Modeling Spatial Relationships with ArcGIS Tools | Spatial Analytics with ArcGIS

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Spatial Analytics with ArcGIS

By : Eric Pimpler

4 (1)

Spatial Analytics with ArcGIS

4 (1)

By: Eric Pimpler

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

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 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 Distributions with ArcGIS Tools

Measuring geographic centrality

The Standard Distance and Directional Distribution tools

Summary

Analyzing Patterns with ArcGIS Tools

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

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

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

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

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

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

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

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

Summary

In this chapter, two linear regression tools were used to model the relationships between various demographic, socio-economic, and geographic factors and the incidence of burglary in Denver County, CO. Using the OLS tool, a combination of variables were identified as the best model for understanding burglary in this context. The model was then improved using the Geographically Weighted Regression tool, which introduced spatial variability into the model. Finally, the Exploratory Regression tool was used to quickly identify combinations of variables that best fit a model. In the next chapter, we'll examine a variety of data conversion tools found in the Utilities toolset. These tools are often used in conjunction with other tools found in the Spatial Statistics Tools toolbox.

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