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Studies of police organizations typically involve examining predictors consistent with structural contingency theory while comparatively fewer have examined the applicability of institutional theory. The purpose of the current study is to examine the influence of institutional factors on the elaboration of organizational structure, specifically the incorporation of a crime analysis unit into a police organization's structure.

Data were obtained using a 2002 mail survey of law enforcement agencies focusing on the crime analysis function and environmental factors influencing the organization of that function. Additional information complementing the survey data was obtained through telephone interviews with representatives from 12 of the surveyed departments.

Multivariate results show that, consistent with contingency theory, size is an important predictor of structural elaboration. Analyses and interview responses suggest that institutional factors, particularly accreditation standards, may play some role in shaping organizational structures.

The survey was returned by 160 agencies for a response rate of 56 percent but analyses were based on relatively small samples of 67‐77 agencies. Telephone interviews were conducted using a purposive, non‐probability sampling method.

The results of this exploratory study provide insight into factors contributing to the adoption of structures and strategies in policing.

The research is one of only a small number of studies in policing to explicitly test propositions derived from institutional theory, particularly the concept of institutional isomorphism.

The purpose of this paper is to examine national survey data of police agencies in the USA to explore the current state of crime analysis integration to patrol crime reduction work.

The data examined in this paper are from a national quantitative survey which sought to understand how crime analysis results are used by officers as well as higher ranking personnel in the patrol division and what types of strategies are implemented using crime analysis.

The findings show that the routine use of crime analysis is not well integrated. Despite the low integration, however, some differences were found. Management uses crime analysis the most overall, but officers and first-line supervisors use tactical crime analysis more routinely than management, where management personnel use evaluation most routinely. Tactical crime analysis is used most often for directed patrol, strategic for both directed patrol and general information, and evaluation for both general information and crime prevention. Analysis of using analysis proactively shows that agencies use tactical crime analysis most proactively, followed by the strategic crime analysis, then evaluation.

The study relies on self-report surveys, so the results may suffer from some of the general limitations of self-reports. Also, the study resulted in a lower response rate than surveys of police agencies typically achieve. Although responding and non-responding agencies were comparable in terms of population size, number of officers, and region of the country, the response rate was about 55 percent. However, it is a possibility based on the analysis results that non-responses may reflect a disinterest in the topic or the lack of integration of crime analysis.

This is the first national survey that focussed specifically on crime analysis integration in patrol work for crime reduction. The value of the results presented here are in the description of the current state of crime analysis integration in the USA which has not been investigated in such depth before and the identifications of gaps in both research in practice.

The purpose of this paper is to introduce architecture of an Intelligent Decision Support System to fulfill the emerging responsibilities of law enforcement agencies.

The proposed Intelligent Police System (IPS) is designed to meet the emerging requirements and provide information at all levels of decision making by introducing a multi-level structure of user interface and crime analysis model. The proposed framework of IPS is based on data mining and performance measurement techniques to extract useful information like crime hot spots, predict crime trends and rank police administration units on the basis of crime prevention measures.

IPS has been implemented on actual Indian crime data provided by National Crime Records Bureau (NCRB), which illustrates effectiveness and usefulness of the proposed system. IPS can play a vital role in improving outcome in the crime investigation, criminal detection and other major areas of functioning of police organization by analyzing the crime data and sharing of the information.

The research in intelligent police information system can be enhanced with some important additional features which include web-base management system, geographical information system, mobile adhoc network technology, etc.

IPS can easily be applied to any police system in the world and can equally be useful for any law enforcement agencies for carrying out homeland security effectively.

The research reported in this manuscript is outcome of the research project funded by NCRB. This paper is the first attempt to build framework of IPS for Indian police who deal with large volume and high rate of crimes that are unmatched to any police force of the world.

Traditional crime linkage studies on serial sexual assaults have relied predominantly on a binary crime linkage approach that has yielded successful results in terms of linkage accuracy. Such an approach is a coarse reflection of reality by focussing mainly on the outcome of an offence, neglecting the forceful differences due to the intricate offender-victim interaction. Only few researchers have examined sexual assaults through the lens of a sequence analysis framework. This paper aims to present the first empirical test of offence sequence-based crime linkage, moving beyond exploratory analyses.

