Facility location in cities : the optimal location of emergency units within cities
Bibliographic record
Abstract
Over the past four decades, there has been an increasing interest in the problem of effective facility location in many societies. The question arises as to how many schools, hospitals, ambulances, warehouses, fire stations, emergency centers are needed and their respective locations to achieve a prescribed level of service. This book is concerned with the problem of locating emergency facilities efficiently and effectively in cities. We develop three new heuristic methods with the objective of increasing accessibility to these facilities and thus reducing their response time. This book details the development, implementation, and testing of the three new heuristics in addition to applying our best heuristic to the location of ambulance stations in the Perth metropolitan area in Western Australia. Furthermore, we also developed an effective method for locating new facilities among old facilities. This book is well suited for students, lecturers, researchers, and anyone who is interested in the location and operation of emergency facilities in some major cities such as New York, Austin, Los Angeles, Denver, Vancouver, Bangkok, Taipei, Santo Domingo and Belfast.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".