Development of a Synthetic Population and its Baseline Mobility Tool Ownership for the Province of Nova Scotia
Bibliographic record
Abstract
This study focuses on developing a synthetic population for Nova Scotia to update the base-year synthesis for an agent-based integrated transport and land use model. It proposes a novel population synthesizer, developed in Python programming language. Utilizing the 2016 hierarchical public use microdata file (PUMF) and census data, the iterative proportional updating algorithm was employed to generate the synthetic data at the county level, followed by conditional Monte Carlo simulation for micro spatial unit assignment to the dissemination area. A feedforward neural network (FNN) model is used to tackle missing value issues in the PUMF data and added the capacity to synthesize base-year mobility tool ownership. The addition of missing values enhanced the distribution accuracy of the PUMF against census totals. The synthesis process generated individuals with an error percentage of 0.07% compared to the actual population. Most household-level and individual-level attributes had an error between −0.5% and +0.5%, while a few attributes showed errors slightly outside this range. The synthetic data shows a close joint distribution to the PUMF. Household vehicle ownership, driver’s license holders, and transit pass ownership were added to the synthetic data using a FNN model. The FNN model achieved an overall accuracy of 52% for vehicle ownership, with 87% predictions within ±1 vehicle per household. Although the model demonstrates 90% overall accuracy for driver’s license ownership, and 92% for transit pass owners, it showed lower precision and recall for non-driver’s license holders and transit pass holders because of their low dataset representation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".