Offence accounts from 90 serial sexual assault and rape victims from the UK were analysed and sequentially coded. Sequence analysis allowed to compare all offences combinations regarding their underlying sequence of events. The resulting comparison was transformed and plotted in two-dimensional space by multidimensional scaling analysis for a visual inspection of linkage potential. The transformed proximities of all offences were used as predictors in a receiver operating characteristic analysis to actually test their discriminatory accuracy for crime linkage purpose.

Sequence analysis shows significant discriminatory accuracy for crime linkage purpose. However, the method does perform less well than previous binary crime linkage studies.

Several limitations due to the nature of the data will be discussed.

The practical limitations are as follows: the study is a potential practical value for crime analysts; it is a complimentary methodology for statistical crime linkage packages; it requires automated coding to be useful; and it is very dependent on crime recoding standards.

The exploratory part of this study has been published in a book chapter in 2015. However, to the best of the authors’ knowledge, the succinct test of crime linkage accuracy is the first of its kind.

This paper aims to demonstrate the benefit of applying crime script analysis at a macro level to expose money laundering as an interconnected series of events that contradict understanding which defines money laundering as a series of typological studies.

Using previously captured data and detailed research studies alongside the professional experience of the author, this study creates a horizontal crime script for three distinct methods of money laundering – to determine characteristic nodes and identify pathways.

Despite significant research into money laundering typologies, this paper demonstrates that the process of money laundering cannot be separated into typological studies. By applying crime script analysis to each method of money laundering prior to analysing the nodes, activities and behaviours, money laundering can be identified as a series of interconnected services, business formalities and management activities.

This study is of value to government policymakers, regulators and financial institutions considering future preventative measures. It is also of value to financial investigators and law enforcement agencies intent on investigating money laundering. By applying the method of analysis outlined in this paper to each method of money laundering and then combining them together, it is possible to understand money laundering as a holistic process, in which webs of interconnected criminal events can be identified for investigative and preventative purposes.

Interest in crime script analysis is now supporting understanding of different crime types. This study goes beyond the analysis of individual types of money laundering and applies it in a “test format” against three methods of money laundering to create, for the first time, a holistic or macro appreciation of the interconnected series of events, activities and behaviours which exist beyond the characteristics routinely captured in money laundering typology reports.

The purpose of this paper is to examine the influence of reference data, input address quality, and crime type on completeness and positional accuracy of street geocoded crime events.

Existing data were analyzed using ArcGIS, including crime incident information, street network reference data, and address point and/or parcel reference data. Geocoding completeness was determined by the overall match rate. Positional accuracy was determined by comparing the Euclidian distance between street geocoded locations of crime events to the corresponding address point/parcel geocoded location.

Results indicate that match rates vary by reference data, input address quality, and crime type. Local street centerline files consistently produced match rates that were as good as – and in many cases superior to – other types of reference data, including commercial data. Greater variability in positional accuracy was observed across reference data when crime type and input address quality was considered, but results were consistent with positional accuracy analysis conducted using data from other disciplines.

Results provide researchers and practitioners with valuable guidance and insight into one of the most basic – albeit fundamental – procedures related to the spatio‐temporal analysis of crime, suggesting that reference data required to produce geocoded crime incidents successfully and of high quality does not necessarily mean a large financial investment on the part of law enforcement agencies or researchers interested in the geospatial analysis of crime.

Prior to this investigation, a comprehensive examination of the impact of data quality on geocoded crime events was absent from the literature.

The purpose of this paper is to show that despite the critical importance of using accurate data when identifying geographic patterns and studying hotspots, few have explored the data quality issues introduced by Geographic Information Systems (GIS) software applications. While software manufacturers provide some information about the address matching process, critical details are left out or are buried in technical, and sometimes proprietary, jargon. The purpose of this paper is to address these issues.

The paper demonstrates, with three datasets of 100 cases each, how the assumptions built into popular GIS software produce systematically missing data during the data importing process commonly referred to as address matching.

