Naturally, as a Geographer the wikileaks release of facilities that the US believes critical to its security was interesting. Much like the chaps over at Floatingsheep some of us (Martin Austwick, James Cheshire, Peter Baudains, Alex Braithwaite) took it upon ourselves to map out the reported list. Martin came up with the following visualisation that I think is really rather nice.

Naturally, the same sort of caveats that floatingsheep note also applies here – most of these points are only accurate to the city level, such as for locating industries, or at the country-level such as for locating mines, or oil pipelines, where the features were not immediately apparent on maps. The uncertainty inherent means that the pattern of facilities is best understood from the global perspective, and it is from this vantage that I’ll attempt a brief analysis.

Firstly, the most interesting pattern I note is connected to the Spatial Division of Labour (aka new international division of labour) which is a concept extolled by globalisation theorists such as Doreen Massey, Paul Krugman etc. Essentially- look at how globalised a system of critical facilities the US identifies – the kind of bordered protectionism that we might have once expected from a hegemon is missing. Indeed, we could see this globally distributed network of critical sites as evidence of neo-imperialism and the reach of neo-liberalism, this is the corperate extension of the American empire, and in a sense, it maps the USs perceived vulnerabilities. It is the modern reproduction of the trade empires previously established by the European powers, and very much in this tradition it is the developing world countries, on the whole, that are responsible for raw materials production, and natural resources, and the developed ‘western’ world that is responsible for technology and medical products. The apparent primacy of the west is also demonstrated by the vastly larger numbers of critical sites that exist in the west, as opposed to the developing world.

Further to this, there is an interesting balance that is mediated by geopolitics and the nation state, on the one hand it is notable that the US privilleges ports as transport hubs (but, perhaps strangely, not railways or airports) demonstrating the global reach of trade and movement, but also heavily emphasises the importance of it’s physical land borders with Canada and Mexico, reiterating the perceived importance of the US as a nation state, a physical, defensible entity. In some sense this approach is reminiscent of Halford MacKinder’s ‘heartland’ hypothesis – the US, in this document is attempting to demonstrate, and encapsulate itself as a heartland state with a global reach and network of operations.

The importance of some resources is emphasised, and tends to relate to energy, such as the “Nadym Gas Pipeline Junction”, which is denoted “the most critical gas facility in the world”, likewise, Yemen’s “Bab al-Mendeb” Shipping lane is seen as “a critical supply chain node”. However, it is neither these remarks, nor the references to certain products being crucial to the Patriot missile production that really define the data here, rather it is the whole, which defines an incredibly diverse set of global interests for the US. The diversity of US interest in this map, both by type of critical facility identified and by where they occur reminds me of the chess-board analogy to geopolitics. I can’t remember who originally cited this analogy, or where I heard it, nonetheless it goes something like this: essentially a hegemonic entity, such as the US, is actually playing at geopolitics on a number of different chessboards. One chessboard relates to what the US do militarily, and certainly there are critical facilities on this list that best fit into this game, secondly there is a diplomatic chessboard, which relates to the persuasive power that the US has, how well it can negotiate and leverage its position as a functional hegemon. The military chessboard is perhaps best articulated by the positioning of military bases, and deployment of military personnel; the diplomatic chessboard is perhaps best understood in terms of the locations of embassies and diplomats. Additionally, with respect to the critical facilities, we might see a corperate chessboard, articulated through the interests of US companies and understood in terms of the locations of multi-nationals, and flows of currency from host countries back to the US. Finally, the US might also play chess on the basis of deterrence, this is the reasoning behind nuclear armaments etc. and to some extent the presence of a US list of facilities considered critical is curious evidence of what the US considers its reach and responsibilities, as well as what it has vested interest in maintaining oversight on. It might suggest that repercussions for challenging these listed site would be harsher that they might otherwise have been.

Here is a quick confirmation that you can use Python to draw very detailed maps; using the previously specified method I was unable to get python to draw all UK OAs due to their great number (c.220,000) and high complexity (c.50,000,000) vertices. Additionally I was unable to use the generalised OA boundaries for the UK from UKBorders as they contain topological errors that the shapefile reader cannot deal with. ArcGIS is obviously a bit clever in how it handles bad topologies. So I extracted all the vertices and fed them into shapely polygons, and visualised them in the same way, but without reading shapefiles directly into python and was able to output this:

This method has had an impact on the speed of computation as it can take roughly 25 minutes to output this map. The map looks pretty good, aside from a slightly odd polygon in the Bristol channel. Nevertheless, coupled with the operations that shapely, and other geo-libraries, can do this si increasing indication of the maturity of GIS in a variety of platforms. Oh, and it’s all free!

Recently I’ve been doing some work involving the transportation problem. The Transportation Problem is an allocation optimisation problem that requires the optimal assignment of demand, in my case patients by Output Area, to known, fixed, supply points, in my case doctors surgeries (General Practices). Rather than use a euclidian or manhattan metric to model distance from the demand site to the supply site I have used public transport travel times from TfL.

Initially this seemed a difficult task, and early attempts only provided partial, or non-optimal, solutions. However, once I had found the linear programming functionality in R through the package LPSolve it became very easy to create a model with the constraints I wanted and get a solution very quickly. Key to the success of the R package was the ability to set the constraints I needed, crucially integer constraints so that people were not subdivided, and constraints on the number of patients doctors could take.

Mapping the outcomes in ArcGIS was straightforward due to R’s built in csv-export funtionality.

Here is an example of the output.

The legend denotes the 44 physical practices in the London borough of Southwark, some doctors exist on the same site and so these practices were agglomerated. The grey areas represent unallocated demand caused by capping the size of the General Practices. In another definition of the model I ran I set the GPs up as uncapacitated so that all the demand would be satisfied. This model uses data from 2006, I have data from 2009 for which I will also run the model.

Some earlier work I did on this is available in the Proceedings of GISRUK ’09.