# VISUAL AND SPATIAL ANALYSIS: Advances in Data Mining, Reasoning and Problem Solving

Editors

## Preface

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Visual problem solving has been successful for millennia. The Pythagorean Theorem was proved by visual means more than 2000 years ago. The entire study of geometry existed as a visual problem-solving field more than one and a half millennia before René Descartes invented symbolic coordinates. Albert Einstein wrote in 1953 that the development of Western Science is based on two great achievements: the invention of the formal logic system (in Euclidean geometry) and reasoning based on systematic experimentation during the Renaissance. In the context of this book, it is important to notice that the formal logical system in Euclidean geometry was visual.

Consider two other important historical examples of visual problem solving and decision making. Maritime navigation by using the stars presents an example of sophisticated visual problem solving and decision-making. Then 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 the city map. Of course, there continue to be many current examples of advanced visual problem solving and decision-making.

This book presents the current trends in visual problem solving and decision-making making a clear distinction between the visualization of an already identified solution and visually finding a solution. Thus, the book focuses on two goals:

1. displaying a result or solution visually, and
2. deriving a result or solution by visual means.
The first goal has two aspects: goal 1a displaying results to a novice and goal 1b convincing a decision maker. Recently mass media (US News and World Report, Dec. 2003, p.30) reported that intelligence analysts knew the danger of coming September 11 but convincing decision makers was one of their major challenges: “There were people who got it at the analyst level, at the supervisory level, but all of us were outnumbered”. A novice simply does not know the subject but has no prejudice, priorities, special interests or other preconceived notions that may prevent the digesting of information. Decision makers may have all of these characteristics.

The second goal 2 is even more difficult to achieve especially for non-structured problems. Obviously, there are many intermediate goals that fall between the two extremes and the journey from goal 1 to goal 2 is not a short, non-stop flight. This is the reason that we consider this book to be the first step in a future series “Visual decision making and problem solving.”

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