geoskill-insurance-risk-mapping

保险风险制图 | Insurance Risk Mapping

Multi-hazard Expected Annual Loss (EAL) mapping for property / catastrophe insurance, providing a spatial basis for underwriting pricing, loss reserving, and risk diversification.

Core model EAL = Σ_h P_h · Asset · V_h(I_h): P_h = 1/recurrence interval is the annual exceedance probability of hazard h, Asset is the per-pixel asset value, and V_h is the vulnerability curve (linear or Sigmoid) mapping hazard intensity to a loss ratio ∈ [0, 1]. Flood, wind, and seismic losses are computed separately and stacked to yield per-pixel annual expected loss and risk class.

Dependencies / 依赖

pip install 'numpy' 'rasterio' 'scipy'

Usage / 使用方法

Basic usage

python geoskill-insurance-risk-mapping.py --bbox 116.0 39.0 117.0 40.0 [other arguments]

Example 1 (Synthetic scenario, offline)

python geoskill-insurance-risk-mapping.py --bbox 116 39 117 40 --synthetic --output-dir ./out

Example 2 (Real data (Asset/Flood/Wind/Seismic))

python geoskill-insurance-risk-mapping.py --input data.tif --output-dir ./out

Example 3 (Switch to Sigmoid vulnerability curve)

python geoskill-insurance-risk-mapping.py --input data.tif --curve sigmoid --output-dir ./out

Example 4 (Custom risk classes)

python geoskill-insurance-risk-mapping.py --input data.tif --class-breaks 5 50 500 --output-dir ./out

Output / 输出

Data Source / 数据源 / Source

Multi-band GeoTIFF with band order Asset / Flood / Wind / Seismic intensity. Or use --synthetic to generate physically consistent simulated data (fully offline).

Privacy / 隐私声明 / Privacy

• Runs offline by default; --synthetic mode is fully offline with no network access.
• All processing is performed locally; user data is never uploaded.

License / License

MIT

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name: geoskill-insurance-risk-mapping description: 'Multi-hazard probability times asset value times vulnerability curves to compute expected loss for insurance risk mapping'

保险风险制图 | Insurance Risk Mapping

面向财产/巨灾保险的多灾种年期望损失 (Expected Annual Loss, EAL) 制图，为承保定价、准备金与风险分散提供空间依据。

核心模型 EAL = Σ_h P_h · Asset · V_h(I_h)：P_h = 1/重现期为第 h 种灾害年超越概率，Asset 为像元资产价值，V_h 为把灾害强度映射为损失比 ∈[0,1] 的脆弱性曲线（线性或 Sigmoid）。对洪水、风灾、地震分别计算后叠加，得到逐像元年期望损失与风险等级。

依赖

pip install 'numpy' 'rasterio' 'scipy'

使用方法

基本用法

python geoskill-insurance-risk-mapping.py --bbox 116.0 39.0 117.0 40.0 [其他参数]

示例 1（合成场景，离线）

python geoskill-insurance-risk-mapping.py --bbox 116 39 117 40 --synthetic --output-dir ./out

示例 2（真实数据（Asset/Flood/Wind/Seismic））

python geoskill-insurance-risk-mapping.py --input data.tif --output-dir ./out

示例 3（改用 Sigmoid 脆弱性曲线）

python geoskill-insurance-risk-mapping.py --input data.tif --curve sigmoid --output-dir ./out

示例 4（自定义风险分档）

python geoskill-insurance-risk-mapping.py --input data.tif --class-breaks 5 50 500 --output-dir ./out

输出

数据源 / Source

多波段 GeoTIFF，波段顺序 Asset / Flood / Wind / Seismic 强度。 或使用 --synthetic 生成物理一致的模拟数据（完全离线）。

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，不上传用户数据。

License

MIT