Comments (2)
- The zonal step seem to introduce an overestimation error ONLY for total Pop; when tested on DOM against original code and QGIS zonal, the error is between 1% and 50%. The raster creation is checked to be correct according to exported byproducts.
with mp.Pool(cores) as p:
# Get total exposure for each ADM region
func = partial(zonal_stats_partial, raster=exp_ras, stats="sum")
stats_parallel = p.map(func, np.array_split(adm_data.geometry, cores))
exp_per_ADM = list(it.chain(*stats_parallel))
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Thanks Arthur for refining the code.
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Related Issues (20)
- Extend to more return periods HOT 1
- CCDR development plan HOT 2
- New heat stress data HOT 5
- Update Landslide data HOT 1
- Framework for Return Period calculation
- Update air quality hazard and vulnerability
- Tropical cyclones (strong wind): regionalized impact function HOT 1
- Drought hazard: better indicator and projections
- Exposure datasets: migrate to other sources HOT 1
- Align data analytics pipeline and risk dashboard HOT 2
- Curves for flood damage on crops HOT 4
- Ranking of risk: combining indices, comparability in time and space
- TEMP - Global hazard data
- Disaster data collection and elaboration
- Impact function used in classification?
- Auto-collection of Population data layers HOT 1
- Heat stress aggregation HOT 1
- Coastal flood - wrong CSV output HOT 7
- data(.tif) files in notebook/EXP HOT 1
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