Mining 911 calls in New York city: Temporal patterns, detection and forecasting

Alex Chohlas-Wood, Aliya Merali, Warren Reed, Theodoros Damoulas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The New York Police Department (NYPD) is tasked with responding to a wide range of incidents that are reported through the city's 911 emergency hotline. Currently, response resources are distributed within police precincts on the basis of high-level summary statistics and expert reasoning. In this paper, we describe our first steps towards a better understanding of 911 call activity: Temporal behavioral clustering, predictive models of call activity, and anomalous event detection. In practice, the proposed techniques provide decision makers granular information on resource allocation needs across precincts and are important components of an overall data-driven resource allocation policy.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence for Cities - Papers Presented at the 29th AAAI Conference on Artificial Intelligence, Technical Report
PublisherAI Access Foundation
Pages2-8
Number of pages7
ISBN (Electronic)9781577357155
StatePublished - 2015
Event29th AAAI Conference on Artificial Intelligence, AAAI 2015 - Austin, United States
Duration: Jan 25 2015Jan 30 2015

Publication series

NameAAAI Workshop - Technical Report
VolumeWS-15-04

Other

Other29th AAAI Conference on Artificial Intelligence, AAAI 2015
Country/TerritoryUnited States
CityAustin
Period1/25/151/30/15

ASJC Scopus subject areas

  • General Engineering

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