The Role of Spatial Context Information in the Generalization of Geographic Information
Using Reducts to Indicate Relevant Attributes
Bibliographic Data
| ID | 22032474 |
|---|---|
| Authors | Anna Fiedukowicz (0000-0001-9609-8185, Warsaw University of Technology, corresponding author) |
| Year | 2020 |
| Volume | 9 |
| Issue | 1 |
| Pages | 37 |
| Publication date | 2020-01-10 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9010037 |
| OpenAlex | W2999170151 |
| Language | EN |
| References cited | 41 |
Generalization of geographic information enables cognition and understanding not only of objects and phenomena located in space but also the relations and processes between them. The automation of this process requires formalization of cartographic knowledge, including information on the spatial context of objects. However, the question remains which information is crucial to the decisions regarding the generalization (in this paper: selection) of objects. The article presents and compares the usability of three methods based on rough set theories (rough set theory, dominance-based rough set theory, fuzzy rough set theory) that facilitate the designation of the attributes relevant to a decision. The methods are using different types (levels of measurements) of attributes. The author determines reducts and their cores (common elements) that show the relevance of attributes stemming from the spatial context. The fuzzy rough set theory method proved the least useful, whereas the rough set theory and dominance-based rough set theory methods seem to be recommendable (depending on the governing level of measurement)
Data mining · Dominance-based rough set approach · Fuzzy cognitive map · Fuzzy logic · Fuzzy set · Generalization · Machine learning · Membership function · Rough set · Spatial analysis · Statistics · Computer Science · Data Management and Algorithms · Geographic Information Systems Studies · Mathematics · Rough Sets and Fuzzy Logic · Artificial Intelligence
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| Citation velocity | historical |
|---|---|
| Highly cited | No |