Another day, another few new charts! 🙂
I have included 4 charts here. When I presented the traditional, explanatory criteria, I created a visualization of how each subcorridor performed along each of these criteria. But I began to wonder: which indices were driving the performance of each criteria? So in these charts, I stack the contribution of the indices to the criteria:
What can you learn from this?
- Weightings really matter. Looking at the congestion chart, you can see the pink index ( Travel Demand Index) dominates the brownish one ( Congestion index ). This is mostly because of the 5:2 weightings that Project Connect selected for those indices, and I maintained the same.
- If you track the orange “Affordability Index” at the base of the “Connections” criteria around the compass point, you can see it grow from a tiny amount in West Austin, peaking in East Austin, then start dropping again as it makes its way back around the compass. This is a large driver of the “Connections” index, and is definitely making me question whether “Affordability” should be its own metric, separated from the rather junky “Centers” and “Consistency” metrics.
- The Ridership criteria is a more self-contained picture into one factor than the Connections criteria. Although they both have multiple colors, picking a single color for the Ridership criteria will give you a similar picture as picking all 3: the 3 measures covary. For the Connections criteria, this criteria may be useful for scoring, but not so much for gaining insight into characteristics of the subcorridors.
The next thing I began to wonder was about normalization. As I discussed earlier today, Project Connect uses min-max normalization on all measures. That is, it finds the minimum and maximum values any subcorridor score on a measure, then scale all the values from the minimum to the maximum. I mentioned that I think 0 to maximum might be better, an idea I got from a coworker of mine. The idea is that if you take, say, a measure like population density (measured in say, residents / acre), with 4 subcorridors scoring 65, 75, 95, 110 then normalize it along a min-max scale, you get the same values (0, .22, .67, 1) as if you have 4 subcorridors scoring 5, 15, 35, 50. But in the first case, the subcorridors are within a factor of 2, while in the second case, the subcorridors are within a factor of 10! Normalizing by max alone would result in scores of (.60, .68, .86, 1) versus scores of (0.1, 0.3, 0.7, 1). A clear difference!
So I decided to rerun the whole analysis, normalizing by max rather than max and min. That results in this chart:
What did we learn? A lot! We can see that the congestion criterion, while showing dramatic differences above, shows very mild differences below. Basically, all subcorridors are within a factor of 2 of one another. Twice as much traffic is important, for sure. But now look at the ridership criteria: the dramatic differences from one subcorridor to another maintained themselves. While traffic might change as a factor of 2, ridership might change as a factor of 6! Focusing solely on the blue “Current Ridership” index, we can see that MLK goes from non-existent (by definition, as the smallest subcorridor) in the far above chart, to a very small value in the near above chart. It really is the case that Lamar and ERC score large multiples higher than MLK; that is not an artifact of min-max normalization like the large differences in congestion were.
Now, some caveats: some of this may be a result of the types of measures that went into each index. I have not yet assessed that.
Also, some notes about these charts:
- I have moved from showing the subcorridors in alphabetical order to using the order Project Connect prefers, around the compass from West Austin to MoPac to Lamar, etc.
- I realize the colors are too similar. Sorry. Fixing that takes time and I wanted to get this out there tonight, before I go to bed.
- In these charts, I have used the “Including West Campus in Lamar and MoPac” and “Eliminating negative weighting on present” data variants presented in this post. I could rerun them for the other variants if there’s interest.
I also present Project Connect’s criteria, broken down by index (using min-max and max-only normalization):
What else do we learn from this?
- Constraints and growth is a ridiculous category for getting a handle on subcorridor performance. These are two unrelated indices thrown together for no good reason.
- Similarly, the System criteria is mostly Ridership, but oddly diluted by throwing the unrelated Connectivity index in it.
- The “Core” metric is almost entirely affordability, along the familiar compass pointing East toward affordability.