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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.

A number of crisis situations, such as natural disasters, have affected the planet over the past decade. The outcomes of such disasters are catastrophic for the infrastructures of modern societies. Furthermore, after large disasters, societies come face-to-face with important issues, such as the loss of human lives, people who are missing and the increment of the criminality rate. In many occasions, they seem unprepared to face such issues. This paper aims to present an automated social media and crowdsourcing data mining system for the synchronization of the police and law enforcement agencies for the prevention of criminal activities during and post a large crisis situation.

The paper realized qualitative research in the form of a review of the literature. This review focuses on the necessity of using social media and crowdsourcing data mining techniques in combination with advanced Web technologies for the purpose of providing solutions to problems related to criminal activities caused during and after a crisis. The paper presents the ATHENA crisis management system, which uses a number of data mining techniques to collect and analyze crisis-related data from social media for the purpose of crime prevention.

Conclusions are drawn on the significance of social media and crowdsourcing data mining techniques for the resolution of problems related to large crisis situations with emphasis to the ATHENA system.

The paper shows how the integrated use of social media and data mining algorithms can contribute in the resolution of problems that are developed during and after a large crisis.

The purpose of this paper is to identify a workable methodology to prioritise those crime scenes which have the greatest opportunity of a forensic recovery to enable effective Crime Scene Investigator (CSI) resource deployment.

The motivation behind this work stemmed from an abundance of volume crime scenes that required examination and a lack of resources that could be deployed. Within a data mining application environment, two supervised learning algorithms were used to model Northamptonshire Police's forensic data to provide a computer‐based model that could predict the outcome of finding a forensic sample at the currently unattended scene of a crime.

Based on past data, a computer model could be produced to predict the probability of finding useful fingerprints, DNA and/or footwear marks at the scene of a volume crime. In this paper, volume crime means burglary dwelling, burglary in commercial buildings, theft of and theft from motor vehicles. The model was 68 percent accurate. CSIs were 41 percent accurate in their predictions. This has been tested within five different police forces each having differing computer systems, demonstrating that the methodology is portable.

The model, when connected to either a crime recording system or an incident recording system, can produce a prioritised crime scene attendance list within minutes and assess crimes/incidents as they are reported. This list can be seamlessly used in conjunction with other attendance criteria if required, e.g. vulnerable victim, etc.

This paper provides a scientific solution to CSI resource attendance management being proved in five different UK police forces.

The purpose of this paper is to develop a method for the discovery of knowledge in emergency response databases based on police incident reports, generating information that identifies local criminal demands that allow the selection of the appropriate policing strategies portfolio to solve the problem.

The developed model uses a methodology for the discovery of knowledge involving text mining techniques using Latent Dirichlet Allocation (LDA) integrated with the ELECTRE I multicriteria method.

The developed method allowed the identification of the most common criminal demands that occurred from January 1 to December 31, 2016, in the policing areas studied. One of the crimes does not occur homogeneously in a particular locality. In this study, it was initially observed that 40 per cent of the crimes identified in the Integrated Public Safety Area 5, or AISP-5, (historical city center of RJ) had no correlation with AISP-19 (Copacabana - RJ), and 33 per cent of crimes crimes in AISP-19 were not identified in AISP-5. This finding guided the second part of the method that sought to identify which portfolio of policing strategies would be most appropriate for the identified demands. In this sense, using the ELECTRE I method, eight different scenarios were constructed where it can be identified that for each specific criminal demand set there is a set of policing strategies to be applied.

The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics.

The developed methodology contributes in a complementary way to the identification of criminal practices and their characteristics based on reports of police occurrences stored in emergency response databases. The knowledge generated through the identification of criminal demands allows law enforcement decision makers to evaluate and choose among the available policing strategies, which best suit the reality they study, and produce the reduction of criminal indices.

It is possible to infer that by choosing appropriate strategies to combat local crime, the proposed model will increase the population’s sense of safety through an effective reduction in crime.

The originality of the study lies in the integration of text mining techniques, LDA and the ELECTRE I method for detecting crime in a given location based on crime reports stored in emergency response databases, enabling identification and choice, from customized policing strategies to particular criminal demands.

