Pakistan Crimes Analysis (2000–2024)

Comprehensive national crime trend analysis with socioeconomic correlations

Overview

This project examines 25 years of crime data in Pakistan (2000–2024) and explores how crime rates correlate with key socioeconomic indicators. Through statistical analysis and data visualization, I investigate whether literacy, economic development, unemployment, poverty, and urbanization have measurable relationships with national crime patterns.

The goal is to provide evidence-based insights that can inform policy decisions, resource allocation, and crime prevention strategies.

Dataset

The dataset integrates crime statistics with socioeconomic indicators spanning 25 years.

What's Included

Crime Data (9 categories tracked): Murder Attempt to Murder
Kidnapping / Abduction Dacoity Robbery Burglary Cattle Theft Other Theft Others (miscellaneous offenses)

Socioeconomic Variables: Population (Census data + projections) National literacy rate GDP per capita (USD) Unemployment rate Poverty rate Urbanization rate

Data Composition

Period	Records	Source	Quality
2012–2017	54	Pakistan Bureau of Statistics	✓ Verified official data
2000–2011, 2018–2024	171	Statistical projection	Modeled using OLS regression

Total: 225 records (25 years × 9 crime types)

Key Findings

Based on analysis of verified data (2012–2017):

Crime & Socioeconomic Correlations

Literacy Rate
Correlation with crime rate: r = −0.633 Interpretation: Higher literacy is associated with lower crime rates Significance: p = 0.178 (moderate evidence)

Economic Development
Correlation with crime rate: r = −0.468
Interpretation: Higher GDP per capita correlates with reduced crime Significance: p = 0.349

Long-term Trends
Total crimes recorded (2000–2024): 16.3 million incidents Population growth: +72.5% (142M → 245M) Literacy improvement: +14.5 percentage points (45% → 59.5%) Crime rate trend: Declining despite population growth

Crime Distribution

• Most common: "Others" category (79.6% of all crimes)
• Violent crimes: Murder + Attempt to Murder = 4.3% of total
• Property crimes: Burglary + Robbery + Theft = 10.4% of total

Visualizations

Comprehensive Dashboard

This dashboard presents:

1. Key Statistics: Total crimes, average crime rate, population growth, literacy gains
2. Time Series: Crime trends by category (2000–2024)
3. Correlation Heatmap: Relationships between crime and socioeconomic factors
4. Scatter Plots: Crime vs. literacy and crime vs. economic development
5. Crime Distribution: Breakdown by offense type
6. Contextual Trends: Evolution of literacy, unemployment, and poverty

Methodology

Data Collection

Crime data comes from the Pakistan Bureau of Statistics (PBS), the official government source for national statistics. Socioeconomic indicators were compiled from:

Population: Pakistan Census 1998, 2017, 2023 + PBS demographic projections Literacy: Pakistan Economic Survey (annual publications 2000–2024), UNESCO Institute for Statistics, Pakistan Social & Living Standards Measurement (PSLM) surveys Economic Data: World Bank Open Data, State Bank of Pakistan annual reports Poverty & Development: World Bank poverty estimates, UNDP Human Development Reports

Statistical Approach

For verified period (2012–2017): Direct extraction from PBS Crime Statistics Portal No estimation or modeling required

For extended coverage (2000–2011, 2018–2024):

1. Calculated per-capita crime rates from verified data
2. Fitted linear regression models for each crime type
3. Applied trends to historical and future population estimates
4. Added realistic variance (±5%) to avoid artificial smoothness
5. Clearly tagged all projections in Data_Source column

Correlation Analysis: Pearson correlation coefficients calculated Two-tailed significance tests performed Limited to verified data only (2012–2017) for robustness

Data Integrity

All projections are: Based on established statistical methods Transparent and reproducible Clearly documented in the datasetConservative (using linear trends, not complex models)

