Growth and Change of Retail Opportunity in Greater Toronto Area
Bibliographic record
Abstract
During the last decade, rapid changes have occurred in the retail economy of North America epitomised by the pace and location of development, and a functional transformation of retailing. Despite these changes, few studies have examined the space-time dynamics of retailing with a view to understanding the role of retail in urban growth management. Using data from a longitudinal database of Canadian commercial activity, this study explores spatiotemporal trends in retail development within the Greater Toronto Area (GTA) during the period 1996 to 2005. The paper begins with an overview of the historic periods of retail development in Canada. Attention then turns to regional analyses focused on the spatial evolution of retail structure across the GTA. Weighted bivariate Gaussian kernel estimation and centrographic statistics are used to describe spatial patterns of retailing classified by opening year and format. Retail format categories include: (1) community and neighborhood convenience, (2) enclosed regional and super regional malls, and (3) power centers. The results highlight the recent wave of power retail development that has swept across the GTA. These auto-dependent power centers have been built predominantly in areas of new residential growth located at the fringes of the GTA. The paper concludes with a discussion of the implications of the research findings for the development of models designed to simulate urban growth and change. The conclusions also explore the planning and policy gap between the emerging retail reality and smart growth strategies that have overlooked the role and potential impact of retail planning and development on metropolitan regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".