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.

1. JUMP Bike Impact on
Campus Accessibility
Lily Z
COMMLD 520 B: Community Data Science

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. 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. 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. 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. 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. 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. 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. East Campus Edge Ramp

10. Mary Gates Main Entrance Ramp

11. Liberal Arts Quad Ramp

12. Suzzallo-Red Square Ramp

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. 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. Alternative Breakdown of Bike Barrier
Incidents Per Location
East Campus Top Ramp

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. 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. 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. 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. 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/