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The world is experiencing an unprecedented rate of urbanisation, with 70% of its population expected to live in urban cities by 2050 (United Nations, 2018). This is driving demand for smart city development projects, that address city related issues such as traffic congestion, inadequate housing, excessive crime rates, strains on utilities like energy and water, amongst others. The volume and analysis of data associated with these activities, call for a mechanism for managing data in smart cities.This paper is aimed at developing a data governance framework for managing data generated by smart cities, and improving the way they operate as well as the quality of life of their citizens. The paper will highlight the characteristics of smart cities, the importance for cities to become smart, how the smartness of cities can be measured, and which cities around the world are considered smart. It will also discuss why data is important for the success of smart cities, the types and sources of data typically present in smart city settings and the data management challenges that plague smart cities globally.A sequential mixed methods research approach incorporating qualitative and quantitative data collection and analysis methods was employed, following a multi-step approach starting with analysis of existing frameworks and interviews with subject matter experts. This was followed by a survey of industry professionals, for insights on key framework components, and the use of case studies and questionnaires for framework evaluation.Throughout the research process, the focus of smart cities on people rather than technology was prominent. As such, the most important theme of the framework was identified as "People", particularly in their roles as data stewards and data owners. The remaining five themes of the recommended data governance framework are, Process, Technology, Data, Protection and Valuable Insights. [ABSTRACT FROM AUTHOR]

This paper focuses on crime status prediction through an ensemble methodology applied to extensive datasets obtained from catalog.data.gov, specifically targeting Los Angeles crime incidents since 2020. The research methodology comprises meticulous data collection, rigorous preprocessing, exploratory data analysis, model selection, and comprehensive model evaluation. Initial challenges included data inaccuracies and privacy-preserving measures in location data, necessitating thorough cleaning and transformation processes. Exploratory data analysis revealed crucial insights, including the 'Status' attribute's limited correlation, crime code distributions, areawise crime counts, and temporal patterns. To address class imbalance within 'Status', the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the dataset. Model evaluation highlighted the superiority of random forest models employing 10 and 20 decision trees, alongside KNN, which demonstrated consistent high accuracy, balanced precision-recall trade-offs, and notable F1 scores in crime status prediction. [ABSTRACT FROM AUTHOR]

The rapid increase in the number of vehicles has led to increasing traffic congestion, traffic accidents, and motor vehicle crime rates. The management of various parking lots has also become increasingly challenging. Vehicle-type recognition technology can reduce the workload of humans in vehicle management operations. Therefore, the application of image technology for vehicle-type recognition is of great significance for integrated traffic management. In this paper, an improved faster region with convolutional neural network features (Faster R-CNN) model was proposed for vehicle-type recognition. Firstly, the output features of different convolution layers were combined to improve the recognition accuracy. Then, the average precision (AP) of the recognition model was improved through the contextual features of the original image and the object bounding box optimization strategy. Finally, the comparison experiment used the vehicle image dataset of three vehicle types, including cars, sports utility vehicles (SUVs), and vans. The experimental results show that the improved recognition model can effectively identify vehicle types in the images. The AP of the three vehicle types is 83.2%, 79.2%, and 78.4%, respectively, and the mean average precision (mAP) is 1.7% higher than that of the traditional Faster R-CNN model. [ABSTRACT FROM AUTHOR]

Recent years have seen widespread application of crowd counting and detection technology in areas as varied as urban preventing crime, station crowd statistics, and people flow studies. However, getting accurate placements and improving audience counting performance in dense scenes still has challenges, and it pays to devote a lot of effort to it. In this paper, crowd counting models are proposed based on the YOLOv5 algorithm, and four YOLOv5 models (YOLOv5l, YOLOv5m, YOLOv5s, YOLOv5x) were built for the purpose of comparing the models and increasing the accuracy of crowd identification as each model contains certain characteristics such as Filter sizes. Each model was trained on a human dataset (indoor and outdoor) for the purpose of comparing the results of each model and showing which model reaches higher accuracy in detecting people. Through this study and practical experiments conducted on each model, it was found that the best model is YOLOv5x, and YOLOv5l, where the accuracy of detecting humans reached more than 96%, while YOLOv5s reached more than 92%, and YOLOv5m reached the lowest accuracy, which is 91%. [ABSTRACT FROM AUTHOR]

Immigration has long been a controversial social issue on both sides of the Atlantic Ocean. The prevailing narrative is generally negative and is mainly promoted by domestically focused, conservative political parties. The key points argued by such anti-immigrant groups are that incoming migrants 1) take jobs from existing residents, 2) burden society due to higher social spending, and 3) worsen crime rates and potentially lead to terrorism. Germany ranks second (after the USA) in terms of immigration. It has a long history of accepting refugees fleeing political instability or simply seeking a better economic opportunity. In this paper, I research the robust migration waves into Germany after the recent wars in Afghanistan, Iraq, Syria, and Ukraine and the concurrent performance of the domestic economy. My findings have revealed that contrary to the adverse mainstream views, high immigration did not coincide with weaker employment or economy. In fact, historical data indicates a more robust domestic economy and employment after the heightened immigration. Multiple factors impact the economy; hence a direct causation effect cannot be asserted. I highlight why such a favorable outcome occurred and what lessons could be drawn for other countries with high immigration. [ABSTRACT FROM AUTHOR]

