Spatial Transferability of a Microresidential Mobility Model in the Integrated Land Use, Transportation, and Environment Modeling System
Bibliographic record
Abstract
This paper presents the spatial transferability analysis of a micro-behavioral model from the residential mobility component of the integrated land use, transportation, and environment (ILUTE) modeling system developed and implemented in the Greater Toronto and Hamilton Area, Ontario, Canada. The study examined whether ILUTE could be spatially transferred with the current model components to a different geographic area: Halifax, Nova Scotia, Canada. The residential mobility component within ILUTE is a continuous-time, hazard-based duration model, developed with retrospective survey data from the Residential Mobility Survey 2 in the Greater Toronto and Hamilton Area. This study developed a similar continuous-time, hazard-based duration model for the residential mobility decisions of households in Halifax on the basis of retrospective survey data from a household mobility and travel survey. The model results suggested that households in Halifax and the Greater Toronto and Hamilton Area exhibited profound differences in residential mobility decisions. Sociodemographic, dwelling, and neighborhood characteristics significantly affected residential mobility decisions in the Greater Toronto and Hamilton Area. The effects of land use and accessibility measures were noteworthy for Halifax. For instance, home-to-work distances in Halifax affected the decision to move; however, such an effect could not be confirmed in the Greater Toronto and Hamilton Area. Households' first periods of residence after household formation in a residential location were shorter in Halifax than in the Greater Toronto and Hamilton Area. It was concluded that the direct transfer of micromodels from one spatial context to another could be difficult.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".