Inclusion of directional indicators and zip codes are more important than previously thought. The results highlight the critical need to provide complete descriptions of research methodology. All geographic analyses must be accompanied with: information about the hit rate (percent of cases plotted), details about the software and process used to import tabular crime data, information about the software parameters set for the importation process (geocoding preferences), reference information about the street file used; and, an examination of the missing cases to identify some of the sampling error. When forecasting crime issues or identifying hot spots, analysts must be cognizant of the differential impact this bias will have on the generalizability of the results.

The paper explores previously neglected issues in data quality introduced by GIS software applications.

There is a lack of understanding in the literature on the spatial relationships between crime and house price. This paper aims to test the impact of spatial effects in the housing market, how these are related to the incidence of crime and whether effects vary by the type of crime.

The analysis initially explores univariate and bivariate spatial patterns in crime and house price data for the Belfast Metropolitan Area using Moran’s I and Local Indicator Spatial Association (LISA) models, and secondly uses spatial autoregression models to estimate the role of crime on house prices. A spatially weighted two-stage least-squares model is specified to analyse the joint impact of crime variables. The analysis is cross sectional, based on a panel of data.

The paper illustrates that the pricing impact of crime is complex and varies by type of crime, property type and location. It is shown that burglary and theft are associated with higher-income neighbourhoods, whereas violence against persons, criminal damage and drugs offences are mainly associated with lower-priced neighbourhoods. Spatial error effects are reduced in models based on specific crime variables.

The originality of this paper is the application of spatial analysis in the study of the impact of crime upon house prices. Criticisms of hedonic price models are based on unexplained error effects; the significance of this paper is the reduction of spatial error effects achievable through the analysis of crime data.

This paper aims to present the evaluation results of a practice‐based research partnership to develop and implement a new police organizational model for crime reduction into one police agency which was implemented based on the best practices of problem‐oriented policing, hot spots policing, and Compstat.

A qualitative process evaluation of organizational changes and an impact evaluation examining specific crime types was conducted over seven years to determine increased efficiency, collaboration, and effectiveness of the police department's crime reduction strategies.

The process evaluation found that the agency improved its crime analysis capabilities as well as its coordination and communication, expanded its problem solving activities, and made a significant cultural shift towards incorporating problem solving and accountability throughout the organization. The impact evaluation found that the crimes addressed in the implementation – theft from vehicle – did decrease overall and when compared to other crime types and to neighboring jurisdictions.

The limitations of the research are that these results are from one agency and that the impact evaluation is not conclusive.

The work was carried out over seven years in which the collaboration between the researcher and the agency was seminal. The model developed can be used by other police departments, and a key finding was that strong leadership played the most important role in the implementation of the crime reduction strategies and accountability practices.

Abuja, the capital city of Nigeria, is one of the fastest growing capital cities in sub-Saharan Africa. Recently, the city is experiencing an alarming rate of burglary and violent crimes, while the city planning management frameworks lacks adequate and effective crime mapping, monitoring and management techniques necessary for achieving liveable and safe environment for habitation despite its grandiose spatial planning and aesthetically appealing architectural design as a modern city. Based on police crime records (2007–2018) and geospatial analysis, this paper aims to provide adequate understanding on the interplay of land use configuration and burglary crime formation in residential neighbourhoods of Abuja, Nigeria.

The methods used for the purpose of data collection includes; field survey, Block Environmental Inventory, while inferential statistics and Geographic Information System tools was used for data analysis. The analysis established that Lagos, Nsukka and Enugu Streets are hotspots; while Chief Palace street, Ladoke Akintola and Oka-Akoko streets were found to be cold spots.

This study, however, established the applicability of crime pattern, opportunity theory and routine activity theory in understanding the rising burglary crime events in the study areas and the link between physical characteristics of street block typology and burglary crime pattern. The results of the analysis has in a way affirmed the positions of the theories, while disagreed with them in cases where the results indicated contrary outcome.

This paper concluded with inference drawn from the results that supported mixed-use development but with built-in crime prevention through environmental design strategies as effective burglary crime prevention mechanisms that contribute to crime rate reduction.