Crime Economics and Supplementary Data for Crime Analysis

This study examines the relationship between socio-economic factors and crime distribution using a dataset that includes variables such as <b>unemployment rates, literacy rates, per capita income, and population density</b>. The analysis explores how these factors influence crime rates across different regions, comparing urban and rural areas to identify variations in crime patterns due to economic and social disparities. Additionally, the study investigates <b>cultural and psychological influences</b> on criminal activities. The findings offer valuable insights for policymakers to develop more effective crime prevention strategies.<br>This dataset supports the manuscript ‘Crime and Socio-Economic Inequalities: Leveraging Deep Learning and Generative AI for Comprehensive Analysis.’ It includes:- CrimeEconomicsData.csv: Original dataset with 114 observations across 10 socio-economic variables (Per Capita Income, Population Density, Unemployment, Literacy Rate, Happiness Index, Crime Rate).- supplementary_data.zip: Contains: - table_ii_metrics.csv: Performance metrics (Accuracy, Precision, Recall, F1-Score, ROC-AUC) for machine learning and deep learning models in Table II. - figure_2_confusion_matrices.csv: Confusion matrix data for each model, supporting Figure 2’s visualizations. - README.txt: Description of the files and their purpose.Preprocessed datasets are not included, as preprocessing steps (e.g., mean imputation, standardization, PCA) are detailed in the manuscript and can be replicated using CrimeEconomicsData.csv.