An Algorithmic Political Geography Of The USA
Anyone interested in geographic economics and regionalism will find this new study from Garrett Dash Nelson and Alasdair Rae fascinating. The authors used an algorithmic approach to identify the megaregions of the US based on commute data. It demonstrates that in almost all areas of the country, the current political borders are not a good reflection of the economic interests of the inhabitants.
Garrett Dash Nelson and Alasdair Rae, An Economic Geography of the United States: From Commutes to Megaregions
The emergence in the United States of large-scale “megaregions” centered on major metropolitan areas is a phenomenon often taken for granted in both scholarly studies and popular accounts of contemporary economic geography. This paper uses a data set of more than 4,000,000 commuter flows as the basis for an empirical approach to the identification of such megaregions. We compare a method which uses a visual heuristic for understanding areal aggregation to a method which uses a computational partitioning algorithm, and we reflect upon the strengths and limitations of both. We discuss how choices about input parameters and scale of analysis can lead to different results, and stress the importance of comparing computational results with “common sense” interpretations of geographic coherence. The results provide a new perspective on the functional economic geography of the United States from a megaregion perspective, and shed light on the old geographic problem of the division of space into areal units.
Note the NYC region – where I live – where the political boundary between NY state and New Jersey (and a speck of Pennsylvania) are shown to be irrelevant, and in fact, problematic: consider Governor Christie’s blocking the construction of new tunnels from NJ to NYC, for example.
The NY-Connecticut border stands out as one of the few borders that show up algorithmically, which the authors suggest indicates some border-related economic barrier to commuting: perhaps the NYC tax system, where those that commute to work in NYC have to pay city taxes as well as to the state?
Also note that the High Plains lack enough commute data to warrant even a name on their new partitioning.
In a perfect future world, political boundaries would be fluid, not fixed. The residents of the NYC region, for example, would have an equal voice in issues that affect them equally, and those outside the region – like those in the new megaregions of Upstate NY and Philly – would have little say.
The longer that old political boundaries persist – reflecting dictates of long-dead kings, or the expansion west a hundred years ago or more – the greater the societal costs for their inflexibility and their mismatch with actual economic reality.
I’d like to see the analysis carried out at a finer granularity, so that the ‘counties’ and ‘cities’ of the new ‘state’ of New York City could be determined in the same way, as opposed to political considerations of decades or centuries ago.
One last observation: handing over the political geography to an algorithm will likely be a political plank in future elections, taking away the lever of gerrymandering based on political parties, and determining voting districts based on human activity and connection.
I don’t want to rehash the recent presidential elections, but I wonder how these regions would have voted, and what allocation of electoral votes they would have had. Don’t you?
beautiful work