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.

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.

Objectives: To analyse the spatial distribution of rates of COVID‐19 cases and its association with socio‐economic conditions in the state of Pernambuco, Brazil.Methods: Autocorrelation (Moran index) and spatial association (Geographically weighted regression) models were used to explain the interrelationships between municipalities and the possible effects of socio‐economic factors on rates.Results: Two isolated clusters were revealed in the inner part of the state in sparsely inhabited municipalities. The spatial model (Geographically Weighted Regression) was able to explain 50% of the variations in COVID‐19 cases. The variables proportion of people with low income, percentage of rented homes, percentage of families in social programs, Gini index and running water had the greatest explanatory power for the increase in infection by COVID‐19.Conclusions: Our results provide important information on socio‐economic factors related to the spread of COVID‐19 and can serve as a basis for decision‐making in similar circumstances.