Mapping Health and GP Access | Python

🗺️ Project: Spatial Analysis of Health Conditions & Substance Abuse in Stockport, UK | Python

GitHub link for Code and Dataset: https://github.com/nipunkalraa/Spatial-Data-Science-Analysis-of-Health-Metrics-and-Healthcare-Accessibility-in-Stockport-UK

🎯 Aims

Analyze spatial patterns of chronic health conditions and substance abuse.
Identify areas with high healthcare needs.
Use geospatial methods to uncover spatial dependencies and inequalities.

🛠️ Python Packages Used

pandas & numpy 📊 – Data processing and numerical operations.
matplotlib & seaborn 📈 – Data visualization and statistical plotting.
geopandas 🌍 – Geospatial data handling.
folium 🗺️ – Interactive map creation.
pysal & libpysal 🏠 – Spatial econometrics and clustering.
esda 🔍 – Exploratory spatial data analysis (spatial autocorrelation).
esda (Getis-Ord 𝐺𝑖∗)🔥 – Used to detect hotspots of high and low values in spatial data.

📌 Key Findings

Certain regions showed significant clustering of poor health and substance abuse.
Getis-Ord 𝐺𝑖∗ analysis revealed hotspots where health issues were concentrated.
The study highlighted inequalities in healthcare access, particularly in areas with low GP registration rates.

📢 Summary

This project combined spatial data analysis with health statistics to better understand how location influences healthcare needs. By leveraging Python’s geospatial tools, we mapped high-risk areas and provided insights that could inform public health policies and interventions.