This is such a cool paper @youngjae.cha Just finished reading through, and I had a couple questions:
- For this paragraph: “Finally, controlling for the prepandemic life expectancy, median age, occupants per room, and political orientation simultaneously, walkable zip codes still had fewer cases than less walkable zip codes. The greater the proportion of Black and Hispanic residents there were, the more confirmed cases there were (see Table 2). In terms of the number of deaths, the walkability main effect disappeared, but the interaction effect remained significant. Wealthier zip codes and zip codes with longer prepandemic life expectancy had fewer deaths (see Table 2). The greater the proportion of Black and Hispanic residents there were, the more COVID-19-related deaths there were.” ==> Am I right to interpret this as meaning that, when area racial composition is added to the controls listed above, the walkability effect loses statistical significance in cases and deaths? Or is that the incorrect interpretation?
- What is meant by the “interaction effect remained significant”?
I also found the research you linked to on walkability being linked to greater social mobility super interesting.
@youngjae.cha - are you still affiliated with the Oishi Lab? (just looked up your work). Does your research today still have a similar focus?
If so, I wanted to introduce you to @Song_Gao_UW-Madison who’s done a lot of work in this area. Check out “Intracounty modeling of COVID-19 infection with human mobility: assessing spatial heterogeneity with business traffic, age, and race”: Slack
@Song_Gao_UW-Madison Did your research ever include walkability as one if its areas of analysis? If so, was curious what you would expect the relative effects of walkability to be vs some of the variables you mentioned when you shared your work.
@youngjae.cha I also wanted to intro you to a couple people who’ve layered interesting modeling in similar areas of research. @Xiao_Huang_University_of_Arkansas has applied time-series clustering on our dwell times to explain disparities and exposure and modeled the effects of inequalities. Slack
and potentially most interesting and relevant to you, @Esteban_Moro_MIT shared a paper two weeks ago that found that 55% of experienced income segregation is due to mobility! @undefined - how (if at all) did you handle walkability in your analysis?