Trade-offs and synergies for urban Production-Living-Ecological spatial Patterns-Comparison study between Fuzhou, China, and Saskatoon, Canada
Bibliographic record
Abstract
Rapid industrialization and urbanization have significantly changed urban spatial patterns, resulting in the urban ecosystem degradation and urban spatial conflicts. The challenge requires the urban spatial planning more sophisticated for developing eco-city models in the perspective of urban land multifunctionality. The Production-Living-Ecological(PLE) spatial pattern is proposed for effective eco-city planning in Chinese urban cases. Given the differing climatic and cultural contexts, are the PLE spatial patterns comparable between cities from different continents? This study aims to compare the characteristics of PLE spatial patterns and the trade-offs & synergies of PLE spaces between Fuzhou city, China and Saskatoon, Canada for developing the eco-city models. First, the paper identified the PLE spaces by integrating multi-source data, then analyzed the PLE spatial agglomeration characteristics by using the average nearest neighbor and kernel density analysis, finally detected the trade-offs and synergies between functional spaces by Spearman correlation and bivariate spatial autocorrelation. The results showed the distinctly different PLE spatial patterns and the trade-offs & synergies of PLE spaces between the two eco-cities in Fuzhou, China and Saskatoon, Canada in 2022. (1) For the PLE space composition, the percentages of ecological space in Fuzhou and Saskatoon were 64.6% and 36.4%, respectively, while the proportions of the most suitable residential space in two cities from POI data were 2.4% and 4.1%, respectively. (2) For PLE spatial agglomeration, ecological space in Fuzhou was characterized with a random distribution with the average nearest neighbor index of 1.19, and scattered as small patches in urban hilly area covered with ever-green broadleaf trees, while in Saskatoon the index was less than 1.00 with a clustered distribution in numerous city parks covered with grass and shrubs; Fuzhou’s multifunctional spaces were clustered in the central urban area surrounded by ring roads and in Changle District, while Saskatoon’s were dispersed with large patches. (3) For the trade-offs & synergies of PLE space, the ecological spaces in two cities were suppressed. In Fuzhou, the trade-off area ratio of the ecological space to other fuctional spaces was ranged 50% to 58%, while in Saskatoon, it was 40% to 47%. (4) The PLE spatial pattern can clearly sketch the different eco-city frameworks in different continents. Fuzhou’s eco-city model was characterized by “high ecological space/compacted living space/strong trade-off between ES and other spaces” and Saskatoon’s was featured with “low ecological space/spacious residential space with high livability/ weak trade-off between ES and other spaces”. Therefore, Fuzhou faced more challenges of intense spatial competition in the context of dense population. Our findings reveals the practical requirements for optimizing urban space and functions in terms of economic, ecological, and livability considerations. Additionally, they would provide valuable insights for long-term urban spatial planning and development strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".