Spatial Modeling for Homicide Rates Estimation in Pernambuco State-Brazil

Abstract

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.

Spatial-temporal patterns of homicide in socioeconomically deprived settings: violence in Alagoas, Brazil, 2006‒2015

“…Another study performed in the neighboring state of Pernambuco found that homicides are clustered in the metropolitan region of the capital Recife. Similar to our results this highlighted the importance of the Human Development Index indicators in building a prevention strategy [44]. A similar study located in the nearby state of Bahia observed a clear spatial expansion of homicides outward from the major urban centres, this being nearly congruent with our observations in Alagoas [45].…”

How Is Urban Greenness Spatially Associated with Dockless Bike Sharing Usage on Weekdays, Weekends, and Holidays?

“…Initially, the Moran's I for residuals of all three backward stepwise linear regression models was significantly positive (Moran's I > 0.2, p-value = 0.001), which suggests that the global regression assumption of residual independence was invalid. Therefore, GWR can be applied to solve this problem [44,47,[55][56][57]. The results were significantly improved by GWR in the following aspects (Table 5).…”

“…Secondly, spatial models have never been applied in studies of association between urban greenness and dockless bike sharing usage, hence resulting in the lack of the exploration of the spatially varying impacts of urban greenness on bike sharing usage [4,5]. The main purpose of the research is to explore and compare the spatial associations between eye-level and overhead level greenness, and the usage of dockless bike sharing on weekdays, weekend, and holidays in a center area of Shenzhen, China [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57].…”

“…Secondly, the AICc of the weekdays model declined from 220.333 in the global model to 204.794 in the GWR, and that of the weekend and holidays models also decreased. The GWR model is also capable of identifying the areas that best fit the model based on the local R-square [57]. Hence, the model's local performance to explore the dockless bike sharing usage of different metro station buffers can be visualized.…”

“…Meanwhile, the northwestern and northeastern regions had a lower local R-square, suggesting that there might be extra explanatory variables affecting the bike sharing usage in those areas. The GWR model is also capable of identifying the areas that best fit the model based on the local R-square [57]. Hence, the model's local performance to explore the dockless bike sharing usage of different metro station buffers can be visualized.…”