Spatial Analysis & GIS

Geospatial workflows for mapping health and environmental indicators onto Rwanda’s administrative geography. This section covers the Python toolchain (GeoPandas + Matplotlib), the country’s boundary hierarchy, a worked climate-mapping pipeline, and a reusable chart library for health-data presentation.

The source notebooks and their input layers live in the repository under analysis/spatial-analysis/, so every map here can be reproduced end to end.

All datasets used in this section are aggregate — sector- and district-level climate values and illustrative placeholder chart data. No patient-level or facility-level operational data is included.

Administrative hierarchy

Rwanda’s geography nests through five levels. Analyses join indicator data to the level that matches the data’s granularity — climate data is mapped at the sector level (admin3).

Level	Unit	Shapefile
`admin1`	Province	`rwa_adm1_2006_NISR_WGS1984`
`admin2`	District	`rwa_adm2_2006_NISR_WGS1984`
`admin3`	Sector	`rwa_adm3_2006_NISR_WGS1984`
`admin4`	Cell	`cell_boundary`
`admin5`	Village	`Village_boundary`

All layers use the WGS84 geographic coordinate system (NISR, 2006 boundaries).

Explore

Getting StartedSet up the Python geospatial environment and load your first boundary shapefile.Climate MapsThe end-to-end pipeline: clean ERA5 climate data, join to sectors, and render binned choropleths.Visualization GuideA reusable, styled chart library — bar, column, thermometer, scatter, and double-scale — for health reporting.Data ReferenceCatalogue of boundary and environmental layers, their sources, CRS, and repository paths.