Abstract
Knowledge extraction from spatial big data (SBD) with advanced analytics has become a major trend in research and industry. Meanwhile, the increasingly complex SBD and its analytics face proliferating challenges posed by uncertainties in them. Linked to various characteristics of SBD, the uncertainties emerge and propagate in each stage of SBD analytics. To avoid unreliable knowledge and losses resulting from the uncertainties and to ensure the value of authentic knowledge, this article proposes uncertainty-based SBD analytics. Uncertainty-based SBD analytics strive to understand, control, and alleviate uncertainties and their propagation in each stage of geographic knowledge extraction. Key topics involved in uncertainty-based SBD analytics include, for example, place-based heuristics for learning urban structure and place-based analytics on broader knowledge extraction tasks; dealing with the biases and inferencing the semantics in cell phone tracking data; quality assessment of unstructured spatial user-generated contents and the rectification of location shifts and time elapses between humans' activities and corresponding online contents they generate; and uncertainty handling in sophisticated black-box analytics with SBD such as deep learning. Challenges and the latest advances in each of these topics are presented, and further research for addressing these challenges is suggested in this article.
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