Sequence analysis of local indicators of spatio-temporal association for evolutionary pattern discovery
The Local Indicators of Spatial Association (LISA) is one of the most widely used methods for identifying local patterns of spatial association in geographical elements. However, the dynamic trends of spatial-temporal (S-T) autocorrelation remain poorly understood, yet capturing these patterns is es...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | GIScience & Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2025.2487292 |
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| author | Jianing Yu Hengcai Zhang Peixiao Wang Jinzi Wang Feng Lu |
| author_facet | Jianing Yu Hengcai Zhang Peixiao Wang Jinzi Wang Feng Lu |
| author_sort | Jianing Yu |
| collection | DOAJ |
| description | The Local Indicators of Spatial Association (LISA) is one of the most widely used methods for identifying local patterns of spatial association in geographical elements. However, the dynamic trends of spatial-temporal (S-T) autocorrelation remain poorly understood, yet capturing these patterns is essential for analyzing the evolution of spatial processes. To fill the gap, we propose a novel S-T LISA methodology to automatically discover co-occurrences LISA subsequences over time by incorporating sequence analysis techniques. First, we extend the classical LISA to a dynamic context, and clarify the definition, properties, and classification of S-T LISA sequences. Second, we adopt an enhanced Hamming distance to quantify the similarity of LISA sequences, followed by hierarchical clustering to group similar LISA sequences. Next, an improved FP-Growth algorithm is applied to identify frequent patterns. Finally, we conduct experiments using grid-scale social media check-in records and city-scale carbon emission data to discover significant evolutionary patterns. The results verified the applicability of the proposed method in both human and physical geography. The proposed approach outperforms traditional S-T cube methods in its ability to automatically capture dynamic, complex, and transient S-T association trends as well as irregular outliers. The integration of sequence analysis with LISA statistics presented in this article provides an effective framework for identifying evolutionary patterns of S-T association. |
| format | Article |
| id | doaj-art-93511eda5dbe4a4da84e3dda1ad00607 |
| institution | DOAJ |
| issn | 1548-1603 1943-7226 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj-art-93511eda5dbe4a4da84e3dda1ad006072025-08-20T03:06:17ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262025-12-0162110.1080/15481603.2025.2487292Sequence analysis of local indicators of spatio-temporal association for evolutionary pattern discoveryJianing Yu0Hengcai Zhang1Peixiao Wang2Jinzi Wang3Feng Lu4State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaThe Local Indicators of Spatial Association (LISA) is one of the most widely used methods for identifying local patterns of spatial association in geographical elements. However, the dynamic trends of spatial-temporal (S-T) autocorrelation remain poorly understood, yet capturing these patterns is essential for analyzing the evolution of spatial processes. To fill the gap, we propose a novel S-T LISA methodology to automatically discover co-occurrences LISA subsequences over time by incorporating sequence analysis techniques. First, we extend the classical LISA to a dynamic context, and clarify the definition, properties, and classification of S-T LISA sequences. Second, we adopt an enhanced Hamming distance to quantify the similarity of LISA sequences, followed by hierarchical clustering to group similar LISA sequences. Next, an improved FP-Growth algorithm is applied to identify frequent patterns. Finally, we conduct experiments using grid-scale social media check-in records and city-scale carbon emission data to discover significant evolutionary patterns. The results verified the applicability of the proposed method in both human and physical geography. The proposed approach outperforms traditional S-T cube methods in its ability to automatically capture dynamic, complex, and transient S-T association trends as well as irregular outliers. The integration of sequence analysis with LISA statistics presented in this article provides an effective framework for identifying evolutionary patterns of S-T association.https://www.tandfonline.com/doi/10.1080/15481603.2025.2487292Spatial-temporal autocorrelationlocal indicators of spatial association (LISA)dynamic evolutionary patternssequence analysishierarchical clusteringfrequent pattern mining |
| spellingShingle | Jianing Yu Hengcai Zhang Peixiao Wang Jinzi Wang Feng Lu Sequence analysis of local indicators of spatio-temporal association for evolutionary pattern discovery GIScience & Remote Sensing Spatial-temporal autocorrelation local indicators of spatial association (LISA) dynamic evolutionary patterns sequence analysis hierarchical clustering frequent pattern mining |
| title | Sequence analysis of local indicators of spatio-temporal association for evolutionary pattern discovery |
| title_full | Sequence analysis of local indicators of spatio-temporal association for evolutionary pattern discovery |
| title_fullStr | Sequence analysis of local indicators of spatio-temporal association for evolutionary pattern discovery |
| title_full_unstemmed | Sequence analysis of local indicators of spatio-temporal association for evolutionary pattern discovery |
| title_short | Sequence analysis of local indicators of spatio-temporal association for evolutionary pattern discovery |
| title_sort | sequence analysis of local indicators of spatio temporal association for evolutionary pattern discovery |
| topic | Spatial-temporal autocorrelation local indicators of spatial association (LISA) dynamic evolutionary patterns sequence analysis hierarchical clustering frequent pattern mining |
| url | https://www.tandfonline.com/doi/10.1080/15481603.2025.2487292 |
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