New insights on relationships between street crimes and ambient population: Use of hourly population data estimated from mobile phone users’ locations

The purpose of this research is to examine relationships between occurrences of snatch-and-run offences and hourly population estimated from mobile phone users’ locations, with particular focus on differences between daytime and nighttime. Using an hourly population dataset allows us to count the so-called ‘ambient population’ by hour of day to accurately quantify the influence of such population as capable guardians and suitable targets in a framework of routine activity theory. Our major findings based on logistic regression models are that (1) the effects of ambient population and (2) its temporal change are large, and the effects differ between daytime and nighttime. During the daytime, snatch-and-run offences are less likely to occur in areas where hourly population density is expect to increase, possibly because offenders are highly sensitive to the risk of being detected by other people. On the other hand, offences at night occur even in relatively crowded areas, and they are only weakly related to population change. In addition, our study found that (3) snatch-and-run offences are more likely to occur in or near local town centres and (4) socially vulnerable neighbourhoods are only targeted at night. We attempted to explain this in terms of offenders’ characteristics and motivations depending on time of day.

[1]  Peter K. B. St. Jean,et al.  Pockets of Crime: Broken Windows, Collective Efficacy, and the Criminal Point of View , 2007 .

[2]  Richard Block,et al.  Robberies in Chicago: A Block-Level Analysis of the Influence of Crime Generators, Crime Attractors, and Offender Anchor Points , 2011 .

[3]  Dean P. Anderson,et al.  SCALE-DEPENDENT SUMMER RESOURCE SELECTION BY REINTRODUCED ELK IN WISCONSIN, USA , 2005 .

[4]  Jerry H. Ratcliffe,et al.  The Hotspot Matrix: A Framework for the Spatio‐Temporal Targeting of Crime Reduction , 2004 .

[5]  Carlo Ratti,et al.  Cellular Census: Explorations in Urban Data Collection , 2007, IEEE Pervasive Computing.

[6]  Martin A. Andresen Location Quotients, Ambient Populations, and the Spatial Analysis of Crime in Vancouver, Canada , 2007 .

[7]  Bryan F. J. Manly,et al.  Estimating a resource selection function with line transect sampling , 2002, Adv. Decis. Sci..

[8]  Rebecca Meaney,et al.  Commuters and marauders: an examination of the spatial behaviour of serial criminals , 2004 .

[9]  Martin A. Andresen Unemployment and crime: A neighborhood level panel data approach. , 2012, Social science research.

[10]  F. Neuhaus Emergent Spatio-temporal Dimensions of the City: Habitus and Urban Rhythms , 2015 .

[11]  J. Fieberg,et al.  Comparative interpretation of count, presence–absence and point methods for species distribution models , 2012 .

[12]  S. Cherry,et al.  USE AND INTERPRETATION OF LOGISTIC REGRESSION IN HABITAT-SELECTION STUDIES , 2004 .

[13]  Ai Ishikawa,et al.  RELATIONSHIP BETWEEN DISTANCE OF VISIBLE ROAD ON ROAD NETWORK AND OCCURRENCE OF SNATCH , 2008 .

[14]  Lawrence E. Cohen,et al.  Social Change and Crime Rate Trends: A Routine Activity Approach , 1979 .

[15]  Michael Batty,et al.  Environment and Planning B: Planning and Design , 1996 .

[16]  D. McFadden Conditional logit analysis of qualitative choice behavior , 1972 .

[17]  Martin A. Andresen Crime Measures and the Spatial Analysis of Criminal Activity , 2006 .

[18]  Martin A. Andresen,et al.  Spatio-temporal crime hotspots and the ambient population , 2015 .

[19]  Christopher R. Herrmann Street-Level Spatiotemporal Crime Analysis: Examples from Bronx County, NY (2006–2010) , 2013 .

[20]  Alex Pentland,et al.  Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data , 2014, ICMI.

[21]  Peter Flemming,et al.  Crime Prevention , 2014, Encyclopedia of Social Network Analysis and Mining.

[22]  Kurt C. VerCauteren,et al.  Animal and Plant Health Inspection Service 2010 An evaluation of three statistical methods used to model resource selection , 2017 .

[23]  Y. Ishikawa,et al.  Destination Choice of the 1995–2000 Immigrants to Japan: Salient Features and Multivariate Explanation , 2008 .

[24]  Gary Higgs,et al.  Visualising space and time in crime patterns: A comparison of methods , 2007, Comput. Environ. Urban Syst..

[25]  W. Bernasco,et al.  Go where the money is: modeling street robbers’ location choices , 2013 .

[26]  Jerry H. Ratcliffe,et al.  TESTING FOR TEMPORALLY DIFFERENTIATED RELATIONSHIPS AMONG POTENTIALLY CRIMINOGENIC PLACES AND CENSUS BLOCK STREET ROBBERY COUNTS , 2015 .

[27]  Carlo Ratti,et al.  Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network , 2009 .

[28]  C. Ratti,et al.  Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis , 2006 .

[29]  Bryan F. J. Manly,et al.  Resource Selection by Animals , 1993, Springer Netherlands.

[30]  Robert D. Crutchfield,et al.  Crime Rate and Social Integration The Impact of Metropolitan Mobility , 1982 .

[31]  Hans R. Zuuring,et al.  Relationships among grizzly bears, roads and habitat in the Swan Mountains, Montana , 1996 .

[32]  Tomoki Nakaya,et al.  Visualising Crime Clusters in a Space‐time Cube: An Exploratory Data‐analysis Approach Using Space‐time Kernel Density Estimation and Scan Statistics , 2010, Trans. GIS.

[33]  K. Poole,et al.  Winter habitat selection by female moose in western interior montane forests , 2006 .

[34]  V. Clarke,et al.  WHERE ANGEL FEARS TO TREAD: A TEST IN THE NEW YORK CITY SUBWAY OF THE ROBBERY/DENSITY HYPOTHESIS , 2006 .

[35]  William Smith,et al.  Determining How Journeys-to-Crime Vary: Measuring Inter- and Intra-Offender Crime Trip Distributions , 2009 .

[36]  W. Frey,et al.  Multivariate explanation of the 1985–1990 and 1995–2000 destination choices of newly arrived immigrants in the United States: the beginning of a new trend? , 2007 .