Multi-Scalar Analysis of Transit-Oriented Development for New Start Commuter Rail
Bibliographic record
Abstract
This paper reports a novel, multi-scalar approach to analyzing population growth and employment development by economic sector within commuter rail transit-oriented development zones. To demonstrate, five new-start commuter rail systems are examined – Rail Runner (New Mexico), FrontRunner (Utah), Northstar (Minnesota), Music City Star (Tennessee) and Westside Express (Oregon). Using ESRI Business Analyst, a series of multi-scalar data tables were created to examine transit-oriented development (TOD) zones adjacent to stations for each system. Data for these zones were compiled for circular zones with radii of a quarter-mile, half-mile, one-mile, and 2-miles, and compared to the same data for the greater metropolitan areas. The datasets allow multi-scale sensitivity analysis across commuter rail systems, TOD zones, and metropolitan areas. Results from the analysis show greater population growth within proximity to the rail stations: within half-mile, the population growth was 26.5 percent; just beyond from half-mile to 2-miles, population growth was 14.6 percent; and for the metropolitan areas, population growth was 22.0 percent. Employment patterns in the zones show an elevated employee to resident ratio within half-mile of the stations, which diminishes as distances increase. The types of employment associated with this proximity are skewed toward professional, administrative and information service sectors. This paper adds to the existing literature on transit-oriented development by providing a new means of examining the impacts of commuter rail stations at varying scales.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".