Computing and Information AN INTERACTIVE GEOSPATIAL ANALYSIS PLATFORM FOR FACILITY LOCATION DECISION-MAKING
Bibliographic record
Abstract
Abstract. The Facility Location Problem is an important research topic in spatial analysis. This paper focuses on the Static and Mobile Facility Location (SMFL) Problem, which aims to identify those static and mobile facility locations that serve a target area most efficiently and equally. This paper formalizes the SMFL problem as a bi-objective model and then solves the model by using a novel heuristic algorithm, named Static and Mobile Facility Location Searching (SMFLS). The algorithm consists of two steps: static facility location searching and mobile facility location searching. In order to solve the model for large datasets efficiently, a clustering-based heuristic method is proposed for the static facility location searching while the mobile facility location searching is implemented using a greedy heuristic method. Experiments on synthetic datasets demonstrate the efficiency of the SMFLS algorithm. In addition, with the aim of conducting facility location decision-making conveniently and efficiently, in this paper, an interactive geospatial analysis platform, named Geospatial Analysis Platform using Interactive Maps (GAPIM) is proposed by combining the bi-objective models and the SMFLS algorithm with an interactive map. Experiments on Alberta public health service data are conducted, with the results demonstrating the efficiency and practicality of the platform.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".