Note: I don’t intend for this to be an academic post as even I tend to glaze over when presented with equations and fancy-arsed academic language. However if readers would like to read more I’d recommend tracking down Luc Anselin’s work as a starting place and then graduating to others as the reading takes you. Just don’t expect me to run through complex formulae here; the truth is they’re intensely boring and nobody likes them. Ahem.

Geography matters. In the UK, for example, it’s fairly straightforward to portray how densely concentrated Labour’s vote share is in the towns and cities. Similarly it’s straightforward to show the density of Conservative vote share in the south east of England outside those cities.

However it’s more interesting to judge these concentrations over time. So one can say that since 1997 Labour’s performance in the UK has been in retreat. That the party once had an electoral reach which took in large parts of the south east of England stretching out towards the south west, much of Scotland and the towns and cities in between. By 2015 its performance had been reduced to a stubborn husk of seats clinging on to the vestiges of what came before and tightly packed around its town and cities. Scotland? Shrivelled and died. The south east? A long, slow death. Even its towns and cities are being nibbled at. The use of cluster maps allows us to see this process unfold over time.

The same is true for the United States. We now have enough county results in to be able to compare the 2016 election results to that of the previous two cycles, 2008 and 2012.

First, some boring pretext. You are about to see some maps with colours. The data used to create the maps are the US county results for the 2008, 2012 and 2016 US Presidential Elections from Dave Leip’s Election Atlas (thanks Dave!). The red counties are counties where there is high vote share for that candidate surrounded by counties (Queen contiguity) with equally high vote share for that candidate. The blue counties are counties where there is low vote share for that candidate surrounded by counties with equally low vote share for that candidate. There are a world of statistics I would be expected to describe here but I’m keeping it real here so academics will have to come to peace with it! REMEMBER: RED IS HIGH…….BLUE IS LOW*

Below are the maps for the performance of the Democrats since 2008.

Clustering of Obama vote share, 2008 (Moran’s I = 0.598)


Clustering of Obama vote share, 2012 (Moran’s I = 0.674)


Clustering of Clinton vote share, 2016 (Moran’s I = 0.642)


Where the +/- change in vote share for the Democrats has clustered between 2008 and 2016


The final map shows where the change over that period has clustered. In that map the red areas show where an increase in vote share has clustered, whereas blue shows where a decreased has clustered. There are significant spatial patterns to both Democratic support (unsurprising) and the change since 2008 (perhaps surprising). Firstly, and most obviously, the Democrats have clearly lost ground in the Rust Belt. Except if it is a belt the owner of that belt has had one too many happy meals.

The size of the chunk of land (shaded in blue) taken in by this reduction in Democratic fortunes in the Mid West takes in much of Ohio, Pennsylvania, northern Michigan, West Virginia, Kentucky, Illinois, Missouri, Kansas, Iowa, Minnesota, Wisconsin, North and South Dakota and across into Wyoming.

At the same time there has been an improvement in Democratic fortunes elsewhere. Since 2008 the Democrats have filled in the Acela corridor, Virginia, parts of the Carolinas and urban areas across the I-20 corridor in Alabama, Mississippi and Louisiana. Their improvement in Texas and California has continued too.

The maps speak for themselves. I tend to spend a good while looking through them before writing extensive commentary. This piece is the first stage: present the maps. Tomorrow….the Republicans.

*The light blue counties indicate Low vote share surrounded by High vote share. The pink counties indicate High vote share surrounded by Low vote share.