Factors influencing temporal patterns in crime in a large American city: A predictive analytics perspective.

BACKGROUND: Improving the accuracy and precision of predictive analytics for temporal trends in crime necessitates a good understanding of the how exogenous variables, such as weather and holidays, impact crime. METHODS: We examine 5.7 million reported incidents of crime that occurred in the City of Chicago between 2001 to 2014. Using linear regression methods, we examine the temporal relationship of the crime incidents to weather, holidays, school vacations, day-of-week, and paydays. We correct the data for dominant sources of auto-correlation, and we then employ bootstrap methods for model selection. Importantly for the aspect of predictive analytics, we validate the predictive capabilities of our model on an independent data set; model validation has been almost universally overlooked in the literature on this subject. RESULTS: We find significant dependence of crime on time of year, holidays, and weekdays. We find that dependence of aggressive crime on temperature depends on the hour of the day, and whether it takes place outside or inside. In addition, unusually hot/cold days are associated with unusual fluctuations upwards/downwards in crimes of aggression, respectively, regardless of the time of year. CONCLUSIONS: Including holidays, festivals, and school holiday periods in crime predictive analytics software can improve the accuracy and precision of temporal predictions. We also find that including forecasts for temperature may significantly improve short term crime forecasts for the temporal trends in many types of crime, particularly aggressive crime.

Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement.

In this paper, we present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In our approach, we provide analysts with a suite of natural scale templates and methods that enable them to focus and drill down to appropriate geospatial and temporal resolution levels. Our forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method, which we apply in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity. We also present a novel kernel density estimation technique we have developed, in which the prediction process is influenced by the spatial correlation of recent incidents at nearby locations. We demonstrate our techniques by applying our methodology to Criminal, Traffic and Civil (CTC) incident datasets.

Bristle Maps: a multivariate abstraction technique for geovisualization.

We present Bristle Maps, a novel method for the aggregation, abstraction, and stylization of spatiotemporal data that enables multiattribute visualization, exploration, and analysis. This visualization technique supports the display of multidimensional data by providing users with a multiparameter encoding scheme within a single visual encoding paradigm. Given a set of geographically located spatiotemporal events, we approximate the data as a continuous function using kernel density estimation. The density estimation encodes the probability that an event will occur within the space over a given temporal aggregation. These probability values, for one or more set of events, are then encoded into a bristle map. A bristle map consists of a series of straight lines that extend from, and are connected to, linear map elements such as roads, train, subway lines, and so on. These lines vary in length, density, color, orientation, and transparency—creating the multivariate attribute encoding scheme where event magnitude, change, and uncertainty can be mapped as various bristle parameters. This approach increases the amount of information displayed in a single plot and allows for unique designs for various information schemes. We show the application of our bristle map encoding scheme using categorical spatiotemporal police reports. Our examples demonstrate the use of our technique for visualizing data magnitude, variable comparisons, and a variety of multivariate attribute combinations. To evaluate the effectiveness of our bristle map, we have conducted quantitative and qualitative evaluations in which we compare our bristle map to conventional geovisualization techniques. Our results show that bristle maps are competitive in completion time and accuracy of tasks with various levels of complexity.