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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.

This paper focusses on analysing the impact of crime on the housing market in Los Angeles (LA) County. By looking at different types of crime instead of general crime measures and controlling by spatial dimension of prices and crime as well as endogeneity, a model is developed that allows for the understanding of how a specific crime impacts the housing market transaction price. To perform the analysis, the paper merges different data sets (crime, housing transaction and census data) and then computes the distances to crucial transport modes to control the accessibility features affecting housing prices. The latter allows estimating the association of housing prices and crime in the distance and estimating the impact on housing depending on it.

This paper focusses on the following crimes: aggravated assault, burglary (property crime), narcotics, non-aggravated assault and vandalism. The paper shows firstly how incidents of reported crime are distributed across space and how they are related to each other – thus highlighting crime models with spatial influences. Secondly, the research utilises instrumental variables within the methodology to estimate house prices using spatial analysis techniques while controlling for endogeneity. Thirdly, it estimates the direct impact of crime on house prices and explores the impact of housing and neighbourhood features.

Results suggest that house transaction prices and crime are closely correlated in two senses. Housing prices are endogenously negatively associated with the levels of narcotics and aggravated assaults. For narcotics, the impact of distance is shorter (1,000 m). However, for burglary, vandalism and non-aggravated assaults, the price reaction suggests a positive association: the further away the crime occurs, the higher the prices. The paper also shows the large spatial association of different crimes suggesting that they occur together and that their accumulation would make negative externalities appear affecting the whole neighbourhood.

The use of a huge database allows interesting findings, but one limitation can be to not have longer time observations to identify the crime evolution and its impact on housing prices.

Large implications as the relationship identified in this paper allow defining precise policies to avoid crime in different areas in LA. In addition, crime has significant but quantitative small effects on LA housing transaction prices suggesting that the effect depends on the spatial scale as well as lack on information about where the crimes are committed. Lack on information suggests low transparency in the market, affecting the transaction decision-taken process, affecting the risk perception and with relevant implications over household welfare.

This paper relates the spatial association among crimes defining the hotspots and their impacts on housing transaction prices.

The purpose of this paper is to conduct an independent analysis of all existing geographic profiling software packages to determine if any one is more accurate than the others or if any of the software systems are any more accurate than simple spatial distribution strategies at locating the home base of serial offenders.

An analysis was conducted of all existing geographic profiling software as well as three spatial distribution methods of profiling. Differences in accuracy were assessed using four different methods; dichotomous profile accuracy, simple error measurement, profile error distance, and average top profile area.

Results indicate that not only are the different profiling software systems no more accurate than the spatial distribution control methods, but that accuracy in general was marginal at best. In addition results indicated that certain crimes, such as commercial robbery, were particularly difficult to profile and that the number of crimes in a series was not by itself a good indicator of success of a profile.

The paper shows that future research needs to focus more on determining how various factors such as city type, crime type, road network and spatial aspects of a crime series (dispersion and search area) impact profiling accuracy. In addition future research should also endeavor to determine whether these advanced strategies are substantially more accurate than other simple profiling strategies such as human prediction. Finally, future research should also seek to examine geographic profiling in a real world setting and how geographic profiling impacts the success of open investigations.

Practically, this study casts doubt not only on the overall accuracy of profiling strategies in predicting the likely home location of an offender, but also on whether probability strategies are substantially better than spatial distribution strategies.

This research was the first to independently analyze all of the existing geographic profiling systems against control methods for the purpose of determining the accuracy of these different methods.

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.

Changing workplace demographics reflect a rising number of women in the traditionally male-dominated field of business. The purpose of this study is to investigate how upwardly mobile women may impact the commission and type of white-collar crime, contributing to the scarce literature on gender distinctions in criminal behavior while comparing criminal trends globally. Women’s increased representation in positions of power in business provides them with increased fraud opportunities prompting the authors to ask: in their areas of opportunity, do women and men commit the same types of white-collar crime and at the same rates, and how does this phenomena vary globally?

Using a database from the Institute for Fraud Prevention, 5,441 fraud cases are examined from 93 nations for the annual periods from 2002 until 2011. Ordinal logistic regression methods are used to test for differences in gendered criminal behavior by fraud offense type controlling for age, position, education, compensation level and country context.

Internationally, results from the study indicate that female fraudsters are three times more likely than male fraudsters to commission crimes of asset misappropriation in the workplace. Upon further investigation, stratifying the data by geographical region, findings from the study demonstrate that female fraudsters are more likely than male fraudsters to commit asset misappropriation in the following geographical regions: Africa (three times as likely), Asia (twice as likely), Canada (three times as likely), China (five times as likely), Europe (twice as likely), the Middle East (four times as likely), Oceania (four times as likely), the United Kingdom (eight times as likely) and the United States of America (twice as likely).

Evidence from this study should be of importance to multinational enterprises, auditors and fraud examiners, as asset misappropriation constitutes 87 per cent of all fraud cases globally. Further, these findings prompt the need for researchers to develop this area of research.

Financial crime continues to represent a crime type that costs billions of dollars per year. It is likely more widespread than any other criminal offence. Despite this, it remains an area that is often ignored, or at best neglected by police. Police agencies typically fail to invest resources and training in upskilling police in financial crime investigation. The current study evaluates an agency-wide training initiative undertaken by the Queensland Police Service (QPS), Australia.

