spatial-epidemiology
CommunityDetect disease clusters and map risk
Education & Research#moran's i#smr#spatial epidemiology#disease mapping#lisa clustering#space-time clustering#gis analytics
Authorxjtulyc
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Spatial epidemiology turns messy geography and outbreak timing into evidence by quantifying whether disease rates cluster, where hotspots are located, and how risk varies across areas over space and time.
Core Features & Use Cases
- Global spatial autocorrelation (Moran's I): measures whether disease rates are spatially clustered versus random.
- Local hotspot detection (LISA): identifies HH/LL clusters and HL/LH spatial outliers to produce interpretable cluster maps.
- Standardized disease burden (SMR): computes indirect standardized mortality/morbidity ratios for fair comparisons across populations.
- Space-time interaction (Knox test): evaluates whether cases cluster jointly in space and time rather than independently.
- Disease mapping: generates choropleth maps for communicating results to public-health stakeholders.
Quick Start
Use the spatial-epidemiology skill to compute Moran's I and generate a LISA cluster map from your disease rate GeoDataFrame.
Dependency Matrix
Required Modules
pysallibpysalgeopandasnumpymatplotlibscipy
Components
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: spatial-epidemiology Download link: https://github.com/xjtulyc/awesome-rosetta-skills/archive/main.zip#spatial-epidemiology Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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