An Indicator Model of the Spatial Quantification and Pattern Analysis of Urban Sustainability: A Case Study of Cincinnati, Ohio
Bibliographic record
Abstract
Shen & Guo (2014) have recently developed an array of urban sustainability indicators (USIs) as a tool to measure urban sustainability. Using 2006 data for Saskatoon, Saskatchewan, Canada, they developed a theoretical integrated USI model with a hierarchical index system, to spatially monitor urban sustainability using geo-matic approaches and further statistically detect its spatial patterns. The purpose of this study is to apply Shen and Guo’s general approach to a major American city, Cincinnati, Ohio, utilizing U.S. census data from 2010, to test its utility beyond the original Canadian test case. In doing so, the model and its indicator structure were modified for the American context after a further review of sustainability indicators. Unlike Shen and Guo, however, the model is not subjectively weighted. Nevertheless, the revised model similarly applied both statistical analysis and geo-statistical analysis to explore how urban sustainability was spatially distributed and what spatial patterns (random, dispersed or clustered) for the indices could be found among Cincinnati’s census tracts. This work confirms Shen and Guo’s conclusion that geo-matic tools can be applied to detect spatially urban sustainability patterns, which can be provided visually for urban planners, managers and administrators for use in future policy making and implementation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".