1. Classification Map

Transcription

1 GEO 426 : Choropleth Maps Tanita Suepa 1. Classification Map All of my classification maps use red sequential scheme because the intensity of red can identify the severity of breast cancer in each area (dark red means to concern about this disease in that areas) and map users can also compare between different methods by using the same sequential scheme. 1.1 Manual Classification This map uses manual classification based on natural breaks which adjusts break values along natural grouping of data. The result of this classifation provides the best representation when comparing to the other maps. Logical breaks of this method have positive affected to data characteristic because it minimize differences between data values in the same class and maximize differences between classes. The histogram clearly shows that the break values are placed where there are the gaps between clusters of values, such as the middle group (group 3, 25-26) has more specific and narrower range than other classes because of the high number of values and the value differences from other classes; therefore, this range should be seperated from class 2 and 4. This method produces natural groups that is suitable for this data and it also deals with outliers. However, the obvious problem of manual method is that break values can vary among map makers. 1

2 Figure 1 Manual Classification Map Figure 2 Histogram of Manual Classification 2

3 1.2 Equal Interval Classification This method is easy for map users to intrepret the data. The legend limits contain no missing value. Nevertheless, this approach fails to represent the distribution of data along the number of line so some classess have few observations, such as class 1 ( ) and class 5 ( ) when comparing to other methods. Figure 3 Equal Interval Classification Map 3

4 Figure 4 Histogram of Equal Interval Classification 4

5 1.3 Defined Interval Classification Defined Interval Classification is a convinent technique to class the data and easy for map useres to understand the legend because there are only three classes. However, the limitation of this method is that map makers cannot add more number of classes (the default is 3 classes and cannot change it). With this limitation, this technique does not provide a good representation of data. As shown in the map, most of US. Area fall in the second class, so map users cannot distinquish the different of data in each area. The result of this classification is not appropriate to represent US breast cancer. Figure 5 Defined Interval Classification Map 5

6 Figure 6 Histogram of Defined Intervall Classification 6

7 2. Change Maps 2.1 Standard Deviation Classification This map uses Standard Deviation Classification with diverging scheme. The diverging scheme emphasizes on the highest and lowest values that are appropriate for change map and this scheme is easy to understand the legend. This technique considers the distribution of data along the number of line, it computes data intervals by using the mean and standard deviation. The legend of SD classification is no gaps and it represents dividing point and displays a contrast of values above and below it, as shown in positive and negative classes on the map that related to the population change from the mean value. The histogram of this data has normal distribution, so SD classification is suitable for mapping this distribution. However, the disadvantage is that it works well only with data that are normally distribution and it also requires some basic statistic to understand map. Figure 7 Standard Deviation Classification Map 7

8 Figure 8 Histogram of Standard Deviation Classification 8

9 2.2 Natural Breaks Classification This map uses natural breaks with diverging scheme, it is also good to emphasize the highest and lowest changes. The variety of hues helps distinquish symbol categories that are classifed in three groups; the first three classes represent the low changes of population, the middle class shows little change and another three classes show the high changes. The natural breaks produce variable class width; as a result, class breaks are defined differently. Nevertheless, natural breaks will show the good result for uneven distribution but this data has normal distribution so natural break classification is not the best for this particular data. Figure 9 Natural Breaks Classification Map 9

10 Figure 10 Histogram of Natural Breaks Classification 10