Spatial Decision Support Systems: Principles and Practices
Features
- Provides thorough coverage of the roots of SDSS
- Integrates SDSS research from a variety of disciplines, including the geosciences
- Includes case studies and a vast array of example applications covering the core principles of SDSS
- Explains how to tailor SDSS to the reader’s own environment
- Integrates sample code segments throughout
Summary
Although interest in Spatial Decision Support Systems (SDSS) continues to grow rapidly in a wide range of disciplines, students, planners, managers, and the research community have lacked a book that covers the fundamentals of SDSS along with the advanced design concepts required for building SDSS.
Filling this need, Spatial Decision Support Systems: Principles and Practices provides a comprehensive examination of the various aspects of SDSS evolution, components, architecture, and implementation. It integrates research from a variety of disciplines, including the geosciences, to supply a complete overview of SDSS technologies and their application from an interdisciplinary perspective.
This groundbreaking reference provides thorough coverage of the roots of SDSS. It explains the core principles of SDSS, how to use them in various decision making contexts, and how to design and develop them using readily available enabling technologies and commercial tools. The book consists of four major parts, each addressing different topic areas in SDSS:
- Presents an introduction to SDSS and the evolution of SDSS
- Covers the essential and optional components of SDSS
- Focuses on the design and implementation of SDSS
- Reviews SDSS applications from various domains and disciplines—investigating current challenges and future directions
The text includes numerous detailed case studies, example applications, and methods for tailoring SDSS to your work environment. It also integrates sample code segments throughout. Addressing the technical and organizational challenges that affect the success or failure of SDSS, the book concludes by considering future directions of this rapidly emerging field of study.
Table of Contents
Introduction
Spatial Decision Making
What Are Spatial Decisions?
Types of Spatial Decisions
Spatial Decision-Making Problems
Spatial Decision-Making Process
Need for Decision Support Systems
Definition of SDSS
SDSS Characteristics
Types or Flavors of SDSS
Origins of SDSS
Core Drivers for the Development of Spatial Decision Support Technology
Information and Communication Technology
Spatial Data Availability
Applications
Users, Developers, and User Interfaces
Spatially Explicit Modeling
Expert Domain Knowledge
DSS-Based Evolution
DSS to SDSS
GIS-Based Evolution
GIS to SDSS
SDSS Progression
Introduction Phase (1976–1989)
Integration Phase (1990–2000)
Implementation Phase (2000s)
Related and Important Literature
Important Contributors to SDSS Development
Suggested Readings
DSS
GIS
SDSS
Components of SDSS I: Geographic Information Systems
Components of Traditional DSS and GIS
Components of SDSS
Geographical Information Systems (GIS) Overview
History of Spatial Information and Data Use
Definitions of GIS
Coordinate Systems
Data Models
Vector Data Model
Raster Data Model
Raster versus Vector
Spatial Data Collection
Database Management
Data Considerations
Spatial Data Exploration, Processing, and Analysis
Map Data Exploration
Data Identification, Examination, and Query
Vector Processing and Analysis
Buffering
Spatial Overlay
Pattern Analysis and Spatial Statistics
Routing and Network Analysis
Raster Data Analysis
Local Operations
Neighborhood Operations
Zonal Operations
Data Visualization and Cartography
GIS Software
Appendix A: Spatial Data Sources for the United States
Appendix B: Global Spatial Data Sources
Appendix C: Links for Lists of Commercial and Open Source GIS Software
Components of SDSS II
Model Management Component
Modeling Techniques in SDSS
Generic Models
Boolean Overlays
Weighted Linear Combination
Analytical Hierarchy Process
Ordered Weighted Approach
Artificial Neural Networks
Cellular Automata
Genetic Algorithms
Agent-Based Models
Fuzzy Modeling Techniques
Application-Specific Models
Dialog Management Component
Stakeholders Component (SC)
Knowledge Management Component
SDSS Software
Existing SDSS Software
GIS Software Used in SDSS
Problem-Specific SDSS
Domain-Level SDSS
Generic SDSS
IDRISI Macro Modeler
ArcGIS ModelBuilder
ERDAS IMAGINE
Open Source Software
Open-SDSS
Building SDSS Software
SDSS Software Components
Common Software for Utilization in SDSS Development
Spatial Data Collection, Management, Analysis, and Visualization Software
Relational Database Management Software
Modeling Software
Knowledge Management Software
SDSS Development by Software Integration
Integration Technologies
Integration Strategies
Integration Issues
Design and Development of SDSS from Scratch
Enabling Technologies for the Development of Desktop SDSS
Programming Languages
Application Development Environments
Spatial Libraries
SDSS Generator—Geonamica
Web-Based SDSS Development and Architecture
Cloud Computing
Building Desktop SDSS
SDSS Development Considerations
SDSS Development Process
SDSS Development Examples
Spreadsheet-Based AHP SDSS (Microsoft Excel)
SpreadsheetSDSS Plug-in
Customizing Existing Desktop GIS (ArcGIS)
Creation of a New Generic SDSS Program
Building Web-Based SDSS
Web-Based SDSS Developed with ArcGIS Server
Web-Based SDSS for Environmentally Sensitive Areas
Web-Based SDSS Development with Open Source Software
Software Installation
Software Used
Architecture Used and Implementation
Open Source SDSS Download and Execution
