International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025
p-ISSN: 2395-0072
www.irjet.net
Crime Forecasting: Leveraging Data Science for Public Safety Boddeda Jahnavi1, Koti Sai Satya Meghana2, Seeku Bhavana3, Manchikanti Chinna Venkata Reddy4, Reddi Swapna5, K Soma Sekhar 6 1,2,3,4 B.Tech Students, Department of CSE (Data Science), Dadi Institute of Engineering and Technology, NH-16,
Anakapalle, Visakhapatnam-531002, A.P
5 Assistant Professor, Department of CSE (DS& ML), Dadi Institute of Engineering and Technology , NH-16,
Anakapalle, Visakhapatnam-531002,A.P
6 Assistant Professor, Department of BSH, Dadi Institute of Engineering and Technology , NH-16, Anakapalle,
Visakhapatnam-531002,A.P ------------------------------------------------------------------------***------------------------------------------------------------------------Abstract: Crime analysis and prediction play a crucial role in enhancing public safety and optimizing law enforcement efforts. This study leverages data mining and machine learning techniques to analyze historical crime data, identify patterns, and predict crime-prone areas. The proposed system integrates advanced algorithms, including Naïve Bayes for crime classification, Apriori for pattern identification, and Decision Trees for crime prediction based on key factors such as location, time, and socio-economic conditions. Unlike traditional statistical methods and Geographic Information Systems (GIS), which primarily focus on crime trend visualization, our approach incorporates real-time data integration and temporal analysis to improve predictive accuracy. The system also employs heat maps and GIS tools for better visualization and resource allocation. By addressing challenges such as data sparsity, lack of real-time data, and limited time-based pattern analysis, the proposed framework enhances crime forecasting capabilities. This research aims to assist law enforcement agencies in proactive decision-making, reducing crime rates, and ensuring effective resource deployment for a safer society.
Keywords: Crime Analysis, Data Mining, Geographic Information System, Data Visualization, Apriori Algorithm, Pattern Identification, Decision Trees.
1. INTRODUCTION Crime is a significant societal concern that affects public safety, economic stability, and social well-being. Law enforcement agencies have traditionally relied on statistical methods and Geographic Information Systems (GIS) to analyze past crime data. However, these conventional approaches are limited in their predictive capabilities, making it challenging to prevent crimes before they occur. With the rapid advancement of technology, data mining and machine learning techniques have emerged as powerful tools for crime analysis and prediction. This research explores how crime data, when analyzed using advanced computational techniques, can reveal hidden patterns, trends, and correlations that help predict potential crime hotspots. By leveraging algorithms such as Naïve Bayes for crime classification, Apriori for pattern identification, and Decision Trees for forecasting, the proposed system aims to enhance the accuracy and efficiency of crime prediction. The integration of real-time data further strengthens proactive decision-making by law enforcement agencies. The proposed system goes beyond conventional crime mapping by incorporating machine learning models that analyze historical and socio-economic data to predict crime occurrences with higher precision. The goal is to improve public safety, optimize resource allocation, and support evidence-based policymaking. This paper discusses the existing challenges in crime prediction and presents a data-driven solution that enhances crime forecasting capabilities through an integrated and intelligent approach. 1.1 Motivation The motivation behind this research stems from the increasing complexity of crime patterns and the limitations of traditional crime analysis techniques. The rising crime rates in urban and rural areas necessitate a proactive approach to crime prevention. Law enforcement agencies face challenges such as inefficient resource allocation, delayed responses, and an inability to predict crime occurrences. Machine learning and data mining provide an opportunity to enhance crime prevention strategies by analyzing vast amounts of crime data and extracting meaningful insights. By leveraging predictive
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