Spatial Models of Consumer Choice for Retail Outlets: Theory and Practice in Physics of Marketing
Bibliographic record
Abstract
A theoretical review and an assessment of applications of the spatial models of consumer choice for retail outlets are presented here. As they are also called gravitational models in marketing, they remain relevant in today’s retail world despite the explosion of e-commerce. The major objective here is to emphasize the theoretical significance of these models, analyze their practicality in the market, and refer to their validity today. The focus would be on the original Reilly’s model of the early thirties of the last century, and the two most significant improvements over it, Converse’s model of the late forties, and Huff’s model of the early sixties. After a detailed exposition of these models and their applications, a critical analysis is introduced to address the retail site selection, radius of the trading area, limitations of the models, and some criticism.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".