Detailed Explanation and Code
Python Modules
View Templates
SDSS Applications
Reference Collection, Database Creation, and Web-Portal Development
Literature Compilation
SDSS Database Development
Web Portal Development
Publication Sources
SDSS Application Domains
Natural Resources Management
Environmental
Urban
Agriculture
Utility/Communication/Energy and Transportation
Business
Other Major Application Domains
SDSS Challenges and Future Directions
Technical Challenges
Spatial Data Management Component Challenges
Model Management Component
Model Selection or Development
Model Integration
Model Usability and Interpretation of Results
Dialog Management Component Challenges
User Interfaces
Output Presentation and Evaluation
Technological Challenges
Social, Policy, and Organizational Challenges
Educational Challenges
Future Trends and Directions
Each chapter starts with Learning Objectives and an Introduction, and concludes with a Summary and References
Author Bio(s)
Dr. Ramanathan Sugumaran is Professor of Geography and Director of GeoTREE Center at the University of Northern Iowa. He has over nineteen years of research experience in remote sensing, geographic information systems (GIS), Global Positioning Systems (GPS), and spatial decision support systems (SDSS) with applications for natural resources and environmental planning and management.
Dr. Sugumaran has served as PI or Co-PI on over $5 million worth of research grants funded by the National Aeronautics and Space Administration (NASA), Raytheon Corp., the National Oceanic and Atmospheric Administration (NOAA), the U.S. Department of Defense (DOD), the U.S. Department of Agriculture (USDA), Missouri Department of Natural Resources (MDNR), the U.S. Department of Transportation (DOT), and the U.S. Fish and Wildlife Service. He has also published numerous journal articles and presented more than one hundred papers at national and international conferences. Dr. Sugumaran has two PhDs—a PhD in geography from the University of Edinburgh in the United Kingdom and one from the University of Baroda, India. For the past ten years, he has developed and taught several courses and advised more than twenty students on their masters theses. Dr. Sugumaran has also been a recipient of several academic awards that include the outstanding graduate faculty teaching award, Outstanding Scholar award, and Veridian Community Engagement Award.
John DeGroote is a GeoInformatics Scientist at the GeoTREE Center at the University of Northern Iowa. He has been actively applying geospatial technologies for environmental and natural resource applications for nine years. He has experience working on a wide range of issues with a diverse set of investigators including hydrologists, soil scientists, ecologists, and economists. He has extensive experience in developing custom GIS and SDSS applications, using programming and database development, for use by researchers and environmental managers. John has authored or co-authored numerous peer-reviewed articles concerning the use of geospatial technologies for a variety of application domains. He has also presented research at numerous national and international conferences.
Editorial Reviews
This timely book begins with coverage of basic geospatial data handling concepts, methods, and materials. … places the development of SDSS concepts within a historical framework of development and treats important system components with a level of detail that is appropriate for students who may have different backgrounds or be at different stages of intellectual development. Coverage then moves on to demonstrate how these components can be assembled into flexible collections that are used to address particular types of applications. It is here, with the illustration of different component assemblages, that the book coheres by demonstrating how an SDSS can be implemented in the form of a traditional desktop system or using distributed, web-based services. This is done in a way that should prove instructive to both students and their teachers. I sincerely hope that you enjoy reading and learning from this book and that it will lead you to contribute new insights. I came away from it wishing that the book had been available to me many years ago when I was beginning to struggle with the SDSS concepts that now seem rather straightforward after having read these chapters.
—Marc P. Armstrong, Professor and Chair, Department of Geography, University of Iowa
Sugumaran (geography) and DeGroote (geo-informatics, both U. of Northern Iowa) explain systems that are designed to help decision makers solve complex spatially related problems and provide a framework for integrating analytical and spatial modeling capabilities, spatial and non-spatial data management, domain knowledge, spatial display capabilities, and reporting capabilities. They cover evolution and trends in spatial decision support systems, geographical information systems and other components, software and building it, building a desktop system and a web-based system, applications, and challenges and future directions.
—In Research Book News, booknews.com, February 2011
The major strength of this book is the wide range of references cited, and every part of the text is supported by references to a wide literature. … [The book] provides readers with several relevant building frameworks and food for thought, especially in the fields of public engagement, planning, and GIS development.
—Cindy Regalado, Department of Civil, Environmental, and Geomatic Engineering, University College London, in Environment and Planning
