Understanding the Drivers of Mobility during the Covid-19 Pandemic in Florida, USA Using a Machine Learning Approach
Bibliographic Data
| ID | 22034840 |
|---|---|
| Authors | Guimin Zhu (0009-0007-5791-1015, University of Maryland, College Park, corresponding author), Kathleen Stewart (0000-0002-4459-4918, University of Maryland, College Park), Deb Niemeier (0000-0002-8937-7159, University of Maryland, College Park), Junchuan Fan (0000-0002-2933-5248, Oak Ridge National Laboratory) |
| Year | 2021 |
| Volume | 10 |
| Issue | 7 |
| Pages | 440 |
| Publication date | 2021-06-28 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10070440 |
| OpenAlex | W3173306427 |
| Language | EN |
| Citations received | 3 |
| References cited | 31 |
As of March 2021, the State of Florida, U.S.A. had accounted for approximately 6.67% of total COVID-19 (SARS-CoV-2 coronavirus disease) cases in the U.S. The main objective of this research is to analyze mobility patterns during a three month period in summer 2020, when COVID-19 case numbers were very high for three Florida counties, Miami-Dade, Broward, and Palm Beach counties. To investigate patterns, as well as drivers, related to changes in mobility across the tri-county region, a random forest regression model was built using sociodemographic, travel, and built environment factors, as well as COVID-19 positive case data. Mobility patterns declined in each county when new COVID-19 infections began to rise, beginning in mid-June 2020. While the mean number of bar and restaurant visits was lower overall due to closures, analysis showed that these visits remained a top factor that impacted mobility for all three counties, even with a rise in cases. Our modeling results suggest that there were mobility pattern differences between counties with respect to factors relating, for example, to race and ethnicity (different population groups factored differently in each county), as well as social distancing or travel-related factors (e.g., staying at home behaviors) over the two time periods prior to and after the spike of COVID-19 cases
Disease · Ethnic group · Geography · Miami · Pandemic · Population · Social distance · Socioeconomics · Sociology · COVID-19 epidemiological studies · Demography · Human Mobility and Location-Based Analysis · Medicine · Urban Transport and Accessibility · Gerontology
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| Unique citing works | 3 |
|---|---|
| Citations per year | 1 |
| Citation span | 2023 - 2025 (3) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |