Spatiotemporal Evolution of Urban Resilience and Spatial Spillover Effects in Guangdong Province, China
Bibliographic record
Abstract
In the context of global environmental changes, the frequency of various disasters and extreme events is increasing, and enhancing urban resilience has become an important guide for current urban development. Previous studies have mainly focused on changes in urban resilience, with less consideration for the impact of spatial spillover effects on urban resilience. Therefore, this paper aims to analyze the temporal and spatial evolution of urban resilience and its spatial spillover effects in Guangdong from 2012 to 2020 based on the urban resilience assessment model, the Getis-Ord Gi* model, and the improved Capello model. The results are as follows: Affected by COVID-19, the urban resilience of Guangdong Province declined from 2019 to 2020, and 42.86% of the cities demonstrated negative growth in their urban resilience. Urban resilience exhibited significant spatially non-equilibrium characteristics among different cities. The urban resilience of the cities in eastern, northern, and western Guangdong, which are the “collapse zone” of urban resilience, was lower than 0.229 from 2012 to 2020. The intensity of urban resilience spillover in Guangdong presented a typical three-level circle structure of “core-subcore-periphery”, which decreased from the core circle to the surrounding circle. COVID-19 caused a 27.21% decrease in the total urban resilience spillover in Guangdong from 2019 to 2020. Finally, we identified critical driving factors of urban resilience using the optimal-parameters geographical detector model. This study can provide a scientific reference for the Chinese government to build resilient cities and improve sustainable urban development.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".