A Comprehensive Business Location Choice Model Leveraging Machine Learning in Systematic Choice Set
Bibliographic record
Abstract
This study develops a comprehensive two-stage location choice framework for business establishments as part of a goods movements modeling. This study aims to formulate a systematic methodology for investigating the location choice of business establishments within Halifax Regional Municipality. This study presents a novel approach that leverages machine learning techniques to generate a systematic choice set, thereby improving the representation of realistic and reasonable location alternatives. Info Canada Business Establishments dataset 2022 is employed to achieve the aim of this study. Combining an unsupervised machine learning technique with the mixed multinomial logit model facilitates a data-driven approach to enhance the precision and robustness of business establishment location choice models. This approach possesses the potential to unveil latent patterns and heterogeneity among potential choice alternatives that may remain obscured when utilizing a conventional multinomial logit model. This thorough analysis offers robust insights into the factors influencing the location choice of business establishments. The findings obtained from this comprehensive study suggest that wholesalers prioritize proximity to highways and positions within business parks for their operations while avoiding higher population density and central business district proximity. Transportation businesses seek larger sites and locations near highways, favoring clustering with related transport companies and valuing accessibility and cost-effectiveness over proximity to business parks or rural settings. The findings of this study could provide valuable insights for commercial vehicle and goods movement modeling, business location strategies, and policymaking concerning sustainable urban development.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".