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                            Library & Multimedia
 Scalable High-Resolution Forecasting of Sparse Spatiotemporal Events With Kernel Methods: A Winning Solution to the NIJ "Real-Time Crime Forecasting Challenge"
NCJ Number
              251488

Author(s)
          Seth  Flaxman;
                        Michael  Chirico;
                        Pau  Pereira;
                        Charles  Loeffler
Date Published

January 2018
Length
              35 pages
Annotation
              This report proposes a generic spatiotemporal event crime forecasting method, which was submitted for the National Institute of Justice's (NIJ's) Real-Time Crime Forecasting Challenge of 2017.
Abstract
              The goal of the challenge was to predict several different types of crime hotspots over varying timeframes for Portland, OR, using calls-for-service data from the Portland Police Bureau (PPB). The proposed solution is a spatiotemporal forecasting model that combines scalable randomized Reproducing Kernel Hilbert Space (RKHS) methods for approximating Gaussian processes with autoregressive smoothing kernels in a regularized supervised learning framework. Although the smoothing kernels capture the two main approaches in current use in crime forecasting - heatmap-based (kernal density estimation and the self-exciting point process (SEPP) models - the RKHS component of the model can be viewed as an approximation to the popular log-Gaussian Cox Process model. For inference, the proposed method discretizes the spatiotemporal point pattern and learns a log intensity function using the Poisson likelihood and highly efficient gradient-based optimization methods. Model hyper-parameters were learned using temporal cross-validation, including quality of RKHS approximation, spatial and temporal kernel length-scales, number of autoregressive lags, bandwidths for smoothing kernels, and cell shape, size, and rotation. Resulting predictions significantly exceeded baseline kernel density estimation (KDE). This project had winning submissions to the competition in each of the different crime types, suggesting that the proposed method is generally applicable. Performance improvement over baseline predictions and competing submissions were particularly large for sparse crimes over short forecasting periods. 12 figures, 2 tables, and 92 references
      
  
          Additional Details
Grant Number(s)
        2016-NIJ-Challenge-0023


  
  
        
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        NIJ-Sponsored


  
  
        
  Publication Type
        Report (Technical Assistance)
                                Report (Study/Research)
                                Report (Grant Sponsored)
                                Program/Project Description
    
  
  
Date Published: January 1, 2018
    
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