GIS 5007 - Choropleth and Proportional Symbol Mapping

 Expanding on last week's lecture and assignment, module 5 focused on choropleth mapping, dot mapping and proportional symbol mapping. Choropleth mapping displays phenomena that are aggregated to areas and are the most common type of thematic map. Most cartographers choose to utilize choropleth mapping when they want to display data that is evenly distributed across an enumeration district, however, it is important to refrain from using raw data. The data should be normalized to avoid presenting false narratives. 

Dot mapping is also a type of thematic mapping. A dot map displays the location of one or more geographic occurrence or phenomena. Quite often one dot can represent a multiple occurrences of phenomenon, at which time the location of the dot is places in a relative position. Dot maps are easy for the reader to understand and can show variations of large quantities, however, it can be difficult to accurately interpret density. 

Proportional symbol mapping consists of, both, proportional and graduated symbol maps, which use a symbol of various sizes to show differences in magnitude of phenomenon. An important distinction between proportional symbol and graduated symbol maps is that proportional symbol maps uses unclassed data, where the symbol size is tied to a specific value, and graduated symbol maps classify the data, where each symbol represents a range of values. Again, it is important to normalize your data. 

Map of European Wine Consumption and Population Density

The lab assignment, to accompany this week's lecture, required students to create a choropleth map in conjunction with a proportional or graduated symbol map. The map I created, pictured above, is a choropleth map showing population density of European countries and wine consumption of European countries utilizing graduated symbols. For both the choropleth and graduated symbol I chose to use the Natural Breaks (Jenks) method of classification. This method divides the data based on naturally occurring breaks, creating classes that contain data values that are similar while also highlighting the difference between the classes. As the data was not normally distributed, this method also placed outliers in its own class, preventing the data from providing a false narrative.


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