Discrete Choice Model with Structuralized Spatial Effects for Location Analysis
Bibliographic record
Abstract
The discrete choice modeling paradigm and in particular the logit model are research topics that have been continuously developed and refined for years in the field of transportation applications. Modeling locational choices, however, differ from modeling transportation choices in that geographically referenced data are used and thereby give specifically spatial choices. A discrete choice model with a systematic specification of the spatial influences in the choice process is presented. The utility function of the model is specified with autoregressive expressions for the deterministic and error components, and the model is evaluated with reference to three alternative models: the standard logit model, a logit model with an autoregressive deterministic term, and the mixed logit model with autoregressive error terms. Two applications are presented: one with simulated data and another with real data for the analysis of location choice in Sendai, Japan. The proposed model shows an improved performance over the three reference models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".