Predicting Crime Locations Using Big Data Analytics and Map-Reduce Techniques

Author Reg. ID: TIJER_154522

Published Paper Id: TIJER2104002

Published In: Volume 8 Issue 4, April-2021

Abstract: Predicting crime locations is a critical component of modern law enforcement strategies, aiming to enhance public safety and optimize resource allocation. This research explores the application of big data analytics and Map-Reduce techniques to improve the accuracy of crime location predictions. As urban areas grow and crime data becomes increasingly complex, traditional methods of crime forecasting often fall short. By leveraging the power of big data and advanced analytics, this study seeks to address these limitations and offer a more robust framework for predicting criminal activity. Data for this study was collected from various sources, including police reports, public crime databases, and social media feeds. The dataset encompasses a broad range of variables such as crime type, location, time of occurrence, and demographic information. The application of Map-Reduce techniques allowed for the distribution of data processing tasks across multiple servers, significantly reducing computation time and enabling real-time analysis. The research employs several big data analytics methods, including spatial clustering, temporal analysis, and predictive modeling. By integrating Map-Reduce, the study was able to scale these methods to handle large datasets efficiently, providing more accurate and timely predictions. The results indicate that big data analytics combined with Map-Reduce techniques significantly enhance the precision of crime location predictions. The analysis revealed distinct crime patterns and trends, which can be used to inform law enforcement strategies and allocate resources more effectively. The study also highlights the benefits of real-time data processing in improving predictive accuracy and responsiveness.

Keywords: • Crime Prediction • Big Data Analytics • Map-Reduce Techniques • Data Processing • Crime Location Forecasting • Spatial Clustering • Temporal Analysis • Predictive Modeling • Real-Time Analysis • Crime Hotspots • Law Enforcement Strategies • Data Integration • Computational Efficiency • Public Safety • Data-Driven Methods

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Page No: 11-21

Country: -, -, India

Research Area: Science and Technology

Published Paper URL: https://tijer.org/TIJER/viewpaperforall?paper=TIJER2104002

Published Paper PDF: https://tijer.org/TIJER/papers/TIJER2104002

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