I’m getting down to the last few weeks of my research project. So far, I’ve been looking at countless measures of performance and service efficiency. However, looking at all these individual measures can be meaningless unless you have a way to compare across.
For example, Phoenix has the highest route miles per service area (which is to say Phoenix has the best coverage of any system). While Houston has the greatest trips per vehicle revenue mile, an indication that Houston is a very packed system. If I want to create broad categories of “winners”, “losers” and the in-between, then I need a way to compare all the possible metrics on the same scale.
Taking all the metrics I have assessed, I created a “scorecard” using scaled values. I scaled everything between 0 and 1. With 0 being the minimum value and 1 replacing the maximum. So for route miles per service area, Phoenix would get a score of 1, since it had the highest value. This scaling allows me to place multiple metrics on the same graph for a comparison.
Looking at this chart at first gave me a headache. It doesn’t make a lot of sense. I had expected to see clumps of points. I thought that places that did well on one metric would do well on others, and vice versa. You see this clumping a little bit with Baltimore, St. Louis and San Jose. For every measure, their score was on the lower end. But for places like Houston, which got a 1 on three of the measures and a 0 on another, its values are all across the scale. I want to represent this data in a different way, to better see the clumping for each city.
I decided to go with a box and whisker plot. The advantage of using this type of graph is it shows the distribution of values. Below is the new chart.
From this graph, one can easily determine that Baltimore, Pittsburgh, and San Jose are among the lesser performing, while Denver, Houston, Los Angeles, Phoenix and Seattle are doing much better. And of course we have a lot of locations that are in the middle.
A big part of this research was to determine which systems appear to be performing better than others. Now, I need to look into construction costs. I want to see if the investments in these systems have been worth it.