Crime forecasting

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.

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.