Applications of Robust Methods in Spatial Analysis
Spatial data analysis provides valuable information to the government as well as companies. The rapid improvement of modern technology with a geographic information system (GIS) can lead to the collection and storage of more spatial data. We developed algorithms to choose optimal locations from thos...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2023/1328265 |
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| _version_ | 1832556845620789248 |
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| author | Selvakkadunko Selvaratnam |
| author_facet | Selvakkadunko Selvaratnam |
| author_sort | Selvakkadunko Selvaratnam |
| collection | DOAJ |
| description | Spatial data analysis provides valuable information to the government as well as companies. The rapid improvement of modern technology with a geographic information system (GIS) can lead to the collection and storage of more spatial data. We developed algorithms to choose optimal locations from those permanently in a space for an efficient spatial data analysis. Distances between neighboring permanent locations are not necessary to be equispaced distances. Robust and sequential methods were used to develop algorithms for design construction. The constructed designs are robust against misspecified regression responses and variance/covariance structures of responses. The proposed method can be extended for future works of image analysis which includes 3 dimensional image analysis. |
| format | Article |
| id | doaj-art-b811e82f89364acfabc85b74e759c41c |
| institution | Kabale University |
| issn | 1687-9538 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-b811e82f89364acfabc85b74e759c41c2025-02-03T05:44:21ZengWileyJournal of Probability and Statistics1687-95382023-01-01202310.1155/2023/1328265Applications of Robust Methods in Spatial AnalysisSelvakkadunko Selvaratnam0Department of Statistical SciencesSpatial data analysis provides valuable information to the government as well as companies. The rapid improvement of modern technology with a geographic information system (GIS) can lead to the collection and storage of more spatial data. We developed algorithms to choose optimal locations from those permanently in a space for an efficient spatial data analysis. Distances between neighboring permanent locations are not necessary to be equispaced distances. Robust and sequential methods were used to develop algorithms for design construction. The constructed designs are robust against misspecified regression responses and variance/covariance structures of responses. The proposed method can be extended for future works of image analysis which includes 3 dimensional image analysis.http://dx.doi.org/10.1155/2023/1328265 |
| spellingShingle | Selvakkadunko Selvaratnam Applications of Robust Methods in Spatial Analysis Journal of Probability and Statistics |
| title | Applications of Robust Methods in Spatial Analysis |
| title_full | Applications of Robust Methods in Spatial Analysis |
| title_fullStr | Applications of Robust Methods in Spatial Analysis |
| title_full_unstemmed | Applications of Robust Methods in Spatial Analysis |
| title_short | Applications of Robust Methods in Spatial Analysis |
| title_sort | applications of robust methods in spatial analysis |
| url | http://dx.doi.org/10.1155/2023/1328265 |
| work_keys_str_mv | AT selvakkadunkoselvaratnam applicationsofrobustmethodsinspatialanalysis |