The QPS mandated completion of an in-house online financial crime training program for all officers, up to and including the rank of senior sergeant. Matched pre- and post-training data of 1,403 officers were obtained.

The research found that police are under-trained in financial crime. The findings suggest that short online training programs can produce important improvements in knowledge and confidence in financial crime investigation. Critically, attitudes about this crime type which may be deterring officers from engaging in financial crime investigation can be improved.

The current research finds that police agencies need to more heavily invest in training officers to investigate financial crime and such investment will have positive outcomes. The first step involves improving knowledge, skills and attitudes towards this crime type. Further research is needed to understand why training, particularly related to attitudinal change, is more effective for different cohorts of police and how future training programs should be adapted to maximise success.

Business victimization is a serious and pervasive issue within the USA. According to recent estimates, roughly 2,058,194 businesses are victimized each year. Of those, approximately 33 percent of business victimization cases are solved. Taken together, it is important for research to examine factors that influence business victimization clearance. The purpose of this paper is to examine how broken windows enforcement, social disorganization, community and police organizational factors influence business robbery clearance using data from Houston, Texas over a two-year period from 2010 to 2012.

Using a hierarchical linear modeling strategy, the current study found no effect of broken windows tactics, social disorganization elements and various organizational, and community characteristics on business robbery clearance.

Significant effects were found for a number of incident and offense characteristics including gang involvement, business type, type of weapon used in the crime, the number of business entities in an area, and racial populous.

To date, few studies have examined factors that influence clearance rates for business robberies. Thus, the current study adds to and extends upon the literature in theoretically relevant ways by exploring how broken windows policing, social disorganization and various community/police organizational variables influence business robbery clearances in a large city.

The purpose of this paper is to examine the impact of unemployment on various types of crime in Greece.

A battery of econometric tests, including Granger causality tests, a variance decomposition analysis, and an impulse response analysis are used to explore the nexus between unemployment and 14 different types of crime.

Out of the 14 different types of criminal activity, only in the case of three – vehicle thefts, robberies and contraband & smuggling – was a nexus with unemployment established.

Given that such criminal activity is primarily motivated and driven by economic gain, a policy implication is that crime thwarting policies need to be complemented by corresponding labour market interventions, especially in periods of recession, when unemployment emerges as a major problem for disadvantaged social groups.

The issue between labour market conditions and crime has not been addressed before in the case of Greece, a country that has experienced a generally growing crime rate.

The purpose of this study is to demonstrate how existing volumes of big city crime data could be converted to significantly useful information by law enforcement agencies using readily available data warehouse and OLAP technologies. During the post-9/11 era, criminal data collection by law enforcement agencies received significant attention across the world. Rapid advancement of technology helped collection and storage of these data in large volumes, but often do not get analyzed due to improper data format, lack of technological knowledge and time. Data warehousing (DW) and On-line Analytical Processing (OLAP) tools can be used to organize and present these data in a form strategically meaningful to the general public. In this study, the authors took a seven-month sample crime data from the City of Houston Police Department’s website, cleaned and organized them into a data warehouse with the hope of answering common questions related to crime statistics in a big city in the USA.

The raw data for the seven-month period was collected from the website in Microsoft Excel spreadsheet format for each month. The data were then cleaned, described, renamed, formatted and then imported into a compiled Access database along with the definition of Facts and Dimensions using a STAR Schema. Data were then transferred to the Microsoft SQL Server data warehouse. SQL Server Analysis Services and Visual Studio Business Intelligent Tool are used to create a Data Cube for OLAP analysis of the summarized data.

To prove the usefulness of the DW and OLAP cube, the authors have shown few sample queries displaying the number and the types of crimes as a function of time of the day, location, premises, etc. For example, the authors found that 98 crimes occurred on a major street in the city during the early working hours (7 am and 12 pm) when nobody virtually was at home, and among those crimes, roughly two-thirds of them are thefts. This summarized information is significantly useful to the general public and the law enforcement agencies.

The authors’ research is limited to one city’s crime data, whose data set might be different from other cities. In addition to the volume of data and lack of descriptions, the major limitations encountered were the lack of major neighborhood names and their relation to streets. There are other government agencies that provide data to this effect, and a standard set of data would facilitate the process. The authors also looked at data for a nine-month period only. Analyzing data over many years will provide time-trend of crime statistics for a longer period of time.

Many federal, state and local law enforcement agencies are rapidly embracing technology to publish crime data through their websites. However, more attention will need to be paid to the quality and utility of this information to the general public. At the time, there exists no compiled source of crime data or its trend as a function of time, crime type, location and premises. There needs to be a coherent system that allows for an average citizen to obtain this information in a more consumable package. DW and OLAP tools can provide this information package.

Having the crime data of a big city in a consumable form is immensely useful for all segments of the constituency that the government agencies serve and will become a service that these offices will be expected to deliver on demand. This information could also be useful in many instances for the decision makers, ranging from those seeking to start a business, to those seeking a place to live who may not necessarily know which neighborhoods or parts of the city are more prone to criminal activity than others.

While there have been few reports of possible use of DW and OALP technologies to study criminal data, the authors found that not many authors used actual crime data, the data sets and formats used in each case are different, results are not presented in most cases and the actual vendor technologies implemented can be different as well. In this paper, the authors present how DW and OLAP tools readily available in most enterprises can be used to analyze publicly available criminal datasets and convert them into meaningful information, which can be valuable not only to the law enforcement agencies but to the public at large.

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.