Abstract Details
Activity Number:
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214
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 4, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract #313688
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View Presentation
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Title:
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Kernel Space-Time Interaction Tests for Identifying Leading Indicators of Crime
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Author(s):
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Seth Flaxman*+ and Daniel Neill and Alex Smola
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Companies:
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and Carnegie Mellon and Carnegie Mellon
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Keywords:
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spatial ;
spatiotemporal ;
criminology ;
kernel methods ;
machine learning ;
independence testing
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Abstract:
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The CityScan software (Neill, 2013) can accurately predict geographic hot-spots of violent crime by detecting emerging clusters of crimes. CityScan has been used for targeted policing by the Chicago Police Department, but its current use relies on monitoring a manually chosen set of leading indicators. To better identify the most predictive leading indicators, we use a geocoded, time-stamped dataset from Chicago of ~9 million calls to 911 between 2007 and 2010, and search for space-time associations with shootings. Correlations can be confounded in the spatio-temporal setting by underlying spatial effects (e.g., "bad" neighborhoods) and temporal effects (e.g., more crimes in the summer). To address this issue, we propose a new kernel-based statistical test for space-time interaction. Like the classical Knox and Mantel tests, our test explicitly controls for separable spatial and temporal dependencies, but unlike the classical tests it can capture non-linear dependencies. We demonstrate its applicability to questions in criminology and predictive policing by using it to test for space-time interactions between disorder and crime and between 911 call types and shootings/homicides.
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