Please check the GitHub link for full reproducible code. https://github.com/nipunkalraa/Spatial-Data-Science-Analysing-Brexit-Trends

🗺️ Project: Analysing Brexit Trends | Python

🎯 Aims

This project explores the spatial patterns in the Brexit referendum results across the UK, correlating them with demographic, economic, and cultural factors using spatial data science techniques.

🛠️ Packages Used & Why

• pandas – Data processing & analysis

• matplotlib, seaborn – Data visualization

• geopandas, geoplot, contextily – Spatial data handling & mapping

• scikit-learn – Statistical analysis

📊 Key Findings

• Higher "Leave" votes correlated with lower education levels, fewer professionals, and higher home ownership.

• Strong "Remain" votes were observed in diverse, urban, and younger populations.

• Spatial patterns showed regional disparities, with London and Scotland leaning towards Remain, while rural regions favored Leave.

🌍 Final Visuals & Insights

• Bar plots highlighted voting trends by region.

• Choropleth maps visualized the linguistic impact of the Brexit voting.

• Spatial data integration revealed how geography influenced the results.

🏆 Summary

This project successfully bridged data science and geography to uncover voting patterns in Brexit. It highlights the power of spatial analytics in understanding real-world political trends.