Search results

CPTED’s premise to the improvement of quality of life (QOL) is crime prevention and safety, and yet there is little concern for the impact of CPTED implementation to QOL when the crime increases after the interventions.

This study systematically analyzed articles both quantitatively and qualitatively.

This study found that the CPTED–QOL relationship discussion was highly inadequate in research. Improvement of QOL has been elevated to an unquestionable and certain truth of CPTED and yet the evidence on this is highly inconclusive.

This study is a contribution to the CPTED–QOL discussion that has been lacking.

Empirical literature is the primary source of understanding how policing can effectively reduce criminal activities. Spatial analyses can identify particular effects that can explain and assist in constructing appropriate regional strategies and policies; nevertheless, studies that use spatial regression methods are more limited and can provide a perspective on specific effects in a more disaggregated regional context.

This research aims to conduct a systematic literature review (SLR) to understand the relationship between crime indicators and police production using spatial regression models. We consider a combination of Kitchenham and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols as a methodological strategy in five bibliographic databases for collecting scientific articles.

The SLR suggests a limited amount of evidence that meets the criteria defined in the research strategy. Several particularities are observed regarding police and criminal production metrics, either in terms of aggregation level, indicator transformations or scope of analysis. A broader time perspective did not necessarily indicate statistical significance compared to models with a single-period sample.

The findings suggest the possibility of expanding efforts by the public sector to provide policing data with the intention of conducting appropriate research using spatial analysis. This step could allow for a more robust integration between the public sector and researchers, strengthening policing strategies, evaluating the effectiveness of public security policies and assisting in the development of strategies for future policy actions.

Limited empirical evidence meets the criteria of spatial regression models with temporal components considering police production and criminality indicators. Constructing an SLR with this scope is an unprecedented contribution to the literature. The discussion can enhance the understanding of approaches for studying the relationship between police efforts and crime prevention.

The paper aims to investigate the effect of GDP growth on crime and to test the hypothesis of nonlinearity. Additionally, we estimate the interaction between GDP and income inequality and examine its impact on the relationship between GDP and homicide rates.

The study utilizes panel data from the Organization for Economic Cooperation and Development (OECD), spanning the period from 2000 to 2018 and estimates dynamic panel GMM models.

We found a nonlinear relationship between GDP and homicide rates, indicating a dual effect of GDP on the occurrence of lethal crimes. Moreover, income inequality conditions the effect of GDP on homicide rates, exerting a significant influence. We conclude that in contexts characterized by high levels of income inequality, GDP growth is more effective in reducing crime, as there is greater potential for improvement.

This paper contributes to the existing literature by providing insights into the complex nonlinearity between economic conditions, income inequality and homicide rates.

This study addresses the ever-increasing cyber risks confronting the global banking sector, particularly in India, amid rapid technological advancements. The purpose of this study is to de velop an innovative cyber fraud (CF) response system that effectively controls cyber threats, prioritizes fraud, detects early warning signs (EWS) and suggests mitigation measures.

The methodology involves a detailed literature review on fraud identification, assessment methods, prevention techniques and a theoretical model for fraud prevention. Machine learning-based data analysis, using self-organizing maps, is used to assess the severity of CF dynamically and in real-time.

Findings reveal the multifaceted nature of CF, emphasizing the need for tailored control measures and a shift from reactive to proactive mitigation. The study introduces a paradigm shift by viewing each CF as a unique “fraud event,” incorporating EWS as a proactive intervention. This innovative approach distinguishes the study, allowing for the efficient prioritization of CFs.

The practical implications of such a study lie in its potential to enhance the banking sector’s resilience to cyber threats, safeguarding stability, reputation and overall risk management.

The originality stems from proposing a comprehensive framework that combines machine learning, EWS and a proactive mitigation model, addressing critical gaps in existing cyber security systems.

The second chapter of this text provides an introduction to policy relating to mental health, addiction and dual diagnosis from three jurisdictions [Ireland, UK and Australia], chosen because of their close links to Irish people and mental health service provision. The chapter begins with an introduction, reflecting on key points raised in Chapter 1 and how they are relevant to this present chapter. A critical exploration of the policies within these three jurisdictional areas is then presented to highlight the strategic direction of mental health and addiction service provision within the three jurisdictions. This includes the acknowledgement that mental health and addiction services need to be integrated as the presence of dual diagnosis in modern society increases at an alarming rate. Finally, this chapter concludes with a link to each of the policies mentioned herein for those who wish to explore these issues further.

The construction industry in emerging economies have suffered from productivity issues related to poor resource management as a result of theft. Therefore, this study aims to carry out an exploratory factor analysis of the key causes of theft in the construction industry in Nigeria.

The methodology entailed a review of the literature which identified 58 causes of construction theft. The causes were operationalized through a Likert-scale questionnaire survey, which was revised in a pilot study with ten industry experts. The questionnaire was further distributed to experienced construction professionals in Nigeria. A total of 63 respondents participated in the study, and the results were analyzed through an exploratory factor analysis.

A Kruskal–Wallis test showed no difference in perception of the various group of respondents, while Cronbach alpha test indicated an acceptable level of internal consistency and reliability. The top causes from the literature review were determined through descriptive statistics. However, a Kaiser–Meyer–Olkin measurement resulted in the exclusion of ten causes, and exploratory factor analysis yielded twenty causes in six dominant factors that together account for 55.7% of the variance. The six dominant factors were general theft prevention measures, site security measures, site layout planning, management of materials and equipment, construction project management and policy and safety management.

The limited amount of research on construction site theft in emerging construction environments such as Nigeria contributes to poor construction productivity. This study advances our knowledge of construction site theft and is of significant value to construction stakeholders in effective material and resource management through theft mitigation measures.

This chapter examines how the nature of World War I catalyzed significant changes in the laws of war, the Treaty of Versailles, the failed Leipzig Trials, and the multiple treaties enacted in the 1920s, with particular focus on the Geneva Convention of 1929.

Between 2006 and 2012, Mexico implemented a “frontal war against organized crime”. This strategy increased criminal violence and triggered negative consequences across the country’s economic, political and social spheres. This study aims to analyse how the magnitude and visibility of criminal violence impact the housing market of Mexico City.

The authors used different violent proxies to measure the effect of the magnitude and visibility of violence in housing prices. The structure of the data set is an unbalanced panel with no conditions of strict exogeneity. To address endogeneity, the authors calculate the first differences to estimate an Arellano–Bond estimator and use the lags of the dependent variable to instrumentalise the endogenous variable.

Results suggest that the magnitude of violence negatively impacts housing prices. Similarly, housing prices are negatively affected the closer the property is to visible violence, measured through narcomessages placed next to the bodies of executed victims. Lastly, housing prices are not always affected when a violent event occurs nearby, specifically, when neighbours or potential buyers consider this event as sporadic violence.

There are only a few studies of violence in housing prices using data from developing countries, and most of these studies are conducted with aggregated data at the municipality or state level. The authors are using geocoded information, both violence events and housing prices, to estimate more disaggregated effects. Moreover, the authors used different proxies to measure different characteristics of violence (magnitude and visibility) to estimate the heterogeneous effects of violence on housing prices.