Spatial Analysis of Crime Report Datasets

Transcription

1 Spatial Analysis of rime Report Datasets Shashi Shekhar 1, Mete elik 1, Betsy George 1, Pradeep Mohan 1, Ned Levine 2, Ronald E. Wilson 3, Pronab Mohanty 4 1 University of Minnesota, Minneapolis, USA, {shekhar, mcelik, bgeorge, mohan}@cs.umn.edu 2 Ned Levine and Associates, Houston, TX, USA, ned@nedlevine.com 3 National Institute of Justice, Washington D., USA, Ronald.Wilson@usdoj.gov 4 Indian Police Service, Government of India, moha0302@umn.edu Abstract Given a spatial crime report dataset, spatial analysis aims at identifying and quantifying spatial relationships and distributions of crime patterns. This article provides an overview of spatial analysis of crime report datasets. It presents examples of spatial crime patterns and describes the features of current analytical tools that are available for use by the law enforcement community. We illustrate the utility of these spatial crime patterns and analytical tools using an example case study in an Indian setting.

2 Spatial Analysis of rime Report Datasets Introduction Spatial analysis can be defined as a broad range of statistical/analytical techniques used to manipulate spatial data with the objective of deriving useful patterns and relationships. In the context of mapping and analysis for public safety, spatial analysis often refers to a broad range of exploratory statistical techniques which are based on several geographic principles and criminological theories. One of the basic questions of spatial analysis on crime datasets is "Why does crime concentrate where it does?" (Buchanan, 2008). Given a spatial crime report dataset, the goal of spatial analysis is to understand and quantify the patterns of spatial distribution of crime. Spatial analysis of crime is often associated with crime mapping (Wilson & Filbert, 2008), which is a hybrid of several social sciences: geography, sociology and criminology. Spatial analysis is conventionally used as a tool to strengthen the statistical and analytical foundations of crime mapping. Spatial analysis of crime reports has many uses, two common examples being strategic and tactical objectives. Spatial analysis methods that focus on identifying concentrations of crime are considered strategic in nature. Law enforcement agencies use strategic analysis to identify suitable force deployment strategies for crime mitigation. On the other hand, tactical analysis is an offender or offender type centric analysis, which aids law enforcement to identify and target a particular offender or a particular type of offenders. Tactical analysis may be suitable in serial crime investigations and to understand patterns of a particular type of crime (e.g. auto-thefts). This article provides an overview of the spatial analysis of crime report datasets by providing some background concepts from environmental criminology and outlines the broad features of the current state of the art. Further, we provide examples of spatial crime patterns of both a strategic and tactical nature. We also describe the features of current analytical tools that are available for use by the law enforcement community. We illustrate the utility of these spatial crime patterns and analytical tools using an example case study in an Indian crime analysis setting. Finally we describe the future trends in spatial analysis of crime reports. Background Spatial analysis of crime is based on three major theories from environmental criminology (Brantingham & Brantingham, 1990) namely, the Routine Activity Theory (RAT), the rime Pattern Theory (PT) and Rational hoice Theory (RT). RAT suggests that the location of a crime is related to the offender s frequently visited areas. PT extends this theory on a spatial model as shown in Figure 1. This spatial model consists of nodes (e.g. frequently visited areas such as home, work, entertainment/ recreation), paths (e.g. routes between nodes), and edges (i.e. boundaries of an activity footprint). PT suggests that crime locations are often close to the edges, i.e. near the offender's activity boundaries, where the residents may not recognize the criminal. In Figure 1, represents the actual crime locations. RT suggests that offenders pick and choose targets based on factors such as availability of escape routes, absence of guardians and appropriate times where the target is most vulnerable etc. These theories form the basis of spatial crime patterns. Hence, any new crime patterns may have to be explained by these theories. Spatial analysis techniques in spatial statistics (Eck et al.(2005), Levine (2004), Schabenberger & Gotway(2005)) are primarily based on several hypothesis and require a lot of computational effort. Home Figure 1: rime Pattern Theory Entertainment & Recreation Work Legend : rime Incident Location

