学术文献库

With the advancement of GPS and remote sensing technologies, large amounts of geospatial and spatiotemporal data are being collected from various domains, driving the need for effective and efficient prediction methods. Given spatial data samples with explanatory features and targeted responses (categorical or continuous) at a set of locations, the problem aims to learn a model that can predict the response variable based on explanatory features. The problem is important with broad applications in earth science, urban informatics, geosocial media analytics and public health, but is challenging due to the unique characteristics of spatiotemporal data, including spatial and temporal autocorrelation, spatial heterogeneity, temporal non-stationarity, limited ground truth, and multiple scales and resolutions. This paper provides a systematic review on principles and methods in spatial and spatiotemporal prediction. We provide a taxonomy of methods categorized by the key challenge they address. For each method, we introduce its underlying assumption, theoretical foundation, and discuss its advantages and disadvantages. Our goal is to help interdisciplinary domain scientists choose techniques to solve their problems, and more importantly, to help data mining researchers to understand the main principles and methods in spatial and spatiotemporal prediction and identify future research opportunities.