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 focus of this paper is a survey of web‐mining research related to areas of societal benefit. The article aims to focus particularly on web mining which may benefit societal areas by extracting new knowledge, providing support for decision making and empowering the effective management of societal issues.

E‐commerce and e‐business are two fields that have been empowered by web mining, having many applications for increasing online sales and doing intelligent business. Have areas of social interest also been empowered by web mining applications? What are the current ongoing research and trends in e‐services fields such as e‐learning, e‐government, e‐politics and e‐democracy? What other areas of social interest can benefit from web mining? This work will try to provide the answers by reviewing the literature for the applications and methods applied to the above fields.

There is a growing interest in applications of web mining that are of social interest. This reveals that one of the current trends of web mining is toward the connection between intelligent web services and societal benefit applications, which denotes the need for interdisciplinary collaboration between researchers from various fields.

On the one hand, this work presents to the web‐mining community an overview of research opportunities in societal benefit areas. On the other hand, it presents to web researchers from various disciplines an approach for improving their web studies by considering web mining as a powerful research tool.

Purpose: This study aims to employ bibliometric analysis to condense multiple studies into a single publication that not only gives insights into the growth and advancement of the research area but also establishes a future research agenda. This study provides a summary of advances in academic research on money laundering. The research includes bibliometric analysis and visualisation of bibliographic data using the Scopus database. The results of the study show that there has been a significant increase in the number of publications in the field of money laundering research, with topics focussed on specific areas. This study will also benchmark existing and preliminary themes, designs, and methodological choices for future money laundering research.

Methodology: With the help of the ‘visualisation of similarities’ (VOS) viewer open-source software, bibliometric analysis was performed using Scopus data. Citation analysis, topic mapping, country collaboration, co-citation analysis, and keyword co-occurrence analysis are some of the approaches used in bibliometric analysis.

Findings: Based on a bibliometric analysis of 1,391 research papers retrieved from the Scopus database over the past three decades (1990–2021), the study identified the most prominent authors, studies, journals, affiliations, and countries in the field of money laundering, as well as the most co-cited authors and journals. The writers also highlight future study issues in the field of money laundering.

Practical implications: The study’s findings might provide academics and practitioners with information on the present state of money laundering research and trend subjects. It can also be used as a guideline for identifying possible research gaps in the existing literature.

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.

Public sector data analytics concerns the process of retrieving data, data analysis, publication of the results as well as re-using the data by government organizations to improve their operations and enhance public policy. This paper aims to explore the use of public sector data analytics in the Netherlands and the opportunities and challenges of this use.

This paper finds 74 applications of public sector data analytics, identified by a Web search and consultation with policymakers. The applications are categorized by application type, organization(s) involved and application domain, and illustrative examples are used to elaborate opportunities and challenges.

Public sector data analytics is most frequently used for inspection and enforcement of social services and for criminal investigation. Even though its usage is often experimental, it raises concerns for scope creep, repeated targeting of the same (group of) individuals, personal data use by third parties and the transparency of governmental processes.

Drawing on desk research, it was not always possible to identify which type of data or which technology was used in the applications that were found. Furthermore, the case studies are illustrative rather than providing an in-depth overview of opportunities and challenges of the use of data analytics in government.

Most studies either perform a literature overview or present a single case study; this paper presents a more comprehensive overview of how a public sector uses data analytics.

This paper aims to present a bibliometric analysis of publications from the Scopus database on fraud detection studies.

The current research used Microsoft Excel to conduct the frequency analysis, VOSviewer for data visualisation and Harzing’s Publish or Perish for citation metrics and analysis.

In alignment with these research results, the publications on fraud detection studies have been consistently increasing since 2005. India was rated first as the most active country in fraud detection research. Tongji University from China was the most active institution that published significant publications related to fraud detection research. A total of 160 scholars from 89 various countries and 160 different institutions published several fraud detections studies with multi-authors’ participation in different languages.

To the best of the authors knowledge, this study is the first study to review fraud detections publications in the Scopus science database.