Large-scale urban infrastructure investments can have unequal distributional effects on underrepresented populations including seniors, youth, and low-income families. These vulnerable populations can have less access to essential services such as hospitals, clinics and schools than the general population that have the economic means to access these services. We are using data science techniques to assess large-scale transit and demographic data to evaluate the interconnections between access to transport and essential services that can improve urban quality of life.
In a fair society, everyone — young or old, high-income or low- — would have equal access to hospitals, schools and other essential services by public transit. A new computer tool being developed and trialed at the University of British Columbia supports the advancement of fairness and equity across the Cascadia region and beyond. Driven by data science and machine learning techniques, the tool enables cities to use open-source data and software to determine how investments in public transportation systems and essential service facilities could be distributed more equitably.
“In every municipality, certain groups of people are unable to benefit from key service infrastructure as fully as others,” says Martino Tran, a professor at UBC’s School of Community and Regional Planning and director of the study, co-authored by Jerome R. Mayaud and Rohan Nuttall. “By further developing these data science tools, the goal is to help cities better understand who these vulnerable groups are and make planning and policy decisions accordingly.”
Using the tool to analyze and compare Vancouver, Seattle and Portland, the team discovered that while Vancouver generally offers timelier access to hospitals and walk-in clinics by public transit — likely due to its compact size and the high density of both its population and transportation network — it also imposes higher transportation costs on its residents.
The study further found that all three cities can improve healthcare access for vulnerable populations like seniors and low-income residents — particularly seniors living in low-income areas, who not only must bear disproportionately high transportation costs, but also have specialized mobility and healthcare needs.
“The members of our communities who rely on transportation and healthcare services most heavily are the ones who have the greatest difficulty accessing them,” says Tran. “As cities around the world spend billions of dollars overhauling their transportation systems, our research and data tools aim to help better balance urban economic growth with the rights of marginalized people.”
In addition to highlighting the diversity of mobility needs existing within cities, the study demonstrates the importance of open datasets to planning research. Relevant public data is currently housed in diverse repositories and formats, but gathering it into centralized repositories in consistent formats would go a long way towards helping planners make our communities more inclusive places in which to live.
The UBC team is currently developing a computer model that will help cities predict how specific planning and policy decisions would impact access to healthcare, education and even employment, including among underserved populations.
Link to the original article can be found here.
For more information: