CrimAnalyzer: Understanding Crime Patterns in São Paulo

São Paulo is the largest city in South America, with high criminality rates. The number and type of crimes varies considerably around the city, assuming different patterns depending on urban and social characteristics. In this scenario, enabling tools to explore particular locations of the city is very important for domain experts to understand how urban features as to mobility, passersby behavior, and urban infrastructures can influence the quantity and type of crimes. In present work, we present CrimAnalyzer, a visualization assisted analytic tool that allows users to analyze crime behavior in specific regions of a city, providing new methodologies to identify local crime hotspots and their corresponding patterns over time. CrimAnalyzer has been developed from the demand of experts in criminology and it deals with three major challenges: i) flexibility to explore local regions and understand their crime patterns, ii) Identification of not only prevalent hotspots in terms of number of crimes but also hotspots where crimes are frequent but not in large amount, and iii) understand the dynamic of crime patterns over time. The effectiveness and usefulness of the proposed system are demonstrated by qualitative/quantitative comparisons as well as case studies involving real data and run by domain experts.

[1]  Sunil Yadav,et al.  Crime pattern detection, analysis & prediction , 2017, 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA).

[2]  Pemetaan Jumlah Balita,et al.  Spatial Scan Statistic , 2014, Encyclopedia of Social Network Analysis and Mining.

[3]  Jerry H. Ratcliffe,et al.  Aoristic analysis: the spatial interpretation of unspecific temporal events , 2000, Int. J. Geogr. Inf. Sci..

[4]  Hsinchun Chen,et al.  Criminal network analysis and visualization , 2005, CACM.

[5]  Martin A. Andresen,et al.  Spatial/Temporal Variations of Crime: A Routine Activity Theory Perspective , 2017, International journal of offender therapy and comparative criminology.

[6]  Tamara Munzner,et al.  A Nested Model for Visualization Design and Validation , 2009, IEEE Transactions on Visualization and Computer Graphics.

[7]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[8]  Daniel A. Keim,et al.  A Visual Analytics Approach for Crime Signature Generation and Exploration , 2016 .

[9]  Marinka Zitnik,et al.  NIMFA: A Python Library for Nonnegative Matrix Factorization , 2012, J. Mach. Learn. Res..

[10]  Hyunsoo Kim,et al.  Sparse Non-negative Matrix Factorizations via Alternating Non-negativity-constrained Least Squares , 2006 .

[11]  Pierre Goovaerts,et al.  Applying Geostatistical Analysis to Crime Data: Car-Related Thefts in the Baltic States. , 2010, Geographical analysis.

[12]  Abbas Sharifi,et al.  Crime Mapping and Spatial Analysis , 2003 .

[13]  David Windridge,et al.  Criminal pattern identification based on modified K-means clustering , 2016, 2016 International Conference on Machine Learning and Cybernetics (ICMLC).

[14]  Tong Wang,et al.  Learning to Detect Patterns of Crime , 2013, ECML/PKDD.

[15]  Timo Kostiainen,et al.  Generative probability density model in the self-organizing map , 2001 .

[16]  Jin Chen,et al.  A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP) , 2006, IEEE Transactions on Visualization and Computer Graphics.

[17]  Michael Kenneth Townsley,et al.  Visualising space time patterns in crime: the hotspot plot , 2008 .

[18]  J. Ratcliffe.  Crime Mapping: Spatial and Temporal Challenges , 2010 .

[19]  J. Ord,et al.  Local Spatial Autocorrelation Statistics: Distributional Issues and an Application , 2010 .

[20]  Ned Levine,et al.  CrimeStat IV: A Spatial Statistics Program for the Analysis of Crime Incident Locations, Version 4.0 , 2013 .

[21]  Zhang Ying.  Analysis of Crime Factors Correlation Based on Data Mining Technology , 2016, 2016 International Conference on Robots & Intelligent System (ICRIS).

[22]  Cláudio T. Silva,et al.  Wavelet-based visualization of time-varying data on graphs , 2015, 2015 IEEE Conference on Visual Analytics Science and Technology (VAST).

[23]  Jerry H. Ratcliffe,et al.  Aoristic Signatures and the Spatio-Temporal Analysis of High Volume Crime Patterns , 2002 .

[24]  Daniel A. Keim,et al.  Interpretation of Dimensionally-reduced Crime Data: A Study with Untrained Domain Experts , 2017, VISIGRAPP.

[25]  Jouko Lampinen,et al.  On the generative probability density model in the self-organizing map , 2002, Neurocomputing.

