Geography of crime research dates back to the early 1800s, most of which in English and in the context of the United States and Europe, but with a growing and significant literature studying the developing world. We contribute to this literature through an application of social disorganization theory in a Latin American context: Campinas, Brazil. We consider a number of property and violent crime types using census tracts as the spatial unit of analysis. Implementing a spatial regression method, we find support for social disorganization theory, but not as strong as similar studies in Europe and North America. However, because of the context of Campinas, Brazil, a different understanding of the local conditions proves to be important for understanding the geography of crime in this context. The implications of these results are discussed in the context of theoretical developments as well as crime prevention initiatives.

The Caatinga biome is the largest and wettest semi-arid environment globally, occupying 10% of the Brazilian territory. Half of the original vegetation from the Caatinga has already been deforested. This research aims to identify the factors associated with Caatinga deforestation (2010–2016). Therefore, the researchers used Geographically Weighted Regression to analyse the drivers of deforestation at the municipal level. Our results showed that five factors are crucial for understanding the spatial variability of deforestation in the Caatinga biome: agricultural credit, cattle, goats, distance from roads, and mining activities. The results were interpreted carefully since the semi-arid Biome is marked by regional asymmetries, mixing subsistence agriculture with agribusiness (MATOPIBA and Vale do Rio São Francisco). The outcome maps allow us to discuss the implication of spatial arrangements and intervention policies towards sustainability.

The reporting of rape to police is an important component of this crime to have the criminal justice system involved and, potentially, punish offenders. However, for a number of reasons (fear of retribution, self-blame, etc.), most rapes are not reported to police. Most often, the research investigating this phenomenon considers incident and victim factors with little attention to the spatio-temporal factors of the rape. In this study, we consider incident, victim, and spatio-temporal factors relating to rape reporting in Campinas, Brazil. Our primary research question is whether or not the spatio-temporal factors play a significant role in the reporting of rape, over and above incident and victim factors. The subjects under study are women who were admitted to the Women’s Integrated Healthcare Center at the State University of Campinas, Brazil, and surveyed by a psychologist or a social worker. Rape reporting to police was measured using a dichotomous variable. Logistic regression was used to predict the probability of rape reporting based on incident, victim, and spatio-temporal factors. Although we find that incident and victim factors matter for rape reporting, spatio-temporal factors (rape/home location and whether the rape was in a private or public place) play an important role in rape reporting, similar to the literature that considers these factors. This result has significant implications for sexual violence education. Only when we know why women decide not to report a rape may we begin to work on strategies to overcome these hurdles.

Homicide rates have been increasing worldwide, especially in Latin America, where it is considered one of the most lethal of the continents. Despite that, the occurrence of homicides are not homogeneous in time and space on the continent or in the Brazilian cities. Therefore, the main objective of this study is to present a spatial analysis of homicides in the state of Pernambuco, Brazil, between the years of 2016 and 2019, by the use of an exploratory analysis of spatial homicide data with five variables that could explain its occurrence. In addition to that, it was applied the Global and Local Moran’s Index, Ordinary Least Squares (OLS) regression, and Geographically Weighted Regression (GWR), all implemented in the Geographic Information System (GIS) software. Thus, the distribution of clusters revealed a spatial autocorrelation for homicide rates, confirming a spatial dependence. This data also showed the polarization of the rate between the coast and the interior of the state of Pernambuco.