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
| File | Format | Description |
|---|---|---|
case_density.tif |
GeoTIFF | Case kernel density (method=kde/all) |
scan_result.json |
JSON | Most likely cluster (LLR/RR/center, method=scan/all) |
accessibility.tif |
GeoTIFF | Distance to nearest facility |
health_report.json |
JSON | Environmental correlation and accessibility statistics |
output-manifest.json |
JSON | Run manifest |
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;
--syntheticmode requires no network at all. - All processing is performed locally; user data is never uploaded.
License / License
MIT
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
| 文件 | 格式 | 说明 |
|---|---|---|
case_density.tif |
GeoTIFF | 病例核密度（method=kde/all） |
scan_result.json |
JSON | 最可能聚集（LLR/RR/中心，method=scan/all） |
accessibility.tif |
GeoTIFF | 到最近设施距离 |
health_report.json |
JSON | 环境相关性与可达性统计 |
output-manifest.json |
JSON | 运行清单 |
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