Identifying high amenity zones in the U.S. and China with advanced GIS techniques
Bibliographic record
Abstract
This paper applies a variety of GIS techniques to examine the spatial pattern and extent of high amenity zones (HAZ) in selected cities of the U.S. and Guangzhou, China. HAZs are adjacent to downtowns and represent high density upscale residential areas whose residents support neighborhood retailing and service employment that can often result in the misclassification of the areas as employment centers. To define the HAZ consistently across U.S. cities, we first develop a weighted median job density measure for the census tracts of each city and include census tracts that are in the upper quartile in job density and have an employment residence ratio below 1.25, the critical value used to define job centers. In the absence of employment data for China at a census tract scale, Starbucks coffee houses are mapped as they were found in an earlier study to be spatially coincident with high amenity zones in the U.S. Google Earth and GPS field work allowed us to locate each Starbucks for Guangzhou China. Preliminary findings from Guangzhou and the designation of its Tian He District as an HAZ allows for some cross cultural comparisons of the HAZ concept.
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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 | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".