  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.
