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Crime and Socioeconomic Analysis in Mexico (2015–2024)

This project analyzes crime trends in Mexico from 2015 to 2024, integrating crime statistics with socioeconomic data such as poverty indicators and population distribution.

📂 Files Included

  • Dataset&Poverty.ipynb: Jupyter notebook visualizations exploring the correlation between poverty and crime.
  • Population&Crime.ipynb: Jupyter notebook analyzing crime trends, population data, and spatial distributions.

📊 Data Sources

🔍 Key Analyses

  • Time series of monthly and annual crime rates
  • Crime rates per 100,000 inhabitants
  • Correlation matrix with poverty, healthcare, and education indicators
  • Crime rate maps and comparisons between states
  • Design inspired by Tufte and Nathan Yau’s visualization principles

📌 Highlights

  • Interactive map using Plotly and GeoJSON
  • Regression and correlation plots
  • Normalization of crime counts based on population
  • Insight into underreporting and data limitations

🛠 Tools Used

  • Python (Pandas, Plotly, Seaborn)
  • Jupyter Notebook
  • HTML Export (from Jupyter or Dash)
  • GitHub

📄 License

MIT License – feel free to use and adapt with credit.

🙋 About Me

Guadalupe Armenta Mendoza
MSc Data Science & Analytics (2024/25)
Looking for roles in data science, analytics, or data visualization.