Statistical maps analyze many different atmospheric parameters and can also run into multiple problems in trying to calculate and display the data. This is where a Modifiable Areal Unity Problem (MAUP) comes into play; it is a potential source of error that can affect spatial studies which utilize aggregate data sources. Geographical data is often aggregated in order to present the results of a study in a more useful context, and spatial objects are the aggregating zones used to show results of some spatial phenomena. These zones are often arbitrary in nature and different areal units can be just as meaningful in displaying the same base level data. In this example, the prevalence in HIV AIDs in adults was calculated for every country in the world. This amount was also normalized to the total population within that country and the resulting values were placed along a color scale ranging from green to red (low to high). One can easily see with this type of map that the countries in Southern Africa have a high rate of HIV among its adults, and the same is with Africa.
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