E4DRR/ea-exposure

East Africa Exposure Grid (Overture Maps, 0.05°)

Gridded exposure dataset for the East Africa IBF work, derived from Overture Maps (OSM-derived) at 0.05° over S −15, N 25, W 20, E 53. Built by the pipeline in icpac-igad/ea-ibf-climada under exposure/pipeline/.

Complete East Africa region — all 38 land tiles. 372,000 land cells (660×800 grid), 137,673 urban cells, 58,780 ocean (seabar) cells, and ~188 million Overture building footprints aggregated. Ocean-facing area is excluded by the 5×5° land-tile mask and the per-cell ocean flag.

Reproduce

Analyse directly (no download):

import pandas as pd
df = pd.read_csv("hf://datasets/E4DRR/ea-exposure/outputs/ea_exposure_grid_0p05_scored.csv")

Regenerate raw values from Overture (raw parquet is not stored here — re-fetched live from Overture S3, no key). Pipeline: icpac-igad/ea-ibf-climada exposure/pipeline/:

python download_overture.py --tile 36        # raw download (buildings, roads, places, land, water)
python aggregate_to_grid.py --tile 36 --no-concat
python aggregate_places.py  --tile 36        # 23 pl_<class> counts
# all 38 tiles: run_pipeline.py → aggregate_places.py → aggregate_to_grid.py --merge-only → compute_exposure.py

Contents

Path	What
outputs/ea_exposure_grid_0p05.csv	merged per-cell grid (raw layer aggregates)
outputs/ea_exposure_grid_0p05_scored.csv	same + exposure composite score
outputs/ea_exposure_0p05.tif	exposure score as a 0.05° EPSG:4326 COG (660×800; ocean = nodata)
grid_csv/{sno}.csv	per-tile aggregates (one file per 5×5° tile)
buildings_1km/ea_exposure_buildings_0p01.parquet	1 km building-vulnerability grid (2.53 M populated cells; footprint-size distribution incl. median + small-building fraction)
buildings_1km/09_median_footprint_1km.png	median footprint map (small = informal/dense)
buildings_1km/10_small_building_frac_1km.png	<40 m² fraction map (slum signal)

Per-cell schema

ix, iy, lon, lat, tile_sno, bld_count, bld_area_m2, road_km, road_km_{primary,secondary,tertiary,other}, place_count, urban, seabar, landcover_class (+ exposure in the scored CSV).

Buildings: bld_count = number of footprints, bld_area_m2 = total footprint area (UTM). Places: place_count = all POIs; plus 23 class-count columns pl_<class> — pl_atm, pl_bakery, pl_bank, pl_bar, pl_bus_station, pl_cafe, pl_church, pl_cloth_store, pl_convenience_store, pl_department_store, pl_funeralhome, pl_gas_station, pl_hospital, pl_lodging, pl_mosque, pl_movie_theater, pl_parking, pl_temple, pl_restaurant, pl_shopping_mall, pl_super_market, pl_taxi_stand, pl_trainstation — folded from Overture's 880+ category taxonomy (the rest stay in place_count only).

Cell centre follows lon = WEST + ix*0.05 + 0.05/2, lat = SOUTH + iy*0.05 + 0.05/2; urban = ≥20 buildings; seabar = 1 for ocean cells. Layers: buildings, roads (Overture segment), places (POIs), land cover, water (ocean mask).

Provenance

Source: Overture Maps (buildings, transportation, places, base land/water). Exposure score = 0.50·norm(bld_area) + 0.20·norm(bld_count) + 0.20·norm(road_km)

• 0.10·norm(place_count), p99-capped; ocean cells nodata.