Shared rain

This April has been the wettest April on record in the UK, while parts of the country are also in official drought – leading to headlines of the wettest drought on record.
The miserable weather was (is) a good opportunity to finally produce a high-resolution version of the map series that I created during my PhD research and which I presented at last year’s conference of the Society of Cartographers in Plymouth. The maps are not new, and each individual maps can be viewed and downloaded here, but if you are viewing this on a higher resolution display, you may enjoy the map series in all its detail:

As described in the above linked pages, the animation shows precipitation data in relation to the world’s population distribution based on a gridded population cartogram (population data used here comes from SEDAC’s GPWv3 database). The precipitation data used in the map series is based on long term records and interpolations published on and as described by Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005 (Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978). The maps therefore gives more space to highlight how and where rainfall patterns directly affect the human populations on the planet, adding another analytical dimension to the data display.
While a gridded cartogram display adds further analytical value for a visual examination of the data from a different perspective, the raw data itself can also be used in less serious ways to analyse the interrelation of population and rain which was one of the outtakes of my first looks at the original data. Using the absolute rainfall and the total population, one can calculate the rainfall per person in an area, which is a fairly useless calculation that reduces the appearance of rain in the most populous areas and tells very little about how people perceive the long term weather conditions, as the following map extract shows for Europe:

Map of Shared Rainfall(click for larger view)

The outcome of combining population and rainfall data is a map display that results in a nice visualisation that should be taken as nothing else than a conceptual map of geospatial data that demonstrates, how two different dimensions of data interrelate. It can be useful for an exploration of the data from different perspectives, but is certainly less useful as a geographic representation of how population and climate conditions are related (geodeterminism, anyone?). The main reason for showing this map here is the comparison of the effective display of the interrelations between the two data dimensions using a gridded population cartogram projection, which does a very similar combination of the data as the ‘rainfall density’ map, but results in a much more useful visualisation of the topic. While the gridded population cartogram allows to distinguish between the human and the physical space (which is represented in the grid cells), the quantitative information of the rainfall patterns is preserved and can be understood from a people’s perspective (and may be useful to explain how some of these patterns can indeed be explained in a determinist way: many of the driest regions on the planet match the least populated places, though the global settlement patterns follow a more complex set of variables that all contribute to where we live in what densities). Combining the two dimensions in a conventional map projection is hard to achieve, as this is limited to the extent of the physical space. A simple choropleth display of the two data values is not appropriate when calculating the two against each other as in the above map (and thus not allowing to know whether a high data value results from low population values of from high amounts of precipitation). If at all, then this is useful for an exploratory analysis of how the two values interact.
Apparently there are other solutions to display multi-dimensional data in conventional maps, but these often are more complex and harder to read. This may also be the case for a gridded population cartogram when it is seen for the first time, but once the basic concept of the map is understood, it can be used to read and understand any other geospatial data from the human perspective.
These are two ways of showing the same data – mapping rainfall as a shared pain (or blessing, as it is for most of the world’s population). What the two versions of mapping the data show is that the way how one analyses data and how one puts it on a map matters a lot. There is not a single good or bad way to put geospatial data on a map, but they way it is processed and visualised. Bad maps don’t have to be wrong, but may simply miss out on an adequate presentation of their underlying data.

One good start for an advanced insight into how geospatial data can be analysed sensibly can be found in the book Geospatial Analysis by Mike de Smith, Mike Goodchild and Paul Longley, which is also available as a freely accessible online edition: Only a appropriate – and sensible – analysis of data is then suitable for the subsequent geovisualisation in form of a map (as covered in many good books, such as the upcoming Cartographer’s Toolkit that gives a little bit space for a gridded cartogram as well). Far too often the two worlds of analysis and visualisation remain disparate worlds, with both sides remaining quite ignorant of what the other side of the same coin has to offer…

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