No description available.

In this research, we propose to use deep learning to predict crimes in small neighborhoods (regions) of a city, by using historical crime data collected from the past. The motivation of crime predictions is that if we can predict the number crimes that will occur in a certain week then the city officials and law enforcement can prepare resources and manpower more effectively. Due to inherent connections between geographic regions and crime activities, the crime numbers in different regions (with respect to different time periods) are often correlated. Such correlation brings challenges and opportunities to employ deep learning to learn features from historical data for accurate prediction of the future crime numbers for each neighborhood. To leverage crime correlations between different regions, we convert crime data into a heat map, to show the intensity of crime numbers and the geographical distributions. After that, we design a deep learning framework to learn from such heat map for prediction. In our study, we look at the crime reported in twenty different neighbourhoods in Vancouver, Canada over a twenty week period and predict the total crime count that will occur in the future. We will look at the number of crimes per week that have occurred in the span of ten weeks and predict the crime count for the following weeks. The location of where the crimes occur is extracted from a database and plotted onto a heat map. The model we are using to predict the crime count consists of a CNN (Convolutional Neural Network) and a LSTM (Long-Short Term Memory) network attached to the CNN. The purpose of the CNN is to train the model spatially and understand where crimes occur in the images. The LSTM is used to train the model temporally and help us understand which week the crimes occur in time. By feeding the model heat map images of crime hot spots into the CNN and LSTM network, we will be able to predict the crime count and the most likely locations of the crimes for future weeks. / Includes bibliography. / Thesis (MS)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

Thesis (PhD.(Geology))--University of Pretoria, 2008. / Abstract in English. Includes bibliographical references (leaves 243 - 249).

Thesis (M.A.)--Duquesne University, 2007. / Title from document title page. Abstract included in electronic submission form. Includes bibliographical references (p. 73-77) and index.

Poorly integrated crime analysis may be a detriment to crime reduction efforts and financial resources. The purpose of this research is to identify deficiencies and successes in crime analysis integration and to understand which agency factors are related. Using the Stratified Model of Problem Solving, Analysis, and Accountability and data from a national PERF survey of police agencies, this study quantifies the levels of production and consumption-based integration disconnect as well as other important agency factors. To determine which agency factors contribute most to integration disconnect, bivariate correlation and multiple regression analyses are used to examine the relationships, while controlling for agency type, centralization, officers per analyst, crimes per officer, and agency size. Findings indicate that production- and consumption-based disconnect are positively related to one another and that passive patrol-analyst interactions, an agency’s analysis integration disconnect. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Thesis (Ph. D.)--University of Chicago, Dept. of Economics, June 2000. / Includes bibliographical references. Also available on the Internet.

Thesis (Ph.D.) -- Mississippi State University. Department of Sociology, Anthropology, and Social Work. / Title from title screen. Includes bibliographical references.

No description available.

The low amount solved residential burglary crimes calls for new and innovative methods in the prevention and investigation of the cases. There were 22 600 reported residential burglaries in Sweden 2017 but only four to five percent of these will ever be solved. There are many initiatives in both Sweden and abroad for decreasing the amount of occurring residential burglaries and one of the areas that are being tested is the use of prediction methods for more efficient preventive actions. This thesis is an investigation of a potential method of prediction by using neural networks to identify areas that have a higher risk of burglaries on a daily basis. The model use reported burglaries to learn patterns in both space and time. The rationale for the existence of patterns is based on near repeat theories in criminology which states that after a burglary both the burgled victim and an area around that victim has an increased risk of additional burglaries. The work has been conducted in cooperation with the Swedish Police authority. The machine learning is implemented with convolutional long short-term memory (LSTM) neural networks with max pooling in three dimensions that learn from ten years of residential burglary data (2007-2016) in a study area in Stockholm, Sweden. The model's accuracy is measured by performing predictions of burglaries during 2017 on a daily basis. It classifies cells in a 36x36 grid with 600 meter square grid cells as areas with elevated risk or not. By classifying 4% of all grid cells during the year as risk areas, 43% of all burglaries are correctly predicted. The performance of the model could potentially be improved by further configuration of the parameters of the neural network, along with a use of more data with factors that are correlated to burglaries, for instance weather. Consequently, further work in these areas could increase the accuracy. The conclusion is that neural networks or machine learning in general could be a powerful and innovative tool for the Swedish Police authority to predict and moreover prevent certain crime. This thesis serves as a first prototype of how such a system could be implemented and used.

Indiana University-Purdue University Indianapolis (IUPUI) / Law enforcement agencies across the U.S. use maps of crime to inform their practice and make efforts to reduce crime. Hotspot maps using historic crime data can show practitioners concentrated areas of criminal offenses and the types of offenses that have occurred; however, not all of these hotspot crime mapping techniques produce the same results. This study compares three hotspot crime mapping techniques and four crime types using the Predictive Accuracy Index (PAI) to measure the predictive accuracy of these mapping techniques in Marion County, Indiana. Results show that the grid hotspot mapping technique and crimes of robbery are most predictive. Understanding the most effective crime mapping technique will allow law enforcement to better predict and therefore prevent crimes.