Data-driven predictive policing and crime analysis
Table Of Contents
<p>1. Introduction<br> 1.1 Background of predictive policing<br> 1.2 Challenges in traditional policing methods<br> 1.3 Objectives of the project<br>2. Literature Review<br> 2.1 Evolution of predictive policing techniques<br> 2.2 Data sources and features for crime analysis<br> 2.3 Ethical considerations in predictive policing<br>3. Data Collection and Preprocessing<br> 3.1 Sources of crime data and demographic information<br> 3.2 Data cleaning and feature engineering<br>4. Geospatial Analysis for Crime Hotspot Detection<br> 4.1 Spatial clustering and hotspot identification<br> 4.2 Temporal patterns and crime seasonality<br>5. Predictive Modeling for Crime Prediction<br> 5.1 Machine learning algorithms for crime prediction<br> 5.2 Model evaluation and validation<br></p>
Thesis Abstract
<p> This project focuses on the application of data-driven techniques in predictive policing and crime analysis. Traditional policing methods often rely on reactive responses to criminal activities. Data-driven approaches leverage historical crime data, demographic information, and environmental factors to predict and prevent criminal incidents. This project will explore the use of machine learning algorithms, geospatial analysis, and predictive modeling to develop a predictive policing system. The goal is to improve resource allocation, crime prevention, and community safety. <br></p>Thesis Overview
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