Purpose: This paper examines the role of institution in the combating crime in India. This study also assesses institutions for controlling property crime in India in the post-reform era. Design/methodology/approach: Crime and socio-economic data are taken from National Crime Record Bureau and the Reserve Bank of India, respectively. Twenty major Indian states are selected for the study purpose for the period of 1994–2019. Fixed effect panel data technique is used for analysis purpose. Findings: Property crime rate declines with economic growth, while it increases with financial development. Findings of fiscal policy instruments are different. Own tax is positively associated with property crime in India, while non-tax fiscal instruments such as fine, penalty, and so on, are inversely related to it. Property crime rate is inversely related to institutional factors like charge sheet and conviction rate. Research limitations/implications: Further research is needed for other crimes in India. State-level data are used here for analysis purpose; however, spatial or cluster analysis techniques might provide more insights for combating crimes in India. Practical implications: This study suggests that economic growth and fiscal instrument along with institutional development are essential to control property crime in India. Social implications: Government should take steps to improve the law-and-order system to control property crime across states. Originality/value: Impact of non-tax fiscal instrument reduces property crime while that of own tax is increases it in India. These findings are unique and added certain insight in the study. Institutional roles are captured its performances like charge sheet and convict rate, which are significantly reduce property crime in Indian states. Least square dummy variable model is applied to capture individual state effects. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2023-0063 [ABSTRACT FROM AUTHOR]

Cities attract a large number of inhabitants due to their more advanced industrial and commercial sectors and more abundant and convenient living conditions. According to statistics, more than half of the world's population resides in urban areas, contributing to the prosperity of cities. However, it also brings more crime risks to the city. Crime prediction based on spatiotemporal data, along with the implementation of multiple unmanned drone patrols and responses, can effectively reduce a city's crime rate. This paper utilizes machine learning and data mining techniques, predicts crime incidents in small geographic areas with short timeframes, and proposes a random forest algorithm based on oversampling, which outperforms other prediction algorithms in terms of performance. The research results indicate that the random forest algorithm based on oversampling can effectively predict crimes with an accuracy rate of up to 95%, and an AUC value close to 0.99. Based on the crime prediction results, this paper proposes a multi-drone patrol response strategy to patrol and respond to predicted high-crime areas, which is based on target clustering and combined genetic algorithms. This strategy may help with the pre-warning patrol planning within an hourly range. This paper aims to combine crime event predictions with crowd-sourced cruise responses to proactively identify potential crimes, providing an effective solution to reduce urban crime rates. [ABSTRACT FROM AUTHOR]

Abnormal behavior is called an abnormal behavior that deviates from the same normal standard as the average. The installation of public CCTVs to prevent crimes is increasing, but the crime rate is rather increasing recently. In line with this situation, artificial intelligence research using deep learning that automatically finds abnormal behavior in CCTV is increasing. Deep learning is a type of artificial intelligence designed based on artificial neural networks, and the quality of learning data is important for high accuracy in the development of artificial intelligence through deep learning. This paper verifies whether learning data for abnormal behavior detection is suitable as learning data which is being constructed using an MPED-RNN model for binary classification to determine whether there is an abnormal behavior by frame using skeleton data of a person based on an autoencoder. As a result of the experiment, the unsupervised learning-based MPED-RNN model used in this paper is not suitable for verifying images with a similar number of frames with and without abnormal behavior, such as the corresponding data, and it is judged that appropriate results can be derived only when verified with a supervised learningbased model. [ABSTRACT FROM AUTHOR]

Purpose: Under Malaysian Law, the maintenance and management of common property have traditionally been viewed as insoluble challenges in the self-organisational efforts for high-rise low-cost housing developments. As the population increases, more shared resources become heavily exploited with few willing to contribute towards the sustainability of resources. Many researchers argued a better way would be to convert these shared resources into a private or state-managed entity. Hence, with that assumption, this paper aims to examine how better physical characteristics can result in greater collective action. Design/methodology/approach: This research paper adopted a quantitative method approach to determine how the standard of physical characteristics influenced the quality of collective action in high-rise low-cost housing. The method included a questionnaire survey of 519 parcel holders chosen via stratified purposeful random sampling from four high-rise low-cost housing areas. Findings: The paper found a positive correlation between the quality of physical characteristics and the collective action gained. The findings highlighted that the standard of workmanship of high-rise low-cost units (including the common properties), the crime rate in the neighbourhood and the condition of lift systems – when improved – were significant positive predictors of collective action. Practical implications: Housing developers should pay attention to the quality of both low-cost units and related common properties, as this endeavour will assist greatly in future collective action management. Originality/value: The study was justified in terms of its originality as few research studies adopted the social-ecological system framework that focused on the correlation between the quality of physical characteristics and the collective action of parcel holders. [ABSTRACT FROM AUTHOR]

The increasing prominence of the problem of censored data in various fields has made studying how to perform parameter estimation and variable selection in censored mixed-effects models one of the hotspots of current research. In this paper, considering the situation that the response variable is restricted by the bilateral limit, a double-penalty Bayesian Tobit quantile regression model was constructed to carry out parameter estimation and variable selection in the interval-censored mixed-effects model, and at the same time, the fixed-effects and random effects coefficients are compressed in Tobit's mixed-effects model, so as to reduce the estimation error of the model at the same time as the variable selection of the model is carried out. The posterior distribution of each unknown parameter was derived using the conditional Laplace prior and the mixed truncated normal distribution of interval-censored data, and then the Gibbs sampling algorithm for unknown parameter estimation was constructed. Through Monte Carlo simulation, it was found that the new method is more advantageous than the classical method in terms of variable selection and parameter estimation accuracy in various situations, such as different model sparsity, different data censoring ratios and different random error distributions, and the model is able to realize automatic variable selection. Finally, the new method is used to analyze the correlation between the crime rate and various economic indicators in China. [ABSTRACT FROM AUTHOR]

