> ## Documentation Index > Fetch the complete documentation index at: https://docs.mapxagent.com/llms.txt > Use this file to discover all available pages before exploring further. # Public Facility Siting · Dar es Salaam > Diagnose school and clinic coverage in Dar es Salaam, measure the unserved demand, then compare candidate sites and recommend where to build with a maximum-coverage model. Public Facility Siting answers the last mile of public-service planning: **given what already exists, where should the next schools and clinics go?** The scenario runs the full arc — coverage diagnosis, demand gap, candidate screening and siting comparison — and reports how many currently unserved residents each recommended site would bring within reach. The Dar es Salaam run as delivered: ward shortfall rankings, demand/supply quadrants and ranked MCLP sites for schools and clinics ## What the scenario ships Seven layers cover the Dar es Salaam region. | Layer | Data | Role in the study | | - | - | - | | Education facilities | OSM education POIs (2,223) | Raw supply for the school analysis | | Health facilities | Healthsites.io (1,463) | Raw supply for the clinic analysis | | Road network | OSM roads (102,464 lines) | Walking/driving network for service areas and site snapping | | Watercourses | OSM waterways, 11,160 lines | 30 m riparian setback applied to candidates | | Ward boundaries | OCHA COD-AB ADM3 (90 wards, 1,667 km²) | Shortfall ranking, candidate scope and balance checks | | Building footprints | Overture (481,681 polygons) | Built-up intensity screen for candidate cells | | Population | WorldPop, 100 m | Demand and coverage weights | *All layers come from the [built-in catalog](/en/concepts/builtin-datasets) with source and attribution attached, and they are not limited to Dar es Salaam: the catalog provides the same datasets for other cities at the same specification — switch the study area, add the same entries, and this chapter's method chain transfers in minutes.* ## How the study works | Stage | Question | What MapX runs | Output | | - | - | - | - | | 1. Coverage diagnosis | Who is outside the standard today? | Select features (class screen), Overlay clip, Service area (800 m schools / 1000 m clinics), Overlay erase, Zonal statistics, Top N | Ward shortfall layer and a top-10 ranking per class | | 2. Demand gap | Where is demand high and supply low? | Grid at 500 m, Zonal statistics, Quadrant (median split), 1 km demand grid | Pressure quadrants (diagnostic) and the unserved-population demand layer | | 3. Siting comparison | Which new sites serve the most people? | Grid inside the worst wards, Overlay erase (30 m corridor), Zonal statistics/summary, Centroid points, Location-covering (MCLP), Location-allocation (k-median) | Recommended sites, the efficiency comparison, marginal coverage curve and residual gaps | In short, the study first audits the existing schools and clinics against their service standards to locate shortfall wards and unserved population; it then screens candidate grid cells inside the worst wards for setback and build-out conditions; finally a maximum-coverage model picks the recommended sites that serve the most unserved residents, with a second model as an efficiency reference, delivering recommended sites, a marginal coverage curve and a report. ## Run it The screenshots below come from this scenario's reference workspace. Each step names the platform surface it uses, so you can follow the same path in your own project. **Suggested question:** *"Run a public-facility siting study for Dar es Salaam: diagnose the coverage and demand gaps of existing schools and clinics, then compare candidate sites for new facilities and recommend where to build; deliver layers, charts and a report."* ### 1. Open the scenario Open **Scenarios** in the top menu, select this card and choose **Open scenario**. The whole bundle imports at once: every layer keeps its default style, legend, source and attribution, and the camera flies to the study area. Use the **Layers** panel to switch layers on and off before you type anything. The Dar es Salaam scenario opened in a fresh project: the layer bundle imported, the Layers panel expanded, and the chat panel confirming the import with the suggested question ### 2. Ask the suggested question and review the plan Send the **suggested question** in the box above — or edit it first. [Plan mode](/en/concepts/plan-mode) records the supply-set definitions, the radius per class, the demand basis, the number of sites and the report language before the run starts. The Dar es Salaam plan document with the Plans panel open on the left; plan sections and recorded decisions readable ### 3. Approve the plan and run the analysis The chain screens the supply set by facility class, builds the status-quo **service areas** (800 m schools, 1000 m clinics), erases them from the wards to get the shortfall, prepares the unserved demand on a grid, screens the candidate cells and then runs the two siting models: maximum coverage (MCLP) as the recommendation and k-median as the efficiency comparison. The **Analysis** panel keeps every run's operator, inputs, parameters and result layer, and the workflow graph in the centre visualises the whole chain. The analysis view for Dar es Salaam: the Analysis panel lists every operator run on the left, with the workflow graph in the centre ### 4. Style the result on the map Recommended sites are ranked circles in the class hue, the shortfall rankings and demand/supply quadrants stay as hidden reference layers, and the legend states which model each marker belongs to. The style editor on a deliverable layer: classed-colour breaks and label settings, with the layer editing panel highlighted in red ### 5. Turn the result into charts and a report The marginal coverage curve answers whether the next site still earns its place, and the before/after bars show what the recommended sites add. Both are **[charts](/en/concepts/charts)**; the **[report](/en/concepts/reports)** carries the before/after pair and every disclosure about capacity, crowding and fairness. A chart from this scenario in the chart editor, with the editing area highlighted in red The scenario report opened in the report canvas, with an executive summary, figures and embedded maps ## Example output The screenshots above come from one example run with the default settings on the shipped data. They show the shape of the deliverables, not what your run will return. A finished run hands back a named, reusable set of deliverables instead of a single map: * **Layers** — the recommended sites for each facility class, the comparison sites, their service areas, the residual gaps, the shortfall rankings and the demand/supply quadrants. * **Tables** — the ward balance table, the selected-site list and the feasibility counts for the candidate filters. * **Charts** — the marginal coverage curve and the before/after coverage comparison. * **Report** — the siting study with the supply-set definitions, the before/after pair and the capacity, crowding and fairness disclosures. The layer names follow the run language, but the deliverable set is stable enough to plan a handover around. ## Related * [Scenarios](/en/concepts/scenarios) — the full catalog and how scenarios work * [15-minute Neighbourhood Coverage](/en/concepts/scenarios/15min-life-circle-coverage) — the coverage audit that motivates siting * [Urban Growth Monitor](/en/concepts/scenarios/urban-growth-monitor) — where growth is heading, for forward-looking siting * [Analysis](/en/concepts/analysis) — network and overlay tools, scale guards and run semantics * [Reports](/en/concepts/reports) — assembling the siting package This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.