Geospatial Intelligence and
Law Enforcement
Journal
all Crimes have to happen somewhere
Below are some research papers about using GIS in law enforcement field:
Theft in Price-Volatile Markets: On the Relationship between Copper Price and Copper Theft (2011)
Sidebottom, A., Belur, J., Bowers, K., Tompson, L. and Johnson, S. D.
Abstract: Recently, against a backdrop of general reductions in acquisitive crime, increases have been observed in the frequency of metal theft offences. This is generally attributed to increases in metal prices in response to global demand exceeding supply. The main objective of this article was to examine the relationship between the price of copper and levels of copper theft, focusing specifically on copper cable theft from the British railway network. Results indicated a significant positive correlation between lagged increases in copper price and copper cable theft. No support was found for rival hypotheses concerning U.K. unemployment levels and the general popularity of theft as crime type. An ancillary aim was to explore offender modus operandi over time, which is discussed in terms of its implications for preventing copper cable theft. The authors finish with a discussion of theft of other commodities in price-volatile markets.
Back to the Future: using space-time patterns to better predict the location of street crime (2010)
Tompson, L; Townsley, M
Abstract: Crime analysts attempt to identify regularities in police recorded crime data with a central view of disrupting the patterns found. One common method for doing so is hotspot mapping, focusing attention on spatial clustering as a route to crime reduction (Chainey & Ratcliffe, 2005; Clarke & Eck, 2003). Despite the widespread use of this analytical technique, evaluation tools to assess its ability to accurately predict spatial patterns have only recently become available to practitioners (Chainey, Tompson, & Uhlig, 2008). Crucially, none has examined this issue from a spatio-temporal standpoint. Given that the organisational nature of policing agencies is shift based, it is common-sensical to understand crime problems at this temporal sensitivity, so there is an opportunity for resources to be deployed swiftly in a manner that optimises prevention and detection. This paper tests whether hotspot forecasts can be enhanced when time-of-day information is incorporated into the analysis. Using street crime data, and employing an evaluative tool called the Predictive Accuracy Index (PAI), we found that the predictive accuracy can be enhanced for particular temporal shifts, and this is primarily influenced by the degree of spatial clustering present. Interestingly, when hotspots shrank (in comparison with the all-day hotspots), they became more concentrated, and subsequently more predictable. This is meaningful in practice; for if crime is more predictable during specific timeframes, then response resources can be used intelligently to reduce victimisation.
1st Paragraph: Law enforcement professionals are continually implementing new technologies that improve how they serve the public. As different types of hardware and software solutions are deployed in a police agency, it becomes increasingly difficult to integrate these tools. An agency can quickly become overwhelmed with large volumes of data.
GIS for Crime Analysis: Geography for Predictive Models (2000)
Jorge Ferreira, Paulo João and José Martins
A kernel density estimation method for networks, its computational method and a GIS‐based tool (2008)
Atsuyuki Okabea, Toshiaki Satohb & Kokichi Sugiharac
When, where, if, and but’: qualifying GIS and the effect of streetlighting on crime and fear (2006)
Rachel Pain, Robert MacFarlane, Keith Turner, Sally Gill
Abstract: The term crime analysis refers to a concept and to a discipline practiced in the policing community. It includes analysis of more than just a crime, which is why some authors refer to it as public safety analysis. However, over the last few years crime analysis has become a general term that includes a lot of research subcategories: intelligence analysis, criminal investigative analysis, tactical crime analysis, strategic crime analysis, operation analysis and administrative crime analysis. Crime mapping and spatial analysis complements all of them and plays a crucial role in defining new forms of representation and visualization to better understand crime and to respond adequately to the problem of criminality. A new worldwide socio-economical order lead to an increasing number on crime rates and raised the need to find new ways to handle information about criminality. To better understand its causes, local, regional and national security authorities turned to new decision support tools such as Geographic Information Systems (GIS) and other information technologies to find better solutions. To understand the magnitude of all the variables involved it is necessary to spatially capture and correlate them. Only by doing that it´s possible to quantify and qualify some hidden aspects of the phenomena. The city of Lisbon with is new proposed administrative division, reducing from 53 to 24 “freguesias” (minimum administrative division and similar to parish’s) implies an enormous degree of uncertainty in the observation and location of criminal data. As the crime is not treated with an exact point, but at the level of parish, it implies that larger parishes are treated by the average crime regardless of place of occurrence. This research combines statistical methods (cluster analysis) and spatial models created with GIS, based on police crime reports. It also details a framework for short-term tactical deployment of police resources in which the objective is the identification of areas where the crime levels are high (enough) to enable accurate predictive models as well as to produce rigorous thematic maps. In recent years police services have engaged on proactive and IntelligenceLed Policing (ILP) methods. This advance was coincident with the recognition of law-enforcement solutions at local level. This paper also engages an approach to ILP as a methodology to provide the necessary tools for Decision Support System (DSS) of police departments.
Abstract: We develop a kernel density estimation method for estimating the density of points on a network and implement the method in the GIS environment. This method could be applied to, for instance, finding ‘hot spots’ of traffic accidents, street crimes or leakages in gas and oil pipe lines. We first show that the application of the ordinary two‐dimensional kernel method to density estimation on a network produces biased estimates. Second, we formulate a ‘natural’ extension of the univariate kernel method to density estimation on a network, and prove that its estimator is biased; in particular, it overestimates the densities around nodes. Third, we formulate an unbiased discontinuous kernel function on a network. Fourth, we formulate an unbiased continuous kernel function on a network. Fifth, we develop computational methods for these kernels and derive their computational complexity; and we also develop a plug‐in tool for operating these methods in the GIS environment. Sixth, an application of the proposed methods to the density estimation of traffic accidents on streets is illustrated. Lastly, we summarize the major results and describe some suggestions for the practical use of the proposed methods.
Abstract: Geographical information systems (GIS) are increasingly used in England and Wales as a tool to monitor crime and aid community-safety planning. This is despite the widely known limitations of police-recorded data on crime victimisation, and concerns about the quality and specificity of available data on fear of crime. Meanwhile, improving streetlighting is a popular strategy both for improving community safety and for reducing fear of crime. In this paper we report on research carried out in Northumberland, northeast England, which aimed to identify locations most in need of new streetlighting. First, GIS crime hotspot maps and lighting coverage maps were analysed to identify potential areas to target. Qualitative rapid appraisal techniques were then used in these areas to explore local residents’ perceptions and understandings of the relationships between streetlighting, victimisation, and fear of crime. The qualitative data were used to interpret the hotspot maps further, and to inform the location and type of streetlighting interventions. The research demonstrates that people’s experiences of crime and fear, and their understandings of the relationships these have to streetlighting, are complex and reflective. At most, streetlighting was held to have a marginal and even then contradictory influence on the problems of crime and fear that people face. The implications are considered. We conclude that qualifying the outputs of GIS mapping was essential in this case, and has wide potential in critical policy research to promote more inclusive knowledge and more effective decisionmaking.