I am honored and excited to be appointed to a second term as chair of the Mapping Science Committee of the U.S. National Academies of Sciences, Engineering and Medicine.
The Mapping Science Committee organizes and oversees National Research Council studies that provide independent advice to society and to government at all levels on geospatial science, technology, and policy. It also addresses aspects of geographic information science that deal with the acquisition, integration, storage, distribution, and use of spatial data. Through its studies, the committee promotes the informed and responsible development and use of spatial data for the benefit of society.
We have a excellent committee for the 2021-2023 term – accomplished scientists, professionals and leaders in the field, spanning a wide range of geospatial science, technologies and applications:
- Harvey Miller, chair, The Ohio State University
- Stewart Fotheringham, Arizona State University
- Oceana Francis, University of Hawai’i at Manoa
- Hendrik Hamann, IBM T.J. Watson Research Center
- Kristen Kurland, Carnegie Mellon University
- Marguerite Madden, University of Georgia
- Keith Masback, Plum Run, LLC
- Kathleen Stewart, University of Maryland
- Eric Tate, University of Iowa
I’m quoted in an article in CQ Roll Call , a news site covering Congress and the White House, about the drastic budget faced by public transit systems unless Congress acts soon. You may not be surprised to learn I’m against that.
As recovery bills languish, transit systems cut service – Jessica Wehrman, Roll Call, 2 December 2020
New paper: Liu L, Miller HJ, Scheff J (2020) The impacts of COVID-19 pandemic on public transit demand in the United States. PLOS ONE 15(11):e0242476. https://doi.org/10.1371/journal.pone.0242476
The COVID-19 pandemic and related restrictions led to major transit demand decline for many public transit systems in the United States. This paper is a systematic analysis of the dynamics and dimensions of this unprecedented decline. Using transit demand data derived from a widely used transit navigation app, we fit logistic functions to model the decline in daily demand and derive key parameters: base value, the apparent minimal level of demand and cliff and base points, representing the initial date when transit demand decline began and the final date when the decline rate attenuated. Regression analyses reveal that communities with higher proportions of essential workers, vulnerable populations (African American, Hispanic, Female, and people over 45 years old), and more coronavirus Google searches tend to maintain higher levels of minimal demand during COVID-19. Approximately half of the agencies experienced their decline before the local spread of COVID-19 likely began; most of these are in the US Midwest. Almost no transit systems finished their decline periods before local community spread. We also compare hourly demand profiles for each system before and during COVID-19 using ordinary Procrustes distance analysis. The results show substantial departures from typical weekday hourly demand profiles. Our results provide insights into public transit as an essential service during a pandemic.