#### 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

## Using the Geographically Weighted Regression tool

Geographically Weighted Regression (GWR) is a local form of linear regression for modeling spatially varying relationships. GWR constructs a separate equation for each feature. What this means is that the relationships we're trying to model can and often change across the study area. For example, in our study, we might find that a high percentage of renters are an important predictor of burglary in one area of the county but a weak predictor in others.

GWR works by creating a local model of the variables or process that you are attempting to understand. It fits a regression equation to every feature in the study area. The variables of features that fall within the bandwidth of each target feature are incorporated into the equation. The shape and size of the bandwidth are dependent upon user input for criteria such as the kernel type, bandwidth method, distance, and number of neighbors.

GWR creates an output feature class and table. The output...