Spatio-temporal cokriging crime predictions using social media data: a multi-type case study in San Jose, California

Evidence about the relationship between lighting and crime is mixed. Although a review of evidence found that improved road/ street lighting was associated with reductions in crime, these reductions occurred in daylight as well as after dark, suggesting any effect was not due only to changes in visual conditions. One limitation of previous studies is that crime data are reported in aggregate and thus previous analyses were required to make simplifications concerning types of crimes or locations. We addressed this issue by working with a UK police force to access records of individual crimes. We used these data to determine whether the risk of crime at a specific time of day is greater after dark than during daylight, using a case and control approach to analyse ten years of crime data. We compared counts of crimes in 'case' hours, that are in daylight and darkness at different times of the year, and 'control' hours, that are in daylight throughout the year. From these counts we calculated odds ratios as a measure of the effect of darkness on risk of crime. The results supported our three hypotheses: 1) The risk of overall crime occurring after dark was greater than during daylight (OR: 1.28, 95%CI: 1.23-1.34); 2) The risk of crime occurring after dark varied depending on crime category, with five out of fourteen crime categories having odds ratios greater than 1.0; and 3) The risk of crime occurring after dark varied depending on geographical area, with 25 out of 172 Middle Super Output Areas in South Yorkshire having odds ratios greater than 1.0. Our results suggest darkness increases the risk of Bicycle Theft, Burglary, Criminal damage, Robbery - personal, and Vehicle offences, and that some areas may be at more risk of crime occurring after dark than others. These findings suggest the crime types where outdoor lighting may help reduce the risk of crime after dark.

Evidence about the relationship between lighting and crime is mixed. Although a review of evidence found that improved road / street lighting was associated with reductions in crime, these reductions occurred in daylight as well as after dark, suggesting any effect was not due only to changes in visual conditions. One limitation of previous studies is that crime data are reported in aggregate and thus previous analyses were required to make simplifications concerning types of crimes or locations. We will overcome that by working with a UK police force to access records of individual crimes. We will use these data to determine whether the risk of crime at a specific time of day is greater after dark than during daylight. If no difference is found, this would suggest improvements to visual conditions after dark through lighting would have no effect. If however the risk of crime occurring after dark was greater than during daylight, quantifying this effect would provide a measure to assess the potential effectiveness of lighting in reducing crime risk after dark. We will use a case and control approach to analyse ten years of crime data. We will compare counts of crimes in 'case' hours, that are in daylight and darkness at different times of the year, and 'control' hours, that are in daylight throughout the year. From these counts we will calculate odds ratios as a measure of the effect of darkness on risk of crime, using these to answer three questions: 1) Is the risk of overall crime occurring greater after dark than during daylight? 2) Does the risk of crime occurring after dark vary depending on the category of crime? 3) Does the risk of crime occurring after dark vary depending on the geographical area?

Evidence about the relationship between lighting and crime is mixed. Although a review of evidence found that improved road / street lighting was associated with reductions in crime, these reductions occurred in daylight as well as after dark, suggesting any effect was not due only to changes in visual conditions. One limitation of previous studies is that crime data are reported in aggregate and thus previous analyses were required to make simplifications concerning types of crimes or locations. We will overcome that by working with a UK police force to access records of individual crimes. We will use these data to determine whether the risk of crime at a specific time of day is greater after dark than during daylight. If no difference is found, this would suggest improvements to visual conditions after dark through lighting would have no effect. If however the risk of crime occurring after dark was greater than during daylight, quantifying this effect would provide a measure to assess the potential effectiveness of lighting in reducing crime risk after dark. We will use a case and control approach to analyse ten years of crime data. We will compare counts of crimes in ‘case’ hours, that are in daylight and darkness at different times of the year, and ‘control’ hours, that are in daylight throughout the year. From these counts we will calculate odds ratios as a measure of the effect of darkness on risk of crime, using these to answer three questions: 1) Is the risk of overall crime occurring greater after dark than during daylight? 2) Does the risk of crime occurring after dark vary depending on the category of crime? 3) Does the risk of crime occurring after dark vary depending on the geographical area?

