Homicide mortality is a worldwide concern and has occupied the agenda of researchers and public managers. In Brazil, homicide is the third leading cause of death in the general population and the first in the 15-39 age group. In South America, Brazil has the third highest homicide mortality, behind Venezuela and Colombia. To measure the impacts of violence it is important to assess health systems and criminal justice, as well as other areas. In this paper, we analyze the spatial distribution of homicide mortality in the state of Goias, Center-West of Brazil, since the homicide rate increased from 24.5 per 100,000 in 2002 to 42.6 per 100,000 in 2014 in this location. Moreover, this state had the fifth position of homicides in Brazil in 2014. We considered socio-demographic variables for the state, performed analysis about correlation and employed three clustering algorithms: K-means, Density-based and Hierarchical. The results indicate the homicide rates are higher in cities neighbors of large urban centers, although these cities have the best socioeconomic indicators.
As far as we know no paper studied the homicide rates focused on the Goi 'as state. Furthermore, we did not find any work using clustering algorithms to analyze the spatial distribution of homicides. The techniques and tools commonly used on this topic are: estimation of regression coefficients @cite_10 @cite_17 ; spatial scan statistics @cite_6 ; SaTScan software methods @cite_20 ; Bayesian approach with Monte Carlo Markov Chain algorithm @cite_3 ; Moran's Global index @cite_9 ; descriptive statistics or correlation techniques @cite_2 @cite_12 ; estimable functions and negative binomial regression @cite_8 ; SSPS software tools @cite_7 ; and multiple regression analysis (stepwise method) @cite_14 .
@cite_14 @cite_7 @cite_8 @cite_9 @cite_3 @cite_6 @cite_2 @cite_10 @cite_12 @cite_20 @cite_17
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OBJECTIVE To analyze the spatial distribution of homicide mortality in the state of Bahia, Northeastern Brazil. METHODS Ecological study of the 15 to 39-year old male population in the state of Bahia in the period 1996-2010. Data from the Mortality Information System, relating to homicide (X85-Y09) and population estimates from the Brazilian Institute of Geography and Statistics were used. The existence of spatial correlation, the presence of clusters and critical areas of the event studied were analyzed using Moran’s I Global and Local indices. RESULTS A non-random spatial pattern was observed in the distribution of rates, as was the presence of three clusters, the first in the north health district, the second in the eastern region, and the third cluster included townships in the south and the far south of Bahia. CONCLUSIONS The homicide mortality in the three different critical areas requires further studies that consider the socioeconomic, cultural and environmental characteristics in order to guide specific preventive and interventionist practices. This study demonstrate that the empirical literature on the structural convariates of homicide rates contains inconsistent findings across different time periods and different geographical units. This apparent variance of findings may be due to statistical or methodological artifacts of particular studies, such as different time periods covered, units of analysis, samples, model specification, and problems of statistical analysis and inference. A baseline regression model using 11 structural covariates is estimated for cities, metropolitan areas, and states in 1960, 1970, and 1980. The empirical estimates of this model exhibit instability because of high levels of collinearity among several regressors. Principal components analysis is applied to simplify the dimensionally of the structural covariate space. Reestimation of the regression model then indicates that the apparent inconsistencies across time and social space are greatly reduced. The theoretical significance of the findings for substantive theor... Objectives. We modeled the spatiotemporal movement of hotspot clusters of homicide by motive in Newark, New Jersey, to investigate whether different homicide types have different patterns of clustering and movement.Methods. We obtained homicide data from the Newark Police Department Homicide Unit’s investigative files from 1997 through 2007 (n = 560). We geocoded the address at which each homicide victim was found and recorded the date of and the motive for the homicide. We used cluster detection software to model the spatiotemporal movement of statistically significant homicide clusters by motive, using census tract and month of occurrence as the spatial and temporal units of analysis.Results. Gang-motivated homicides showed evidence of clustering and diffusion through Newark. Additionally, gang-motivated homicide clusters overlapped to a degree with revenge and drug-motivated homicide clusters. Escalating dispute and nonintimate familial homicides clustered; however, there was no evidence of diffusion. ... The objective was to evaluate correlations between suicide, homicide and socio-demographic variables by an ecological study. Mortality and socio-demographic data were collected from official records of the Ministry of Health and IBGE (2010), aggregated by state (27). The data were analyzed using correlation techniques, factor analysis, principal component analysis with a varimax rotation and multiple linear regression. Suicide age-adjusted rates for the total population, men and women were 5.0, 8.0, and 2.2 per 100,000 inhabitants respectively. The suicide rates ranged from 2.7 in Para to 9.1 in Rio Grande do Sul. Homicide for the total population, men and women were 27.2, 50.8, and 4.5 per 100,000, respectively. The homicide rates ranged from 13.0 in Santa Catarina to 68.9 in Alagoas. Suicide and homicide were negatively associated, the significance persisted among men. Unemployment was negatively correlated with suicide and positively with homicide. Different socio-demographic variables were found to correlate with suicide and homicide in the regressions. Suicide showed a pattern suggesting that, in Brazil, it is related to high socioeconomic status. Homicide seemed to follow the pattern found in other countries, associated with lower social and economic status.