Finding ways to effectively map population data is a big issue in spatial data visualization. The standard practice uses choropleth maps that simply colour administrative units based on the combined characteristics of the people that live there (see below).
These maps are popular with cartographers for a couple of reasons. You get a clear sense that the map is depicting some form of aggregation (or grouping) so readers of the map are (hopefully) less tempted to think that everything or everyone in that particular unit are the same. Mapping in this way is often the simplest option as names of the administrative units often come with the data you are interested in so they can be easily linked. Ultimately the underlying data are at household level and choropleth’s colour areas (such as parks etc) where nobody lives. For example the River Thames is running through the map above. Oliver O’Brien has sought to remedy some if these drawbacks by clipping the standard choropleth to building outlines (see first map and below).
I think this has resulted in a great visual improvements to the standard maps, and they closely resemble the iconic maps of Charles Booth. The question is, has Ollie gone too far? The reason the maps levitra look better is because they have massively increased the implied precision of the data. This is what makes the increasingly popular dot density maps so eye-catching (but potentially very misleading). You are more likely to think that the inhabitants of each building (if, indeed there are any) are exactly as the colour suggests, but we know that the final colour is based on a number of the surrounding households (approx. 125 in this case). The obvious solution is to map household level data but this clearly isn’t possible for reasons of confidentiality in addition to the fact that grouping households makes statistical sense in many applications. The counter to this argument is that if people are encouraged to look for their own house it will be abundantly clear (to them at least) that the implied category is unrepresentative and they view the map more critically. This implied precision, called the ecological fallacy, affects our lives daily with anything from insurance premiums, to public services and marketing but we don’t notice it because it isn’t mapped. By revealing it in such a visually appealing way, do these maps compound the problem or educate us about it? Click here for Ollie’s explanation of the maps.