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Spatial Analytics with ArcGIS

Book Description

Pattern Analysis and cluster mapping made easy

About This Book

  • Analyze patterns, clusters, and spatial relationships using ArcGIS tools
  • Get up to speed in R programming to create custom tools for analysis
  • Sift through tons of crime and real estate data and analyze it using the tools built in the book

Who This Book Is For

This book is for ArcGIS developers who want to perform complex geographic analysis through the use of spatial statistics tools including ArcGIS and R. No knowledge of R is assumed.

What You Will Learn

  • Get to know how to measure geographic distributions
  • Perform clustering analysis including hot spot and outlier analysis
  • Conduct data conversion tasks using the Utilities toolset
  • Understand how to use the tools provided by the Mapping Clusters toolset in the Spatial Statistics Toolbox
  • Get to grips with the basics of R for performing spatial statistical programming
  • Create custom ArcGIS tools with R and ArcGIS Bridge
  • Understand the application of Spatial Statistics tools and the R programming language through case studies

In Detail

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.

Style and approach

Filled with live examples that you can code along with, this book will show you different methods and techniques to effectively analyze spatial data with ArcGIS and the R language. The exciting case studies at the end will help you immediately put your learning to practice.

Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.

Table of Contents

  1. Preface
    1. What this book covers
    2. What you need for this book
    3. Who this book is for
    4. Conventions
    5. Reader feedback
    6. Customer support
      1. Downloading the example code
      2. Downloading the color images of this book
      3. Errata
      4. Piracy
      5. Questions
  2. Introduction to Spatial Statistics in ArcGIS and R
    1. Introduction to spatial statistics
    2. An overview of the Spatial Statistics Tools toolbox in ArcGIS
      1. The Measuring Geographic Distributions toolset
      2. The Analyzing Patterns toolset
      3. The Mapping Clusters toolset
      4. The Modeling Spatial Relationships toolset
    3. Integrating R with ArcGIS
    4. Summary
  3. Measuring Geographic Distributions with ArcGIS Tools
    1. Measuring geographic centrality
      1. Preparation
      2. Running the Central Feature tool
      3. Running the Mean Center tool
      4. Running the Median Center tool
    2. The Standard Distance and Directional Distribution tools
      1. Preparation
      2. Running the Standard Distance tool
      3. Running the Directional Distribution tool
    3. Summary
  4. Analyzing Patterns with ArcGIS Tools
    1. The Analyzing Patterns toolset
      1. Understanding the null hypothesis
      2. P-values
      3. Z-scores and standard deviation
    2. Using the Average Nearest Neighbor tool
      1. Preparation
      2. Running the Average Nearest Neighbor tool
      3. Examining the HTML report
    3. Using Spatial Autocorrelation to analyze patterns
      1. Preparation
      2. Running the Spatial Autocorrelation tool
      3. Examining the HTML report
    4. Using the Multi-Distance Spatial Cluster Analysis tool to determine clustering or dispersion
      1. Preparation
      2. Running the Multi-Distance Spatial Cluster Analysis tool
      3. Examining the output
    5. Summary
  5. Mapping Clusters with ArcGIS Tools
    1. Using the Similarity Search tool
      1. Preparation
      2. Running the Similarity Search tool
      3. Interpreting the results
    2. Using the Grouping Analysis tool
      1. Preparation
      2. Running the Grouping Analysis tool
      3. Interpreting the results
    3. Analysing real estate sales with the Hot Spot Analysis tool
      1. Explanation
      2. Preparation
      3. Running the Hot Spot Analysis tool
    4. Using the Optimized Hot Spot Analysis tool in real estate sales
      1. Preparation
      2. Running the Optimized Hot Spot Analysis tool
      3. Interpreting the results
    5. Creating Hot Spot maps from point data using the Optimized Hot Spot Analysis tool
      1. Preparation
      2. Running the Optimized Hot Spot Analysis tool
    6. Finding outliers in real estate sales activity using the Cluster and Outlier Analysis tool
      1. Preparation
      2. Running the Cluster and Outlier Analysis tool
      3. Interpreting the results
    7. Summary
  6. Modeling Spatial Relationships with ArcGIS Tools
    1. The basics of Regression Analysis
      1. Why use Regression Analysis?
      2. Regression Analysis terms and concepts
    2. Linear regression with the Ordinary Least Squares (OLS) tool
      1. Running the Ordinary Least Squares tool
      2. Examining the output generated by the tool
    3. Using the Exploratory Regression tool
      1. Running the Exploratory Regression tool
      2. Examining the output generated by the tool
    4. Using the Geographically Weighted Regression tool
      1. Running the Geographically Weighted Regression tool
      2. Examining the output generated by the tool
    5. Summary
  7. Working with the Utilities Toolset
    1. The Calculate Distance Band from Neighbor Count tool
      1. Running the Calculate Distance Band from Neighbor Count tool
      2. Using the maximum distance as the distance band in the Hot Spot Analysis tool
    2. The Collect Events tool
      1. Data preparation
      2. Executing the Collect Events tool
      3. Using the Collect Events results in the Hot Spot Analysis tool
    3. The Export Feature Attribute to ASCII tool
      1. Exporting a feature class
    4. Summary
  8. Introduction to the R Programming Language
    1. Installing R and the R interface
    2. Variables and assignment
    3. R data types
      1. Vectors
      2. Matrices
      3. Data frames
      4. Factors
      5. Lists
      6. Reading, writing, loading, and saving data
    4. Additional R study options
    5. Summary
  9. Creating Custom ArcGIS Tools with ArcGIS Bridge and R
    1. Installing the R-ArcGIS Bridge package
    2. Building custom ArcGIS tools with R
      1. Introduction to the arcgisbinding package
        1. The arcgisbinding package functionality - checking for licenses
        2. The arcgisbinding package functionality - accessing ArcGIS format data
        3. The arcgisbinding package functionality - shape classes
        4. The arcgisbinding package functionality - progress bar
      2. Introduction to custom script tools in ArcGIS
        1. The tool_exec() function
        2. Creating the custom toolbox and tool
        3. Exercise - creating a custom ArcGIS script tool with R
    3. Summary
  10. Application of Spatial Statistics to Crime Analysis
    1. Obtaining the crime dataset
      1. Data preparation
      2. Getting descriptive spatial statistics about the crime dataset
      3. Using the Analyzing Patterns tool in the crime dataset
    2. Using the Mapping Clusters tool in vehicle theft data
      1. Modeling vehicle theft with Regression Analysis
        1. Data preparation
        2. Spatial Statistical Analysis
    3. Summary
  11. Application of Spatial Statistics to Real Estate Analysis
    1. Obtaining the Zillow real estate datasets
    2. Data preparation
    3. Finding similar neighborhoods
      1. The Similarity Search tool
      2. The Grouping Analysis tool
    4. Finding areas of high real estate sales activity
      1. Running the Hot Spot Analysis tool
    5. Recommendations for the client
    6. Summary