Abstract
The purpose of this dataset is to allow for an accessibility analysis and mapping project to be conducted on the University of British Columbia (UBC), Vancouver Campus. In a university setting, campus navigation is a foundational part of seizing opportunities, networking with other scholars, and having an all-around positive student experience. Yet, inaccessible features of an urban landscape (like stairs, rough terrain, or steep slopes) often leave mobility-limited individuals at a great disadvantage or cut off from certain opportunities. The accessibility analysis and mapping project (AAMP) is geared to try and answer what and where barriers to wheelchair accessibility exist on the UBC campus. To do this, (1) two cost paths for accessible and inaccessible terrain were calculated and compared to identify barriers to accessibility, (2) a least-cost path analysis is conducted to test if wheelchair routes are statistically longer than walking routes, and (3) a wayfinding map geared toward wheelchair users is created with the intention of increasing campus navigation equity and as a visualization for urban planners to see where campus accessibility improvements need to be made. It was discovered that 10% of the total walkable path area was some sort of accessibility barrier to wheelchair users. Through a visual investigation and comparison with the previous literature, three main types of barriers were identified on the campus. Next, an online map was created of the study site which highlighted accessibility barriers and difficult terrain. Finally, the paper ends with a discussion around why certain types of accessibility barriers exist on the campus and what urban planners can do to fix these and create more equitable wayfinding experiences across urban landscapes.
MGEM Student: Mary Kristen
Community Partner: UBC Campus + Community Planning
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Cite this project
Kristen, Mary, 2021, “Accessibility Analysis and Mapping at the University of British Columbia”, https://doi.org/10.5683/SP2/HVOGCW, Scholars Portal Dataverse, V1