Representing Dynamic Spatial Processes Using Voronoi Diagrams: Recent Developements
Bibliographic record
Abstract
Geographic space is typically conceptualized either as discrete objects or as continuous fields. Considerable efforts have been carried out for the representation and management of the spatial data, based on the object view of the space. However field-based data models are less developed in GIS especially when it comes to the modeling and representation of dynamic fields. Dynamic phenomena such as urban dynamics, air pollution, fire propagation, etc. are examples of dynamic fields with important spatial and temporal components. These phenomena should be represented in GIS in order to help users and decision makers in different disciplines to better understand and predict their dynamic behaviour. The limitations of GIS for modeling and simulation of those phenomena are mostly related to the 2D and static nature of their spatial data structures. In this paper, we explore the potentials of the Voronoi diagram as an alternative spatial data model that allows realistic representation of the spatial dynamic fields in 2D and 3D spaces. The paper presents how different types of Voronoi diagrams for points in two and three dimensional spaces as well as Voronoi diagrams for line segments and polygons could be effectively used in different contexts to represent and simulate different dynamic spatial fields and processes.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".