Technologies Used

• Python 3.12 – Data analysis and visualization
• Pandas – Data manipulation and cleaning
• NumPy – Numerical computations
• SciPy – Statistical testing (correlation, regression)
• Matplotlib – Visualization framework
• Seaborn – Statistical data visualization
• Jupyter Notebook – Interactive analysis environment
• Microsoft Excel / LibreOffice Calc – Multi-sheet data workbook

Data Sources

All data is from official, publicly accessible sources:

Primary Sources

1. Pakistan Bureau of Statistics (PBS)
   Crime Statistics Portal: https://social.data.gov.pk/
   Pakistan Census 1998, 2017, 2023

2. World Bank Open Data
   GDP, poverty, urbanization: https://data.worldbank.org/

3. Pakistan Economic Survey
   Annual reports (2000–2024): https://www.finance.gov.pk/

4. UNESCO Institute for Statistics
   Literacy data: http://uis.unesco.org/

5. State Bank of Pakistan
   Economic indicators: https://www.sbp.org.pk/

Data Availability

• Fully verified: 2012–2017 (PBS Crime Portal)
• Projected: 2000–2011, 2018–2024 (statistical extrapolation clearly documented)

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Limitations

This analysis has important constraints to keep in mind:

1. Projected Data: Crime figures for 2000–2011 and 2018–2024 are statistical estimates, not official records. They assume linear trends, which may not capture non-linear changes.

2. Reporting Accuracy: Crime statistics depend on reporting rates. Underreporting (especially of certain crimes) may affect accuracy.

3. National Level Only: Provincial or district-level breakdowns are not available for the full 25-year period. Regional variations are not captured.

4. Correlation vs. Causation: Statistical correlations do not prove causation. Multiple factors influence crime beyond those analyzed here.

5. COVID-19 Impact: The 2020–2021 period may show unusual patterns due to pandemic-related lockdowns and reporting changes.

6. "Others" Category: This broad category (79.6% of crimes) includes many offense types, limiting granular analysis.

Future Work

Potential extensions of this analysis:

• Incorporate provincial-level data (2012–2017) for regional comparisons
• Add time-series forecasting models (ARIMA, Prophet)
• Machine learning classification: predict crime category based on socioeconomic factors
• Panel regression analysis with fixed effects
• Integrate additional variables: education spending, police per capita, judicial efficiency
• Interactive dashboard using Plotly/Dash for real-time exploration
• Comparison with neighboring countries (India, Bangladesh, Afghanistan)

Contributions are welcome! See Contributing below.

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Contributing

I welcome suggestions, corrections, and contributions to improve this analysis.

How to Contribute

1. Fork this repository
2. Create a feature branch: git checkout -b feature/your-feature-name
3. Commit your changes: git commit -m "Add your message"
4. Push to the branch: git push origin feature/your-feature-name
5. Open a Pull Request

Issues

If you find errors or have questions:

• Open an issue on GitHub
• Provide detailed information (what you found, what you expected, screenshots if relevant)

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License

This project is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).

You are free to:

• ✓ Share — copy and redistribute the material
• ✓ Adapt — remix, transform, and build upon the material for any purpose, even commercially

Under the following terms:

• Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made
• ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license

See LICENSE file for full details.

Citation

If you use this dataset or analysis in academic work, please cite:

Rabbani, M. (2024). Pakistan Crimes Analysis (2000–2024): National trends with socioeconomic correlations. GitHub repository. https://github.com/Rabbani-bot/pakistan-crimes-analysis

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Contact

Mohammad Rabbani
Data Analyst & Research Consultant

• LinkedIn: www.linkedin.com/in/rabbani-analyst
• Kaggle: (https://www.kaggle.com/rabbani0123)
• Email: mrabbani777tcd@gmail.com
• Location: Mingora, Khyber Pakhtunkhwa, Pakistan

Let's Connect!

I am always interested in discussing data analysis, criminology research, development economics, or potential collaboration opportunities. Feel free to reach out!

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⭐ If you find this project useful, please consider starring it on GitHub! It helps others discover this work.

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Last updated: February 2026