Statistical Analytics & Data Visualization Toolkit examining Indian Crime Trends, Conviction Densities, and Police Infrastructure
An empirical data analytics suite developed in R to evaluate state-level crime statistics across India, modeling relationships between law enforcement density, judicial conviction efficiency, Indian Penal Code (IPC) violations, and emerging cybercrime trends.
Understanding spatial and demographic variations in crime statistics is essential for evidence-based policymaking and police resource allocation. Crime Rate Analysis Suite processes Indian state-level statistics to generate 10 high-resolution analytical visual reports:
- State-Level IPC vs SLL Crime Distribution: Historical trends and ratio compositions across major union territories and states.
- Law Enforcement & Judicial Infrastructure: Evaluating police personnel density against judicial conviction rates using multi-variable correlation heatmaps and bubble scatter plots.
- Emerging Cybercrime vs. Traditional Crime Ratios: Spatial mapping of digital offense acceleration vs. traditional property/violent crime rates.
The toolkit includes 10 automated analytical graphing modules (R/graph_01.R through R/graph_10.R):
- 📈 Graph 1 — Historical Trends (
graph_1_trends.png): Time-series trajectory of crime volume index across reporting years. - 🧩 Graph 2 — Crime Composition (
graph_2_composition.png): Categorical breakdown of violent, property, and cybercrime proportions. - 📊 Graph 3 — Grouped State Comparison (
graph_3_grouped_comparison.png): Comparative bar metrics across state clusters. - 📦 Graph 4 — Dispersion & Outlier Boxplot (
graph_4_boxplot_distribution.png): Variance distribution and statistical outliers in regional crime rates. - 🌡️ Graph 5 — Multi-Variable Correlation Heatmap (
graph_5_correlation_heatmap.png): Cross-correlation matrix between literacy rates, police density, and conviction percentages. - 🎯 Graph 6 — Radar Safety Profile (
graph_6_radar_profile.png): Multi-axis radar metrics comparing regional safety indices. - 🫧 Graph 7 — Police Density vs. Conviction Rate (
graph_7_police_conviction_bubble.png): 3D-mapped bubble plot correlating police force size, caseload, and conviction outcomes. - 🍭 Graph 8 — State Lollipop Ranking (
graph_8_lollipop_ranking.png): Ranked state index highlighting high-efficiency vs. under-resourced regions. - 📉 Graph 9 — Conviction Density Distribution (
graph_9_conviction_density_hist.png): Kernel density estimation histogram of conviction probabilities. - 💻 Graph 10 — Cybercrime Growth Ratios (
graph_10_cyber_vs_traditional_ratio.png): Ratio escalation analysis of digital crimes.
- Language: R (v4.x+)
- Environment: RStudio
- Data Manipulation:
dplyr,tidyr,readr - Visualization:
ggplot2,scales,gridExtra,ggradar - Dataset: Empirical state-level Crime in India dataset (
data/crimerate_dataset.csv)
Crime_Rate_Analysis/
├── R/
│ ├── graph_01.R ... graph_10.R # 10 modular analytical graphing scripts
├── data/
│ └── crimerate_dataset.csv # Standardized CSV dataset
├── plots/ # Generated high-resolution output charts
│ ├── graph_1_trends.png
│ ├── graph_5_correlation_heatmap.png
│ └── graph_7_police_conviction_bubble.png
├── presentation/ # Technical reports & slide decks
│ └── final_presentation_r_fixed.pdf
├── main_code.r # Unified master execution script
├── .gitignore
├── LICENSE
└── README.md
- Clone the repository:
git clone https://github.com/BhavyAtkotiya/Crime_Rate_Analysis.git cd Crime_Rate_Analysis - Install Required Packages:
Launch R or RStudio and execute:
install.packages(c("ggplot2", "dplyr", "tidyr", "readr", "scales"))
- Execute Analysis Pipeline:
Run the master pipeline script to regenerate all 10 analytical plots:
source("main_code.r")
This project is licensed under the MIT License — see the LICENSE file.