Spatial-temporal patterns of self-organization: A dynamic 4D model for redeveloping the post-zoning city
Bibliographic record
Abstract
Time is the main axis for understanding the functional, economic, and social aspects of self-organized redevelopment. When such processes are intensive and are conducted contemporaneously by large numbers of urban agents on different spatial and temporal scales and as a result of different motivations, urban planning is fragmented into multiple simultaneous and unexpected projects. The post-zoning era in urban planning stemmed from a recognition of this kind of complexity of urban dynamics and the need for a flexible planning system. Web-based geographic information systems (GIS) and planning support systems (PSS) are employed widely as digital tools to support planning practices. Still, the solutions tend to be isolated implementations that do not achieve sophisticated management of the complex temporal-spatial urban dynamics of self-organization. To this end, the article presents a useful set of multidimensional (2D, 3D, and 4D) planning tools that can be implemented by municipal planning departments to improve planning practices with relative ease. This toolbox facilitates the real-time updating of changes to individual buildings and allows all parties to see where delays are occurring, where they are impacting one another, and where environments of accelerated development are evolving in nearby urban plots. Identifying redevelopment clusters enables the formulation of an urban time-based planning policy. Using a spatial-temporal toolbox for planning, we argue, can facilitate recognition of the potential of self-organization as the leading form of contemporary urban planning.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".