Development and Evaluation of an Open-Source Network Distance Tool for QGIS: A Huff Model Case Study
Bibliographic record
Abstract
<p>The Huff Model allows researchers to model retail catchment areas using the distance from consumers to stores. To represent the real world as closely as possible, network distance should be used as an input for the Huff Model, but existing tools are either expensive or very slow. The goal of this research is to develop a new, open-source tool to calculate network distance and illustrate the tool’s role as an input to the Huff model on a case study examining major grocery store catchment areas in the City of Toronto. The new tool was developed in Python as a script to be executed in QGIS. To improve upon existing tools, the Python library igraph was utilized, which helped decrease the run time of calculations compared to an existing tool by a factor of 268, while maintaining accuracy of the output. The case study found that some catchment areas for Metro grocery stores are very large and there might be an opportunity for a competitor to move in to capitalize on an underserved market.</p>
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.004 |
| 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".