3 Main Focus of the Article In this section, we outline two simple examples of crime patterns and describe the features of existing analytical tools to identify them. Other spatial analysis methods are available; readers may refer to Levine Figure 2a: rime Hotspots in a Large US ity Figure 2b: Spatial Network Hotspots in the same city. (2004) for more examples. Most spatial analysis of crime reports that has a strategic objective identify spatial concentrations of crime called crime hotspots. rime hotspots refer to groups or areas of crime that require increased law enforcement attention for crime mitigation. For example, Figure 2a shows crime hotspots in a large US city; the hotspots are illustrated using ellipses. Law enforcement agencies are particularly interested in analytical frameworks that identify crime hotspots constrained by spatial networks, such as road networks. This raises questions such as "What is the structure of a crime hotspot on a spatial network?" Figure 2b illustrates the structure of crime hotspots in the same US city, constrained by the street network. Tactical analysis of crime is offender/offender type centric and aims to answer questions such as, "Why do certain types of offenders take certain routes at certain times?" The most common types of tactical crime analysis are geographic profiling (Rossmo, 2000) which helps to identify key areas of activity of a particular offender. Techniques such as Journey To rime (Levine, 2004) are used to identify the frequent routes taken by particular types of offenders. For example, Figure 3c illustrates the frequent train routes taken by offenders to commit auto-theft crimes in Baltimore-ounty, USA. Figure 3a shows the input local train network, Figure 3b shows the lines connecting crime locations and offender homes and Figure 3c shows the frequent routes on the local train network by the offenders to commit crimes. This pattern reveals that law enforcement agencies may have to focus crime reduction efforts on access intersections between train networks and road networks in order to target auto-theft offenders. rime Analysis Tools: There are several tools for both strategic and tactical crime analysis. Some commonly used tools are rimestat (Levine, 2004) and Rigel: Geographic Profiling (Rossmo, 2000). We describe the features of rimestat, which is a spatial statistical tool for the analysis of crime incidents (Levine 2004). rimestat is widely used by crime analysts and practitioners. The program is Windowsbased and interfaces with most desktop GIS programs. The purpose is to provide supplemental statistical tools to aid law enforcement agencies and criminal justice researchers in their crime mapping efforts. rimestat is being used by many police departments around the country as well as by criminal justice and Figure 3a: Baltimore-ounty Train Network Figure 3b: Lines connecting crime locations and offender residences. Figure 3c: Journey to rime

4 other researchers. The tool contains utilities such as Spatial Description, Distance Analysis, Hotspot Analysis and rime Travel Demand Modeling. One of the major shortcomings of rimestat is its lack of computational scalability to analyze large crime datasets. Application ase Study The validity of spatial data analysis in getting insights regarding crime pattern analysis in the Indian context can be inferred from the experience of one of the co-authors in There was a spate of unsolved robberies in and around Kolar, a town in Karnataka. On doing a detailed examination the police authorities found that the crimes were invariably occurring in and around a railway track leading from the Guntakal Railway Junction in neighboring Andhra Pradesh state to Bangalore. Further analysis led to the crucial breakthrough that a particular professional gang from Guntakal had recently been released from prison and had the same modus operandi as seen in the current robberies. This led to the discovery and arrest of the gang members. But, the entire process took nearly two to three years and in the meantime the crimes continued apace. Figure, 4a illustrates the train route originating from Guntakal train station and Figure, 4b illustrates the areas where the property offences and burglaries had occurred. The crime locations are illustrated by red ellipses. If analytical tools such as rimestat had been used by law enforcement in the above scenario the crime could have likely been solved much earlier. However, it would be more helpful if current analytical tools and methods took the temporal semantics of spatio-temporal data sets into account rather than just analyzing their space-time interaction. A more general way to fit spatial data analysis to crime analysis in the Indian context would be to include the Modus Operandi Bureaux or MOB files maintained by all police stations across the country. They reveal the nature and methodology of particular gangs or professional property offenders and this data can then be integrated with real-time spatial occurrences to obtain accurate spatio-temporal profiles of offenders. This can optimize resource utilization by the Indian Police which is chronically starved of both material resources and manpower. Future Trends Many crime activities are spatio-temporal in nature; Ratcliffe (2004) identifies a Hotspot matrix and describes a framework for spatio-temporal hotspot analysis. However, in a practical setting not only are the type of crime activities time dependent, but so is the transportation network. The time dependence of the transportation network varies the level of opportunity, the number of targets and the number of guardians. Figure 4a: Train route originating from Guntakal station in Andhra Pradesh that was used by offenders. Figure 4b. Ellipses showing the areas of Property offences and burglaries committed by offenders along the Train route originating from Guntakal station. ourtse y: Google Maps (best viewed in color) ourtsey: Google Maps (best viewed in color)