[26]  Peter Lindstrom,et al.  TTHRESH: Tensor Compression for Multidimensional Visual Data , 2018, IEEE Transactions on Visualization and Computer Graphics.

[27]  Ping Chen,et al.  Understanding the spatial distribution of crime based on its related variables using geospatial discriminative patterns , 2013, Comput. Environ. Urban Syst..

[28]  Ken Pease,et al.  THE TIME COURSE OF REPEAT BURGLARY VICTIMIZATION , 1991 .

[29]  Peter J. van Koppen,et al.  The Time to Rob: Variations in Time of Number of Commercial Robberies , 1999 .

[30]  Gang Wang,et al.  Crime data mining: a general framework and some examples , 2004, Computer.

[31]  Yang Xiang,et al.  Visualizing criminal relationships: comparison of a hyperbolic tree and a hierarchical list , 2005, Decis. Support Syst..

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

[33]  Hsinchun Chen,et al.  COPLINK: Visualization for Crime Analysis , 2003, DG.O.

[34]  Anton Borg,et al.  Crime Hotspots: An Evaluation of the KDE Spatial Mapping Technique , 2015, 2015 European Intelligence and Security Informatics Conference.

[35]  Spencer Ch.  The Utility of Hotspot Mapping for Predicting Spatial Patterns of Crime , 2008 .

[36]  Daniel A. Keim,et al.  Space‐in‐Time and Time‐in‐Space Self‐Organizing Maps for Exploring Spatiotemporal Patterns , 2010, Comput. Graph. Forum.

[37]  S. Chainey,et al.  Mapping Crime: Understanding Hot Spots , 2014 .

[38]  Michael Leitner,et al.  Visualization of Crime Trajectories with Self-Organizing Maps : A Case Study on Evaluating the Impact of Hurricanes on Spatio-Temporal Crime Hotspots , 2011 .

[39]  J. Ratcliffe.  A Temporal Constraint Theory to Explain Opportunity-Based Spatial Offending Patterns , 2006 .

[40]  Neil Stuart,et al.  When is a hotspot a hotspot? A procedure for creating statistically robust hotspot maps of crime , 2002 .

[41]  Sheng Wu,et al.  An visual analytics approach to explore criminal patterns based on multidimensional data , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[42]  Rachel Boba,et al.  Crime analysis with crime mapping , 2009 .

[43]  Daniel A. Keim,et al.  Making machine intelligence less scary for criminal analysts: reflections on designing a visual comparative case analysis tool , 2018, The Visual Computer.

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

[45]  Simone D. J. Barbosa,et al.  CrimeVis: An Interactive Visualization System for Analyzing Crime Data in the State of Rio de Janeiro , 2017, ICEIS.

[46]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[47]  S. Adorno.  Democracy in Progress in Contemporary Brazil: Corruption, Organized Crime, Violence and New Paths to the Rule of Law , 2013 .

[48]  Martin A. Andresen.  Mapping crime prevention: What we do and where we need to go , 2017, CrimRxiv.

[49]  D. Osgood.  Statistical Models of Life Events and Criminal Behavior , 2010 .

[50]  Hans Hagen,et al.  Understanding hotspots: a topological visual analytics approach , 2015, SIGSPATIAL/GIS.

[51]  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.

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

[53]  S. Shekhar,et al.  Crime Hotspot Detection: A Computational Perspective , 2016 .

[54]  Hui Chen,et al.  Multi-view display coordinated visualization design for crime solving analysis: Vast challenge 2014: Honorable mention for effective use of coordinated visualizations , 2014, 2014 IEEE Conference on Visual Analytics Science and Technology (VAST).

[55]  Creto Augusto Vidal,et al.  MSKDE - Using Marching Squares to Quickly Make High Quality Crime Hotspot Maps , 2016, 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI).

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

[57]  Sergio J. Rey,et al.  PySAL: A Python Library of Spatial Analytical Methods , 2010 .

[58]  Jiang Wu,et al.  Understanding Spatiotemporal Patterns of Multiple Crime Types with a Geovisual Analytics Approach , 2013 .

[59]  Paul A. Zandbergen,et al.  Kernel density estimation and hotspot mapping: examining the influence of interpolation method, grid cell size, and bandwidth on crime forecasting , 2014 .

[60]  C. Tappert,et al.  A Survey of Binary Similarity and Distance Measures , 2010 .

[61]  S. Chainey,et al.  GIS and Crime Mapping , 2005 .

[62]  R. Webster,et al.  Kriging: a method of interpolation for geographical information systems , 1990, Int. J. Geogr. Inf. Sci..