14
January 2016
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
BOOK
REVIEW
Scale in Spatial Information and
Analysis
Jingxiong Zhang, Peter Atkinson, Michael F.
Goodchild, CRC Press
367 pp., 8 B/W Illustrations, ISBN 9781439829370, (hardcover),
$110.46.
Reviewed by
EunHye Yoo, Associate Professor,
Department of Geography, University at Buffalo, The
State University of New York (SUNY), Buffalo, NY,
USA.
This book aims to address critical issues in scale of geospatial
data, including its representation and measurements as
well as scaling, using a scientific approach. Authors built up
their discussion from geospatial data structures, a concept
and theory of random fields, and gradually reviewed an
extensive list of potential solutions toward domain-specific
scale related problems. Highly advanced levels of technical
details are provided, which will be great resources to
graduate students and researchers in the related fields.
This book has 12 chapters, which can be summarized
into three main themes. Chapters 1 and 2 provide a good
introduction of the scale issues and basic concepts in
geospatial data. It is important to read chapter 1 to get the
sense of direction that the following chapters are heading to.
In the following chapters (chapter 3, 4, and 5), topical issues
on “scale of measurements andmodels”, such as geostatistical
and lattice models, are introduced. The discussion is solid in
terms of the breath of technical coverage, although readers
who are unfamiliar with the literature on remote sensing
might be intimidated by mathematical equations and
terms. The scaling issues --- upscaling and downscaling ---
are covered by chapters 6, 7, and 8. In practice the scale of
observed or measured data are not necessarily compatible
with the desirable scale of models, analysis, and predictions,
which require changing scales of data available into that of
target. Numerous solutions have been proposed and used to
address this problem across various disciplines, but not a
unique solution exists to address all different criteria and
applications as authors pointed out. The list of potential
solutions that authors reviewed in this book for both
upscaling and downscaling is extensive and competent in
their technical details. Similar to previous chapters, however,
some technical details might be challenging if readers
are less familiar with Geostatistics or Remote Sensing.
Interestingly authors also discussed scale in relation to
various systems that are available for acquiring data for
modeling environment and social processes in chapters 9,
10, and 11. Readers should be aware that these chapters are
again heavy on the remote sensing literature while the depth
of discussion is thorough. Lastly, authors also incorporate
the connection between the effects of scale on uncertainty
assessment or modeling. Unfortunately the discussion is
rather brief, but key literature and ideas are presented.
To sum up, this book is another excellent resource
for researchers who are dealing with geospatial data,
particularly remote sensing data/images. Authors
successfully convinced readers that scale is one of the
most important issues in data acquisition, representation,
analysis/modeling, and communications (visualization).
Authors’ extensive knowledge and experiences on these
issues are reflected in each chapter, although most
discussions and the selected methods were customized
for remote sensing applications rather than general
geographical/environmental applications.
Perhaps this book could have been more resourceful and
timely relevant by including temporal and/or spatio-temporal
scaling issue upon the emergence of big spatiotemporal
data. By doing so, perhaps some technical details might be
shortened by referring readers to the existing literature,
which will make the book more condense and efficient.
Lastly, the readability might have been improved by adding
more diagrams or graphical materials.
Photogrammetric Engineering & Remote Sensing
Vol. 82, No. 1, January 2016, pp. 14.
0099-1112/16/14
© 2015 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.83.1.14
