Visualizing Spatial Data

Stephen P. Kaluzny, Silvia C. Vega, Tamre P. Cardoso and Alice A. Shelly
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Stephen P. Kaluzny: MathSoft, Inc., Data Analysis Products Division
Silvia C. Vega: MathSoft, Inc., Data Analysis Products Division
Tamre P. Cardoso: MathSoft, Inc., Data Analysis Products Division
Alice A. Shelly: MathSoft, Inc., Data Analysis Products Division

Chapter 3 in S+SpatialStats, 1998, pp 18-66 from Springer

Abstract: Abstract This chapter introduces exploratory data analysis (EDA) and visualization techniques for spatial data in S-Plus and S+SpatialStats. EDA involves methods of describing data and its structure in order to formulate hypotheses and check the validity of assumptions. In general, we will be concerned with the distribution of data and violations of local and global stationarity (as defined in chapter 1)—including trends and outliers. Although a researcher uses EDA techniques throughout spatial analysis and modeling, this chapter focuses on initial explorations—before analysis has been attempted. Analysis techniques for specific types of spatial data — geostatistical data, lattice data, and spatial point patterns, will be discussed in chapters 4, 5, and 6. In this chapter you will learn to do the following tasks: Use basic tools of EDA in S-Plus (section 3.1). Apply basic EDA techniques to geostatistical data (section 3.2) Apply basic EDA techniques to lattice data (section 3.3). Apply basic EDA techniques to point pattern data (section 3.4). Apply hexagonal binning (section 3.5).

Keywords: Spatial Data; Point Pattern; Data Frame; Exploratory Data Analysis; Sids Rate (search for similar items in EconPapers)
Date: 1998
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DOI: 10.1007/978-1-4615-7826-0_3

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