Visualisation Types

Visualisation type: Chloropleth map


A chloropleth maps is a thematic map in which geographical areas or regions are coloured in proportion to a numeric variable. Chloropleth maps are widely used and provide an easy way to visualise how a measurement varies across a geographic area. The boundaries of the geographic areas can be based on administrative regions (e.g. countries, neigbourhoods), geographic coverage zones of (public) service facilities (e.g. healthcare, police) or any other devision of a geographic area. Which devision to be used will depend on the datavisualisation purpose. Both policy domain, map readibility, privacy and detail level of available data must be considered.

When best to use
When data is only available or collected at a regional detail level, a chloropleth map is the most logical geographic visualisation type to be used. For example mobile phone signals can for technical reasons only be geographically located with a limited precision. Signals and derived mobile phone data are assigned to areas determined by the positions, range and direction of the telecommunication antennas. Chloropleth maps are also deliberatly used by aggregating more detailed geographic information (such as point locations) to a larger region. This can be done for privacy reasons to prevent identification of individual information. But aggregating to a regional level is also usefull to make maps more readable, to highlight only the general geographic trends or to adapt the datavisualisation to zones familiar to the policy makers.
How to read:
Chloropleth maps only provide generalized statistics of the geographic areas. Therefore no conclusions should be drawn about individuals or locations on a more detailed geographic level. The borders of the areas might be arbitrary or drawn with low geographic precision. This must be taken into account when analysing differences across area borders. In chloropheth maps larger areas draw more attention and tend to have more weight in the interpretation. This bias can be avoided by visualising the data on equally sized geographic areas, for example rectangles or hexagonals. The downside of these grid maps or hexbin maps is that areas do not correspond to familiar geographic regions with recognizable shapes and orientation.

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