Where Broken Windows Should Be Fixed

Objectives: Introduce a systematic method to identify areas with similar levels of disorder (from serene, to “tipping,” to crime-ridden) which is crucial for a valid empirical test of Broken Windows Theory (BWT). Methods: Systematic social observation data are used of almost 2,000 locations in the city of Amsterdam, the Netherlands. Spatially constrained hierarchical agglomerative clustering is used to aggregate individual observation locations to form homogeneous areas. Davies–Bouldin index and intraclass correlation coefficient are used to objectively identify the optimum number of clusters. Results: The newly identified areas differ from administrative neighborhoods as well as hot spots of disorder. The regionalization method provides a tentative solution to both the “zonation” and “aggregation” problems of the modifiable areal unit problem liable to affect empirical studies of BWT. Conclusions: Hot spot analysis fails to identify areas with moderate levels of disorder, which impedes testing the basic precept of BWT. Our results may partly explain why the evidence on the effectiveness of order maintenance policing remains inconclusive. We suggest that randomized controlled trials of order maintenance policing should be performed on these new areas rather than in hot spots of disorder.

[1]  A. Getis.  The Analysis of Spatial Association by Use of Distance Statistics , 2010 .

[2]  Geoffrey M. Jacquez,et al.  Spatial Cluster Analysis , 2008 .

[3]  Linda Steg,et al.  The Spreading of Disorder , 2008, Science.

[4]  David Thacher,et al.  Order Maintenance Policing , 2014 .

[5]  Lawrence W. Sherman,et al.  General deterrent effects of police patrol in crime “hot spots”: A randomized, controlled trial , 1995 .

[6]  Raúl Ramos Lobo,et al.  Supervised regionalization methods: A survey , 2006 .

[7]  D. Oberwittler,et al.  Why Small Is Better: Advancing the Study of the Role of Behavioral Contexts in Crime Causation , 2009 .

[8]  J. Wilson,et al.  BROKEN WINDOWS: THE POLICE AND NEIGHBOURHOOD SAFETY , 1982 .

[9]  Stephen W. Raudenbush,et al.  1. Ecometrics: Toward a Science of Assessing Ecological Settings, with Application to the Systematic Social Observation of Neighborhoods , 1999 .

[10]  Ralph B. Taylor,et al.  Multilevel Longitudinal Impacts of Incivilities: Fear of Crime, Expected Safety, and Block Satisfaction , 2003 .

[11]  Sue-Ming Yang,et al.  Assessing the Spatial–Temporal Relationship Between Disorder and Violence , 2010 .

[12]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Ralph B. Taylor,et al.  Ecological assessments of community disorder: Their relationship to fear of crime and theoretical implications , 1996, American journal of community psychology.

[14]  Jill P. Mesirov,et al.  Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data , 2003, Machine Learning.

[15]  Robert J. Sampson,et al.  Systematic Social Observation of Public Spaces: A New Look at Disorder in Urban Neighborhoods1 , 1999, American Journal of Sociology.

[16]  A S Fotheringham,et al.  The Modifiable Areal Unit Problem in Multivariate Statistical Analysis , 1991 .

[17]  Stephen D. Gottfredson,et al.  Block Crime and Fear: Defensible Space, Local Social Ties, and Territorial Functioning , 1984 .

[18]  Anthony A. Braga,et al.  Problem-oriented policing in violent crime places: A randomized controlled experiment , 1999 .

[19]  Stan Openshaw,et al.  Modifiable Areal Unit Problem , 2008, Encyclopedia of GIS.

[20]  Wesley G. Skogan,et al.  MEASURING WHAT MATTERS: CRIME, DISORDER AND FEAR , 1997 .

[21]  R. Simes,et al.  An improved Bonferroni procedure for multiple tests of significance , 1986 .

[22]  Elizabeth R. Groff,et al.  The Philadelphia Foot Patrol Experiment: A Randomized Controlled Trial of Police Patrol Effectiveness in Violent Crime Hotspots , 2011 .

[23]  J. Figueira-Mcdonough.  : Disorder and Decline: Crime and the Spiral of Decay in American Neighborhoods , 1992 .

[24]  Anthony A. Braga,et al.  POLICING CRIME AND DISORDER HOT SPOTS: A RANDOMIZED CONTROLLED TRIAL* , 2008 .

[25]  Travis C. Pratt,et al.  Revisiting Broken Windows Theory: Examining the Sources of the Discriminant Validity of Perceived Disorder and Crime , 2010 .

[26]  Marc Ouimet,et al.  Aggregation bias in ecological research: How social disorganization and criminal opportunities shape the spatial distribution of juvenile delinquency in Montreal , 2000 .

[27]  Diansheng Guo,et al.  Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP) , 2008, Int. J. Geogr. Inf. Sci..

[28]  A. Reiss,,et al.  SYSTEMATIC OBSERVATION OF NATURAL SOCIAL PHENOMENA , 1971 .

[29]  Yili Xu,et al.  Discovering the Impact of Community Policing: The Broken Windows Thesis, Collective Efficacy, and Citizens’ Judgment , 2005 .

[30]  P. Moran.  Notes on continuous stochastic phenomena. , 1950, Biometrika.

[31]  M. Fortin,et al.  Spatial pattern and ecological analysis , 1989, Vegetatio.

[32]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[33]  Geoffrey M. Jacquez,et al.  Space-time visualization and analysis in the Cancer Atlas Viewer , 2005, J. Geogr. Syst..

[34]  John Wooldredge,et al.  EXAMINING THE (IR)RELEVANCE OF AGGREGATION BIAS FOR MULTILEVEL STUDIES OF NEIGHBORHOODS AND CRIME WITH AN EXAMPLE COMPARING CENSUS TRACTS TO OFFICIAL NEIGHBORHOODS IN CINCINNATI , 2002 .

[35]  Lena Vogler.  The Handbook Of Geographic Information Science , 2016 .

[36]  John R. Hipp.  Block, Tract, and Levels of Aggregation: Neighborhood Structure and Crime and Disorder as a Case in Point , 2007 .

[37]  G. P. Patil,et al.  Spatially constrained clustering and upper level set scan hotspot detection in surveillance geoinformatics , 2006, Environmental and Ecological Statistics.

[38]  L. Anselin.  Local Indicators of Spatial Association—LISA , 2010 .

[39]  P. Wilcox,et al.  Busy Places and Broken Windows? Toward Defining the Role of Physical Structure and Process in Community Crime Models , 2004 .