#### 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

## Summary

In this chapter, we used many of the tools found in the `Spatial Statistics Tools` toolbox to analyze vehicle theft in Seattle, WA. After downloading the data and doing some initial data preparation, which is often the most time consuming aspect of any GIS project, we used a variety of tools to get a better understanding of the data. Initially, we used some basic descriptive statistical tools to get a general understanding of the data. The `Central Feature` tool gave us an idea of where vehicle theft is centered in the area, and the `Directional Distribution` tool was used as a basic tool for understanding both the distribution and the directionality of the data. Later, we used the `Average Nearest Neighbor` tool to determine if the data formed a clustered, dispersed, or randomly spaced pattern. In our case, the data exhibited a strongly clustered pattern. Next, the `Hot Spot Analysis` tool was run, and it produced an output that indicated hot spots of vehicle theft in the central and north...