Computational Cartography (Part 1)

In the sixth grade my Social Studies teacher lent me a book which had a whole section devoted to alternative maps of the US. What would it look like with a dozen states? Or 200? What if state lines were drawn according to climate and vegetation patterns, or ancestral ethnicities, or any other speculative idea? I'm fascinated by maps generally, and these were no exception.

Here's a example of a speculative map of the US by Neil Freeman that's been making the rounds in the last few days:

The United States redrawn as Fifty States with Equal Population
"Electoral college reform," Neil Freeman

My first reaction to seeing this was: Cool. Now how to do I automate this? Those two thoughts go together in my head a lot. Rather than just idly speculating how to get a machine to do this, I'm actually going to give it a try. Or at least give a try at a first approximation.

Freeman's method was initialized algorithmically, but a lot of the decisions were subjective. He also uses a lot of data (drainage basins, commuting patterns) that would be too much of a pain for me to track down an integrate for this sort of spare-time project.

I'm starting by doing everything on a county-by-county basis, which is a useful limit to work with. Without limiting myself to some relatively coarse-grained pre-existing boundaries the computational possibilities would be staggeringly vast, and the result would probably look too foreign anyway. Limiting things to the 3,033 counties and county-equivalents (Louisianan parishes, Alaskan boroughs, Virginian independent cities, ...) will make things more manageable.

I'll need population data for each county, which I can get from the Census Bureau. I'll also need an adjacency graph of counties, which I thought would be tricky, but it turns out the Census Bureau has me covered their too. The location of each county is given in that first link. For the final rendering I'll need the shape of each county. Location is simple, but I'll have to do some digging for shape.

Methods... methods... hmmm.

A clustering approach seems like a natural way to start. Something like k-means where you start with some arbitrary, incorrect centers for each state, and move them around until each state is approximately equally sized. That would at least give you something to refine from.

K-means is related to Learning Vector Quantization, which was an ancestor of Self-Organizing Maps, so that's another direction to explore. I'm not sure how you'd enforce the population similarity complaint off the top of my head. Or, for that matter, how you'd want to encode any of the information in feature space. (This 2011 paper by Skupin and Esperbé seems like a solid lead [pdf].) I'm not sure how an SOM would work for this but I have a strong feeling there's potential down that road.

There's definitely a greedy hill-climbing approach that could be easily implemented. Which means there's a Simulated Annealing approach as well. No way of knowing a priori how effective either would be.

Those techniques — and many others — will need a fitness function. It would be simple enough to write one based on the inequality in state populations. (Ratio of smallest to largest, Gini coefficient, ...) There should be a penalty for non-compact states, perhaps based on perimeter-to-area raio. But that may cause a problem for states along coast lines or rivers. Perhaps use the adjacency graph and penalize states with large diameters, weighted by their actual distance in geographic-space, relative to the number of counties. In any event, Hawaii and Alaska may need special attention for any distance-based rules.

Genetic Algorithms might come in handy, especially if you already had a reasonable starting point to refine from. How would a chromosome be represented though? Each county is one gene, taking available values of 1-50? The state space would be huge. Something better than the naive approach would be needed.

I'm sure there are notes in either my Computational Geometry or GIS notebooks that would come in handy. I'll have to flip through those next time I'm in the lab. Maybe my Algorithmic Game Theory notes as well; I seem to remember some relevant problems on siting cell towers that may be adaptable.

This should give me plenty to get started with. I've got a lot of other irons in the fire, but hopefully in a few days I'll be back with another post laying out my preliminary efforts.

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2 Responses to Computational Cartography (Part 1)

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