Investigating the Nonlinear Relationship between Takeout Order Demand and Built Environment under Different Periods of COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has hit the global restaurant business hard, especially dine-in. However, it has also provided opportunities for online dining, with takeout becoming a fulcrum for the economic resilience of the urban restaurant industry. Currently, research on the factors affecting takeout order demand under the pandemic has been inadequate. Therefore, this study uses multisource data from Nanjing to explore the changes in takeout order demand as the pandemic develops. And based on the Light gradient boosting machine (Light GBM) model, the nonlinear relationship between the built environment and order demand under different periods of pandemic is investigated, and the important factors affecting the demand are obtained. The results show that daily orders on average during COVID-19 decline by 25.6% than before COVID-19, while during the stabilization phase of the pandemic, they are 20.0% higher than before COVID-19. According to the relative importance ranking of factors in the model, land use diversity and road design influence takeout the most and the crucial influencing factors vary across pandemic periods. In the postpandemic era, special attention needs to be paid to the impact of the number of restaurants, colleges, offices, and main roads on takeout services. In addition, the thresholds of key built environment factors through partial dependency plots can enhance operators’ understanding of takeout services and provide suggestions for the spatial layout of takeout resources. While satisfying people’s dietary needs, the role of takeout in restoring the restaurant economy can be better utilized.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".