Churn and change along commercial strips: Spatial analysis of patterns in remodelling activity and landscapes of local business
Bibliographic record
Abstract
Commercial strips are common within metropolitan regions throughout the world and particularly within Canada and the USA. Planners have identified these linear clusters of commercial land use as a form of auto-oriented sprawl on the one hand, and as fertile ground for local independent businesses on the other. Despite the rapid churn of businesses in a number of gentrifying central cities, few studies have examined the distribution or cumulative impacts of commercial remodelling or its relationship to larger scale urban transformations. In this research, we demonstrate methods used to identify spatial patterns in central city remodelling activity. Getis Ord Gi*, also known as hot spot analysis, is used to identify clusters of reinvestment activity associated with locally owned restaurant and retail businesses. Associations with differences in urban form are observed, including clustering of independently owned restaurant and retail businesses along areas of commercial strips with smaller lots. Theories on the location of clusters in older buildings are also tested, with mixed results. In addition, we use a Redevelopment Impact Index to capture the degree of external modification to commercial buildings and the nature of changes in building usage. Point density analysis is used to identify areas where commercial remodels are likely to add up to entertainment and leisure zones. The results of statistical tests show some association between proximity to the restaurant and retail clusters and new, mixed use development. Thus, we illustrate methods of examining emerging landscapes of local restaurant and retail business and their relationship to larger scales of redevelopment. This methodology has applications in the study of incubation and retention of local businesses, land use planning and redevelopment along commercial strips, and gentrification studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".