Determining the optimal location for a large organic food store in Montreal
Bibliographic record
Abstract
In this thesis, the optimal location for a large format, organic food retail store is determined using the Huff Model in Montreal, Canada. The Huff Model has been widely used in marketing analysis to determine the optimal location in a variety of contexts. First, the study used Statistics Canada 2001 food expenditure data for Montreal to generate a double log linear food expenditure model for Montreal consumers. Variance Inflation Factors were calculated to test if there were multicollinearity problems, and a Breusch-Pagen test was done to test for heteroskedasticity. Neither of the results showed any statistical problems. Second, AC Nielson survey results were used to facilitate the organic food expenditure calculation process. Third, the travel distance from all census tracts in Montreal to the candidate store locations were calculated using the Manhattan distance calculator (McLafferty and Grady, 2005). Finally, the Huff Model was used to calculate an attractiveness index for each candidate location. The conclusion from the results of the empirical analysis was that, among the 45 candidate locations, which are scattered all across Montreal, 5445 de Gaspe gained the highest attractiveness index. This location is situated close to relatively affluent and highly populated areas of the city, and is also very accessible. Not only is this just a few blocks from two metro stations, and close to city bus routes, it is also adjacent to several major streets such as Saint-Laurent to the west, Saint-Denis to the east, Rosemont to the north and Saint-Joseph to the south. This thesis has provided a new application of the Huff model, which could be used in various markets, and has provided an interesting combination of models from the field of Economic Geography, and Agricultural Economics.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".