5 In the context of criminological theories such as the routine activity theory and the crime pattern theory, this also affects the nature of crime patterns on spatio-temporal networks. urrent methodologies have three main limitations: (1) they do not adequately model richer temporal semantics beyond space-time interaction, (2) they do not satisfactorily explain the cause of detected hot spot locations on spatial networks, such as roads, trains and (3) they do not effectively model heterogeneities across spatial networks, e.g. multi-modal urban transportation modes (such as train/metro networks and roads). With the increase in the amount of crime reports that are being archived, law enforcement agencies, public policy agencies and state agencies require computationally efficient analytical methods that can scale to very large datasets. urrent tools such as rimestat take around 2.5 hours to compute crime hotpots on a database of crime reports. Useful access methods such as Self-Join Indices were proposed by Mohan et al. (2008) which reduced the response time by about a factor of 40. Scalable spatiotemporal analytical frameworks for analyzing large crime report databases would be required. Also, existing spatial database management system vendors need to support access methods such as the self-join index so that spatial analysis tools can take advantage of such computationally efficient frameworks. onclusion A broad overview of the current trends in spatial analysis of crime report datasets was presented. This article highlighted new spatial and spatio-temporal crime patterns that have firm theoretical and statistical foundations which are very useful to law enforcement agents to mitigate crime. The article also highlights the utility of these patterns by providing a case study of their applicability in an Indian crime mitigation setting. We also provide directions highlighting the need for improved spatio-temporal pattern semantics and scalable analytical support for analyzing large crime report datasets. Acknowledgments The authors would like to thank Kim Koffolt for her timely comments to improve the readability of this article. The authors would like to thank Michael Robert Evans for his timely comments on the article. The authors would also like to thank the members of the Spatial Database Research group at the University of Minnesota for their helpful comments. This work was supported by grants from NSF, USDOD, NIJ and Microsoft. References 1. elik, M., Shekhar, S., George, B., Rogers, J.P., Shine, J.A. (2007). Discovering and Quantifying Mean Streets: A Summary of Results. Retrieved October 25, 2007, Technical Report: TR07-025, University of Minnesota, Department of omputer Science and Engineering Website: 2. Eck, J., hainey, S., ameron, J., Leitner, M., Wilson, R.E. (2005). Mapping rime: Understanding Hot Spots. Retrieved January 28, 2008, NIJ Special Report, U.S Department of Justice, Office of Justice Programs, National Institute of Justice. Website: 3. Mohan, P., Shekhar, S., Levine, N., Wilson, R.E., George, B., elik, M. (2008). Should SDBMS support the Join Index?: A case study from rimestat. In Proceedings of 16 th AM SIGSPATIAL International onference on Advances in Geographic Information Systems (AM GIS 2008), alifornia, USA: Association of omputing Machinery. 4. Levine, N. (2004). rimestat: A spatial statistics program for the analysis of rime incident locations, version 3.1. Retrieved 2004, Ned Levine and Associates: Houston, TX/ National Institute of Justice, Washington D.. Website: 5. Ratcliffe, J.H.(2004) The Hotspot Matrix: A Framework for the Spatio-temporal targeting of rime Reduction. Police Practise and Research.5(1), Brantingham, P.L and Brantingham, P.L (1990).Environmental riminology. Long Grove, IL, USA: Waveland Press. 7. Rossmo, K.D (2000).Geographic Profiling.UK: R Press 8. Wilson, R.E, Filbert, K.M (2008), rime Mapping and Analysis. Encyclopedia of GIS 2008: Buchanan, M. (2008, April 30). Sin ities: The Geometry of rime. New Scientist, Schabenberger, O., & Gotway,.A. (2005). Statistical Methods for spatial data Analysis. UK: R Press