source Dijkstra gives the network time of every node; the inhabitant walks to the node that minimises total time.

Siting. The gain of a candidate cell is the uncovered population it would bring within the hour; several centres are placed greedily, reaching at least 1 − 1/e of the optimum.

Load. Betweenness centrality counts the shortest paths crossing each road section.

Read with253 facilities mapped in OpenStreetMap, 218 of them in Estuaire and none in Nyanga. The figures measure what the map carries, not access to care on the ground.

Maps, tables and limits in the case study →

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