Book Image

Spatial Analytics with ArcGIS

By : Eric Pimpler
Book Image

Spatial Analytics with ArcGIS

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.

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Table of Contents (16 chapters)
Title Page
Title Page
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Free Chapter
1
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
2
Measuring Geographic Distributions with ArcGIS Tools
Measuring Geographic Distributions with ArcGIS Tools
Measuring geographic centrality
The Standard Distance and Directional Distribution tools
Summary
3
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
4
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
5
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
6
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
7
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
8
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
9
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
10
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

Using the Mapping Clusters tool in vehicle theft data

In the last section, our analysis revealed that vehicle theft in Seattle is clustered. Now, we'll expand our analysis to include the use of several tools found in the Mapping Clusters toolset, including Hot Spot Analysis, Grouping Analysis, and Cluster and Outlier Analysis:

  1. Let's start the clustering analysis by running the Hot Spot Analysis tool. Define Seattle_NHood_VehicleTheft as the input feature class and NormVT as the analysis field. You can define the name and location of the output feature class. For the Conceptualization of Spatial Relationships parameter, use your knowledge of the neighborhood boundaries and the dataset to select an appropriate value. The following screenshot shows the output using CONTIGUITY_EDGES_CORNERS. You may also want to run this tool multiple times with different values for the spatial relationship parameter to see the effect on the output:
  1. Now run the Cluster and Outlier Analysis tool with the same input...
Previous Section
End of Section 3
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