Talking Headways Podcast: People Based Accessibility
This week on the Talking Headways podcast, we’re joined by Dr. Wendy Zhou to discuss her recent USC dissertation on dynamic accessibility — you know, how static maps without a time-based dimension don’t tell the whole story on access and how it’s time for a “people-based” accessibility.
And you know that there’s three ways to play the weekly Talking Headways game:
- Put on some headphones and click “play” in the player below:
- Read an excerpt of our exhilarating conversation below the player.
- Read the entire text (with some transcription typos) here.
Here’s the excerpt:
Jeff Wood: How did you get interested in transportation policy?
Wendy Zhou: I grew into later in life. My interest started in urban planning, not specifically transportation, when I did my college in China, and then I did my Masters in Berkeley.
I was not a specific transportation person. I was more environmental. For example, when I did my research in undergrad, I visited different communities to see how people actually interact with neighborhood green space. So I know people use the infrastructure differently, and the urban planning benefit different people, so that’s kind of how I get interested in urban planning in the first place.
And it’s about how people live in a city that’s got me interested in the first place. And then, I got more exposed to transportation when I was in USC, where I work with my adviser, Geoff Boeing.
Jeff Wood: Geoff’s been on the show before.
Wendy Zhou: Oh cool. That’s when I got more exposed to transportation planning and transportation policy. One thing that kind of bugs me is the accessibility map that I see in, for example, the USDA’s food desert. It’s based on the home census tract and it draws a one-mile buffer around the centroid of census tract to see whether, for example, there are supermarkets within that buffer.
It bothers me because it’s not the case from my experience of how I live. I live in San Gabriel, and where I live is actually marked as a food desert on that map, and I was shocked because I never feel that way. And then I realized because I go to school that has a Trader Joe’s. And sometimes I drop off my husband in Pasadena, where his shuttle is, and there are Whole Foods, like every grocery store there.
It occurs to me I never buy grocery close to my home, and I think maybe perhaps how we measure it can be different from how people living there and experienced accessibility, and that’s the idea that interests me and the dissertation idea came from.
Jeff Wood: Obviously that’s an issue, but I’m curious what led you to think that this was something that needed extra research, that the static maps weren’t quite working for planners and others who are thinking about these issues like food deserts?
Wendy Zhou: That’s a great question. … Take smartphone data, which was not a thing that we used to have as planners. With that data, we can know where people are across different times of day, which should be a better information to just proxy people from where they live. So that’s the new opportunity of this research.
And I think the problem is more based on my own experience because I know how you proxy from people’s home is not what people experience. We know there are data there and know how people experience it differently and want to see how they come together.
Jeff Wood: So what do access metrics look like now? You did basically a review of about 160 different papers. I’m curious what it looks like currently in the world.
Wendy Zhou: So it’s still based on places. That’s most likely still using census tracts, census blocks, and then use that place to proxy people’s home location.
So for example, “more Asians live in that place,” or “more White people” or “more Black people,” and use that to proxy different people’s accessibility. So basically it’s based on their home. But I see the development is more like the time fact, like people are more incorporating the time sensitive, like transit data.
Unlike decades ago, maybe more people are using just straight line distances or like network distances. But now they more incorporated the time difference, the travel time difference by bike, for example, by transit, by drive. So that’s kind of seeing like the temporal dimension they’re incorporating. But most likely they’re based on places like the home census tract.
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