Szczegóły publikacji
Opis bibliograficzny
Supporting the editing of dot maps using the spectral clustering algorithm / Natalia DZIUBA, Stanisław SZOMBARA // Polish Cartographical Review ; ISSN 2450-6974. — 2025 — vol. 57 iss. 1, s. 58–74. — Bibliogr. s. 74, Abstr. — Publikacja dostępna online od: 2025-08-16
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 162006 |
|---|---|
| Data dodania do BaDAP | 2025-09-04 |
| Tekst źródłowy | URL |
| DOI | 10.2478/pcr-2025-0004 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Polish Cartographical Review |
Abstract
Automation of map production is an important subject of work of many scientists. Particular attention should be paid to dot distribution maps, the editing of which is very time-consuming and complicated due to the lack of support in GIS programs. The aim of this research was to develop a method supporting automation of dot map creation. In the study, equal-size spectral clustering algorithm was used, which was modified with a function equalizing the number of points in clusters. Spatial data on residential buildings and statistical data on the population were integrated to calculate theoretical population distributions. These data were entered into the spectral clustering algorithm based on a predefined dot value. The output clusters were then visualized in ArcGIS Pro, where manual adjustments, such as the definition of the dot size and the dispersion of overlapping markers, completed the map editing process. The results showed that the algorithm successfully created clusters representing the population distribution with an acceptable margin of error of the dot map of less than 5% for the entire county (study area – County of Pszczyna, Poland). The adaptation of the equal-size spectral clustering algorithm for cartographic purposes shows its potential to support automation of the dot distribution map editing process. The study found that reducing the input dot value slightly below the target value improved clustering precision, resulting in more consistent clusters. Despite these successes, the method has limitations, including partial reliance on manual corrections in densely populated areas where overlapping dots could not be fully automatically resolved. These issues underscore the need for further refinement to achieve full automation.
