#### 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

## Analysing real estate sales with the Hot Spot Analysis tool

The `Hot Spot Analysis` tool examines features and their attributes to identify statistically significant hot spots and cold spots using the `Getis-Ord Gi*` statistics. It provides a visual clustering of high, low, and not significant values. Each feature is assigned as a hot spot, cold spot, or not significant. To perform the analysis, a neighborhood is defined for each feature. How a neighborhood is defined is critical to the output of the tool as it is the neighborhood that is examined in relation to the study area for assignment of each feature.

### Explanation

At a high level, let's examine what occurs during the hot spot analysis process. Each feature in the dataset has an attribute value that is being measured in the analysis. A neighborhood is assigned to each feature. This neighborhood is critical to the analysis. You must decide how the neighborhood is to be defined as part of the input parameters of the tool. If you don't have...