A method to evaluate task-specific importance of spatio-temporal units based on explainable artificial intelligence. Issue 10 (3rd October 2021)

• Record Type: Journal Article
• Authors: Cheng, Ximeng; Wang, Jianying; Li, Haifeng; Zhang, Yi; Wu, Lun; Liu, Yu
• Is Part Of: International journal of geographical information science. Volume 35:Issue 10(2021)
• Page Start: 2002
• Page End: 2025
• Publication Date: 2021-10-03
• Subjects: Deep learning -- tensor -- spatio-temporal dependence -- task-specific -- classification; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2020.1805116
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Big geo-data are often aggregated according to spatio-temporal units for analyzing human activities and urban environments. Many applications categorize such data into groups and compare the characteristics across groups. The intergroup differences vary with spatio-temporal units, and the essential is to identify the spatio-temporal units with apparently different data characteristics. However, spatio-temporal dependence, data variety, and the complexity of tasks impede an effective unit assessment. Inspired by the applications to extract critical image components based on explainable artificial intelligence (XAI), we propose a spatio-temporal layer-wise relevance propagation method to assess spatio-temporal units as a general solution. The method organizes input data into an extensible three-dimensional tensor form. We provide two means of labeling the spatio-temporal tensor data for typical geographical applications, using temporally or spatially relevant information. Neural network training proceeds to extract the global and local characteristics of data for corresponding analytical tasks. Then the method propagates classification results backward into units as obtained task-specific importance. A case study with taxi trajectory data in Beijing validates the method. The results prove that the proposed method can evaluate the task-specific importance of spatio-temporal units with dependence. This study also attempts to discover task-related knowledge using XAI.