Crime rates in Australia have declined or been steady over the past decade yet prison populations are at all-time highs. Similarly, unemployment rates have been low but unemployment for those ex-prisoners seeking work is very high. In this paper, we draw on the findings of an Australia-wide survey of government-funded employment service providers who support working-aged Australians including ex-prisoners in their search for work. We find that the heterogeneity of the needs of ex-prisoners coupled with the frugality and inflexibility of government policies and practices frustrates the abilities of these agencies to provide services to this target group. [ABSTRACT FROM AUTHOR]

People often refer to, and think of, the United Nations (UN) as a monolith, a single organization. This demand makes statistics provided by IOs more than the sum of national statistics and places on IOs a strong accountability to meet the expectations for excelling in providing the most authoritative and independent international statistics. All too often, references to official statistics implicitly refer to official national statistics, but official statistics also comprise official international statistics. [Extracted from the article]

This paper presents an attempt at establishing an association between crime levels and prison populations across European countries. We observe that the situation in Central and Eastern European countries differs distinctly from the rest of Europe. Building on this, we offer justification that is methodologically based on correlations and regressions of country incarceration rates on crime rates, with reference to governance indicators. Our cross-sectional analysis uses data on crime and prisoner rates by offence from Eurostat and SPACE for the year 2018. The paper's empirical analysis is preceded by a discussion of the challenges faced when attempting to compare crime between countries in Europe. A review of research focused on relationships between incarceration and crime follows, with the emphasis on the deterrence effect and the prison paradox. Typically, this stream of research uses microdata covering a single country or limited to a smaller geographic area. International comparisons are rare, and are usually based on time series and trend analyses. The quantitative approach applied here is based on recognizing two clusters of countries: the Central and Eastern European (CEE) cluster and the Western European (WE) cluster. We show that the observation of higher prisoner rates and lower crime rates for CEE countries is confirmed with regression analysis. Our study encompasses four types of offences: assault, rape, robbery, and theft. The final section of the paper presents an attempt to incorporate Worldwide Governance Indicators into the analysis of the association between incarceration and crime rates. The results confirm that crime rates in WE countries are distinctly higher than in CEE countries, while incarceration rates in WE are significantly lower than in CEE countries. We think this is due to a higher percentage of crimes being reported and the greater accuracy of police statistics in WE countries. The prison population in each country is largely determined by its criminal and penal policies, which differ substantially between CEE and WE countries (e.g., in terms of frequency of imposing prison sentences and the length of imprisonment). These tendencies result in higher incarceration rates in CEE countries, despite lower crime rates when compared to WE countries. [ABSTRACT FROM AUTHOR]

The increasing pervasiveness of ICT technologies and sensor infrastructures is enabling police departments to gather and store increasing volumes of spatio-temporal crime data. This offers the opportunity to apply data analytics methodologies to extract useful crime predictive models, which can effectively detect spatial and temporal patterns of crime events, and can support police departments in implementing more effective strategies for crime prevention. The detection of crime hotspots from geo-referenced data is a crucial aspect of discovering effective predictive models and implementing efficient crime prevention decisions. In particular, since metropolitan cities are heavily characterized by variable spatial densities of crime events, multi-density clustering seems to be more effective than classic techniques for discovering crime hotspots. This paper presents the design and implementation of MD-CrimePredictor (Multi- Density Crime Predictor), an approach based on multi-density crime hotspots and regressive models to automatically detect high-risk crime areas in urban environments, and to reliably forecast crime trends in each area. The algorithm result is a spatio-temporal crime forecasting model, composed of a set of multi-density crime hotspots, their densities and a set of associated crime predictors, each one representing a predictive model to forecast the number of crimes that are estimated to happen in its specific hotspot. The experimental evaluation of the proposed approach has been performed by analyzing a large area of Chicago, involving more than two million crime events (over a period of 19 years). This evaluation shows that the proposed approach, based on multi-density clustering and regressive models, achieves good accuracy in spatial and temporal crime forecasting over rolling prediction horizons. It also presents a comparative analysis between SARIMA and LSTM models, showing higher accuracy of the first method with respect to the second one. [ABSTRACT FROM AUTHOR]

This research examined the impact of crime on rental values of residential properties in Ikono South, Ikono, Akwa Ibom State. In order to achieve the stated aim, the objectives were to assess the trend of crime in Ikono South, Ikono Local Government Area, to evaluate the trend of rental values of residential properties in Ikono South, Ikono Local Government Area and to establish the relationship between crime and property values in Ikono South, Ikono Local Government Area. The research work adopted the cross-sectional survey type of design. The research population comprised occupiers of residential real estate in the study area. Stratified random sampling method was used to stratify Ikono South District into thirty-six villages forming the Ikono South District. Disproportionate stratified random sampling was then used to select 10 respondents from each of the villages forming the Ikono South District, thereby bringing the sample size to 360. Data from 2015 to 2022 was obtained from the questionnaire and was analysed using both descriptive and inferential statistics. Findings from the study revealed that there was a consistent year-to-year increase in crime rate between 2015 and 2022. The study also found out varying rental values, with the maximum rental value of N240,000.00 per annum observed in 2022 in Ekang village, while the minimum rental value of N70,000.00 per annum was observed in 2015 and 2016 in Ukpom Ikot Akpakam village in the study area. The regression results showed a statistically significant negative relationship between crime and rental value. This implied that as crime rate increases, the rental value decreases. The study then concluded that increase in crime rate in the study area has a negative effect on the rental value of residential properties. [ABSTRACT FROM AUTHOR]

