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July 19th, 2010
Handbook of Applied Spatial Analysis

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cda_displayimageSpatial data are important to scientific studies and research. They are the raw pieces of information that inform us about the processes involved in many fields. Scientists involved in oceanography, forestry, soil science, energy, human geography, health, transportation, urban planning and many other field collect, manage, process and visualize both simple and complex processes using spatial information. In many cases we find that these activities using both commercially available tools and freely available tools. In some cases, due to the nature of the work under study, specially designed and programmed software tools and technologies are developed that enable the active pursuit of spatially related data and methodologies.

 

 

 

 

 

 

 

The Spatial Turn
Interdiscplinary perspectives
Edited by Barney Warf and Santa Arias
Handbook of Applied Spatial Analysis
Software Tools, Methods and Applications

  
Edited by

Manfred M. Fischer
Arthur Getis


 


 811 p. 380 illus., 190 in color., Hardcover ISBN: 978-3-642-03646-0
2010


Review by Jeff Thurston

 

 

The Handbook of Applied Spatial Analysis is a compilation of these tools and technologies. It includes a wealth of information about analysis tools used for many purposes including spatial statistics, space-time analysis, geosurveillance, exploratory data analysis, spatial econometrics, filtering, clustering, remotely sensed data and image processing.

Some of the tools and technologies are well known (ie. ArcGIS, SAS, Python, STARS), but many of them are more well known within their respective professional communities. Yet others may be completely new to readers, revealed only through the editorial compilation efforts of this book.

Several authors present the various chapters, beginning with the initial chapter that is focused upon GI software tools. The spatial analysis of case datasets are described and include pattern analysis, cluster analysis and modeling spatial relationships – all based on the use of ArcGIS. Spatial statistics using SAS is outlined using examples such as Boston housing studies. GeoDa, a widely available and popular program originating from the U.S. Centre for Spatially Integrated Social Science (CSISS) is elaborated upon from concept through to working examples and applications.

Considerable attention is paid to exploratory data analyisis in this book, and that can be attributed to the more widely available tools and the number of tools becoming available in recent times. One can also speculate that as computational engines have become more widely available, then functionality has also increased.

Whereas previously only the most highly funded parties might gain access to the spatial analysis computational power available, that is not necessarily the case today as many tools within this book can be initiated from desktop terminals to run within cloud environments at a reasonable cost. This has also resulted in advances in user interfaces – they are simpler to use.

More highly intelligent geographic information systems (GIS) are now crossing static boundaries and entering into dynamic processing. As pointed out, research has led to new techniques, and new tools, for studying dynamic movements and change throught time, for example. The editors manage to describes the literature crossing a period through the mid 1990s to about 2006 that readily captures these developments. This book is filled with different software, sources included to download or links to the original sites are provided. In some cases portals to the discovery of large repositories of software are pointed out with addresses available.

Spatial statistics and geostatistics are explored fully in this text. The nature of geo-georeferenced spatial information is discussed and clustering, filtering, variograms explored. Multi-level modeling addresses issues that connect individuals into higher level hierarchy, usually through aggregation techniques. Readers may find it interesting that such techniques link directly to work involving image processing.

Part D of the book expands upon topics related remotely sensed data. Satellite imagery is explored and neural networks involving multi-level modeling are discussed. The relationship of human capital and economic factors is presented. Population  distributions, income analysis techniques and multi-equation econometric models are included. A considerable level of detail pertaining to the analysis of health factors and disease is presented and techniques for analyzing this information is available.

In summary, this book provides a wealth of information about analyzing spatial information for professionals involved in research and scientific pursuit. It does not focus solely on theory, instead, it includes an abundance of background that involves real tools, methods and applications people are using to solve real problems across a wide number of fields using spatial data.

Examples are included and links to the resources are included. The editors have done an excellent job to bring together conceptual knowledge and application pursuits while empowering readers with the information they need to find these resources and try some of the included techniques using the examples to guide them. The book will be attractive to higher level students and professionals, however, anyone interested in spatial statistics and analysis would find benefits exploring this book. There are not many books as up-to-date with such a wide coverage of spatial analysis tools and techniques along with supporting resource information.

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Jeff Thurston is co-founder and co-editor of V1 Magazine / Asian Surveying and Mapping for Vector1 Media. He is based in Berlin, Germany.

 

 

 


 

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