Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A heuristic multi-criteria classification approach incorporating data quality information for choropleth mapping
Despite conceptual and technology advancements in cartography over the decades, choropleth map design and classification fail to address a fundamental issue: estimates that are statistically indifferent may be assigned to different classes on maps or vice versa. Recently, the class separability concept was introduced as a map classification criterion to evaluate the likelihood that estimates in two classes are statistical different. Unfortunately, choropleth maps created according to the separability criterion usually have highly unbalanced classes. To produce reasonably separable but more balanced classes, we propose a heuristic classification approach to consider not just the class separability criterion but also other classification criteria such as evenness and intra-class variability. A geovisual-analytic package was developed to support the heuristic mapping process to evaluate the trade-off between relevant criteria and to select the most preferable classification. Class break values can be adjusted to improve the performance of a classification.
A heuristic multi-criteria classification approach incorporating data quality information for choropleth mapping
Despite conceptual and technology advancements in cartography over the decades, choropleth map design and classification fail to address a fundamental issue: estimates that are statistically indifferent may be assigned to different classes on maps or vice versa. Recently, the class separability concept was introduced as a map classification criterion to evaluate the likelihood that estimates in two classes are statistical different. Unfortunately, choropleth maps created according to the separability criterion usually have highly unbalanced classes. To produce reasonably separable but more balanced classes, we propose a heuristic classification approach to consider not just the class separability criterion but also other classification criteria such as evenness and intra-class variability. A geovisual-analytic package was developed to support the heuristic mapping process to evaluate the trade-off between relevant criteria and to select the most preferable classification. Class break values can be adjusted to improve the performance of a classification.
A heuristic multi-criteria classification approach incorporating data quality information for choropleth mapping
Sun, Min (Autor:in) / Wong, David / Kronenfeld, Barry
2017
Aufsatz (Zeitschrift)
Englisch
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| Online Contents | 1999
Research Papers - Ogive-based Legends for Choropleth Mapping
| Online Contents | 2006
Unclassed Animated Choropleth Maps
| Online Contents | 2007
An Alternative to Maximum Contrast Symbolization for Classed Choropleth Mapping
| Online Contents | 2005
Incorporating Data Quality Information in Mapping American Community Survey Data
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