This paper explores the spatio-temporal patterns of household crimes in Durban, South Africa, using precinct-level population and decadal crime data for burglary and robbery at residential areas and Malicious Damage to Property (MDP) in the Municipality. Crime rates and Moran I statistic were computed for the precincts, while maps depicted spatio-temporal variations in the crimescape. While the rates of MDP (−7.5%) and robbery (−4.2%) reduced, burglary (5.0%) escalated across many neighbourhoods. Both robbery and MDP showed significant spatial dependence. The momentum for crime appears to have shifted to the sparsely populated suburbs, with implications for future policies and crime-fighting strategies. [ABSTRACT FROM AUTHOR]

This paper deals with the analysis of the relationship between locations and types of crime observed in the Czech Republic. Cluster analysis of crime data based on the recursive Bayesian mixture estimation algorithm is used to identify crime hotspots and estimate local models of crime type. The experiments report that the 2D configuration of the algorithm allows the detection of crime hotspots online. The 3D configuration provides 29% more accurate crime type models than 2D clustering and alternative data mining algorithms. For the data set used, it was determined in which crime hotspots the most serious and most frequent types of crime can be expected to occur with the highest probability. The limitation of the study is the artificial support of the 3D clusters by the fully continuous data vector with the recoded values of the crime type. The potential use of the algorithm is expected in online web applications for sharing information on criminal offenses managed by the Police of the Czech Republic with the public and local government entities in the Czech Republic. [ABSTRACT FROM AUTHOR]

The study of change over time in crime rates is as old as criminology itself. A fundamental question about crime rates is whether they are moving up or down. This special issue of the Journal of Contemporary Criminal Justice brings together recent research on trends in crime and related issues, such as arrests, imprisonment, terrorism, and reporting crimes to the police. These papers show that the study of crime trends is alive and well in criminology and point to innovative data sources, methods, and topics that deepen our understanding of how and why crime rates move up and down over time. [ABSTRACT FROM AUTHOR]

This paper adopts a conventionalist approach to shed light on the measurement and reification problems that underlie the quantification of desistance from crime in the scientific literature. Analysis of 100 papers spanning three decades indicates that approaches based on theoretical classification have recently lost ground in favor of more sophisticated techniques aimed at empirically identifying subgroups. These techniques convey the impression of objectiveness among statistics users and consumers and, as a result, the classification "desisters" and "persisters" are increasingly reified. Findings suggest that the quantification of desistance is intimately linked to the maintenance of a classification system that constitutes delinquency as a stable category and contributes to "making" up new kinds of people over which institutions can legitimately intervene. [ABSTRACT FROM AUTHOR]

As the number of foreigners residing in South Korea has increased, the overall scale of crimes committed by non-Korean nationals and the associated public anxiety have both risen. This paper seeks to explore the trends in violent crimes committed by foreigners in Korea by examining crime statistics from the Korean National Police Agency and immigration data from the Korean Ministry of Justice. Additionally, this paper empirically examines the attributes of the home countries of foreign criminals who are arrested in Korea. The results of our analysis, based on the violent crime statistics for foreign nationals of twelve types from 2003 to 2019, reveal that foreigners from higher-income countries with better educational environments are more likely to be involved in violent criminal activities. Individuals from countries with relatively strong law enforcement are less likely to engage in violent crime while residing in South Korea. [ABSTRACT FROM AUTHOR]

Crime analysis is important for social security management. With the advance of crowd sensing techniques, abundant multisource crowd sensed data could be used for crime analysis. The occurrence of crimes usually has some patterns in terms of temporal and spatial aspects. Investigating the spatio-temporal correlation of crimes could provide more useful cues for crime analysis and help discover underlying crime patterns. In this paper, we conduct a spatio-temporal study to understand urban crimes leveraging multisource crowd sensed data, including crime data, meteorological data, POI distribution, and taxi trips. Specifically, we first present monthly temporal trends and spatial distribution of crimes. We then investigate the spatio-temporal correlation using meteorological data (e.g., weather conditions and air temperature) and POI distribution and taxi trips. It is found that taxi trips and air temperature have a strong correlation with the crime, and some POI categories have a valuable correlation with the crime, e.g., College & University. We also find that Overcast days would witness more crime than other weather conditions. [ABSTRACT FROM AUTHOR]

