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Table Of Contents

Spatial Analytics with ArcGIS

By : Eric Pimpler

4 (1)

Buy this Book

Spatial Analytics with ArcGIS

4 (1)

By: Eric Pimpler

Buy this Book

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.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Introduction to Spatial Statistics in ArcGIS and R

Introduction to spatial statistics

An overview of the Spatial Statistics Tools toolbox in ArcGIS

Integrating R with ArcGIS

Summary

Measuring Geographic Distributions with ArcGIS Tools

Measuring geographic centrality

The Standard Distance and Directional Distribution tools

Summary

Analyzing Patterns with ArcGIS Tools

The Analyzing Patterns toolset

Using the Average Nearest Neighbor tool

Using Spatial Autocorrelation to analyze patterns

Using the Multi-Distance Spatial Cluster Analysis tool to determine clustering or dispersion

Summary

Mapping Clusters with ArcGIS Tools

Using the Similarity Search tool

Using the Grouping Analysis tool

Analysing real estate sales with the Hot Spot Analysis tool

Using the Optimized Hot Spot Analysis tool in real estate sales

Creating Hot Spot maps from point data using the Optimized Hot Spot Analysis tool

Finding outliers in real estate sales activity using the Cluster and Outlier Analysis tool

Summary

Modeling Spatial Relationships with ArcGIS Tools

The basics of Regression Analysis

Linear regression with the Ordinary Least Squares (OLS) tool

Using the Exploratory Regression tool

Using the Geographically Weighted Regression tool

Summary

Working with the Utilities Toolset

The Calculate Distance Band from Neighbor Count tool

The Collect Events tool

The Export Feature Attribute to ASCII tool

Summary

Introduction to the R Programming Language

Installing R and the R interface

Variables and assignment

R data types

Additional R study options

Summary

Creating Custom ArcGIS Tools with ArcGIS Bridge and R

Installing the R-ArcGIS Bridge package

Building custom ArcGIS tools with R

Summary

Application of Spatial Statistics to Crime Analysis

Obtaining the crime dataset

Using the Mapping Clusters tool in vehicle theft data

Summary

Application of Spatial Statistics to Real Estate Analysis

Obtaining the Zillow real estate datasets

Data preparation

Finding similar neighborhoods

Finding areas of high real estate sales activity

Recommendations for the client

Summary

Using the Exploratory Regression tool

The Exploratory Regression tool can be used to evaluate combinations of exploratory variables for OLS models that best explain the dependent variable. This data-mining tool does a lot of work for you in finding variables that are well suited. This tool can save you a lot of time. The results of this tool are written to the progress dialog, the Result window, and an optional report file. In this section, you will use the Exploratory Regression tool to analyze the variables attached to the Denver_Census_Tracts layer used in the OLS section of the course to identify combinations of variables that may be appropriate for a model of burglary incidents.