PE&RS November 2015 - page 833

November 2015
Big Data: Techniques and Technologies
in Geoinformatics
Edited by Hassan A. Karimi
CRC Press, Taylor & Francis Group: Boca Raton, FL. 2014. xiv
and 298 pp., 111 B/W illustrations, index. Hardcover. $126.70
(Amazon). ISBN 979-1-4665-8651-2. Ebook version also
Reviewed by:
Sally E. Goldin, Ph.D. Foreign Expert,
Department of Computer Engineering, King Mongkut’s
University of Technology Thonburi, Bangkok,Thailand.
Big Data: Techniques and Technologies in Geoinformatics
attempts to familiarize the reader with current challenges,
trends and techniques in dealing with the huge volumes of
spatially-referenced data now available. Structured as a set
of fourteen independent chapters by different sets of authors,
the book explores its subject from a wide range of perspectives,
from theoretical frameworks to highly specific computational
examples. The editor does not identify the intended audience,
and indeed, different chapters appear to address readers with
differing goals and levels of expertise. In general, the book
will most likely be of interest to geography, earth science and
geoinformatics professionals who are curious about the “big
data” explosion and how it relates to traditional approaches in
their disciplines.
The chapters do not fit into any obvious or stated
organizational framework. Several (Chapter 2, Yang et al.;
Chapter 4, Deng and Li; Chapter 5, Liu et al.; Chapter 13,
Liang and Huang; Chapter 14, Reed) describe efforts to build
comprehensive architectures for assembling, integrating and
delivering geospatial big data. Others (Chapter 8, Evans et
al.; Chapter 10, Zhang; Chapter 12, Assam and Seidl) can
be viewed as case studies. These chapters describe specific
problem areas where geospatial inputs exhibit the “3V”
characteristics of big data, that is, high volume, high velocity
and high variety, and present novel approaches, including
new algorithms, to solve those problems. Chapters 6 and 7,
both by Terence van Zyl, provide a more structured approach
to the issue of geospatial computation in the big data era.
Chapter 6 focuses on computational complexity of well-known
geoinformatics algorithms and considers how these algorithms
must be reworked or abandoned for new ones because prior
assumptions about data volume no longer hold. Chapter
7 offers a detailed review of machine learning techniques
appropriate to geospatial big data. These two chapters, which
can serve as tutorials on how geoinformatics processing and
storage techniques need to change in the face of big data
challenges, come closest to what I expected when I picked up
the book. Chapter 1, written by the editor and his colleague
M.H. Sharker, discusses distributed and parallel computing
infrastructures in extensive but sometimes confusing or even
contradictory detail, with little reference to the geospatial
This book includes some fascinating content. Assam and
Seidl, for instance, from RWTH Aachen University, describe
a largely successful attempt to integrate unstructured but
semantically rich data streams from social media with GPS
location readings in order to identify contextually “important”
locations. Jianting Zhang, from City College of New York,
presents new, more efficient algorithms for frequent
sequence mining in taxi trip data by incorporating road
network information. The chapter describing the design and
implementation of the GEOSS Clearing House, by a research
group from the Center for Intelligent Spatial Computing
at George Mason University, shows how the metadata for
available geospatial data sets are in themselves “big data”.
This chapter also illustrates the fact that standards for data
representation and storage do not provide a full solution to
problems of interoperability, simply because there are multiple
The curious geographer or earth scientist who represents
the hypothetical audience for this volume will find much
of interest within its pages. Unfortunately, this book also
has many flaws. Because it makes no attempt to articulate
the conceptual links between chapters or to organize them
Photogrammetric Engineering & Remote Sensing
Vol. 81, No. 11, November 2015, pp. 833–834.
© 2015 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.81.11.833
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