However, I think Brian makes an excellent point: the most important question is what is the *goal* of redistricting. If we can come up with an objective criteria for measuring the 'goodness' of a map, then it really doesn't matter how it is drawn or by whom. That would effectively decouple the political issues from the technical ones.
In the debate (generally, not just here) I've heard a range of criteria/concerns that people want to maximize:
1. Equality Population (P)
2. Community Connectedness (C)
3. Geographic Compactness (G)
4. Consistency with existing Districts (D)
I've been thinking about this some more, and wanted to be a little more precise. Here's my best attempt to define Objective Districting Criteria -- please let me know your thoughts.
-- Ernie P.
Objective Districting, Draft 2, 1/9/2005
Ernest Prabhakar <[EMAIL PROTECTED]>
In order to objectively evaluate the goodness of a redistricting proposal, we need to evaluate various quantities for each proposed district, which are then combined over all districts to generate a unique figure of merit ("M"); the proposal with the best M is then objectively the best redistricting option based on all the available information.
I realize that it may be difficult to reach consensus on these criteria, and the appropriate weighting to be used. However, I believe it is still easier -- and far more important -- than trying to reach agreement over a process for redistricting. Armed with objective criteria, a redistricting panel of, say, retired judges can focus on validating and evaluating submissions (i.e., judging), which is a reasonable thing to trust them to do.
I believe there are four valid concerns (criteria) that should be jointly minimized during redistricting.
1. Population variance: p
2. Community disconnect: c
3. Geographical dispersion: g
4. Fragmentation of previous districts: f
While these are all valid, I do not believe they are equally important. In fact, I have listed them in order of importance, which would imply a weighting like M = 4 * p + 3 * c + 2*g * 1*f. The numbers are slightly arbitrary, but the assertion is that these factors are at least qualitatively correct, and politically neutral, which is what we are trying to achieve. Even weighting them all equally (M = p + c + g + f) would still improve representation over time.
Of course, to use any sort of weighting requires that we be able to a) calculate p, c, g, and f for each proposed district, and b) normalize them so one unit of "badness" is roughly consistent across all four criteria. Fortunately, there are reasonable measures or proxies for all of these:
1. Population variance: p[i]
To obtain p[i] for District 'i', we need three numbers:
P[i] = the population of the proposed district
P_avg, the average population over all districts
S, the standard deviation of the current districting map
Then:
p[i] = (P[i] - P_avg)/S
This, as specified, give us a variable which is 0 if ideal, and +1/-1 if as bad(good) as the existing districting.
2. Community disconnect: c[i]
The best objective measure of community connectedness is traffic flow, as that reflects whom we live, work, learn, and socialize with. Disconnects can thus be modeled by the number of lanes of traffic (L) intersected by the circumference of the district (including rail, etc.): dividing two tightly-coupled neighborhoods is worse than placing the boundary in the middle of nowhere. To normalize, we need to know "R" - the number of roads contained in the district. While neither of those numbers is currently part of the standard State database, they should be trivial to obtain from any commercial GIS system. Since for a random distribution of roads R the number intersecting the boundary would grow as the square root, we get:
c[i] = L / sqrt(R)
3. Geographical dispersion: g[i]
The simplest mathematical measure of dispersion is the moment of inertia (I), which is related to how difficult it is to 'spin' an object, and is determined by how far the population is from the center of mass of the district. The minimum (0) would of course be if all the population lived at the exact center, but a reasonable baseline is a uniformly circular disk, which for a given population P[i] and area A[i] has a moment of inertia equal to (1/2) * P[i] * sqrt(A[i]/PI). This gives a normalization of:
g[i] = 2 * I[i] * sqrt (PI/Area) / P[i]
4. Fragmentation of previous districts: f
While from a purely aesthetic perspective this is less important than the other, intrinsic criteria, continuity of representation is still a valid consideration. A good measure of continuity is the largest overlap of the proposed district with any existing district. This gives an overlap O[i] which is 1 if identical to a current district and can approach zero if a new district is composed entirely of tiny pieces from existing districts. Thus, our normalized values is:
f[i] = 1 - O[i]
Again, I'm not claiming that any of these criteria -- or the means of calculating them -- are completely perfect and reliable. However, I *am* claiming that they are "good", in that a redistricting scheme that does well against these criteria is in generally better than one that does poorly. More importantly, there are only four free parameters (the weightings) which can be manipulated politically, and these must be set ahead of time, making it impossible to 'rig' the system to favor one party over another.
This system is not perfect, but I submit that it is vastly preferable to the current system of letting politicians pick their voters (rather than vice versa), and more objective even than a panel of judges. Yes, it requires asking voters to trust some slightly abstract mathematics (though nothing beyond a bright high-school senior), but that still seems more reasonable than trusting politicians. Plus, if the criteria, algorithms, and data are all published freely on the web, then any interested citizen group can easily check the results.
To sum it up: I believe this sort of objective criteria would optimize redistricting to maximize representation, rather than political stagnation. Isn't that worth the price?
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Ernest N. Prabhakar, Ph.D. <DrErnie at RadicalCentrism.org>
The mission of www.RadicalCentrism.org is to help individuals, communities, and systems become sustainably centered — happy, healthy & holy — by being properly rooted in humility, justice & love.
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