Smart Growth and the Future of Commercial Space in the Greater Golden Horseshoe Area
Bibliographic record
Abstract
<p>For a decade and a half, the Greater Golden Horseshoe Area (GGHA) in Ontario, Canada has been undergoing dramatic changes due to provincial smart growth legislation that encouraged high density mixed-use development. For developers of commercial real-estate this has been problematic since commercial mixed-use is not as well understood in comparison with single use commercial properties that have well defined categories and subcategories. Many in the industry have also called into question the wisdom of adding large amounts of new commercial space in mixed-use developments at a time when E-commerce is growing rapidly and contributing to uncertainty about what commercial space demand will look like in the future. </p> <p>Analysis of a property assessment database of 992 commercial mixed-use properties across the GGHA using k-means cluster analysis reveals that commercial mixed-use properties can be divided into 12 distinct clusters or categories. A Delphi survey conducted in 2018 with a panel of 22 experts from various commercial real estate and urban planning backgrounds showed that while E-commerce in nine commercial NAICS categories was expected to grow an average of 137% over the next twenty years, the effect on demand for per capita commercial space was only forecasted to decline by 16% over the same time period. When the per capita commercial space forecast was applied to 20-year population forecasts, this showed that a very moderate amount of new commercial space would be required in the GGHA by 2041. These findings are discussed in the context of both policy guidance implications as well as the COVID-19 pandemic that has raised additional questions about the growth of E-commerce and raises new areas for future research as well as a need for continued research on the topics investigated here. </p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".