How should one choose a data classification method?
Each method provided in the BRFSS Maps section enables the user to choose the data classification method that they feel is most appropriate. There is no single best data classification method; each classification method has advantages and disadvantages. When creating a map, the map user should consider the purpose of the map, the data distribution (if known), and the knowledge level (i.e., mapping and statistical awareness) of the intended audience. The following are brief descriptions of the four data classification methods available to users of the SMART and BRFSS data used in the BRFSS Map application. Equal-interval: In equal-interval classifications, the data ranges for all classes are the same. In other words, the range of the entire dataset is divided by the desired number of data classes, such that each class occupies an equal interval along the range of data values. The major advantage of the equal-interval classification is that the resulting equal intervals may be easy for m
Each method provided in the BRFSS Maps section enables the user to choose the data classification method that they feel is most appropriate. There is no single best data classification method; each classification method has advantages and disadvantages. When creating a map, the map user should consider the purpose of the map, the data distribution (if known), and the knowledge level (i.e., mapping and statistical awareness) of the intended audience. The following are brief descriptions of the four data classification methods available to users of the SMART and BRFSS data used in the BRFSS Map application. Equal-interval: In equal-interval classifications, the data ranges for all classes are the same. In other words, the range of the entire dataset is divided by the desired number of data classes, such that each class occupies an equal interval along the range of data values. The major advantage of the equal-interval classification is that the resulting equal intervals may be easy for m
