#### 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 Average Nearest Neighbor tool

The `Average Nearest Neighbor` tool calculates a nearest neighbor index based on the average distance from each feature to its nearest neighboring feature. For each feature in a dataset, the distance to its nearest neighbor is computed. An average distance is then computed.

The average distance is compared to the expected average distance. In doing so, an Average Nearest Neighbor (ANN) ratio is created which in simple terms is the ratio of observed/expected. If the ratio is less than 1, we can say that the data exhibits a clustered pattern, whereas a value greater than 1 indicates a dispersed pattern in our data.

The ANN ratio created as a result of dividing the observed distance by the expected distance creates a value between 0 and 1. If the ratio is less than 1, we can say that the data exhibits a clustered pattern, whereas a value greater than 1 indicates a dispersed pattern in our data.

### Preparation

If necessary, open `ArcMap` with the   ` C:GeospatialTrainingSpatialStatsDenverCrime...`