| Titre : | CRIME HOTSPOT ANALYSIS AND SUMMARIZATION | | Type de document : | projet fin études | | Auteurs : | Karima ELGARROUSSI, Auteur | | Année de publication : | 2017 | | Langues : | Anglais (eng) | | Catégories : | e-Management et Business Intelligence
| | Mots-clés : | Crime mapping, crime prevention policies, hotspot analysis, mapping methods, spatial and temporal criminal hotspots, frequent patterns. | | Index. décimale : | 1777/17 | | Résumé : | This document is a summary of my work done as part of my graduation project that I accomplished within the University of Houston. My work centered on crime analysis in general, on analyzing crime hotspots and on finding factors that are associated with crime in particular.
The distribution of crime across a region is not random. A number of factors influence where crime occurs, including the physical and social characteristics of the place and the people using the place. Crime mapping can show us where the high crime areas are and help to provide an understanding of the factors that affect the distribution and frequency of crime. This knowledge can help improve crime prevention policies and programs. For example, it can help us to identify at-risk places; and people, to allocate law enforcement resources, and crime victim services and to design suitable crime prevention strategies.
Crime tends to cluster geographically. This has led to the wide usage of hotspot analysis to identify and visualize crime. Accurately identified crime hotspots can greatly benefit the public by creating accurate threat visualizations, more efficiently allocating police resources, and provide a valuable input for crime prediction. Yet existing mapping methods usually identify hotspots without considering factors that are associated with crime. This project focuses on finding and analyzing spatial and temporal criminal hotspots using a real-world crime dataset of the city of Philadelphia and to summarize their characteristics. Moreover, in this study we are interested in finding social factors that are associated with particular types of crimes. To accomplish this goal, we annotate the Philadelphia crime’s dataset with social information in order to find some associations and identify social factors that might affect the safety of neighborhoods and also the paper shows how we used Decision Tree classifier and Naïve Bayesian classifier in order to predict potential crime types . The results of this latter study could be used to raise people’s awareness regarding the dangerous locations, to provide a better understanding of the causes of crime, and to help agencies to predict where and when future crimes occur.
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CRIME HOTSPOT ANALYSIS AND SUMMARIZATION [projet fin études] / Karima ELGARROUSSI, Auteur . - 2017. Langues : Anglais ( eng) | Catégories : | e-Management et Business Intelligence
| | Mots-clés : | Crime mapping, crime prevention policies, hotspot analysis, mapping methods, spatial and temporal criminal hotspots, frequent patterns. | | Index. décimale : | 1777/17 | | Résumé : | This document is a summary of my work done as part of my graduation project that I accomplished within the University of Houston. My work centered on crime analysis in general, on analyzing crime hotspots and on finding factors that are associated with crime in particular.
The distribution of crime across a region is not random. A number of factors influence where crime occurs, including the physical and social characteristics of the place and the people using the place. Crime mapping can show us where the high crime areas are and help to provide an understanding of the factors that affect the distribution and frequency of crime. This knowledge can help improve crime prevention policies and programs. For example, it can help us to identify at-risk places; and people, to allocate law enforcement resources, and crime victim services and to design suitable crime prevention strategies.
Crime tends to cluster geographically. This has led to the wide usage of hotspot analysis to identify and visualize crime. Accurately identified crime hotspots can greatly benefit the public by creating accurate threat visualizations, more efficiently allocating police resources, and provide a valuable input for crime prediction. Yet existing mapping methods usually identify hotspots without considering factors that are associated with crime. This project focuses on finding and analyzing spatial and temporal criminal hotspots using a real-world crime dataset of the city of Philadelphia and to summarize their characteristics. Moreover, in this study we are interested in finding social factors that are associated with particular types of crimes. To accomplish this goal, we annotate the Philadelphia crime’s dataset with social information in order to find some associations and identify social factors that might affect the safety of neighborhoods and also the paper shows how we used Decision Tree classifier and Naïve Bayesian classifier in order to predict potential crime types . The results of this latter study could be used to raise people’s awareness regarding the dangerous locations, to provide a better understanding of the causes of crime, and to help agencies to predict where and when future crimes occur.
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CRIME HOTSPOT ANALYSIS AND SUMMARIZATION / Karima ELGARROUSSI