This paper analyses the spatial trends and patterns of the crime rates in London and explores how socio-economic characteristics affect crime rates with consideration of the geographic context across London. The 2015 London Crime Statistics and Socio-economic Characteristics datasets were used. First, we investigated the spatial patterns of crime rates through exploratory spatial analysis at the ward level. In addition, both the ordinary least square (OLS) model and geographically weighted regression (GWR) model, which allow the effects of factors to vary in spatial scales, were adopted and compared to explore the potential spatially varying effect across London. The results showed that there exists obvious spatial clustering for the crime rate in central London. Both global and local Moran's I values indicated the spatial dependence of crime at the ward level. The GWR model performed better in explaining crime rates than the OLS model. Only two factors, namely, the percentage of children aged from 0 to 15 and employment rates, had significant spatial variability in London. The influences of the percentage of children aged 0 to 15 on crime rates are constantly negative over a spatial scale; however, employment rates positively affect crime rates in the north-western areas near the centre of London. Therefore, this paper focuses more on the spatial perspective, which fills the gap in traditional crime analysis, especially on the spatially varying influence of socio-economic status. [ABSTRACT FROM AUTHOR]

Telecommunication network fraud crimes frequently occur in China. Predicting the number and trend of telecommunication network fraud will be of great significance to combating crimes and protecting the legal property of citizens. This paper proposes a combined model of predicting telecommunication network fraud crimes based on the Regression-LSTM model. First, we find that there is a strong correlation between privacy data illegally sold on the dark web and telecommunication network fraud data. Hence, this paper constructs a Linear Regression model using the privacy data illegally sold on the dark web to predict the number of telecommunication network fraud crimes. Second, an LSTM prediction model is constructed using the data of telecommunication network fraud cases on China Judgments Online based on the time-series feature of telecommunication network fraud crimes. Third, this paper uses the error reciprocal method to combine the two models for prediction. In addition, this paper selects the monthly data set of telecommunication network fraud occurring in 2021 for experimental evaluation. The experimental results show that the accuracy of the Regression-LSTM model constructed in this paper is 86.80%, and the RMSE is 0.149. Compared with the ARIMA, Linear Regression, LSTM, Additive-ARIMA-LSTM, and Multiplicative-ARIMA-LSTM models, the Regression-LSTM model proposed has the highest prediction accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Lecturas de Economia is the property of Universidad de Antioquia, Facultad de Ciencias Economicas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of International Annals of Criminology is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper attempts to provide better understanding of which factors shaped UKIP electoral support in local elections using borough-level data from London, as well as individual-level survey data from respondents residing in London. Results from this study display rising crime, falling turnout, and falling Conservative party vote share led to rises in UKIP vote share. These findings show that while perceptions of immigration and the economy may affect voter choice for Eurosceptic, nationalist, and populist parties, actual change in migration and economic conditions had no effect on voter support for UKIP in local elections. Additionally, there is substantial evidence that rising rates of local crime, and perceptions of rising local crime rates, lead voters to seek out parties, such as UKIP, with a policy platform supporting strengthened criminal justice measures. Finally, UKIP support in local elections is shown to have drawn heavily from former Conservative voters, as opposed to disenchanted Labour supporters. [ABSTRACT FROM AUTHOR]

In post-pandemic, climate-changing societies, the presence of urban greenspace assumes paramount functions, at the same time that socio-economic crises and shocks augment vulnerabilities and insecurities. The recent literature on environmental criminology argues that the geography of crime is not random, and that the presence of greenery, due to its impact on well-being and the environment, can have positive associations with feeling safe; although the opposite effect can occur if spaces are not properly designed or maintained. In this paper, the case study of Porto, Portugal, is presented; one of the municipalities with higher crime rates, that also pledged to double the available greenspace in the near future. As a way to support decision-making, the aim of this study was to present an overall exploratory diagnosis of how street crime patterns, of different typologies, spatially co-exist with greenspaces. Using a 10-year street crime dataset at the segment level, descriptive quantitative methods with the support of GIS have been applied to plot crime's spatial distribution over time, as well as the walking accessibility to greenspaces. The results confirm crime's geographical non-randomness, with distinct categories occupying specific locations, even though there was a consistently proportional distribution in the different distance bands. On the contrary, the cumulative effect of the proximity to greenspaces was variable. Almost half of the city's street crimes (46%) were within a 5 min walking distance of greenspaces, but they were much closer to smaller inner-city urban gardens, with higher densities of street crimes (hot spots), than to larger municipal parks, where lower densities (cold spots) were seen. [ABSTRACT FROM AUTHOR]

The rapid development of digital finance has changed all aspects of human life and has also had a deep impact on the social governance system. This paper constructs an unbalanced panel of data of the theft crime rates for 289 cities in China during 2014–2019 based on the theft criminal judgments published on China's Judicial Documents website and explores the impact of digital financial inclusion on urban theft crime. It shows that there is a significantly negative correlation between digital financial inclusion and the urban theft crime rate, indicating that the development of digital financial inclusion can effectively reduce urban theft crime, which is also confirmed by instrumental variable analysis based on the spherical distance between cities and Hangzhou, and that digital financial inclusion mainly reduces theft crime committed by more serious and highly educated individuals. In addition, mechanism analysis shows that digital financial inclusion can reduce the expected benefits of theft by enhancing payment convenience and raise the opportunity cost by promoting employment. Therefore, in the Internet era, it is essential for China to continuously improve social governance tools that adapt to the development of new technologies to achieve high-quality urban development. [ABSTRACT FROM AUTHOR]

