Abstract
Given a definition of change and a dataset about spatiotemporal (ST) phenomena, change pattern mining is the process of identifying the location and/or time frame of shift in phenomenon. Due to the societal importance of ST change pattern mining for use cases such as disease outbreak, urban sprawl, and climate change, techniques for mining ST change patterns have emerged from a diverse set of research area ranging from time series analysis, remote sensing, to spatial statistics. Related work focus on surveying techniques in specific discipline. It is difficult to compare techniques across disciplines for cross-fertilization, due to the challenges such as homonym, synonym, and change definition diversity. This chapter attempts to provide an interdisciplinary perspective on change pattern mining techniques to promote cross-fertilization of ideas among researchers of diverse backgrounds. It proposes taxonomy of patterns from ST perspective and uses it to compare related concepts from different disciplines.
Published Version
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