Objectives: Assessing the predictive accuracy of using prior crime, place attributes, ambient population, community structural, and social characteristics, in isolation and combined when forecasting different violent and property crimes. Method: Using multilevel negative binomial regression, crime is forecasted into the subsequent year, in 50-m grid-cells. Incidence rate ratio (IRR), Prediction Accuracy Index (PAI), and Prediction Efficacy Index (PEI*) are interpreted for all combined crime generators and community characteristics. This study is partially a test of a crude version of the Risk Terrain Modeling technique. Results: Where crime has been in the past, the risk for future crime is higher. Where characteristics conducive to crime congregate, the risk for crime is higher. Community structural characteristics and ambient population are important for some crime types. Combining variables increases the accuracy for most crime types, looking at the IRR. Taking the geographical area into account, crime history in combination with both place- and neighborhood characteristics reaches similar accuracy as crime history alone for most crime types and most hotspot cutoffs. Conclusions: Crime history, place-, and neighborhood-level attributes are all important when trying to accurately forecast crime, long-term at the micro-place. Only counting past crimes, however, still does a really good job.

Crime is always one of the most important social problems, and it poses a great threat to public security and people. Accurate crime prediction can help the government, police, and citizens to carry out effective crime prevention measures. In this paper, the research on crime prediction is systematically reviewed from a variety of temporal and spatial perspectives. We describe the current state of crime prediction research from four perspectives (prediction content, crime types, methods, and evaluation) and focus on the prediction methods. According to various temporal and spatial scales, temporal crime prediction is divided into short-term prediction, medium-term prediction, and long-term prediction, and spatial crime prediction is divided into micro-, meso-, and macro-level prediction. Spatio-temporal crime prediction classification can be a permutation of temporal and spatial crime prediction classifications. A variety of crime prediction methods and evaluation metrics are also summarized, and different prediction methods and models are compared and evaluated. After sorting out the literature, it was found that there are still many limitations in the current research: (i) data sparsity is difficult to deal with effectively; (ii) the practicality, interpretability, and transparency of predictive models are insufficient; (iii) the evaluation system is relatively simple; and (iv) the research on decision-making application is lacking. In this regard, the following suggestions are proposed to solve the above problems: (i) the use of transformer learning technology to deal with sparse data; (ii) the introduction of model interpretation methods, such as Shapley additive explanations (SHAPs), to improve the interpretability of the models; (iii) the establishment of a set of standard evaluation systems for crime prediction at different scales to standardize data use and evaluation metrics; and (iv) the integration of reinforcement learning to achieve more accurate prediction while promoting the transformation of the application results.

PurposeThis study aims to examine the relationship between law enforcement expenditures, economic indicators and various crime rates in India using data from the National Crime Records Bureau (NCRB) spanning from 2001 to 2021.Design/methodology/approachThe study uses data sourced from the NCRB covering violent crimes, property crimes, economic crimes and total crimes under the Indian Penal Code across 27 states and 2 union territories in India. Crime rates per 100,000 people are calculated using projected population figures from the 2001 and 2011 censuses, adjusting for census periods and growth rates. Economic variables such as per capita real gross state domestic product (GSDP) and total police expenditure are obtained from state government sources, adjusted to constant prices. Pooled ordinary least squares analysis is conducted to explore the relationships between crime rates, conviction rate, economic factors and police expenditure over the three-time points: 2001, 2011 and 2021.FindingsThe study reveals a complex relationship between police expenditure and crime rates across different categories in India. Increased police expenditure is positively associated with higher reporting rates for violent and economic crimes, suggesting that enhanced law enforcement resources may improve crime detection and reporting. However, contrary to some expectations, higher police spending does not necessarily lead to a reduction in overall crime rates. Economic prosperity, measured by per capita GSDP, is found to reduce violent crime rates but is associated with an increase in property and economic crimes, likely due to the greater opportunities for such crimes in wealthier regions.Research limitations/implicationsThe study’s reliance on state-level aggregate data and NCRB crime statistics, which are based on reported incidents, might not fully capture the true extent of criminal activity, particularly in regions with underreporting issues. In addition, the use of projected population data beyond 2001 and 2011 introduces some uncertainty into the analysis. Future research should explore district-level data to better understand regional variations in crime and incorporate social, cultural and innovative policing strategies to provide a more comprehensive analysis.Practical implicationsPolicymakers should recognize the nuanced impacts of police expenditure and economic prosperity on crime rates. While increased law enforcement resources may enhance crime reporting, they do not guarantee a reduction in overall crime rates. Economic growth, while beneficial in reducing violent crimes, may inadvertently increase property and economic crimes due to greater opportunities for criminal activity. These insights suggest the need for targeted interventions that consider both economic factors and law enforcement strategies in crime prevention efforts.Originality/valueThis study contributes to the ongoing debate about the effectiveness of police expenditure in reducing crime by highlighting its limited impact on overall crime rates in India. The findings underscore the importance of understanding the diverse effects of economic prosperity on different crime types, offering valuable insights for policymakers aiming to design evidence-based crime prevention strategies that account for socioeconomic disparities and regional contexts.

