Investigating the Influences of Tree Coverage and Road Density on Property Crime

With the development of Geographic Information Systems (GIS), crime mapping has become an effective approach for investigating the spatial pattern of crime in a defined area. Understanding the relationship between crime and its surrounding environment reveals possible strategies for reducing crime in a neighborhood. The relationship between vegetation density and crime has long been under debate. The convenience of a road network is another important factor that can influence a criminal’s selection of locations. This research is conducted to investigate the correlations between tree coverage and property crime, and road density and property crime in the City of Vancouver. High spatial resolution airborne LiDAR data and road network data collected in 2013 were used to extract tree covered areas for cross-sectional analysis. The independent variables were inserted into Ordinary Least-Squares (OLS) regression, Spatial Lag regression, and Geographically Weighted Regression (GWR) models to examine their relationships to property crime rates. The results of the cross-sectional analysis provide statistical evidence that there are negative correlations between property crime rates and both tree coverage and road density, with the stronger correlations occurring around Downtown Vancouver.

[1]  S. Balasubramanian,et al.  Assessment of Spatial Distribution of Rural Crime Mapping in India: A GIS Perspective , 2013 .

[2]  Fahui Wang,et al.  Where the Jobs Are: Employment Access and Crime Patterns in Cleveland , 2002 .

[3]  Martin A. Andresen,et al.  Crime concentrations and similarities in spatial crime patterns in a Brazilian context , 2015 .

[4]  J. Mennis,et al.  Does vegetation encourage or suppress urban crime? Evidence from Philadelphia, PA , 2012 .

[5]  Yikang Du,et al.  How Do Vegetation Density and Transportation Network Density Affect Crime across an Urban Central-Peripheral Gradient? A Case Study in Kitchener - Waterloo, Ontario , 2016, ISPRS Int. J. Geo Inf..

[6]  Paul E. Bidanset,et al.  Evaluating Spatial Model Accuracy in Mass Real Estate Appraisal: A Comparison of Geographically Weighted Regression and the Spatial Lag Model , 2014 .

[7]  Kun Xie,et al.  Spatial analysis of highway incident durations in the context of Hurricane Sandy. , 2015, Accident; analysis and prevention.

[8]  David Hillier,et al.  Crime prevention through environmental design (CPTED): a review and modern bibliography , 2005 .

[9]  Elizabeth R. Groff,et al.  The Criminology of Place: Street Segments and Our Understanding of the Crime Problem , 2012 .

[10]  John Turner,et al.  Police-reported crime statistics in Canada, 2009 , 2010 .

[11]  Remote Sensing and Spatial Statistics as Tools in Crime Analysis , 2005 .

[12]  Lisa Wood,et al.  Planning safer suburbs: do changes in the built environment influence residents' perceptions of crime risk? , 2013, Social science & medicine.

[13]  F. E. Kuo,et al.  Environment and Crime in the Inner City , 2001 .

[14]  P. Brantingham,et al.  THE INFLUENCE OF STREET NETWORKS ON THE PATTERNING OF PROPERTY OFFENSES , 1994 .

[15]  Martin A. Andresen.  A spatial analysis of crime in Vancouver, British Columbia: a synthesis of social disorganization and routine activity theory , 2006 .

[16]  Jorge Ricardo da Costa Ferreira,et al.  GIS for Crime Analysis: Geography for Predictive Models , 2012 .

[17]  A S Fotheringham,et al.  Geographically weighted Poisson regression for disease association mapping , 2005, Statistics in medicine.

[18]  Sandra Bogar,et al.  Green Space, Violence, and Crime , 2016, Trauma, violence & abuse.

[19]  J. Law.  Health and the Environment: A Geographical Study of Drugs at Different School Neighbourhoods , 2012 .

[20]  Elin Waring,et al.  Coercive mobility and crime: A preliminary examination of concentrated incarceration and social disorganization , 2003 .

[21]  Heith Copes,et al.  ROUTINE ACTIVITIES AND MOTOR VEHICLE THEFT: A CRIME SPECIFIC APPROACH , 1999 .

[22]  H. D. McKay,et al.  Juvenile Delinquency and Urban Areas. , 1943 .

[23]  Matthew Zook,et al.  Artists and Bankers and Hipsters, Oh My! Mapping Tweets in the New York Metropolitan Region , 2014 .

[24]  Charles C Branas,et al.  Greening vacant lots to reduce violent crime: a randomised controlled trial , 2012, Injury Prevention.

[25]  T. M. Waliczek,et al.  The Impact of Community Gardens on Numbers of Property Crimes in Urban Houston , 2009 .

[26]  Jonathan Li,et al.  Derivation of the characteristics of the Surface Urban Heat Island in the Greater Toronto area using thermal infrared remote sensing , 2017 .

[27]  J. Mennis.  Integrating Remote Sensing and GIS for Environmental Justice Research , 2011 .

[28]  Andrew W. Eckerson.  Understanding the Relationship between Tree Canopy and Crime in Minneapolis, Minnesota using Geographically Weighted Regression , 2014 .

[29]  Juan C. Duque,et al.  Using remote sensing to assess the relationship between crime and the urban layout , 2014 .

[30]  Trisalyn A. Nelson,et al.  Predictive crime mapping , 2015 .

[31]  Jane Law,et al.  Bayesian Spatial Random Effect Modelling for Analysing Burglary Risks Controlling for Offender, Socioeconomic, and Unknown Risk Factors , 2012 .

[32]  Austin Troy,et al.  The relationship between tree canopy and crime rates across an urban–rural gradient in the greater Baltimore region - nrs_2012_troy_001 , 2013 .

[33]  Timothy D. Crowe,et al.  Crime prevention through environmental design : applications of architectural design and space management concepts , 1991 .

[34]  Jeffrey P. Prestemon,et al.  The Effect of Trees on Crime in Portland, Oregon , 2012 .

[35]  Yao Li,et al.  Experimental approach for identifying building surface materials based on hyperspectral remote sensing imagery , 2017 .