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Madison Comparison #68

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spencerrecneps opened this issue May 22, 2020 · 3 comments
Open

Madison Comparison #68

spencerrecneps opened this issue May 22, 2020 · 3 comments

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@spencerrecneps
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spencerrecneps commented May 22, 2020

Category Original BNA pybna (normal parking) pybna (low parking)
Overall 50 38 46
People 56 43 53
Core Services 49 27 36
Dentists 38 15 21
Doctors 39 18 23
Grocery 56 33 43
Hospitals 49 12 13
Pharmacies 41 22 27
Social Services 60 27 35
Opportunity 50 35 42
Employment 44 31 38
Higher Education 54 25 26
K12 Education 54 37 46
Tech/Vocational College 42 8 13
Recreation 65 36 39
Community Centers 43 19 25
Parks 75 67 72
Trails 69 n/a* n/a*
Retail 56 39 47
Transit 24 6 9

* In the past we have discussed a better version of the trails score. I've left it out completely here but it would be easy to add a measure for access to trailheads--which is what we've discussed in the past--as long as it can be taken from OSM.

@spencerrecneps
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spencerrecneps commented May 22, 2020

There are three big differences in methodology that are affecting these scores. #3 would apply most significantly to the normal parking scores listed above.

  1. Original BNA does not account for detour and would thus have higher scores as documented in % detour differences #47.
  2. There is no trails score in pybna pending further discussion about the best way to capture access to trails. Since Madison has a lot of great access to trails this is probably affecting the overall score somewhat.
  3. Madison is heavily impacted by the change in our treatment of bike lanes. Many streets with bike lanes flipped from low-stress in the latest online scores to high stress in pybna. Bike lanes adjacent to parking #48

I also ran the pybna scores using destinations I downloaded from OSM at the time of testing, which means there could have been some variation in the destinations adding to the scoring differences.

@spencerrecneps
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spencerrecneps commented May 23, 2020

I built some testing capabilities into pybna that allow us to verify the LTS scores on a variety of road conditions. I'm attaching that here for you to reference. As you can see, we went through each possible condition and checked what the score should be based on our LTS chart against what pybna was scoring. We corrected any discrepancies as part of the validation process.
lts_assessment.xlsx

This is likely another source of variation if there are any cases where the original was inaccurately rating roads or intersections. We did some testing of the original scores but the new method is far more rigorous and gives me much more confidence that our LTS scores are being accurately applied. (Whether the underlying data is accurate is, of course, an entirely different problem.)

@spencerrecneps
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Here are exports of the two Madison runs.
https://www.dropbox.com/s/2wj42i5zutdoolb/madison.zip?dl=1

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