@Article{RamosSilvClarPrat:2021:ToFiBe,
               author = "Ramos, Rafael Blakeley Guimar{\~a}es and Silva, Br{\'a}ulio F. 
                         A. and Clarke, Keith C. and Prates, Marcos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de Minas Gerais (UFMG)} and {University of 
                         California Santa Barbara} and {Universidade Federal de Minas 
                         Gerais (UFMG)}",
                title = "Too Fine to be Good? Issues of Granularity, Uniformity and Error 
                         in Spatial Crime Analysis",
              journal = "Journal of Quantitative Criminology",
                 year = "2021",
               volume = "37",
               number = "2",
                pages = "419--443",
                month = "June",
             keywords = "Crime Mapping, Criminology of Place, Error, Granularity, Scale.",
             abstract = "Objectives: Crime counts are sensitive to granularity choice. 
                         There is an increasing interest in analyzing crime at very fine 
                         granularities, such as street segments, with one of the reasons 
                         being that coarse granularities mask hot spots of crime. However, 
                         if granularities are too fine, counts may become unstable and 
                         unrepresentative. In this paper, we develop a method for 
                         determining a granularity that provides a compromise between these 
                         two criteria. Methods: Our method starts by estimating internal 
                         uniformity and robustness to error for different granularities, 
                         then deciding on the granularity offering the best balance between 
                         the two. Internal uniformity is measured as the proportion of 
                         areal units that pass a test of complete spatial randomness for 
                         their internal crime distribution. Robustness to error is measured 
                         based on the average of the estimated coefficient of variation for 
                         each crime count. Results: Our method was tested for burglaries, 
                         robberies and homicides in the city of Belo Horizonte, Brazil. 
                         Estimated optimal granularities were coarser than street segments 
                         but finer than neighborhoods. The proportion of units 
                         concentrating 50% of all crime was between 11% and 23%. 
                         Conclusions: By balancing internal uniformity and robustness to 
                         error, our method is capable of producing more reliable crime 
                         maps. Our methodology shows that finer is not necessarily better 
                         in the micro-analysis of crime, and that units coarser than street 
                         segments might be better for this type of study. Finally, the 
                         observed crime clustering in our study was less intense than the 
                         expected from the law of crime concentration.",
                  doi = "10.1007/s10940-020-09474-6",
                  url = "http://dx.doi.org/10.1007/s10940-020-09474-6",
                 issn = "0748-4518",
             language = "en",
           targetfile = "ramos_too.pdf",
        urlaccessdate = "09 maio 2024"
}