Spatial analysis for interval-valued data

Symbolic data analysis deals with complex data with symbolic objects, such as lists, histograms, and intervals. Spatial analysis for symbolic data is relatively underexplored. To fill the gap, this paper proposes a statistical framework for spatial interval-valued data (SIVD) analysis. We provide ge...

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Bibliographic Details
Published inJournal of applied statistics Vol. 51; no. 10; pp. 1946 - 1960
Main Authors Workman, Austin, Song, Joon Jin
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 26.07.2024
Taylor & Francis Ltd
Subjects
Data analysis
geostatistics
interval-valued data
Performance prediction
Spatial analysis
Spatial data
Spatial prediction
Statistical methods
symbolic data analysis
temperature
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Summary:Symbolic data analysis deals with complex data with symbolic objects, such as lists, histograms, and intervals. Spatial analysis for symbolic data is relatively underexplored. To fill the gap, this paper proposes a statistical framework for spatial interval-valued data (SIVD) analysis. We provide geostatistical methods for spatial prediction, predictive performance measure for prediction assessment, and visualization for mapping SIVD. The proposed methods are illustrated with both simulated and real examples.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2023.2249636