geoskill-disaster-exposure-assessment

危险区与资产人口空间叠加的暴露量统计

灾害暴露度评估 | Disaster Exposure Assessment

Overlays hazard zones with assets/population to quantify exposure: at the raster level, per-pixel exposure equals the sum of values within hazard zones (exact), aggregated by hazard level; at the vector level, geopandas.sjoin spatially joins asset points with hazard-zone polygons to sum the value of points falling inside hazard zones. Exposure never decreases when hazard zones expand (superset).

Dependencies / 依赖

bash

pip install numpy rasterio scipy geopandas shapely

Usage / 使用方法

Basic usage (synthetic data, offline)

bash

python geoskill-disaster-exposure-assessment.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out

More examples

bash

python geoskill-disaster-exposure-assessment.py --bbox 116 39 117 40 --synthetic --output-dir ./out
python geoskill-disaster-exposure-assessment.py --input region.tif --threshold 1.0 --output-dir ./out
python geoskill-disaster-exposure-assessment.py --bbox 116 39 117 40 --breaks 0.3 1.0 2.0 --synthetic --output-dir ./out
python geoskill-disaster-exposure-assessment.py --bbox 121 31 122 32 --synthetic --quiet --output-dir ./out

Output / 输出

File	Format	Description
exposed_mask.tif	GeoTIFF	Exposed-area mask (any hazard level)
hazard_zone.geojson	GeoJSON	Vectorized hazard-zone boundaries
exposure_stats.json	JSON	Exposed value/population/zone statistics/point exposure

Each run also produces output-manifest.json (run manifest, including input/output/QA summary).

Data Source / 数据源 / Source

Real mode reads a multi-band GeoTIFF (band1=hazard intensity, band2=asset value, band3=population); synthetic mode generates a scenario offline (including asset points).

Privacy / 隐私声明 / Privacy

• Runs offline by default; --synthetic mode requires no network at all.
• All processing is done locally; no user data is uploaded.

License / License

MIT

---

---

name: geoskill-disaster-exposure-assessment description: '危险区与资产人口空间叠加的暴露量统计'

灾害暴露度评估 | Disaster Exposure Assessment

将灾害危险区与资产/人口做空间叠加统计暴露量：栅格逐像元暴露量 = 危险区内价值之和（精确），并按危险等级分区汇总；矢量层面用 geopandas.sjoin 把资产点位与危险区多边形做空间连接，统计落入危险区的点位价值。危险区扩大（超集）时暴露量不减。

依赖

bash

pip install numpy rasterio scipy geopandas shapely

使用方法

基本用法（合成数据，离线）

bash

python geoskill-disaster-exposure-assessment.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out

更多示例

bash

python geoskill-disaster-exposure-assessment.py --bbox 116 39 117 40 --synthetic --output-dir ./out
python geoskill-disaster-exposure-assessment.py --input region.tif --threshold 1.0 --output-dir ./out
python geoskill-disaster-exposure-assessment.py --bbox 116 39 117 40 --breaks 0.3 1.0 2.0 --synthetic --output-dir ./out
python geoskill-disaster-exposure-assessment.py --bbox 121 31 122 32 --synthetic --quiet --output-dir ./out

输出

文件	格式	说明
exposed_mask.tif	GeoTIFF	暴露区掩膜（任一危险等级）
hazard_zone.geojson	GeoJSON	矢量化危险区边界
exposure_stats.json	JSON	暴露价值/人口/分区统计/点位暴露

每次运行还会产出 output-manifest.json（运行清单，含输入/产物/QA 摘要）。

数据源 / Source

真实模式读取多波段 GeoTIFF（band1=危险强度、band2=资产价值、band3=人口）；合成模式离线生成场景（含资产点位）。

隐私声明 / Privacy

• 默认离线运行，--synthetic 模式完全无网络。
• 所有处理在本地完成，不上传任何用户数据。

License

MIT