Estimating Population Counts for Dissemination Areas and Census Tracts in Canada from 2011 to 2021
Bibliographic record
Abstract
Abstract Accurate small-area population estimates are essential for health research and social policy development. While Statistics Canada provides census-year population counts for all geographic units, it does not produce intercensal estimates for Dissemination Areas (DA) and Census Tracts (CT). This study addresses this gap by estimating DA- and CT-level population counts for 2011 to 2021 using census data and interpolation techniques. Population counts were derived from Statistics Canada’s Geographic Attribute Files for 2011, 2016, and 2021. We applied linear interpolation to estimate intercensal population counts (2012–2015 and 2017–2020). We used an areal-weighted interpolation technique to account for boundary shifts due to census geography changes, utilizing Statistics Canada’s Correspondence Files. The final datasets provide consistent population estimates across census cycles, enabling longitudinal and neighbourhood-level analyses. The methodology and accompanying R script, available as supplementary materials, can be adapted for other intercensal periods and other demographic information, promoting transparency and reproducibility in demographic research. This study facilitates data-driven decision-making in public health and policy development by providing a reliable and scalable methodology for estimating intercensal population counts. About the Research Department The Saskatchewan Health Authority Research Department leads collaborative research to enhance Saskatchewan’s health and healthcare. We provide diverse research services to SHA staff, clinicians, and team members, including surveys, study design, database development, statistical analysis, and assistance with research funding. We also spearhead our own research programs to strengthen research and analytic capability and learning within Saskatchewan’s health system. Disclaimer This working paper is for discussion and comment purposes. It has not been peer-reviewed nor been subject to review by Research Department staff or executives. Any opinions expressed in this paper are those of the author(s) and not those of the Saskatchewan Health Authority. Suggested Citation Marouzi Anousheh, Plante Charles. 2025. “Estimating Population Counts for Dissemination Area and Census Tracts in Canada from 2011 to 2021.” MedRxiv. Author Contributions AM conducted the data analysis and prepared the first draft of the article. AM and CP designed the study and directed its implementation, including quality assurance and control. CP supervised the data analysis. CP reviewed, edited, and finalized the text. CP provided the overall guidance and funding for the research project. All authors approved the final version of the manuscript. Funding Statement This research was funded by the Saskatchewan Health Research Foundation (SHRF). Ethics Declaration This study exclusively utilizes publicly available, de-identified population data obtained from Statistics Canada. No human participants, personal identifiers, or confidential information were involved in this research, and therefore, ethical approval was not required. Conflict of Interest The authors declare that they have no conflict of interest. Data Availability All data used in this study is for public use and can be accessed through the Statistics Canada website. Code Availability Codes are available as a supplementary file to this working paper.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".