Purpose: Using data from 2009–2016 across 31 states and union territories, this paper investigates determinants of juvenile delinquency in India as well as explores the nature of the complex relationship between economic variables and crime rate. Design/methodology/approach: The paper employs a panel corrected standard error model due to the presence of heteroskedasticity and contemporaneous correlation. Additionally, due to possible feedback effect from independent variables resulting in endogeneity, a two-step generalized method of moments (GMM) is utilized to estimate a system of equations. Findings: Estimation results indicate that macroeconomic factors – GSDP per capita and adult unemployment rate – are significant in explaining the juvenile crime rate in India. Higher poverty rate and percentage of slums were found to increase juvenile crime. This paper also demonstrates the harmful effects that domestic violence has on juvenile delinquency. Finally, education has a deterring impact on crimes relating to juveniles but deterrence factors do not. Originality/value: While some implications are consistent with those found in previous studies of crime in developed and developing countries, the analysis in this paper also reveals unique results. For example, the adult unemployment rate was negatively correlated with juvenile crime, and an increase in police density exhibits a positive association with the juvenile crime rate. Further analysis of crimes by type (property and violent) reveals additional insights. In addition to that, contrary to hypothesis, by employing GMM estimation, the paper finds no evidence of a negative impact of juvenile delinquency on economic growth. [ABSTRACT FROM AUTHOR]

Climate change is the most relevant discussion point in today's scenario. The research paper explains and analysis the relationship between crime and air pollution. It is important to understand that there is a remarkable increase in crimes in the recent past globally and the same can be correlated to air pollution. Studies suggest that air pollution contributed to the increased crime rate. Recent studies in this context have suggested that air pollution can be directly related to aggressive behavior and which in turn leads to an increase in the crime rate. The paper focuses on harm mitigation rather than retribution when dealing with environmental offences. Awareness of the application of restorative processes in cases of environmental harm is ambiguous and, indeed, exculpatory in nature in present scenario. It is generally accepted that environmental crimes are among some of the most lucrative types of transnational illegal activity. In 2016, their monetary worth is calculated at between US$ 91 and 259 billion annually, most likely the world's fourth largest criminal sector after narcotics, counterfeits, and trade in human beings. In this area, there is indeed a detailed list of violations, i.e., wildlife crimes, illicit mining are few to mention. [ABSTRACT FROM AUTHOR]

Past literature has shown that 1970 amendments to the clean air act (CAA) led to significant reduction in air pollution early 1970s, and that it had positive infant health consequences for the cohorts treated by CAA. Because effects of in-utero and early childhood conditions are persistent, and the health effects can remain latent for years, CAA may impact the future adult outcomes. In this paper, I investigate the impact of the CAA on the future crime. In a difference-in-differences framework, I find that the cohorts that were born in the year of the CAA's first implementation commit fewer crimes 15–24 years later. The magnitude of this impact is about 6%. Property crimes rather than violent crimes are impacted. I also estimate that CAA reduced the ambient air pollution by 17%. These reduced form estimates suggest that a 1% reduction in air pollution reduces future crime rate by 0.35%. [ABSTRACT FROM AUTHOR]

Environmental crime is the perpetration of harms against the environment that violate current law. Environmental crime is a special type of crime, which is becoming more and more complicated in modern social circumstances. The way it is presented shows that it is constantly changing and developing. Environmental crime in the Republic of North Macedonia represents a very significant form of criminality. The opinion is not aware of its consequences leaving behind it and protecting environment is an especially complex process. In this paper the subject of scientific observation is the penal policy in relation to environmental protection in the Republic of North Macedonia. For this aim will be presented the data taken from the statistical Yearbooks of the Republic of North Macedonia State Statistical office for a period of five years. This data’s refer to the number of reported, charged or convinced persons presented for a period of five years. The findings of the research in this work indicate that the detection of criminal offenses in the field of environmental security is at an extremely low level and that the crime rate is pronounced. The purpose of this paper is to raise questions about the most visible components of the penal policy related to the Environment in Republic of Macedonia. The focus of this work is to address the current and emerging threats posed by the environmental crime in the Republic of Macedonia. [ABSTRACT FROM AUTHOR]

This paper attempts to provide new evidence on the indirect cost of neighbourhood crime rates on mental wellbeing of residents. Crime places a heavy burden on the economy, and it receives significant attention from the public. Yet, the connection between crime and health for victims and non-victims is not well captured in the literature. Using spatial methodology, we differentiate the effect on mental wellbeing of residents due to the crime rates in their immediate neighbourhood (local) and crime rates in the surrounding areas (spatial). The main innovation of the paper is to use these crime rates at small geographical level (around 8500 residents) to better translate the changes in crime in the neighbourhoods and their effects on a person's mental wellbeing. This is different to other studies that looked at crime incidences in larger geographical units. Our estimation results show that the increase in local and spatial crime rates against the property negatively affect the mental wellbeing of residents. The negative impact of spatial crime rates against the person is also present and is 6.7 times larger than the effect of property crime rates. [ABSTRACT FROM AUTHOR]

This article examines municipalities on implementation and coordination of social crime prevention programmes in addressing social issues. The White Paper on Safety and Security (1998) clearly proposed that local government should be responsible for implementing and coordinating social crime prevention programmes within areas of concern. Therefore, it is argued that the provincial government is closest to citizens and inimitably placed to contribute to social crime prevention initiatives and readdress the delivery of services to deal with crime prevention successfully. There is little information about this matter in South Africa. This article will reveal the challenges municipalities and the department of social development face on social issues. There seems to be a repetition of social programmes in addressing social issues and there are no programmes targeting youth at risk, juvenile delinquency and child-headed households’ issues. The study concludes that the municipalities should consider the review in social crime prevention strategies to better align its programmes to the provincial mandates. Municipalities should further demonstrate regularly how it responds to social issues that might perpetuate the crime rate. [ABSTRACT FROM AUTHOR]

