Visual and Spatial Analysis
Advances in Data Mining, Reasoning, and Problem Solving


Kovalerchuk, Boris; Schwing, James (Eds.)
2004, XX, 576 p. 16 illus. in color., Hardcover
ISBN: 978-1-4020-2939-4


About this book
Advanced visual analysis and problem solving has been conducted successfully for millennia. The Pythagorean Theorem was proven using visual means more than 2000 years ago. In the 19th century, John Snow stopped a cholera epidemic in London by proposing that a specific water pump be shut down. He discovered that pump by visually correlating data on a city map. The goal of this book is to present the current trends in visual and spatial analysis for data mining, reasoning, problem solving and decision-making. This is the first book to focus on visual decision making and problem solving in general with specific applications in the geospatial domain - combining theory with real-world practice. The book is unique in its integration of modern symbolic and visual approaches to decision making and problem solving. As such, it ties together much of the monograph and textbook literature in these emerging areas.
This book contains 21 chapters that have been grouped into five parts: (1) visual problem solving and decision making, (2) visual and heterogeneous reasoning, (3) visual correlation, (4) visual and spatial data mining, and (5) visual and spatial problem solving in geospatial domains. Each chapter ends with a summary and exercises.
The book is intended for professionals and graduate students in computer science, applied mathematics, imaging science and Geospatial Information Systems (GIS). In addition to being a state-of-the-art research compilation, this book can be used a text for advanced courses on the subjects such as modeling, computer graphics, visualization, image processing, data mining, GIS, and algorithm analysis.


Written for:
Professionals and graduate students in Computer Science, Applied Mathematics, Imaging Science and Geospatial Information Systems (GIS); advanced courses on the subjects such as Modeling, Computer Graphics, Visualization, Image Processing, Data Mining, GIS, and Algorithm Analysis

Table of contents

Preface, Color Plates
PART 1. Visual Problem Solving and Decision Making
1. Decision process and its visual aspects, 2.Information visualization value stack model
PART 2. Visual and Heterogeneous Reasoning
3. Visual reasoning and representation, 4. Representing visual decision making: a computational architecture for heterogeneous reasoning, 5. Algebraic visual symbolism for problem solving: iconic equations from Diophantus to our days, 6. Iconic reasoning architecture for analysis and decision making, 7.Toward visual reasoning and discovery: lessons from early history of mathematics
PART 3. Visual Correlation
8. Visual correlation methods and models, 9.Iconic approach for data annotating, searching and correlating, 10. Bruegel iconic correlation system
PART 4. Visual and Spatial Data Mining
11. Visualizing data streams, 12. SPIN! – an enterprise architecture for data mining and visual analysis of spatial data, 13. XML-based visualization and evaluation of data mining results, 14. Neural-network techniques for visual mining clinical electronencephalograms, 15. Visual data mining with simultaneous rescaling, 16. Visual data mining using monotone Boolean functions
PART 5. Visual and Spatial Problem Solving in Geospatial Domains
17. Imagery integration as conflict resolution decision process: methods and approaches, 18. Multilevel analytical and visual decision framework for imagery conflation and registration, 19. Conflation of images with algebraic structures, 20. Algorithm development technology for conflation and area-based conflation algorithm, 21. Virtual experts for imagery registration and conflation