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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

Introduction to the R Programming Language

The R Project for Statistical Computing, or simply named R, is a free software environment for statistical computing and graphics. It is also a programming language that is widely used among statisticians and data miners for developing statistical software and data analysis.

Although there are other programming languages for handling statistics, R has become one of the most popular languages for statistical routines, offering a package repository with over 6,400 packages. The R language also offers versatile and powerful plotting tools.

In this chapter, you will learn the fundamentals of the R programming language and how it can be applied to GIS analysis. However, this chapter is not a comprehensive guide to the R language. Instead, it will focus on introducing you to some of the most important constructs of the language and the various data types you can expect to encounter...