Comprehensive quantitative analysis of London's urban systems developed during London Data Week, focusing on crime rates, housing demographics, and environmental indicators across 32 boroughs. This project implements advanced data processing pipelines and statistical modeling techniques to analyze urban patterns and trends.
- Automated ETL pipelines for TfL API integration
- Spatial regression modeling for cross-borough analysis
- Interactive choropleth maps and statistical dashboards
- Time-series analysis of urban indicators
- Comprehensive data validation and quality assurance
- Python 3.8+
- Pandas & NumPy for data processing
- SciPy & Statsmodels for statistical analysis
- Folium & QGIS for geospatial visualization
- Data Processing: pandas, numpy, scipy
- Visualization: matplotlib, folium, plotly
- Statistical Modeling: statsmodels, scikit-learn
- Geospatial Analysis: geopandas, shapely
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Data Collection & Processing
- TfL API integration
- Borough-level data aggregation
- Automated data cleaning pipelines
-
Statistical Analysis
- Spatial regression modeling
- Time-series analysis
- Cross-borough pattern identification
-
Visualization & Reporting
- Interactive choropleth maps
- Statistical dashboards
- Temporal trend visualization
london-urban-analysis/
├── notebooks/ # Jupyter notebooks for analysis
├── src/ # Source code
├── data/ # Data directory
└── docs/ # Documentation
- Clone the repository
git clone https://github.com/[your-username]/london-urban-analysis.git- Install dependencies
pip install -r requirements.txt- Navigate to notebooks directory
cd notebooks[Coming Soon: Screenshots and descriptions of key visualizations]
Contributions, issues, and feature requests are welcome! Feel free to check issues page.
This project is MIT licensed.
- GitHub: [@kamalshashwat]
- LinkedIn: [Kamal Shashwat]
- London Data Week organizers
- Transport for London (TfL) for API access
- Camden Council for data access