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The relationship between climate change and violent behavior has been well documented in previous studies. Violence has two dimensions: outward violence (i.e., crime) and inward violence (i.e., suicide). To our knowledge, rigorous empirical studies have not been performed to investigate how climate change affects both criminal and suicidal behavior. This study aims to estimate the effects of climate change on crime and suicide in Japan by using prefecture-level monthly panel data on climate, crime, and suicide between 2009 and 2015. Even after controlling for prefecture, yearly, and monthly effects, we found that many climate factors affected both crime and suicide in Japan. In particular, more aggressive behavior and an increased number of suicides were observed when the average temperature increased. Furthermore, we predicted how changes in the climate of Japan will affect future patterns of criminal and suicidal behavior based on two climate change scenarios.

With the increase of urbanization in cities, there is also the rise of crime incidence. It is important for the police force to stay ahead in proactive policing. In this paper, the researcher presents a simple approach to predict crime in a geographic space using grid thematic mapping and neural networks. The study particularly focuses on the possibility of using historical crime data in predicting future crime incidents. The dataset is divided into monthly, weekly, and daily data called as a snapshot for training the model. The study area, Cebu City, is divided into a square grid of various sizes and crime incidents, from each cell in the grid of each snapshot, will then be recorded. This data is then fed into the neural network to predict possible areas where crime would happen for the next time interval. Initial results have shown that the model is accurate enough to predict crime areas when the data snapshot is divided monthly and weekly and grid size is set to a large size of about 1000 by 1000 meters and 750 by 750 meters. The best model was able to yield an F1 score of 0.95. Although the created model is simple, this can be a stepping stone to further crime modelling and prediction studies. This tool can be used to aid decisions and policy-making in the deployment of police resources throughout the city.

This article analyzes crime development which is one of the largest threats in today's world, frequently referred to as the war on crime. The criminal commits crimes in his free time (when not in jail) according to a non-stationary Poisson process which accounts for fluctuations. Expected values and variances for crime development are determined. The deterrent effect of imprisonment follows from the amount of time in imprisonment. Each criminal maximizes expected utility defined as expected benefit (from crime) minus expected cost (imprisonment). A first-order differential equation of the criminal's utility-maximizing response to the given punishment policy is then developed. The analysis shows that if imprisonment is absent, criminal activity grows substantially. All else being equal, any equilibrium is unstable (labile), implying growth of criminal activity, unless imprisonment increases sufficiently as a function of criminal activity. This dynamic approach or perspective is quite interesting and has to our knowledge not been presented earlier. The empirical data material for crime intensity and imprisonment for Norway, England and Wales, and the US supports the model. Future crime development is shown to depend strongly on the societally chosen imprisonment policy. The model is intended as a valuable tool for policy makers who can envision arbitrarily sophisticated imprisonment functions and foresee the impact they have on crime development.

This paper provides an overview of the current state of knowledge about small business scams. A scam is a form of dishonest action, based upon an invitation to participate in an activity. Victims are encouraged, mislead or induced to voluntarily interact with the perpetrator, and ultimately to willingly surrender over money, information or other valuable resources. Common forms of scams directed towards small business include phishing, false business valuations and sales, fake overpayments, false directory and advertisement listings, bust-outs, blowing, cramming, advance fee fraud and misleading self-employment projects. The limited research evidence available to date suggests that small enterprises are particularly vulnerable to these types of criminal activity, are less inclined to report such events, are likely to be subject to repeat attacks, and are particularly susceptible to online scams. This occurs because small businesses often lack the in-house skills, resources and reporting arrangements needed to effectively detect and prevent scams. The paper also briefly examines the entrepreneurial nature of scammers, and explores some of the emergent literature on the psychology of small business scams that may explain scam propensity. Strategies for combating and avoiding scams are discussed, as are suggestions for future research directions in the area.

This paper analyzes whether the behavior of potential offenders can be guided by information on the actual detection probability transmitted by the policy maker. It is established that, when viewed as a cheap-talk game, the existence of equilibria with information transmission depends on the level of the sanction, the level of costs related to imposing the sanction, and the level of social harm resulting from the offense. In addition, we find that the policy maker (i. e., society as a whole) is not necessarily better off ex ante when more information is transmitted in equilibrium, but that potential offenders always are.

The correlation between hot temperatures and crime is well documented, though the relationship between heat and gun violence faces confounding problems of misreporting and underreporting of crimes. In this research, we utilize ShotSpotter data which record the time and location of gunshots via listening devices that are distributed across select cities, and we link these data with information on temperature variation over time. These data allow us to circumvent the concern that gun violence may be underreported or unobserved in standard sources like the Uniform Crime Reporting records. Here, we show that the marginal effect of a 1F change in the daily maximum temperature increases gunshot counts within a city by approximately 0.6%, and that a 1F change increases the probability that a gunshot occurs. Under expected warming paths, this implies at least a 1.6% increase in gunshots daily, and an increase in the rate of firearm deaths due to assault and suicide.