Vacant and abandoned buildings are common features in many post-industrial US cities, and are consistent predictors of violence. Demolition programs are regularly employed as an urban land use policy to stabilize housing markets and mitigate public health problems including violence. The objective of this research was to examine the effect of vacant building removals on violent and property crimes in Baltimore, MD from 2014 to 2019. We conducted a difference-in-differences analysis using spatio-temporal Bayesian mixed models on six crime types on block faces with and without building removals, before compared with after removal. There were significant reductions in total, violent crimes (with and without assaults), thefts, and burglaries on block faces with building removals relative to their controls. Total crimes decreased 1.4% per mi2 (CrI: 0.5 - 2.3%), which translates to a relative reduction ~ 2.6 total crimes per mi2 per year. The largest relative decreases in crime were found among assaults (4.9%; CrI: 3.4 - 6.3%) and violent crimes (3.0%; CrI: 1.9 - 4.1%). Building removals were associated with relative reductions in crime in Baltimore City. The relative reductions in crime, at building removals compared to at control vacant lots, were found among assaults and violent crimes, the crimes of greatest public health concern. Building removals provide co-benefits to their communities, and may be considered part of a crime reduction strategy compatible with other approaches. A systematic effort to understand the role of care for remaining vacant lots could further inform our findings, and efforts to further decrease violence and improve community health.

Crime and violation are the threat to justice and meant to be controlled. Accurate crime prediction and future forecasting trends can assist to enhance metropolitan safety computationally. The limited ability of humans to process complex information from big data hinders the early and accurate prediction and forecasting of crime. The accurate estimation of the crime rate, types and hot spots from past patterns creates many computational challenges and opportunities. Despite considerable research efforts, yet there is a need to have a better predictive algorithm, which direct police patrols toward criminal activities. Previous studies are lacking to achieve crime forecasting and prediction accuracy based on learning models. Therefore, this study applied different machine learning algorithms, namely, the logistic regression, support vector machine (SVM), Naïve Bayes, k-nearest neighbors (KNN), decision tree, multilayer perceptron (MLP), random forest, and eXtreme Gradient Boosting (XGBoost), and time series analysis by long-short term memory (LSTM) and autoregressive integrated moving average (ARIMA) model to better fit the crime data. The performance of LSTM for time series analysis was reasonably adequate in order of magnitude of root mean square error (RMSE) and mean absolute error (MAE), on both data sets. Exploratory data analysis predicts more than 35 crime types and suggests a yearly decline in Chicago crime rate, and a slight increase in Los Angeles crime rate; with fewer crimes occurred in February as compared to other months. The overall crime rate in Chicago will continue to increase moderately in the future, with a probable decline in future years. The Los Angeles crime rate and crimes sharply declined, as suggested by the ARIMA model. Moreover, crime forecasting results were further identified in the main regions for both cities. Overall, these results provide early identification of crime, hot spots with higher crime rate, and future trends with improved predictive accuracy than with other methods and are useful for directing police practice and strategies.

This Campbell systematic review examines the effects of closed circuit television (CCTV) on property crime and violent crime. The review reports on whether using CCTV results in crime displacement, and also assesses whether using CCTV leads to the spread of crime prevention benefits.The authors found 44 evaluations. The studies were from the United Kingdom, the United States of America, Canada, Norway and Sweden. Most of the studies (34) were from the United Kingdom.CCTV has a modest impact on crime. Effectiveness varies across settings. Surveillance is more effective at preventing crime in car parks, and less effective in city and town centers, public housing, and public transport. CCTV appears most effective in car parks at reducing vehicle crimes such as thefts from cars or stealing cars. The effectiveness of CCTV surveillance is greater when camera coverage of an area is high. CCTV surveillance does not have an effect on levels of violent crime.In all six of the CCTV car park studies, CCTV surveillance was an element in a broader package of crime prevention measures, such as extra security guards, better lighting, and fencing. It is not possible to assess the independent effects of each of these different components. The available evidence does not allow a conclusion as to whether CCTV leads to a displacement of crime or a diffusion of crime prevention benefits to other areas.AbstractBackgroundIn recent years, there has been a marked and sustained growth in the use of CCTV to prevent crime in public space in the U.K., United States, and other Western nations. In the U.K., CCTV is the single most heavily funded crime prevention measure operating outside of the criminal justice system. A key issue is how far funding for CCTV has been based on high quality scientific evidence demonstrating its efficacy in preventing crime. There is concern that this funding has been based partly on a handful of apparently successful schemes that were usually evaluated with less than rigorous designs, done with varying degrees of competence, and done with varying degrees of professional independence from government. Recent reviews that have examined the effectiveness of CCTV against crime have also noted the need for high quality, independent evaluation research.ObjectivesThe main objective of this review is to assess the available research evidence on the effects of CCTV surveillance cameras on crime in public space. In addition to assessing the overall impact of CCTV on crime, this review will also investigate in which settings, against which crimes, and under what conditions it is most effective.Search strategyFour search strategies were employed to identify studies meeting the criteria for inclusion in this review: (1) searches of electronic bibliographic databases; (2) searches of literature reviews on the effectiveness of CCTV in preventing crime; (3) searches of bibliographies of CCTV studies; and (4) contacts with leading researchers. Both published and unpublished reports were considered in the searches. Searches were international in scope and were not limited to the English language.Selection criteriaStudies that investigated the effects of CCTV on crime were included. For studies involving one or more other interventions, only those studies in which CCTV was the main intervention were included. Studies were included if they had, at a minimum, an evaluation design that involved before‐and‐after measures of crime in experimental and control areas. There needed to be at least one experimental area and one reasonably comparable control area.Data collection & analysisNarrative findings are reported for the 44 studies included in this review. A meta‐analysis of 41 of these 44 studies was carried out; the requisite crime data was missing in other 3 studies. The “relative effect size” or RES (which can be interpreted as an incident rate ratio) was used to measure effect size. Results are reported for total crime and, where possible, property and violent crime categories using (mostly) official data. In the case of studies that measure the impact of CCTV programs on crime at multiple points in time, similar time periods before and after are compared (as far as possible). The review also reports on displacement of crime and diffusion of crime prevention benefits.Main resultsThe studies included in this systematic review indicate that CCTV has a modest but significant desirable effect on crime, is most effective in reducing crime in car parks, is most effective when targeted at vehicle crimes (largely a function of the successful car park schemes), and is more effective in reducing crime in the U.K. than in other countries.Reviewers’ conclusionsWe conclude that CCTV surveillance should continue to be used to prevent crime in public space, but that it be more narrowly targeted than its present use would indicate. Future CCTV schemes should employ high‐quality evaluation designs with long follow‐up periods.

Background: Patients with psychiatric illness are at increased risk of committing violent crime more than individuals in the general population.Objective: To estimate the risk of violent crime among psychiatric patients with different diagnoses and the clinical factors mediating this risk.Subjects and method: cross-sectional study was used to analyze data from ElKhanka psychiatric hospital admissions and criminal convictions in 2010-2012. Risk of violent crime in psychiatric patients was compared with type of crime (homicide,set fire,physical and sexual assult), their written diagnoses in medical files and durations of admission and discharge data were considered.Results: We found increased risk of violent crime among patients diagnosed with schizophrenic disorders 80.6%, followed by bipolar disorder 7.3% and mental retardation 8.1%. Most of the crimes committed were homicide 56.4% and physical assault 20.6%.Conclusion: prevention of crimes in psychiatric patients needs more attention. The risk assessment, treatment and duration of admission in these individuals need further examination.

This article examines the influence of CCTV on the realization of the person`s intention to commit a crime. The authors present the results of their own research which was conducted among Hungarian prison population (172 respondents) using a questionnaire method. The questionnaires were of a survey-type with a closed set of questions. The research sought to determine how offenders relate to CCTV, its role in crime prevention, and whether any differences in attitudes towards CCTV can be observed in terms of age and time spent in prison. In the course of the research, it was found that a significant negative correlation can be found between the time spent in a penitentiary institution and the fear of CCTV among those who spent more time in prison. Furthermore, it was also determined that the deterrent power of cameras is comparable to that of uniformed police officers. Research showed that CCTV’s effectiveness depends on factors such as camera placement, real-time monitoring, and integration with police patrols. While studies confirm reductions in certain crime types – particularly property crime and offenses in urban areas – other findings suggest CCTV primarily displaces crime geographically rather than preventing it. Offenders perceive cameras as deterrents in visible, well-monitored spaces, but this effect diminishes with sporadic deployment or inadequate implementation. This finding has significant criminological and national economic significance.

