A temporally cyclic growth model of urban spatial morphology in China: Evidence from Kunming Metropolis
Bibliographic record
Abstract
Rapid urbanisation and complexity of political-economic transition in China has brought about continuous and remarkable changes of urban morphology over the past decades, which were driven by a mixture of spatial, social-economic and institutional forces. Understanding such urban morphological evolution requires new mixed evidence and holistic perspectives. In this paper, it is argued that two dominant types of urban growth in China: low-density expansion and high-density infill, might be driven by different forces at different stages. To interpret the processes of urban development, two easy-to-understand morphological indicators: ‘expansion-induced investment density index’ (EID) and ‘infill-induced investment density index’ (IID), are defined to measure the investment density per unit of developed land and used to compare the morphological changes between different phases in a long period by integrating spatial and socio-economic data. The temporal variation of these indicators suggests a cyclic growth model (CGM), which means the periodic switch between low-density expansion and high-density infill. Using Kunming metropolis as a case study, this paper has confirmed that its urban morphological evolution from 1950 to 2014 was periodically and reciprocally driven by a set of vis-à-vis dualistic dynamics, in which low-density expansion is led by pro-growth infrastructure-oriented public investment, while the high-density infill is activated by collective and rational actions of individual enterprises and their economic behaviours. It is concluded that the confirmed CGM model, together with two morphological indicators, offers a new holistic perspective and method to easily and integrally interpret urban morphological evolution and accordingly has potential theoretical implications for reasonably understanding the urbanisation in China.
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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) | 0.000 | 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".