Spatiotemporal Analysis of Human Mobility based on Land Use Types in the Greater Toronto Area during COVID-19 Pandemic
Bibliographic record
Abstract
The 2019 Coronavirus disease COVID-19 is an infectious respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It undoubtedly poses a huge challenge in terms of public health and social impact worldwide. The Ontario government implemented a series of non-pharmaceutical interventions (NPIs) prior to vaccination to prevent large-scale outbreaks in the Great Toronto Area (GTA), which is the most densely populated region in Ontario. Detecting and analyzing human mobility during the pandemic can help decision makers assess the effectiveness of policy implementation, in order to better respond to similar events in the future. Geotagged Twitter data serves as an important source of volunteered geographic information (VGI). Anonymized geotagged tweet in the GTA in 2020 using the Twitter Academic API are used to analyze inner-city human mobility. The results provide a longer-term insight into how human activity is affected by the pandemic as well as government orders. In this thesis, human mobility spatiotemporal patterns in the GTA are found to be close to patterns founded in the previous studies. People are affected more by the severeness of the first outbreak. More people stay at home rather than in commercial areas, schools, and workplaces. Human mobility in open spaces is affected by seasons besides policy effects. Human mobility in utility and transportation areas is related to the properties of the areas they connect. Most of the policies received significant reflections within one week of release, but milder policies resulted in insignificant human mobility changes. Human mobility patterns in most land use types have moderate correlation with the Google Community Mobility Report. Even so, some limitations still exist.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".