#### Overview of this book

Spatial statistics has the potential to provide insight that is not otherwise available through traditional GIS tools. This book is designed to introduce you to the use of spatial statistics so you can solve complex geographic analysis. The book begins by introducing you to the many spatial statistics tools available in ArcGIS. You will learn how to analyze patterns, map clusters, and model spatial relationships with these tools. Further on, you will explore how to extend the spatial statistics tools currently available in ArcGIS, and use the R programming language to create custom tools in ArcGIS through the ArcGIS Bridge using real-world examples. At the end of the book, you will be presented with two exciting case studies where you will be able to practically apply all your learning to analyze and gain insights into real estate data.
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Introduction to Spatial Statistics in ArcGIS and R
Measuring Geographic Distributions with ArcGIS Tools
Analyzing Patterns with ArcGIS Tools
Modeling Spatial Relationships with ArcGIS Tools
Working with the Utilities Toolset
Introduction to the R Programming Language
Creating Custom ArcGIS Tools with ArcGIS Bridge and R
Application of Spatial Statistics to Crime Analysis
Application of Spatial Statistics to Real Estate Analysis

## The basics of Regression Analysis

Up until this section of the course, we examined tools that help us answer the where questions. Where are crimes occurring? Where do wildfires cluster? Where are the hot spots for real estate sales? However, you may have noticed that these tools don't help determine the why questions. The regression tools found in the `Modeling Spatial Relationships` toolset help with the logical progression of obtaining a deeper understanding of our problem. Tools such as `Ordinary Least Squares` and `Exploratory Regression` help us determine the variables that explain why an observed pattern is present. These explanatory variables can lead to the development of models that can predict the occurrence of these patterns in other places.

### Why use Regression Analysis?

There are essentially three reasons to use Regression Analysis. The first is to gain an understanding of a phenomenon and effect policy or make decisions about appropriate actions to take. The next is to create predictive...