Online Program

Analysis of Weather, Temporal, Population, and Socioeconomic Factors in Determining Crime Rates in Five U.S. Cities and Projections for the Future (303050)

Keywords: Regression, Desicion Tree, Neural Networks, Ensemble, SAS

This presentation is based on the 2014 SAS Analytics and Data Mining Shootout. In this study, we examine the effects of weather, temporal, population density, and socioeconomic variables on crime rate by crime type by census tract within five U.S. cities: Atlanta, Chicago, Denver, Houston, and Sacramento. We were given nine data sets that included crime data, population data, unemployment data, storm data, etc. We chose the hour as the major gradation of the data for our analysis. Data aggregation and imputation, preliminary bivariable analysis, and data visualization were performed before modeling. Three modeling groups—regression, decision tree, and neural network—were used and the best-performing model from each group was combined to construct an ensemble model. Various results were provided for each aspect of interests. Based on the results, recommendations are made for how the law enforcement resources can be more effectively used in reducing crime.