Exploratory data analysis (EDA) is about detecting and describing
patterns, trends, and relations in data, motivated by certain
purposes of investigation. As something relevant is detected in
data, new questions arise, causing specific parts to be viewed in
more detail. So EDA has a significant appeal: it involves
hypothesis generation rather than mere hypothesis testing.
The authors describe in detail and systemize approaches,
techniques, and methods for exploring spatial and temporal data in
particular. They start by developing a general view of data
structures and characteristics and then build on top of this a
general task typology, distinguishing between elementary and
synoptic tasks. This typology is then applied to the description of
existing approaches and technologies, resulting not just in
recommendations for choosing methods but in a set of generic
procedures for data exploration.
Professionals practicing analysis will profit from tested
solutions a" illustrated in many examples a "for reuse in the
catalogue of techniques presented. Students and researchers will
appreciate the detailed description and classification of
exploration techniques, which are not limited to spatial data only.
In addition, the general principles and approaches described will
be useful for designers of new methods for EDA.
General
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