A Markov Chain Monte Carlo Algorithm for Spatial Segmentation
Spatial data are very often heterogeneous, which indicates that there may not be a unique simple statistical model describing the data. To overcome this issue, the data can be segmented into a number of homogeneous regions (or domains). Identifying these domains is one of the important problems in s...
| 出版年: | Information |
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| 主要な著者: | , |
| フォーマット: | 論文 |
| 言語: | 英語 |
| 出版事項: |
MDPI AG
2021-01-01
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| 主題: | |
| オンライン･アクセス: | https://www.mdpi.com/2078-2489/12/2/58 |
| _version_ | 1850376420831789056 |
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| author | Nishanthi Raveendran Georgy Sofronov |
| author_facet | Nishanthi Raveendran Georgy Sofronov |
| author_sort | Nishanthi Raveendran |
| collection | DOAJ |
| container_title | Information |
| description | Spatial data are very often heterogeneous, which indicates that there may not be a unique simple statistical model describing the data. To overcome this issue, the data can be segmented into a number of homogeneous regions (or domains). Identifying these domains is one of the important problems in spatial data analysis. Spatial segmentation is used in many different fields including epidemiology, criminology, ecology, and economics. To solve this clustering problem, we propose to use the change-point methodology. In this paper, we develop a new spatial segmentation algorithm within the framework of the generalized Gibbs sampler. We estimate the average surface profile of binary spatial data observed over a two-dimensional regular lattice. We illustrate the performance of the proposed algorithm with examples using artificially generated and real data sets. |
| format | Article |
| id | doaj-art-e76fdcdbcafb4d88a9605bb539ffd2c1 |
| institution | Directory of Open Access Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| spelling | doaj-art-e76fdcdbcafb4d88a9605bb539ffd2c12025-08-19T22:59:12ZengMDPI AGInformation2078-24892021-01-011225810.3390/info12020058A Markov Chain Monte Carlo Algorithm for Spatial SegmentationNishanthi Raveendran0Georgy Sofronov1Department of Mathematics and Statistics, Macquarie University, Sydney, NSW 2109, AustraliaDepartment of Mathematics and Statistics, Macquarie University, Sydney, NSW 2109, AustraliaSpatial data are very often heterogeneous, which indicates that there may not be a unique simple statistical model describing the data. To overcome this issue, the data can be segmented into a number of homogeneous regions (or domains). Identifying these domains is one of the important problems in spatial data analysis. Spatial segmentation is used in many different fields including epidemiology, criminology, ecology, and economics. To solve this clustering problem, we propose to use the change-point methodology. In this paper, we develop a new spatial segmentation algorithm within the framework of the generalized Gibbs sampler. We estimate the average surface profile of binary spatial data observed over a two-dimensional regular lattice. We illustrate the performance of the proposed algorithm with examples using artificially generated and real data sets.https://www.mdpi.com/2078-2489/12/2/58Markov chain Monte CarloGibbs samplerspatial segmentationbinary data |
| spellingShingle | Nishanthi Raveendran Georgy Sofronov A Markov Chain Monte Carlo Algorithm for Spatial Segmentation Markov chain Monte Carlo Gibbs sampler spatial segmentation binary data |
| title | A Markov Chain Monte Carlo Algorithm for Spatial Segmentation |
| title_full | A Markov Chain Monte Carlo Algorithm for Spatial Segmentation |
| title_fullStr | A Markov Chain Monte Carlo Algorithm for Spatial Segmentation |
| title_full_unstemmed | A Markov Chain Monte Carlo Algorithm for Spatial Segmentation |
| title_short | A Markov Chain Monte Carlo Algorithm for Spatial Segmentation |
| title_sort | markov chain monte carlo algorithm for spatial segmentation |
| topic | Markov chain Monte Carlo Gibbs sampler spatial segmentation binary data |
| url | https://www.mdpi.com/2078-2489/12/2/58 |
| work_keys_str_mv | AT nishanthiraveendran amarkovchainmontecarloalgorithmforspatialsegmentation AT georgysofronov amarkovchainmontecarloalgorithmforspatialsegmentation AT nishanthiraveendran markovchainmontecarloalgorithmforspatialsegmentation AT georgysofronov markovchainmontecarloalgorithmforspatialsegmentation |