@Article{Ramos:2021:GeWeRe,
               author = "Ramos, Rafael Blakeley Guimar{\~a}es",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Improving victimization risk estimation: A geographically weighted 
                         regression approach",
              journal = "ISPRS International Journal of Geo-Information",
                 year = "2021",
               volume = "10",
               number = "6",
                pages = "e364",
                month = "June",
             keywords = "Crime, Denominator dilemma, Geographically weighted regression, 
                         Mapping, Risk, Standardization.",
             abstract = "Standardized crime rates (e.g., homicides per 100,000 people) are 
                         commonly used in crime analysis as indicators of victimization 
                         risk but are prone to several issues that can lead to bias and 
                         error. In this study, a more robust approach (GWRisk) is proposed 
                         for tackling the problem of estimating victimization risk. After 
                         formally defining victimization risk and modeling its sources of 
                         uncertainty, a new method is presented: GWRisk uses geographically 
                         weighted regression to model the relation between crime counts and 
                         population size, and the geographically varying coefficient 
                         generated can be interpreted as the victimization risk. A 
                         simulation study shows how GWRisk outperforms na{\"{\i}}ve 
                         standardization and Empirical Bayesian Estimators in estimating 
                         risk. In addition, to illustrate its use, GWRisk is applied to the 
                         case of residential burglaries in Belo Horizonte, Brazil. This new 
                         approach allows more robust estimates of victimization risk than 
                         other traditional methods. Spurious spikes of victimization risk, 
                         commonly found in areas with small populations when other methods 
                         are used, are filtered out by GWRisk. Finally, GWRisk allows 
                         separating a reference population into segments (e.g., houses, 
                         apartments), estimating the risk for each segment even if crime 
                         counts were not provided per segment.",
                  doi = "10.3390/ijgi10060364",
                  url = "http://dx.doi.org/10.3390/ijgi10060364",
                 issn = "2220-9964",
             language = "en",
           targetfile = "ramos_improving.pdf",
        urlaccessdate = "09 maio 2024"
}