Assessing the Impact of Game Day Schedule and Opponents on Travel Patterns and Route Choice using Big Data Analytics

The transportation system is crucial for transferring people and goods from point A to point B. However, its reliability can be decreased by unanticipated congestion resulting from planned special events. For example, sporting events collect large crowds of people at specific venues on game days and disrupt normal traffic patterns. The goal of this study was to understand issues related to road traffic management during major sporting events by using widely available INRIX data to compare travel patterns and behaviors on game days against those on normal days. A comprehensive analysis was conducted on the impact of all Nebraska Cornhuskers football games over five years on traffic congestion on five major routes in Nebraska. We attempted to identify hotspots, the unusually high-risk zones in a spatiotemporal space containing traffic congestion that occur on almost all game days. For hotspot detection, we utilized a method called Multi-EigenSpot, which is able to detect multiple hotspots in a spatiotemporal space. With this algorithm, we were able to detect traffic hotspot clusters on the five chosen routes in Nebraska. After detecting the hotspots, we identified the factors affecting the sizes of hotspots and other parameters. The start time of the game and the Cornhuskers’ opponent for a given game are two important factors affecting the number of people coming to Lincoln, Nebraska, on game days. Finally, the Dynamic Bayesian Networks (DBN) approach was applied to forecast the start times and locations of hotspot clusters in 2018 with a weighted mean absolute percentage error (WMAPE) of 13.8%

CPC: Crime, Policing and Citizenship - Intelligent Policing and Big Data

Crime, Policing and Citizenship (CPC) – Space-Time Interactions of Dynamic Networks has been a major UK EPSRC-funded research project. It has been a multidisciplinary collaboration of geoinformatics, crime science, computer science and geography within University College London (UCL), in partnership with the Metropolitan Police Service (MPS). The aim of the project has been to develop new methods and applications in space-time analytics and emergent network complexity, in order to uncover patterning and interactions in crime, policing and citizen perceptions. The work carried out throughout the project will help inform policing at a range of scales, from the local to the city-wide, with the goal of reducing both crime and the fear of crime. The CPC project is timely given the tremendous challenging facing policing in big cities nationally and globally, as consequences of changes in society, population structure and economic well-being. It addresses these issues through an intelligent approach to data-driven policing, using daily reported crime statistics, GPS traces of foot and vehicular patrols, surveys of public attitudes and geo-temporal demographic data of changing community structure. The analytic focus takes a spatio-temporal perspective, reflecting the strong spatial and temporal integration of criminal, policing and citizen activities. Street networks are used throughout as a basis for analysis, reflecting their role as a key determinant of urban structure and the substrate on which crime and policing take place. The project has presented a manifesto for ‘intelligent policing’ which embodies the key issues arising in the transition from Big Data into actionable insights. Police intelligence should go beyond current practice, incorporating not only the prediction of events, but also how to respond to them, and how to evaluate the actions taken. Cutting-edge network-based crime prediction methods have been developed to accurately predict crime risks at street segment level, helping police forces to focus resources in the right places at the right times. Methods and tools have been implemented to support senior offices in strategic planning, and to provide guidance to frontline officers in daily patrolling. To evaluate police performance, models and tools have been developed to aid identification of areas requiring greater attention, and to analyse the patrolling behaviours of officers. Methods to understand and model confidence in policing have also been explored, suggesting strategies by which confidence in the police can be improved in different population segments and neighbourhood areas. A number of tools have been developed during the course of the project include data-driven methods for crime prediction and for performance evaluation. We anticipate that these will ultimately be adopted in daily policing practice and will play an important role in the modernisation of policing. Furthermore, we believe that the approaches to the building of public trust and confidence that we suggest will contribute to the transformation and improvement of the relationship between the public and police

Model for Spatiotemporal Crime Prediction with Improved Deep Learning

Crime is hard to anticipate since it occurs at random and can occur anywhere at any moment, making it a difficult issue for any society to address. By analyzing and comparing eight known prediction models: Naive Bayes, Stacking, Random Forest, Lazy:IBK, Bagging, Support Vector Machine, Convolutional Neural Network, and Locally Weighted Learning – this study proposed an improved deep learning crime prediction model using convolutional neural networks and the xgboost algorithm to predict crime. The major goal of this research is to provide an improved crime prediction model based on previous criminal records. Using the Boston crime dataset, where our larceny crime dataset was extracted, exploratory data analysis (EDA) is used to uncover patterns and explain trends in crimes. The performance of the proposed model on the basis of accuracy, recall, and f-measure was 100% outperforming the other models used in this study. The analysis of the proposed model and prediction can aid security services in making better use of their resources, anticipating crime at a certain time, and serving the society better

Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ "Real-Time Crime Forecasting Challenge"

We propose a generic spatiotemporal event forecasting method, which we developed for the National Institute of Justice’s (NIJ) RealTime Crime Forecasting Challenge (National Institute of Justice, 2017). Our method is a spatiotemporal forecasting model combining scalable randomized Reproducing Kernel Hilbert Space (RKHS) methods for approximating Gaussian processes with autoregressive smoothing kernels in a regularized supervised learning framework. While the smoothing kernels capture the two main approaches in current use in the field of crime forecasting, kernel density estimation (KDE) and self-exciting point process (SEPP) models, the RKHS component of the model can be understood as an approximation to the popular log-Gaussian Cox Process model. For inference, we discretize the spatiotemporal point pattern and learn a log-intensity function using the Poisson likelihood and highly efficient gradientbased optimization methods. Model hyperparameters including quality of RKHS approximation, spatial and temporal kernel lengthscales, number of autoregressive lags, bandwidths for smoothing kernels, as well as cell shape, size, and rotation, were learned using crossvalidation. Resulting predictions significantly exceeded baseline KDE estimates and SEPP models for sparse events