Transportation’s Influence on Land Use Development: Historical Spatial-Temporal Approach
Bibliographic record
Abstract
The interrelationships between transportation and land use remain contentious despite extensive research. In particular, the influence of transportation system performance on land use development, although recognized as a lagged relationship, has yet to be fully understood. The existing methodological approaches are critically evaluated, and support for the development of a novel historical spatial–temporal approach is provided. The uniqueness of this approach is based on the creation, modification, and analysis of extensive historical databases (1964 to 1998) of transportation system performance (annual weekday traffic volumes) and microscale parcel level data (land use, actual use, and year of development) for the city of Calgary. All data are in yearly time steps, stored within a geographic information system (GIS) framework. Both data sets are significantly modified to create ( a) a congestion index based on volume/capacity ratios and ( b) development intensity and rates of change based on existing transportation zones. An example focus is on warehousing and logistics land uses, which are critically important to the city’s economy and broadly underexamined within the field. Methods used include scientific visualization, correlation analysis, and the use of GIS spatial analytical approaches. The results support the appropriateness of the data modifications and exemplify the value of scientific visualization. At a systemwide level, the timing and location of warehousing and logistics development are found to be statistically associated with high-congestion areas. For individual transportation zones, the relationship between developments and zone proximal congestion is less clear, although there is weak evidence for the existence of lagged effects.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".