Choropleth Maps

A Choropleth map is a class of thematic maps used to explore statistical data in relation to a geographic area. This is achieved through using predefined geographic units, such as districts or regions, that are represented with graduated colour values. Choropleth maps are useful for delineating area values at both global and local scales and showing relationships between variables entangled within a geographic location. However, a drawback of Choropleth maps is that they do not show detailed information because they use an average numerical value to represent defined areas.

Unclassified Choropleth maps are a good starting point to visualize raw data before classification. They are useful for interpreting high and low density areas through defined colour gradients. The colour gradients are directly proportional to each numerical value. Therefore, it exhibits a wide variety of shading representing the diversity of the data. This allows the viewer to see subtle differences between places through minute changes in colour which can show demographic patterns that would otherwise go unnoticed.

There are various classification methods that can be used for creating a Choropleth map including Natural Breaks, Equal Interval, Quantiles and Manual which will be discussed below. Each classification method uses the same data. The difference between each method is in the spatial presentation of the data which influences the viewers interpretation.

The Natural Breaks (Jenks) classification method divides classes by the natural groupings ingrained in the data. This method of classification reduces variations in data by arranging each grouping so there is less variation in shading. You would use this method to compare the unemployment rates in suburbs across a city. The unemployment rates will be grouped so that suburbs with similar rates will be shown with the same colour. However, this method is not useful for comparing maps created with different data.

The Equal Interval Classification method divides the data into equal sized groups ( i.e., 0-10, 10-20, 20-30). This is best used with proportional data such as the percentage of unemployment in an area. This method emphasizes the amount of a feature relative to other values. This method is most useful for data that has an equal spread rather than data with outliers or unequal values which can create empty classes and distort the data.

The Quantile classification method arranges groups so they have the same quantity of features in each class (i.e., 1-4, 4-9, 9-250). This means the colour gradient will look equally distributed. In other words, Quantile maps try to arrange groups so they have the same quantity in each class. This mapping method is useful for showing relative magnitudes of value. However, it can distort the appearance of the map by positioning similar values in different classes.

The Manual classification method allows you to set the class breaks for your data when there are specific ranges that must be set for emphasis. It is useful for comparing averages across a spatial area overtime. Although, this is the most adaptable method allowing a map maker to define their own classes, class breaks and ranges. The down side is that extra care must be taken with presenting data because it is easy to misrepresent it and distort the data.

"Classification matters because how we group our data into classes is one of the most fundamental aspects of map generalization—the process by which we simplify the real world to fit it on to the page—and small differences in how we do that can dramatically change the look of the map, and thus, its message". - Axis Maps