Enabling a New Data Science for Urban Accessibility for All
PI: Jon Froehlich (UW), jonf@cs.washington.edu, ORCID: 0000-0001-8291-3353
Co PIs: Anat Caspi
AMOUNT & MATCH: $40,000 from PacTrans; $40,000 Match
PERFORMANCE PERIOD: 3/16/2021 – 3/15/2022
STATUS: Completed
CATEGORIES: Accessibility, Pedestrian Safety, Crowd Sourcing, Machine Learning
DESCRIPTION: In this proposal, we aim to leverage Project Sidewalkâs unique cross-regional sidewalk dataset to investigate the following research questions via new data analytics and visualization tools:
- What are the geo-spatial patterns and key correlates of urban accessibility? How does accessible infrastructure correspond to racial and socioeconomic factors or other metrics such as house pricing, school ratings, park density, and transit access.? Who appears to be primarily impacted?
- How do sidewalk patterns compare across cities? What are the main accessibility barriers and how can/should we categorize them? How do these barriers reflect the socio-cultural, economic, and political context of those regions?
- How does urban accessibility change over time? We propose adapting our crowdsourcing + machine learning techniques to examine street scene imagery across time, which will enable new temporal analyses focused on how and where sidewalks and sidewalk accessibility change over time.
DELIVERABLE | DUE DATE | DATE RECEIVED |
Research Project Progress Report #1 | 10/10/2021 | 10/14/2021 |
Research Project Progress Report #2 | 4/10/2022 | N/A |
Research Project Progress Report #3 | 10/10/2022 | N/A |
No Cost Extension Request | 1/15/2022 | |
Draft Report | 1/15/2022 | 8/1/2022 |
Final Project Report | 3/15/2022 | 12/1/2022 |