TABLE OF CONTENTS Shashi Shekhar
University of Minnesota
Pusheng Zhang
University of Minnesota
Yan Huang
University of Minnesota
| Abstract | Spatial Data Mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more difficult than ex tracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation. This chapter provides an overview on the unique features that distinguish spatial data mining from classical Data Mining, and presents major accomplishments of spatial Data Mining research. |
| Keywords: | Spatial Data Mining, Spatial Autocorrelation, Location Prediction, Spatial Out liers, Co-location, Spatial Clustering |
The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the automated discovery of spatial knowledge. Spatial Data Mining (Roddick and Spiliopoulou, 1999; Shekhar and Chawla, 2003) is the process of discovering interesting and previously unknown, but potentially useful patterns from spatial databases. The complexity of spatial data and intrinsic spatial relationships limits the usefulness of conventional Data Mining techniques for extracting spatial patterns. Efficient tools for extracting information from geo-spatial data are crucial to organizations which make decisions based on large spatial datasets, including the National Aeronautics and Space Administration (NASA), the National Geospatial-Intelligence Agency (NGA), the National Cancer Institute (NCI), and the United States Department of Transportation (USDOT). These organizations are spread across many application domains including ecology and environmental management, public safety, transportation, Earth science,...
