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Mapping uncertain geographical attributes: incorporating robustness into choropleth classification design.
- Source :
-
International Journal of Geographical Information Science . Nov2020, Vol. 34 Issue 11, p2204-2224. 21p. - Publication Year :
- 2020
-
Abstract
- Choropleth mapping provides a simple but effective visual presentation of geographical data. Traditional choropleth mapping methods assume that data to be displayed are certain. This may not be true for many real-world problems. For example, attributes generated based on surveys may contain sampling and non-sampling error, and results generated using statistical inferences often come with a certain level of uncertainty. In recent years, several studies have incorporated uncertain geographical attributes into choropleth mapping with a primary focus on identifying the most homogeneous classes. However, no studies have yet accounted for the possibility that an areal unit might be placed in a wrong class due to data uncertainty. This paper addresses this issue by proposing a robustness measure and incorporating it into the optimal design of choropleth maps. In particular, this study proposes a discretization method to solve the new optimization problem along with a novel theoretical bound to evaluate solution quality. The new approach is applied to map the American Community Survey data. Test results suggest a tradeoff between within-class homogeneity and robustness. The study provides an important perspective on addressing data uncertainty in choropleth map design and offers a new approach for spatial analysts and decision-makers to incorporate robustness into the mapmaking process. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13658816
- Volume :
- 34
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- International Journal of Geographical Information Science
- Publication Type :
- Academic Journal
- Accession number :
- 146466319
- Full Text :
- https://doi.org/10.1080/13658816.2020.1726921