Introduction

Yee Leung ()
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Yee Leung: The Chinese University of Hong Kong

Chapter Chapter 1 in Knowledge Discovery in Spatial Data, 2010, pp 1-12 from Springer

Abstract: Abstract Understanding natural and human-induced structures and processes in space and time has long been the agenda of geographical research. Through theoretical and experimental studies, geographers have accumulated a wealth of knowledge about our physical and man-made world over the years. Based on such knowledge, we work for the betterment of the man-land relationship that hopefully will lead to the sustainable development of our man-land system. The quest for knowledge can mainly be summarized into two basic approaches. Based on some assumptions about the underlying mechanisms, we can infer the properties and behaviors of our systems. This is the well-known process of the search for knowledge through deduction. On the other hand, knowledge is often discovered through critical observations of phenomena in space and time. Structures and processes are unraveled by sipping through data that we gathered. With the advancement and rapid development of the information technologies, amassing huge volume of data for research is no longer a problem in any disciplines. It is particularly true in geographical studies where a continuous inflow of various types of data collected by various means is a common ground. The problem is then not having enough data but having too much and too complex a database for the discovery and understanding of structures, processes, and relationships. Useful knowledge is often hidden in the sea of data that awaits discovery.

Keywords: Data Mining; Spatial Data; Knowledge Discovery; Classification Rule; Spatial Database (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/978-3-642-02664-5_1

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