Modeling Temporal-Spatial Correlations for Crime Prediction
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1 Modeling Temporal-Spatial Correlations for Crime Prediction Xiangyu Zhao and Jiliang Tang Michigan State University
2 Background Urban Security and Safety Eg. New York City Weekly Crime Report (NYPD) 1888 felony incident, July 4~10, 2016 Urgent demand for accurate crime prediction
3 Motivation Recent development of new techniques to collect and integrate urban data: Public safety data Weather data Point of interests (POIs) data Human mobility data 311 public-service complaint data
4 Motivation Two challenging questions Q1: What temporal-spatial patterns can be observed about urban crimes Q2: How to model these patterns mathematically for crime prediction
5 Problem Statement { Regions Feature Weight Crime { Dimensions { Time number of crimes h time slots later (or in time t K+h ) : feature vector of n th region in k th time slot Basic Model
6 Q1:Temporal-Spatial Patterns Dc Temporal pattern how crime evolves over time for each region in a city c t and c t + t : the crime number in time t and t + t c = c t c t + t Observations two adjacent time slots time differences t increase Dt(day) similar crime numbers crime difference c increase
7 Dc Q1:Temporal-Spatial Patterns Intra-region temporal correlation Weight Dt(day) Temporal Correlations Time{
8 Q1:Temporal-Spatial Patterns Dc Spatial pattern geographical influence among regions in the city c i and c j : the crime number in region i and region j c = c i c j 4 3 Observations two spatial close regions spatial distance d increase Dd (km ) similar crime numbers crime difference c increase
9 Dc Q1:Temporal-Spatial Patterns { Regions Inter-region spatial correlation Weight Dd (km ) Spatial Correlations
10 Q2:TCP Framework { { Regions Crime Feature Weight { Dimensions Time Spatial Correlations Temporal Correlations ADMM framework for optimizing objective function
11 An Overview of the Crime Prediction System Step 1: Feature Extraction Citizens Government Police TCP Framework Crime Prediction Step 2: TCP region framework Matrices Construction Features Extraction Spatial Correlation Temporal Correlation Step 3: Crime Prediction Crime Stop&Frisk Meteorology POIs Human Mobility 311 Complaint Complaint
12 Experiment Settings Datasets New York City July 2012 June 2013 (365 days) 133 disjointed regions (2km 2km grid) Metric Average root-mean-square-error (RMSE)
13 Experiment Settings Two questions QA: how TCP performs compared to baselines QB: how the temporal and spatial patterns contribute to the performance
14 Experiment Results Baselines: CSI: Cubic Spline Interpolation ARMA: Auto-Regression-Moving-Average LASSO: Lasso Regression LR: Linear Regression stmtl: Spatio-Temporal Multi-Task Learning
15 Experiment Results QA: Overall performance Comparison
16 Experiment Results ar M SE QB: Contribution of Temporal and Spatial Correlations TCP-t: evaluate temporal correlations TCP-s: evaluate spatial correlations 2.00 TCP TCP-t TCP-s day 7 days
17 Experiment Results c Dc Further Probing on Temporal Patterns (a) Dt(day) (b) Jan. Dec.
18 Experiment Results How the spatial distribution of urban crimes varies with respect to days of a week?
19 Future Work More sources Social media, Crime networks More temporal-spatial patterns Weekly periodicity, Hotspot More applications Air quality prediction, Noise Detection
20 Acknowledgements Thanks for the grant IIS and IIS from National Science Foundation (NSF) Thanks for the support from Data Science and Engineering Lab at Michigan State University Authors {zhaoxi35,
21 Thanks
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