Exposure

Exposure#

Exposure identifies what is present in the landscape and how much of it sits in areas where hazard is elevated. It is the second term in the pipeline’s IPCC-aligned framework, \(R = H \times E \times V\).

Definition

Exposure is “the situation of people, infrastructure, housing, production capacities and other tangible human assets located in hazard-prone areas” (UNDRR, 2019). Exposure alone does not imply risk – it becomes risk only once combined with hazard and vulnerability.

In this pipeline#

Exposure is represented as sector-specific layers on the same 0.25 degree analysis grid used by hazard and vulnerability, so all three terms can be multiplied cell-by-cell. Each sector is kept as its own absolute layer (people, hectares, head count, m^2, km, count) plus a 0-1 normalized layer used in risk composition – sectors are never blended into a single generic “exposure index.”

Exposure sectors and data sources#

Sector	Indicator(s)	Source	License
Population	Population count	WorldPop	CC-BY 4.0
Cropland	Cropland fraction (total, irrigated, rainfed split)	ESA WorldCover 10m v200 (2021) + FAO GMIA v5	CC-BY 4.0
Livestock	Cattle, sheep, goat head count	FAO Gridded Livestock of the World v4 (2020)	CC-BY 4.0
Built-up surface	Built-up surface area	JRC GHS-BUILT-S R2023A	CC-BY 4.0
Roads and buildings	Road length, building count	OpenStreetMap (via Geofabrik)	ODbL
Health facilities	Health facility count	Global Healthsites Mapping Project (via HDX)	ODbL

Full citations, acquisition modules, and output file names are in data_provenance.md.

Formula#

Exposure sectors are not weighted-combined the way hazard and vulnerability are – each sector stays its own layer (see “In this pipeline” above). What each sector does go through is robust normalization, so every sector lands on the same 0-1 scale before it multiplies into risk:

\[ E_{norm} = \mathrm{clip}\!\left(\frac{x - p_{5}}{p_{95} - p_{5}},\ 0,\ 1\right) \]

where \(x\) is the sector’s absolute value at a cell and \(p_5\)/\(p_{95}\) are that sector’s 5th/95th percentile across all valid cells (not a global min-max, so a handful of extreme cells – e.g. Addis Ababa’s population count – don’t compress every other cell toward 0). This is the exact robust_percentile_normalize function (src/hydroclim_risk/layers.py) also used by the vulnerability module.

Data used#

See the sector table above and data_provenance.md for exact source files and acquisition modules.

Result#

See “Layer statistics” and “Representative figure” below, or the full per-sector maps in the Exposure GeoTIFF inspection notebook.

Layer statistics (analysis grid, absolute units)#

Computed directly from outputs/exposure_vulnerability/*.tif – the same files the notebooks inspect.

Layer	Valid cells	Min	Mean	Max	Total
Population (people)	1,481 / 2,880	448	70,513	3,299,466	104,430,083
Cropland fraction (0-1)	2,880 / 2,880	0.00	0.14	1.00	–
Cattle head count	2,611 / 2,880	0	33,577	489,389	87,668,763
Built-up surface (m^2)	2,880 / 2,880	0	1,078,737	225,454,809	3,106,762,115
Road length (km)	2,880 / 2,880	0.0	129.3	4,045.3	372,412
Building count	2,880 / 2,880	0	887	81,078	2,554,920
Health facility count	2,880 / 2,880	0	0.27	129	777

Valid-cell counts differ by sector because each source has its own native coverage before resampling (e.g. WorldPop has no population in uninhabited cells); this is expected, not missing data.

Exposure workflow#

Acquire raw sector datasets at native resolution.
Harmonize and resample to the 0.25 degree analysis grid (sum for extensive quantities like population and built-up area, mean for fractions).
Preserve absolute layers and generate normalized (0-1) layers.
Feed exposure layers into risk composition with hazard and vulnerability.

Representative figure#

Exposure population example Population exposure layer, normalized 0-1, Ethiopia-clipped.

Inspect the full layers#

Two notebooks carry the complete, machine-readable detail behind this page. Both are also listed in the left sidebar under this Exposure page.

Exposure GeoTIFF inspection notebook

inspect_exposure_geotiffs.html – absolute and normalized exposure rasters on the analysis grid: metadata, units, source and processing notes, summary statistics, and Ethiopia-clipped map previews.

Raw source inspection notebook

inspect_exposure_vulnerability_raw_sources.html – native-resolution source layers from data/exposure_vulnerability_raw, source-to-config mapping, and raw format/extent checks prior to resampling.