Improved street lighting serves many functions and is used in both public and private settings. The prevention of personal and property crime is one of its objectives in public space, which is the main focus of this systematic review.There are two main theories of why improved street lighting may cause a reduction in crime. The first suggests that improved lighting leads to increased surveillance of potential offenders (both by improving visibility and by increasing the number of people on the street) and hence to increased deterrence of potential offenders. The second suggests that improved lighting signals community investment in the area and that the area is improving, leading to increased community pride, community cohesiveness, and informal social control. The first theory predicts decreases in crime especially during the hours of darkness, while the second theory predicts decreases in crime during both daytime and nighttime.Results of this review indicate that improved street lighting significantly reduces crime. This lends support for the continued use of improved street lighting to prevent crime in public space. The review also found that nighttime crimes did not decrease more than daytime crimes. This suggests that a theory of street lighting focusing on its role in increasing community pride and informal social control may be more plausible than a theory focusing on increased surveillance and increased deterrence. Future research should be designed to test the main theories of the effects of improved street lighting more explicitly, and future lighting schemes should employ high quality evaluation designs with long‐term followups.AbstractBackgroundImproved street lighting is intended to serve many purposes, one of them being the prevention of crime. While street lighting improvements may not often be implemented with the expressed aim of preventing crime – pedestrian safety and traffic safety may be viewed as more important aims – and the notion of lighting streets to deter lurking criminals may be too simplistic, its relevance to the prevention of crime has been suggested in urban centers, residential areas, and other places frequented by criminals and potential victims.ObjectivesThe main objective of this review is to assess the available research evidence on the effects of improved street lighting on crime in public space. In addition to assessing the overall impact of improved street lighting on crime, this review will also investigate in which settings, against which crimes, and under what conditions it is most effective.Search strategyFour search strategies were employed to identify studies meeting the criteria for inclusion in this review: (1) searches of electronic bibliographic databases; (2) searches of literature reviews on the effectiveness of improved street lighting in preventing crime; (3) searches of bibliographies of street lighting studies; and (4) contacts with leading researchers. Both published and unpublished reports were considered in the searches. Searches were international in scope and were not limited to the English language.Selection criteriaStudies that investigated the effects of improved street lighting on crime were included. For studies involving one or more other interventions, only those studies in which improved street lighting was the main intervention were included. Studies were included if they had, at a minimum, an evaluation design that involved before‐and‐after measures of crime in experimental and control areas. There needed to be at least one experimental area and one reasonably comparable control area.Data collection & analysisNarrative findings are reported for the 13 studies included in this review. A meta‐analysis of all 13 of these studies was carried out. The “relative effect size” or RES (which can be interpreted as an incident rate ration) was used to measure effect size. Results are reported for total crime and, where possible, property and violent crime categories using (mostly) official data. In the case of studies that measure the impact of improved street lighting programs on crime at multiple points in time, similar time periods before and after are compared (as far as possible). The review also addresses displacement of crime and diffusion of crime prevention benefits.Main resultsThe studies included in this systematic review indicate that improved street lighting significantly reduces crime, is more effective in reducing crime in the United Kingdom than in the United States, and that nighttime crimes do not decrease more than daytime crimes.Reviewers’ conclusionsWe conclude that improved street lighting should continue to be used to prevent crime in public areas. It has few negative effects and clear benefits for law‐abiding citizens.

This study investigated the relationship between urban park size and community mental health, focusing on neighborhood crime risk as a mediator. Data were collected for 989 urban parks in Alabama, USA, from the Trust for Public Land’s ParkServe database and relevant Alabama cities’ Parks and Recreation Department websites. Park size was measured using Geographic Information Systems. The relative risks of various crime types, including violent and property crimes, were provided by the Environmental Systems Research Institute. Community mental health data from the PLACES database were used to evaluate the prevalence of poor mental health in different communities. Guided by the Stress Reduction Theory, we used mediation analysis to explore whether crime risk mediated the relationship between park size and mental health outcomes. Results indicated that the effect of larger park sizes on reducing poor mental health was fully mediated by the indirect pathway through reduced crime risk, though park sizes alone were not significantly directly associated with a lower prevalence of poor mental health. By allocating resources to create and maintain high-quality urban neighborhood parks, policymakers can foster safer environments that contribute to improved mental health across communities, and, ultimately, build essential infrastructure to support the public’s mental well-being.

