Probabilistic Deep Learning for Highly Multivariate Spatio-Temporal Log-Gaussian Cox Processes
Multivariate spatio-temporal point patterns have become increasingly common due to the advancement of technology for massive data collection. Parameter estimation is vital for understanding the distributional patterns within such data. However, performing estimation using a parametric approach on mu...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11005449/ |
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| author | Achmad Choiruddin Ekky Rino Fajar Sakti Tintrim Dwi Ary Widhianingsih Jorge Mateu Kartika Fithriasari |
| author_facet | Achmad Choiruddin Ekky Rino Fajar Sakti Tintrim Dwi Ary Widhianingsih Jorge Mateu Kartika Fithriasari |
| author_sort | Achmad Choiruddin |
| collection | DOAJ |
| description | Multivariate spatio-temporal point patterns have become increasingly common due to the advancement of technology for massive data collection. Parameter estimation is vital for understanding the distributional patterns within such data. However, performing estimation using a parametric approach on multivariate spatio-temporal point pattern data is challenging due to the curse of dimensionality, making parametric estimation increasingly difficult as data dimensionality grows. Deep learning offers a promising alternative due to its ability to model complex nonlinear patterns in large datasets. Despite limited applications in multivariate point pattern analysis, this study aims to introduce deep learning as a tool for parameter estimation of the multivariate spatio-temporal log-Gaussian Cox Process (LGCP) model. We employ the concept of probabilistic deep learning, ensuring that each estimated parameter follows a certain distribution that aligns with its assumption. We assess our model performance via a simulation study, and analyze the highly multivariate spatio-temporal point pattern data of Barro Colorado Island (BCI). Both the simulation study and application demonstrate our model effectiveness over previous approaches to handle highly multivariate point pattern data. |
| format | Article |
| id | doaj-art-133b13edfec54fa9816271eb61a7daf5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-133b13edfec54fa9816271eb61a7daf52025-08-20T03:26:05ZengIEEEIEEE Access2169-35362025-01-0113947619477610.1109/ACCESS.2025.357047611005449Probabilistic Deep Learning for Highly Multivariate Spatio-Temporal Log-Gaussian Cox ProcessesAchmad Choiruddin0https://orcid.org/0000-0003-2568-2274Ekky Rino Fajar Sakti1https://orcid.org/0009-0006-2777-3566Tintrim Dwi Ary Widhianingsih2https://orcid.org/0000-0003-2585-6895Jorge Mateu3https://orcid.org/0000-0002-2868-7604Kartika Fithriasari4Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, IndonesiaDepartment of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, IndonesiaDepartment of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, IndonesiaDepartment of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, IndonesiaDepartment of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, IndonesiaMultivariate spatio-temporal point patterns have become increasingly common due to the advancement of technology for massive data collection. Parameter estimation is vital for understanding the distributional patterns within such data. However, performing estimation using a parametric approach on multivariate spatio-temporal point pattern data is challenging due to the curse of dimensionality, making parametric estimation increasingly difficult as data dimensionality grows. Deep learning offers a promising alternative due to its ability to model complex nonlinear patterns in large datasets. Despite limited applications in multivariate point pattern analysis, this study aims to introduce deep learning as a tool for parameter estimation of the multivariate spatio-temporal log-Gaussian Cox Process (LGCP) model. We employ the concept of probabilistic deep learning, ensuring that each estimated parameter follows a certain distribution that aligns with its assumption. We assess our model performance via a simulation study, and analyze the highly multivariate spatio-temporal point pattern data of Barro Colorado Island (BCI). Both the simulation study and application demonstrate our model effectiveness over previous approaches to handle highly multivariate point pattern data.https://ieeexplore.ieee.org/document/11005449/Big datadeep learningLGCPmultivariate point patternneural network |
| spellingShingle | Achmad Choiruddin Ekky Rino Fajar Sakti Tintrim Dwi Ary Widhianingsih Jorge Mateu Kartika Fithriasari Probabilistic Deep Learning for Highly Multivariate Spatio-Temporal Log-Gaussian Cox Processes IEEE Access Big data deep learning LGCP multivariate point pattern neural network |
| title | Probabilistic Deep Learning for Highly Multivariate Spatio-Temporal Log-Gaussian Cox Processes |
| title_full | Probabilistic Deep Learning for Highly Multivariate Spatio-Temporal Log-Gaussian Cox Processes |
| title_fullStr | Probabilistic Deep Learning for Highly Multivariate Spatio-Temporal Log-Gaussian Cox Processes |
| title_full_unstemmed | Probabilistic Deep Learning for Highly Multivariate Spatio-Temporal Log-Gaussian Cox Processes |
| title_short | Probabilistic Deep Learning for Highly Multivariate Spatio-Temporal Log-Gaussian Cox Processes |
| title_sort | probabilistic deep learning for highly multivariate spatio temporal log gaussian cox processes |
| topic | Big data deep learning LGCP multivariate point pattern neural network |
| url | https://ieeexplore.ieee.org/document/11005449/ |
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