PhD Research: Urban Barrier Detection

This is what I’m focused on at school!

Where walking pedestrians can reliably use mapping applications to find personalized pedestrian transit routes to their chosen destinations, pedestrians experiencing disability can not because of barriers in the built environment that go unrecognized and unaccounted for. There are various reasons for this:

• Logistically, because the amount of time and money it would cost to survey thousands of kilometres of citywide pedestrian networks manually would be far too expensive.

• Ontologically, because walking pedestrian’s mental models of what constitutes reliable pedestrian transit routes will vary greatly from those of pedestrians experiencing marginalization due to intersectionality or through disabling environments.

• Both aspects: since manual assessments generally use a qualitative measure (“on a scale of 1-5, how bad is it?”), and because of the differing mental models of the assessors, the data will have inconsistent, subjective results.

Permanent or temporal barriers like improper sidewalk grades or overly short crosswalk times block disabled pedestrians but do not impede walking pedestrians and therefore will not enter into their route selection heuristics. Same with issues of intersectionality. This lack of awareness is disability and intersectionality-bias, the mechanism that allows barriers to continue to proliferate in the built environment and go unrecognized in corresponding digital environments, especially in terms of what data is being collected.

This project’s aim is to address both logistical and ontological aspects by harnessing conceptual frameworks in social justice, and technologies like GIS, Cloud Computing, Machine Learning, and LiDAR to objectively identify disability and inclusion barriers in the built environment.

Be well, and thanks for reading!