The routine activity approach to the etiology of crime has gained considerable attention in the last few years. Its propositions have been tested using a variety of data sources and analysis methods. To date, however, the majority of analyses have used data collected within one nation and have employed unidimensional indicators. This article explores the macrostructural tenets of the approach based upon a sample of 52 nations spanning a 25-year period (1960-1984). The findings offer qualified support for the approach and uncover interesting anomalies. First, the model appears to be crimespecific, applying more to property crime than personal crime. Second, the best fitting model is nonlinear and specifies threshold effects. These findings are discussed in light of current research on the routine activity approach. Over the last ten years there has been a explosion in research and a rapid expansion of knowledge concerning a criminological approach variously called routine activity, lifestyle, or opportunity theory.' Because of the relative newness of this approach, the literature is vague on the specific relationships among (1) social structural conditions and routine activities or opportunities and (2) those routine activities or opportunities and the risk of victimization or crime.2 Two prominent models emerge from a perusal of the empirical literature, models that are conceptually very similar but differ by what is empirically investigated and what is assumed a priori. Each model views crime or the risk of victimization as a process whereby social structural change causes a change in the nature and frequency of routine activities and, subsequently, in the levels of risk. However, while one model assumes a specific social structure (e.g., proportion of single-person households, percent of women in the workforce, and amount of leisure time) and then empirically investigates the effect of routine activities on risk (cf. Garofalo 1987; Lynch 1987; Maxfield 1987; Miethe, Stafford & Long 1987), the other model investigates the empirical relationship between social structure and risk while assuming the intervening routine The author wishes to thank James P. Lynch, Michael G. Maxfield, and Sandra Baxterfor their assistance in conceptual development, Peter Basiotis for his aid in data analysis, and Mona Danner for her aid in data management. Direct correspondence to the Department of Justice, Law, and Society, The American University, Washington, DC 20016. ? The University of North Carolina Press Social Forces, September 1991, 70(1):147-163 This content downloaded from 207.46.13.159 on Sun, 23 Oct 2016 05:16:43 UTC All use subject to http://about.jstor.org/terms 148 / Social Forces 70:1, September 1991 activities (i.e., not measuring or testing them within the model) (cf. Cantor & Land 1985; Cohen & Felson 1979). In an attempt to advance our understanding of the efficacy of the routine activity approach in explaining the risk of crime, this article (1) investigates the second and less researched model, where the effect of structural change on crime rates is investigated while assuming the mediating or intervening effect of routine activities, (2) employs a cross-national sample of highly divergent social structures,3 (3) investigates the relative and simultaneous effects of the routine activity approach's central concepts, and (4) devises multiple indicators to measure more precisely the multidimensional nature of the approach's central concepts. In addition, following the tradition of Cohen and Felson (1979), this model is applied to both personal and property crime rates. Elements of the Model Although activity models in the literature incorporate varying explanatory concepts as well as levels of analyses, each includes reference to a central core of three concepts. This core includes consideration of the (1) suitability of the target, (2) proximity of the victim to a pool of motivated offenders, and (3) level of guardianship over the target. The present research relies on this literature and develops a three-part composite model for testing with cross-national data.

Background and Aim: Considering the significance of prevention as one of the most important issues in ethical and legal systems, and increasing the risk of environmental crimes, this study aimed to analyze the prevention of environmental crimes in the light of UN guidelines. Materials and Methods: This analytical study examines the prevention of environmental crimes in the light of the UN guidelines. This analysis is done based on the ethical principle of prevention of harm. Findings: Given the globalization of environmental damages, green criminologists have focused on the principle of no harm. They have recognized environmental harm as the criterion in the definition of the environmental crime rather than criminalization. The United Nations has adopted guidelines for the prevention of crime in 1996 and 2002 in the Economic Council to promote crime prevention programs at the national level. The key principles in these guidelines included: government governance, crime prevention in social and economic policies; participatory prevention; accountability; sustainability; cost-benefit analysis; knowledge-based; compliance with the Human rights; solidarity, Interdependency and differentiation. Conclusion: The utilization of the principles of the United Nations Guidelines focusing on the ethical Principle of Prevention of harm, can help us to prevent environmental crimes. In order to prevent environmental damage and harm, participation of the civil society in the field of protection of environment is recommended through providing access of the citizens and empowering them, as well as, supporting community-based and non-governmental organizations. Please cite this article as: Mirkamali AR, Hajivand A. Analyzing the Prevention of Environmental Crimes in the light of the United Nations Guidelines based on the Ethical Principle of Prevention of Harm. Bioethics Journal 2017; 7(26): 61-75.