Previous research has found that sports games increase certain crimes including theft and vandalism, but the relationship between sports and other offenses such as motor vehicle theft, robbery, and assaults is mixed. Using regular season National Hockey League (NHL) game data and city-level, incident-level crime data from four large U.S. cities, this paper estimates if professional ice hockey games played at home is associated with increases in property crimes, alcohol-related crimes, and assaults during and right after games the over course of four NHL seasons. Results show that NHL games lead to small but significant increases in property crimes and assaults. There is no significant effect for alcohol-related crimes. Implications and suggestions for future work are discussed. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to examine the relationship between two types of property‐related crime and some socio‐economic, demographic and deterrence factors. It employs the conditional autoregressive specification to account for spatial autocorrelation that characterises property‐related crime data, which are aggregated up to the level of municipality. First, the analysis confirms the presence of spatial autocorrelation in the data, which means that neighbouring municipalities exhibit similar levels of property crime rates. In addition, and most importantly, empirical findings show that internal migration, youth and education are important predictors of property‐related crime across municipalities in South Africa for the period under consideration. [ABSTRACT FROM AUTHOR]

U.S. support for border enforcement in Mexico has been ongoing for decades, but in 2014, after the arrival of unprecedented numbers of Central American minors and families to the U.S., even greater pressure was placed on Mexico to seal its border with Guatemala. This paper explores the resulting tensions between Mexican border enforcement policies outlined under Programa Frontera Sur, intended to tighten security and surveillance in the south of the country, and Mexico's 2011 Migration Law, intended to facilitate the protection of migrants' rights. Through ethnographic fieldwork, I examine how the borders of the Mexican state are maintained through the (il)legibility of administrative rules and procedures in the context of conflicting immigration agendas. Specifically, this paper explores the precarious paths to legal protection via regularización de estancia por razones humanitarias, a temporary legal status granted to victims of grave crimes. It traces applicants' circuitous trajectories through bureaucratic processes and evolving enforcement landscapes, noting the costs and contingencies involved in making claims 'legitimate' and legible in the eyes of the state. This paper also brings attention to the positioning of migrant shelters at the margins of state inclusion, where formal and informal mobilities often intersect. As a feminist geopolitical study of state bordering practices, this research is useful in understanding lived impacts and responses to more recent strategies of administrative border enforcement, including the MPP and Title 42 programs. [ABSTRACT FROM AUTHOR]

When designing a patrol route, it is often necessary to pay more attention to locations with high crime rates. In this paper, we study a patrol routing problem for a fleet of patrol cars patrolling a region with a high-crime neighborhood (HCN) consisting of multiple hot spots. Considering the disorder and chaos in the HCN, at least one patrol car is required in the HCN at any given time during the patrol. We call this routing problem the hot spot coverage patrol problem (HSCPP). In the HSCPP, the importance of a patrol location is quantified by a prize, and the prize is collected if a patrol car visits the location. Our objective is to maximize the sum of prizes collected by the patrol cars, obeying all operational requirements. We propose mathematical formulations and develop several solution approaches for the HSCPP. The global approach consists of finding the routing solution for all patrol cars with a single integer programming (IP) formulation. The partition approach involves first partitioning the region geographically and solving the routing problem in each subregion with two IP formulations. Next, we strengthen the partition approach by developing a column generation (CG) approach in which the initial columns of the CG approach are the solutions generated from the partition approach. We conduct a detailed computational case study using instances based on real crime data from Montgomery County, Maryland. To further understand the computational tractability of our solution approaches, we also perform a sensitivity analysis using synthetic instances under various scenarios. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0192) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0192). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

How does organized crime affect foreign direct investment (FDI) in developing countries? Some research examines the effects of crime, such as homicide rates, on FDI. However, we know little about how organized crime in particular might affect such investment. This paper examines organized crime and FDI in Mexican states between 2000 and 2018. This case is important because Mexico is one of the top global recipients of FDI. At the same time, criminal violence has killed hundreds of thousands of people in Mexico in recent years, and scholars seek to understand the violence's wider effects. We explain how organized crime competition, as opposed to crime generally, should shape investors' decisions. Analyses using original data on criminal organization territory find that higher numbers of criminal groups are associated with lower levels of new FDI. Other measures of crime, such as homicide rates or crime rates, are not associated with foreign investment. [ABSTRACT FROM AUTHOR]

This research paper aims to shed a critical light on the moral panic surrounding multiple-perpetrator rape (MPR) in Spain, by exploring the interplay between official statistics of this type of crime, and the depiction in the media. The concordance between media representation and the statistical reality of crime raises questions: (1) how multiple perpetrator assault and its assailants are represented in media narratives (content analysis) is examined, including the content, framing, and underlying themes within the media coverage, with a particular emphasis on the role of nationality, and (2) this representation is compared to official crime statistics (Ministry of Home Affairs report on crimes against sexual freedom and indemnity in Spain (detentions/accused) and the statistics of the sentences passed by various Spanish Provincial Courts between 2005 and 2020 from criminal justice data), and to what extent, they align. If they do not align, do news portrayals influence a moral panic? In conclusion, the idea of tabloid justice (Fox et al., 2001) is not present in the three largest Spanish daily newspapers from 2005 to 2021. There is a misrepresentation of non-Spanish nationalities having the highest rates, Romanians being underrepresented and the French being overrepresented. Media coverage perceives tourists as a certain danger, and is questioning the idea of certain non-Spanish immigrants as 'folk devils.' [ABSTRACT FROM AUTHOR]

