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Data Science for Campus Accessibility - Addressing Access Barriers Caused by Bike Share Bikes

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I utilized basic Python coding to initiate an analysis of the impact JUMP Bike bikes have on campus accessibility. This report contains a description of methods used, data gathered, as well as recommendations for addressing these issues.

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Data Science for Campus Accessibility - Addressing Access Barriers Caused by Bike Share Bikes

  1. 1. JUMP Bike Impact on Campus Accessibility Lily Z COMMLD 520 B: Community Data Science
  2. 2. Bike Barriers Bikes blocking access ramps pose barriers for individuals with limited mobility and create inconvenience for everyone. ADA The Americans with Disability Act protects disabled individuals’ right to access public spaces equally.
  3. 3. Intro Accessibility is a legal and ethical issue. As a public space, the University of Washington has an added level of responsibility to ensure that campus pathways are accessible to all. As Coordinator of the UW Disability and Deaf Cultural Center (D Center), I’ve learned of frequent instances of pathway access barriers due to improperly parked bike share bikes. I chose to investigate 4 ramps near the D Center to get more information this issue. I chose the D Center as a central point because it’s a HUB for disabled students.
  4. 4. Press Coverage (Seattle Times) In addition to first-person reports on-campus, local and national news outlets have also begun to shine a light on this issue.
  5. 5. Methods - Limited access to JUMP Data (1 mil rows - April 4th through May 12th) - Focused on 4 ramps on campus near the D Center (recorded latitude and longitude for each) - Exported data subset using Python with bikeID, ramp, timestamp, and distance from ramp - Converted minute-by-minute data to a range of time per Bike Barrier - Set 4.9 M proximity as this is the approximate accuracy of GPS (filtered to data points less than 3.5 M to focus on closest bikes for visualization)
  6. 6. Data Prep - I started with a CSV file with a subset of 1 million rows of JUMP Bike data ranging from April 4th, 2019 to May 12th, 2019. - The original dataset contained irrelevant data (like battery %), so I wrote code in Python to export a cleaner dataset with only bikeID, ramp, timestamp, and distance from ramp. (See next 2 slides for code) - This code utilizes the haversine formula and creates a target coordinate range for which the dataset is checked for bikes parked in the target locations. - After exporting the desired data, I had to convert data entries into ranges of time per each bike because JUMP Bike had nearly minute-by-minute location data for bikes (which was TOO granular for my purposes).
  7. 7. Code Part 1 (Exporting Data Subset) Code Part 1 (on Page 1 of Linked Google Doc) - Written with Visual Studio Code, Python, Powershell View Full Code
  8. 8. Code Part 2 (Calculating Bike-Ramp Distance) Code Part 2 (on Pages 2-7) of Linked Google Doc) - Written with Visual Studio Code, Python, Powershell View Full Code
  9. 9. East Campus Edge Ramp
  10. 10. Mary Gates Main Entrance Ramp
  11. 11. Liberal Arts Quad Ramp
  12. 12. Suzzallo-Red Square Ramp
  13. 13. Final Data Subset of Bike Barriers (for Visualization) - Using Microsoft Excel and Google Sheets, I organized and analyzed the data exported with my original Python code. - Each of the 4 ramps analyzed had at least one bike barrier incident. - The duration, represented on this chart, indicates that potential access barriers may be lasting up to about 4 hours!
  14. 14. Data Visualization Each ramp checked had at least one Bike Barrier incident. Suzzallo is a hotspot for access issues. Alternative Breakdown of Bike Barriers Per Location
  15. 15. Alternative Breakdown of Bike Barrier Incidents Per Location East Campus Top Ramp
  16. 16. Distribution of Bike Barriers by Day The distribution of access barriers across days of the week suggests that Tues-Fri are the peak days with incidents while the weekend may see a “cool-down” period with fewer. Day of the week
  17. 17. Findings - Highest number of total Bike-Barrier incidents per ramp: 9 (Suzzallo) - Longest duration: 249 Minutes at Suzzallo-Red Square - Each ramp zone had at least one Bike Barrier incident
  18. 18. Recommendations - Bike share companies should geofence locations with important access features like ramps and handrails so they are alerted when bikes pose barriers and users can be alerted or fined when parking in a key access zones - Regularly monitor key areas like Suzzallo for bike-barriers - Increase Monitoring on peak days (Tues-Friday) - UW and bike share companies should collaborate on a campaign to educate our community about the importance of access pathways and proper bike share etiquette
  19. 19. Summary - Campus accessibility is a legal and ethical issue - The UW is responsible for maintaining accessible pathways throughout campus - Currently, bike share bikes are repeatedly parked in spots such that they create barriers to access - We can do better! Report improperly parked bikes: https://depts.washington.edu/uwdrs/2019/01/reporting-improperly-parked-bike-share-at-uw/
  20. 20. Sources ● https://www.google.com/search?rlz=1C1GCEU_enUS828US828&tbm=isch&sa=1&ei=61n4XIKxF-OX0g LM35LYCw&q=concrete+wheelchair+ramp&oq=concrete+wheelchair+ramp&gs_l=img.3..0l2j0i24l3.14 184.17146..17282...0.0..0.106.1055.23j1......0....1..gws-wiz-img.......35i39j0i67.8JAYUomK7PE#imgrc=AnF gor6B1NYJhM: ● https://www.seattletimes.com/seattle-news/transportation/frustrated-by-all-those-bikes-in-the-middle- of-seattle-sidewalks-youre-not-alone/ ● Maps.google.com ● https://sea.jumpbikes.com/opendata/

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