geoskill-public-health-spatial

公共卫生空间分析 | Public Health Spatial Analysis

A spatial analysis toolbox for public health, covering disease cluster detection, environmental association and healthcare accessibility assessment.

Kernel density estimation (KDE) maps case density with a 2D Gaussian kernel to identify hot spots; Kulldorff spatial scan statistics use a circular moving window to maximize the log-likelihood ratio (LLR) and detect the most likely clusters of significantly elevated incidence; Pearson correlation quantifies the association between incidence and environmental factors; the distance to the nearest healthcare facility (Euclidean distance transform) assesses service accessibility.

Dependencies / 依赖

pip install 'numpy' 'rasterio' 'scipy' 'geopandas' 'shapely'

Usage / 使用方法

Basic usage

python geoskill-public-health-spatial.py --bbox 116.0 39.0 117.0 40.0 [other options]

Example 1 (all methods on a synthetic scene, offline)

python geoskill-public-health-spatial.py --bbox 116 39 117 40 --synthetic --output-dir ./out

Example 2 (real population/environment rasters)

python geoskill-public-health-spatial.py --input data.tif --output-dir ./out

Example 3 (KDE only)

python geoskill-public-health-spatial.py --input data.tif --method kde --bandwidth 4 --output-dir ./out

Example 4 (spatial scan only)

python geoskill-public-health-spatial.py --input data.tif --method scan --output-dir ./out

Output / 输出

Data Source / 数据源 / Source

Multi-band GeoTIFF with band order population / environmental factors. Alternatively, use --synthetic to generate physically consistent simulated data (fully offline).

Privacy / 隐私声明 / Privacy

• Runs offline by default; --synthetic mode requires no network at all.
• All processing is performed locally; user data is never uploaded.

License / License

MIT

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name: geoskill-public-health-spatial description: 'Spatial scan statistics, kernel density, environment association and accessibility for public health spatial analysis'

公共卫生空间分析 | Public Health Spatial Analysis

面向公共卫生的空间分析工具集，覆盖疾病聚集探测、环境关联与医疗可达性评估。

核密度估计 (KDE) 用 2D 高斯核对病例点做密度制图识别热点；Kulldorff 空间扫描统计用圆形移动窗口最大化对数似然比 (LLR) 探测发病率显著升高的最可能聚集区；Pearson 相关量化发病率与环境因子的关联；到最近医疗设施的距离（欧氏距离变换）评估服务可达性。

依赖

pip install 'numpy' 'rasterio' 'scipy' 'geopandas' 'shapely'

使用方法

基本用法

python geoskill-public-health-spatial.py --bbox 116.0 39.0 117.0 40.0 [其他参数]

示例 1（合成场景全部方法，离线）

python geoskill-public-health-spatial.py --bbox 116 39 117 40 --synthetic --output-dir ./out

示例 2（真实人口/环境栅格）

python geoskill-public-health-spatial.py --input data.tif --output-dir ./out

示例 3（只做 KDE）

python geoskill-public-health-spatial.py --input data.tif --method kde --bandwidth 4 --output-dir ./out

示例 4（只做空间扫描）

python geoskill-public-health-spatial.py --input data.tif --method scan --output-dir ./out

输出

数据源 / Source

多波段 GeoTIFF，波段顺序 人口 / 环境因子。 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