geoda (2004) - GeoDa An Introduction to Spatial Data...

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GeoDa: An Introduction to Spatial Data Analysis * Luc Anselin, Ibnu Syabri and Youngihn Kho Spatial Analysis Laboratory Department of Agricultural and Consumer Economics University of Illinois, Urbana-Champaign Urbana, IL 61801 USA [email protected], [email protected], [email protected] May 5, 2004 Abstract This paper presents an overview of GeoDa TM , a free software program intended to serve as a user-friendly and graphical introduction to spatial analysis for non-GIS specialists. It includes functionality ranging from simple mapping to exploratory data analysis, the visualization of global and local spatial autocorrelation, and spatial regression. A key feature of GeoDa is an interactive environment that combines maps with statistical graphics, using the technology of dynamically linked windows. A brief re- view of the software design is given, as well as some illustrative examples that highlight distinctive features of the program in applications dealing with public health, economic development, real estate analysis and crim- inology. Key Words : geovisualization, exploratory spatial data analysis, spatial outliers, smoothing, spatial autocorrelation, spatial regression. 1 Introduction The development of specialized software for spatial data analysis has seen rapid growth since the lack of such tools was lamented in the late 1980s by Haining * This research was supported in part by US National Science Foundation Grant BCS- 9978058, to the Center for Spatially Integrated Social Science ( csiss ) and by grant RO1 CA 95949-01 from the National Cancer Institute. In addition, this research was made possible in part through a Cooperative Agreement between the Center for Disease Control and Prevention (CDC) and the Association of Teachers of Preventive Medicine (ATPM), award number TS- 1125. The contents of the paper are the responsibility of the authors and do not necessarily reflect the official views of NSF, NCI, the CDC or ATPM. Special thanks go to Oleg Smirnov for his assistance with the implementation of the spatial regression routines, and to Julie Le Gallo and Julia Koschinsky for preparing, respectively, the data set for the European convergence study and for the Seattle house prices. GeoDa TM is a trademark of Luc Anselin.
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GeoDa 2 (1989) and cited as a major impediment to the adoption and use of spatial statistics by GIS researchers. Initially, attention tended to focus on conceptual issues, such as how to integrate spatial statistical methods and a GIS environ- ment (loosely vs. tightly coupled, embedded vs. modular, etc.), and which techniques would be most fruitfully included in such a framework. Familiar re- views of these issues are represented in, among others, Anselin and Getis (1992), Goodchild et al. (1992), Fischer and Nijkamp (1993), Fotheringham and Roger- son (1993, 1994), Fischer et al. (1996), and Fischer and Getis (1997). Today, the situation is quite different, and a fairly substantial collection of spatial data analysis software is readily available, ranging from niche programs, customized scripts and extensions for commercial statistical and GIS packages, to a bur-
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