Visual Comparison of Moving-Window Kriging Models
Bibliographic Data
| ID | 14701194 |
|---|---|
| Authors | Urška Demšar (0000-0001-7791-2807, National University of Ireland, Maynooth, corresponding author), Paul Harris (0000-0003-0259-4079, National University of Ireland, Maynooth) |
| Year | 2011 |
| Volume | 46 |
| Issue | 4 |
| Pages | 211-226 |
| Publication date | 2011-12-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Cartographica The International Journal for Geographic Information and Geovisualization (JOURNAL) |
| Journal identifiers | ISSN: 0317-7173 • E-ISSN: 1911-9925 |
| Publisher | University of Toronto Press Inc. (UTPress) (PUBLISHER) |
| DOI | 10.3138/carto.46.4.211 |
| OpenAlex | W1996918481 |
| Language | EN |
| References cited | 24 |
Kriging is a spatial prediction method that is core to the geo-statistical paradigm. Commonly applied in the environmental sciences, it enables a prediction at an unsampled location coupled with a measure of confidence in its accuracy. Many variations of kriging exist, some of them complex, especially those that allow many parameters to vary spatially. Calibrating such a kriging model and interpreting its results can therefore be quite daunting. We suggest that visualization and visual exploration can help with this task. In particular, we focus on the moving-window kriging model, evaluating three newly developed robust variants of this model against a basic counterpart. We use star icon maps and plots to visually explore model results to evaluate model parameterization, specification, and performance
Data mining · Focus (optics · Kriging · Machine learning · Process (computing · Task (project management · Visualization · Window (computing · Computer Science · Data Visualization and Analytics · Land Use and Ecosystem Services · Soil Geostatistics and Mapping · Artificial Intelligence
| Highly cited | No |
|---|