This paper investigates whether shocks in oil prices affect the level of crime in Kern County, California's top oil producing county. Monthly data from 2003 to 2018 is employed using a nonlinear autoregressive distributed lag model to test the asymmetric effects of these changes. Results for Kern County indicate that in the long-run, a negative shock in oil prices increases criminal activity while short-run oil price changes have no effect on criminal activity. Results from neighboring counties indicate that positive shocks to oil prices increase criminal activity in wealthy regions. Findings from generalized impulse response functions indicate that criminal activity in Los Angeles, Kings and San Bernardino Counties respond to shocks from Kern County's crime rates. They also indicate that criminal activity in Inyo and Los Angeles Counties influence Kern County's crime rate. [ABSTRACT FROM AUTHOR]

Most evaluations of public works programs in developing countries study their effects on poverty reduction and other labor market outcomes (job creation, earnings, and participation). However, very few look at other collateral effects, such as the incidence of violence. Between 2009 and 2014, El Salvador implemented the Temporary Income Support Program (PATI), which aimed at guaranteeing a temporary minimum level of income to extremely poor urban families for 6 months, as well as providing beneficiaries with experience in social and productive activities at the municipal level. Making use of a panel data set at the municipal level for 2007–2014, with monthly data on different types of crime rates and social program benefits by municipalities, this paper assesses the effects of the program on crime rates in municipalities in El Salvador. There are several possible channels through which the PATI can affect crime. Since the program is associated with cash transfers to beneficiaries, a decrease in economically motivated crimes is expected (income effect). But since the program enforces work requirements and community participation, this could generate a negative impact on crime, because the beneficiaries will have less time to commit crime and because of community deterrence effects. Overall, the paper finds a robust and significant negative impact of the PATI on most types of crimes in the municipalities with the intervention. Moreover, the negative effects of the program on some types of crime rates hold several years after participation. The positive spillover effects for municipalities hold within a radius of 50 km. [ABSTRACT FROM AUTHOR]

Purpose: Taking motivation from South Africa's ranking of 50 out of 50 countries who participated in the Progress in International Reading Literacy Study of Grade four learners in 2016, this paper examines the social and economic outcomes of literacy in South Africa. Design/methodology/approach: Using unemployment, income and crime rates as proxies for social and economic outcomes, the effect of literacy is examined by employing the fixed- and random-effects techniques to estimate a panel data covering nine provinces in South Africa from 2008 to 2017. Findings: The results show that literacy rate worsens unemployment but improves crime rate and income per capita across South African provinces. Practical implications: Policymakers need to consider an expanded view of literacy by extending investments to cover financial and technology literacy in addition to functional literacy to fully maximise the benefits of education. Originality/value: To the authors' best knowledge, this is the first empirical assessment of literacy outcomes in South Africa. [ABSTRACT FROM AUTHOR]

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Boko Haram insurgency brings with it unprecedented consequences, particularly in records of multiple deaths and devastating effects on socioeconomic dynamics of the nation. Since no state would stay without responding to threats to its national security, the federal government of Nigeria adopted both kinetic and non-kinetic approaches to counter the impact of terrorism on its shores. With several attacks from Boko Haram and the resultant consequences in terms of deaths, displacement and economic damages, the group has remained a potent force particularly in the Northeast and the Sahel region. In the midst of these contending realities is the Corona Virus Disease (COVID -19) that broke out from Wuhan, China in December 2019. Within the space of few months, the outbreak spiralled into a public health emergency and global pandemic; with rapid spread, high casualty rate and negative impacts on global health and economy. Accordingly, Nigeria like other nations was hit by the virus, causing disruption in its already fragile economy coupled with internal security threats. As a measure of containment, the global lockdown measures resulted in less economic patronage which translated into vulnerabilities like crime rate increase. While other countries were seeking answers to the question of health security, Nigeria was faced with the double tragedy of Boko Haram on one hand; and health insecurity on the other with attendant consequences. In this view, application of technology in combating insecurity as well as health emergencies will be the way to go, particularly, in a world that is technologically driven. This paper examines the imperative of technology in counter insurgency operations. It will adopt the historical approach in its analysis to build a pragmatic approach to reversing the trend. [ABSTRACT FROM AUTHOR]

Research suggests that the social effects of internal migration may be substantially different from those associated with international immigration. In this paper, I provide the first evidence of the effect of internal migration on crime with panel data from Brazilian microregiões (microregions). Using local labor demand shocks as an instrumental variable, I find that a 10% increase in the in-migration rate translates to a 9.4% increase in the homicide rate in destination areas. I propose that the effect is driven by intermediating labor market effects and not by the migrants themselves. Exploring these possible channels, I do not find that crime-prone migrants drive the results. The effect is only significant in locations with high past crime rates, indicating crime inertia, and in places with a small informal sector, suggesting that the impact of internal migration is conditioned by the ability of local labor markets to accommodate migrants. This finding is supported by a negative effect of in-migration on formal employment in rigid markets and a positive effect on unemployment among young men, with the latter explaining most of the total effect. [ABSTRACT FROM AUTHOR]