Hierarchical Location-Allocation with Spatial Choice Interaction Modeling
Bibliographic record
Abstract
We combine concepts and methods from hierarchical spatial systems, spatial interaction modeling, and location-allocation modeling to derive optimal hierarchical facility systems. We consider models of increasing realism in how spatial interaction is dealt with—the first two are from past literature, the third is new. The p-median model assumes that patrons always travel to the closest facility and that distance minimization best serves them. Several researchers have observed, however, that patients in the developing world frequently bypass lower level facilities to attend more distant higher level ones. Previous location-allocation models have incorporated spatial interaction models attributing attendance and benefits to facility size and distance. In this article, we present a new model that incorporates a spatial choice interaction model attributing attendance and benefits to facility size, distance, and neighborhood accessibility. We demonstrate our approach with 150-node, three-level Oppong's problem of locating healthcare facilities in Suhum District, Ghana. This approach reacts intuitively to changes in accessibility patterns and deals realistically with the bypassing problem.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".