Defining Socially-Based Spatial Boundaries in the Region of Peel, Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The purpose of the project was to delineate a series of contiguous neighbourhood-based "Data Zones" within the Region of Peel (Ontario) for the purpose of health data analysis and dissemination. Zones were to be built on Census Tracts (N = 205) and obey a series of requirements defined by the Region of Peel. This paper explores a method that combines statistical analysis with ground-truthing, consultation, and the use of a decision tree. DATA: Census Tract data for Peel were derived from the 2006 Canadian Census Master file. METHODS: Following correlation analysis to reduce the data set, Principal Component Analysis was applied to the data set to reduce the complexity and derive an index. The Getis-Ord Gi*statistic was then applied to look for statistically significant clusters of like Census Tracts. A detailed decision tree for the amalgamation of remaining zones and ground-truthing with Peel staff verified the resulting zones. RESULTS: A total of 15 Data Zones that are similar with respect to socioeconomic and sociodemographic attributes and that met criteria defined by Peel were derived for the region. CONCLUSION: The approach used in this analysis, which was bolstered by a series of checks and balances throughout the process, gives statistical validity to the defined zones and resulted in a robust series of Data Zones for use by Peel Public Health. We conclude by offering insight into alternative uses of the methodology, and limitations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".