Evaluating the spatial-temporal impact of urban nature on urban vitality in Vancouver: A social media and GPS data approach
Bibliographic record
Abstract
Natural elements in urban environments enhance livability and health, strengthening urban vitality. However, existing research has primarily focused on physical indicators, with limited attention paid to the joint influence of behavioral and perceptual dimensions on urban vitality. To address this gap, this study integrates a spatiotemporal analytical framework encompassing three dimensions: natural elements, human perception, and activity diversity. Focusing on Vancouver, we utilized smartphone-GPS and social media data from 2018 to 2023 to explore temporal (weekdays vs. weekends) and spatial dimensions. Using machine learning techniques (Google vision, K-means and Sentiment analysis) on multivariate social media data, and we also analyzed changes in activity diversity over time. We assessed the multidimensional influences on urban vitality using the Geographically and Temporally Weighted Regression (GTWR) model. Our results show that during the pandemic, attention to nature and outdoor activities increased significantly, while cultural and social activities and transportation initially decreased but quickly recovered. Sentiment scores, natural elements, and human activity preferences significantly influenced urban vitality during COVID-19, with notable spatiotemporal heterogeneity. The pandemic intensified residents' reliance on natural spaces and green transportation, altering the spatial distribution of urban vitality. These findings provide a basis for optimizing natural spaces and sustainable transportation planning in future urban development. • Increased foces on natural spaces and green transport during COVID-19 altered urban vitality patterns. • Sentiment scores, natural elements, and activity diversity significantly impacted urban vitality, especially during the pandemic. • The need for future planning to prioritize natural spaces and green transport.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.001 | 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".