Chapter 1, Introduction to Spatial Statistics in ArcGIS and R, contains an introduction to spatial statistics, an overview to the Spatial Statistics Tools toolbox in ArcGIS, and an introduction to R and the R-ArcGIS Bridge.
Chapter 2, Measuring Geographic Distributions with ArcGIs Tools, covers the basic descriptive spatial statistics tools available through the Spatial Statistics Tools toolset, including the Mean and Median Feature, Central Feature, Linear Directional Distribution, Standard Distribution, and Directional Distribution tools.
Chapter 3, Analyzing Patterns with ArcGIS Tools, covers tools that evaluate whether features or the values associated with features form clustered, dispersed, or random spatial patterns. They also define the degree of clustering. These are inferential statistics that define the probability of how confident we are that the pattern is dispersed or clustered. The output is a single result for the entire dataset. Tools covered in this chapter include Average Nearest Neighbor, High/Low Clustering, Spatial Autocorrelation, Multi-Distance Spatial Cluster Analysis, and Spatial Autocorrelation.
Chapter 4, Mapping Clusters with ArcGIS Tools, covers the use of various clustering tools. Clustering tools are used to answer not only the question of Is there clustering? and Where is the clustering? but also Is the Clustering Statistically Significant? Tools covered in this chapter include Cluster and Outlier Analysis, Grouping Analysis, Hot Spot Analysis, Optimized Hot Spot Analysis, and Similarity Search.
Chapter 5, Modeling Spatial Relationships with ArcGIS Tools, shows how beyond analyzing spatial patterns, GIS analysis can be used to examine or quantify relationships among features. The Modeling Spatial Relationships tools construct spatial weights matrices or model spatial relationships using regression analyses. Tools covered in this chapter include Ordinary Least Squares (OLS), Geographically Weighted Regression, and Exploratory Regression.
Chapter 6, Working with the Utilities Toolset, covers the utility scripts that perform a variety of data conversion tasks. These tools can be used in conjunction with other tools in the Spatial Statistics Tools toolbox. Tools covered in this chapter include Calculate Areas, Calculate Distance Band from Neighbor Count, Collect Events, and Export Feature Attribute to ASCI.
Chapter 7, Introduction to the R Programming Language, covers the basics of the R programming language for performing spatial statistical programming. You will learn how to create variables and assign data to variables, create and use functions, work with data types and data classes, read and write data, load spatial data, and create basic plots.
Chapter 8, Creating Custom ArcGIS Tools with the ArcGIS Bridge and R, covers the R-ArcGIS Bridge, which is a free, open source package that connects ArcGIS and R. Using the Bridge allows developers to create custom tools and toolboxes in ArcGIS that integrate R with ArcGIS to build spatial statistical tools. In this chapter, you will learn how to install the R-ArcGIS Bridge and build custom ArcGIS Tools using R.
Chapter 9, Application of Spatial Statistics to Crime Analysis, shows you how to apply the Spatial Statistics tools and R programming language to the analysis of crime data. After finding and downloading a crime dataset for a major U.S. city, you will perform a variety of spatial analysis techniques using ArcGIS and R.
Chapter 10, Application of Spatial Statistics to Real Estate Analysis, teaches you how to apply the Spatial Statistics tools and R programming language to the analysis of real estate data. After downloading a real estate dataset for a major U.S. city, you will perform a variety of spatial analysis techniques.
