A Prototype of a Machine Learning Workflow to Classify Land use from Housing Market Dynamics. Part of a Longitudinal Analysis of Housing Sales in the Greater Toronto-Hamilton Area
Bibliographic record
Abstract
There is ample evidence of the role of land use and transportation interactions in determining urban spatial structure. The new data sources introduced by increased digitization of human activity, such as Teranet’s dataset of real estate sales, offer opportunities for development of integrated urban models or studies conducting longitudinal analysis of changes in land value distributions. To facilitate this, data from various sources (Census, TTS, etc.) needs to be merged at the land parcel level to enhance datasets with additional attributes, while maintaining the ease of data storage and retrieval. In addition, accurate land use information needs to be added to Teranet records to allow separating sales data by major property types. This thesis proposes a prototype of a workflow to augment Teranet’s dataset with data from multiple sources and use machine learning to classify land use of each Teranet record based on the housing market dynamics.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".