US Election 2012 County-Level Results

I've been experimenting with the 2012 US election results at the county level* published on the Guardian Datablog and comparing percentages for Obama and Romney. One of the most striking things is how Obama won in DC (over 91.4% of the vote) and in the Bronx, NY (91.2%). The highest percentage for Romney was in King County, Texas where he won 95.9% of the vote. The image below shows these patterns and also includes some information on race, with % Hispanic and % African-American mirroring, to a large extent, the percentages of voters choosing Obama. It's not really that simple of course, but there is a correlation. One interesting nugget here is the difference in total votes won in counties with the highest voting percentage for each candidate. In the 5 counties with the highest Obama percentage, almost 850,000 voted for Obama. By contrast, in Romney's top 5 counties the total was just under 20,000 voting for him.

Full size version here

What does any of this mean? It means that the Republican Party probably need to think about how to do better in cities, with Hispanics and with African-Americans, but they already know that. Romney was very successful in areas where not many people live but not successful enough in major cities. It's all pretty obvious but it stands out more when you look at it on a 3D map!

*(for the continental United States, so no Alaska or Hawaii for now)

Why I Like 3D Maps

I do quite a bit of spatial analysis and mapping in my academic research, and some of it ends up on this blog. Over the past few years I've done quite a few 3D* maps - most recently one of population density in China. A comment by map-guru James Cheshire made me think about the 3D issue, hence this post, which attempts to say a little more about why I like using the third dimension, as it were. Also, there's not much about this stuff online at present. The two images below show population density in Europe at NUTS3 level, with a colour scale running from red (high density) to blue (low density). Click the images to enlarge.

*N.B. Data for some parts of Italy, Germany, and the UK are missing, 

but that doesn't matter for now - this is just an example.

The reason I like the addition of the third dimension with this kind of dataset is that you can tell more about the differences between areas within the same statistical category. Essentially, it adds an additional dimension of information that you can not observe from the conventional 2D map above. This is particularly true of the most high density areas in the first map. There is of course an issue here about the relative size of areas and how this might change the population density of different places but that is a different matter since I'm not in control of NUTS3 definitions! For more on this kind of thing I'd recommend looking at Stan Openshaw's work and for more on the utility (or futility?) of choropleths generally Tobler (1973) is an excellent starting point. Gale and Halperin (1982) is also worth a look.

If we assume that the main purpose of a choropleth map is to present and discover spatial patterns then there is sometimes a strong case for using the vertical dimension and extruding polygons using a z-variable (I do this in ArcScene, in case anybody is interested). However, there are some complications and I don't think it is always appropriate to go 3D. For example, depending upon the spatial structure of your data extruded polygons in one area can obscure those in another. There is also the issue of the different size of areas and the way these might have an impact upon the level of extrusion - i.e. if we used 1km cells for the European population density map it would look rather different in 3D - though this is possibly another artifact of the modifiable areal unit problem, as described by Openshaw. I've patched together three different examples from my blog in the image below, just for comparison. Another option would be to follow the example of Ben Hennig and produce population-weighted cartograms.

When I produce these 3D maps (or visualisations) I'm not trying to create a geographically precise rendering of space but rather I'm attempting to draw attention to variations in a dataset in a way which 2D maps can only do to a limited degree. They are abstractions and simplifications but in terms of understanding the world I find it can be an improvement. There is a little bit about it in this Environment and Planning B paper I wrote but I plan to write more about this in the near future (the'near future' in geological terms of course). 

*Also known as 2.5D in the GIS world, but I'll put that to one side for now...

Population Density in New York City

In some recent posts I've been looking at the issue of population density. I did this for London in December and for the continental United States earlier this month. Given the extremely high population density in Manhattan, I thought it would make sense to take a closer look at New York City. So, I took some publicly available NYC GIS data, some 2010 US Census data and went to work. The result is the 3D map image below...

See this image full size

The mapping here is done at the Census Tract level, of which there are about 2,100. These areas have an average population of about 4,000 though there is some considerable variation between areas in that several tracts contain more than 10,000. The spatial patterns above are fairly obvious and, as expected, Manhattan dominates once again. However, the individual Census Tract with the highest population density is actually in Corona, Queens with a figure of 216,000 persons per square mile*.

There's a lot more information on New York City's 2010 Census results on these pages, from the New York City Department of City Planning...

* N.B. It's important to point out here that these areas are much less than a square mile, but I'm using square miles since it is a conventional measure of population density in urban areas).

The IMD in 3D

Before I move on from experimenting with the deprivation data from the new English Indices of Deprivation I thought I'd do a 3D version, just for fun. The images below are the results of my experiments. The first image has labels for various places. The second image does not and is also at a higher resolution.



When you add a third dimension certain places stand out more (e.g. coastal areas) but there is a balance to be struck here, as always... That's enough IMD mapping for now!

Experiments in Colour

I've been experimenting with colour and animations a lot recently. This involves producing a lot of material that ends up on the floor of the editing room, so to speak. So, I made an entirely pointless animation of said cuttings... The one below is an animated gif (nerd speak) of the North West of England and its commuting 'intensity' (i.e. spikes for areas where people commute to). The visual effects are just from my experiments - some just for the sake of art. An assault on the eyeballs, to be sure!

A Lightbox and a 3D Map

A short post today, following the theme of some other recent posts. I've been experimenting with creating different kinds of 3D surfaces with Ordnance Survey data. I've also been experimenting with ways of displaying these online.

So, what's a 'lightbox' - well, it's a way of making an image pop-out on screen with the background darkened. Click the image below to see this in action. The 3D map in the lightbox is just a surface model I created using an OS 1:50,000 colour raster tile for the Inverness/Moray Firth area in the north of Scotland.

Further details of how to integrate lightboxes into blogger (for nerds) can be found here.

Manchester Commuter Inflows

A short post today on visualizing commuting flows. I've used some 3D GIS techniques to create a commuting surface for the North West of England (based on wards). Areas with peaks represent high in-commuting. Manchester dominates the North West pattern, as seen below.

I added in flow lines to this map in order to see which 'peaks' were being by-passed on the way to Manchester, and from where. The lines on the map below represent flows of 25 or more from individual wards in the North West. Not a huge number per ward, but it all adds up. When you think of how many people are doing this and how far they come it is significant however...

This is only really a rough draft, but it does communicate quite a bit of information and it tells a familiar story of urban commuting. Click on the map to see it in full size.