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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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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Seitenzahl: 217
Veröffentlichungsjahr: 2017
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Title Page
BIRMINGHAM - MUMBAI
Copyright
Spatial Analytics with ArcGIS
Copyright © 2017 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
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Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: April 2017
Production reference: 1200417
ISBN 978-1-78712-258-1
www.packtpub.com
Credits
Author
Eric Pimpler
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Reviewer
Ken Doman
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About the Author
Eric Pimpler is the founder and owner of GeoSpatial Training Services (geospatialtraining.com) and has over 20 years of, experience implementing and teaching GIS solutions using open source technology, ESRI and Google Earth/Maps. Currently, he focuses on ArcGIS scripting with Python and the development of custom ArcGIS Server web and mobile applications using JavaScript.
Eric has a bachelor’s degree in geography from Texas A&M University and a master's degree in applied geography with a concentration in GIS from Texas State University.
Eric is the author of Programming ArcGIS with Python Cookbook (https://www.packtpub.com/application-development/programming-arcgis-python-cookbook-second-edition), first and second edition, Building Web (https://www.packtpub.com/application-development/building-web-and-mobile-arcgis-server-applications-javascript) and Mobile ArcGIS Server Applications with JavaScript, and ArcGIS Blueprints (https://www.packtpub.com/application-development/arcgis-blueprints), all by Packt Publishing.
About the Reviewer
Ken Doman is a senior frontend engineer at GEO Jobe, a software development company and ESRI business partner that helps public sector organizations and private sector businesses get the most out of geospatial solutions. Ken has worked with web and geospatial solutions for local and county government, and private industry for over 9 years.
Ken is the author of Mastering ArcGIS Server Development with JavaScript. He has also reviewed several books for Packt Publishing, including Building Web and Mobile ArcGIS Server Applications with JavaScript by Eric Pimpler and ArcGIS for Desktop Cookbook by Daniela Christiana Docan.
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Table of Contents
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
Introduction to Spatial Statistics in ArcGIS and R
Introduction to spatial statistics
An overview of the Spatial Statistics Tools toolbox in ArcGIS
The Measuring Geographic Distributions toolset
The Analyzing Patterns toolset
The Mapping Clusters toolset
The Modeling Spatial Relationships toolset
Integrating R with ArcGIS
Summary
Measuring Geographic Distributions with ArcGIS Tools
Measuring geographic centrality
Preparation
Running the Central Feature tool
Running the Mean Center tool
Running the Median Center tool
The Standard Distance and Directional Distribution tools
Preparation
Running the Standard Distance tool
Running the Directional Distribution tool
Summary
Analyzing Patterns with ArcGIS Tools
The Analyzing Patterns toolset
Understanding the null hypothesis
P-values
Z-scores and standard deviation
Using the Average Nearest Neighbor tool
Preparation
Running the Average Nearest Neighbor tool
Examining the HTML report
Using Spatial Autocorrelation to analyze patterns
Preparation
Running the Spatial Autocorrelation tool
Examining the HTML report
Using the Multi-Distance Spatial Cluster Analysis tool to determine clustering or dispersion
Preparation
Running the Multi-Distance Spatial Cluster Analysis tool
Examining the output
Summary
Mapping Clusters with ArcGIS Tools
Using the Similarity Search tool
Preparation
Running the Similarity Search tool
Interpreting the results
Using the Grouping Analysis tool
Preparation
Running the Grouping Analysis tool
Interpreting the results
Analysing real estate sales with the Hot Spot Analysis tool
Explanation
Preparation
Running the Hot Spot Analysis tool
Using the Optimized Hot Spot Analysis tool in real estate sales
Preparation
Running the Optimized Hot Spot Analysis tool
Interpreting the results
Creating Hot Spot maps from point data using the Optimized Hot Spot Analysis tool
Preparation
Running the Optimized Hot Spot Analysis tool
Finding outliers in real estate sales activity using the Cluster and Outlier Analysis tool
Preparation
Running the Cluster and Outlier Analysis tool
Interpreting the results
Summary
Modeling Spatial Relationships with ArcGIS Tools
The basics of Regression Analysis
Why use Regression Analysis?
Regression Analysis terms and concepts
Linear regression with the Ordinary Least Squares (OLS) tool
Running the Ordinary Least Squares tool
Examining the output generated by the tool
Using the Exploratory Regression tool
Running the Exploratory Regression tool
Examining the output generated by the tool
Using the Geographically Weighted Regression tool
Running the Geographically Weighted Regression tool
Examining the output generated by the tool
Summary
Working with the Utilities Toolset
The Calculate Distance Band from Neighbor Count tool
Running the Calculate Distance Band from Neighbor Count tool
Using the maximum distance as the distance band in the Hot Spot Analysis tool
The Collect Events tool
Data preparation
Executing the Collect Events tool
Using the Collect Events results in the Hot Spot Analysis tool
The Export Feature Attribute to ASCII tool
Exporting a feature class
Summary
Introduction to the R Programming Language
Installing R and the R interface
Variables and assignment
R data types
Vectors
Matrices
Data frames
Factors
Lists
Reading, writing, loading, and saving data
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
Introduction to the arcgisbinding package
The arcgisbinding package functionality - checking for licenses
The arcgisbinding package functionality - accessing ArcGIS format data
The arcgisbinding package functionality - shape classes
The arcgisbinding package functionality - progress bar
Introduction to custom script tools in ArcGIS
The tool_exec() function
Creating the custom toolbox and tool
Exercise - creating a custom ArcGIS script tool with R
Summary
Application of Spatial Statistics to Crime Analysis
Obtaining the crime dataset
Data preparation
Getting descriptive spatial statistics about the crime dataset
Using the Analyzing Patterns tool in the crime dataset
Using the Mapping Clusters tool in vehicle theft data
Modeling vehicle theft with Regression Analysis
Data preparation
Spatial Statistical Analysis
Summary
Application of Spatial Statistics to Real Estate Analysis
Obtaining the Zillow real estate datasets
Data preparation
Finding similar neighborhoods
The Similarity Search tool
The Grouping Analysis tool
Finding areas of high real estate sales activity
Running the Hot Spot Analysis tool
Recommendations for the client
Summary
Preface
The Spatial Statistics toolbox in ArcGIS contains a set of tools for analyzing spatial distributions, patterns, processes, and relationships. While similar to traditional statistics, spatial statistics are a unique set of analyses that incorporate geography. These tools can be used with all license levels of ArcGIS Desktop and are a unique way of exploring the spatial relationships inherent in your data. In addition to using ArcBridge, the R programming language can now be used with ArcGIS Desktop to provide customized statistical analysis and tools.
Spatial Analytics in ArcGIS begins with an introduction to the field of spatial statistics. After this brief introduction ,we’ll examine increasingly complex spatial statistics tools. We’ll start by covering the tools found in the Measuring Geographic Distributions toolset, which provide descriptive spatial statistical information. Next, the Analyzing Patterns toolset will teach the reader how to evaluate datasets for clustering, dispersion, or random patterns. As we move on, you will also be introduced to much more advanced and interesting spatial statistical analysis, including hot spot analysis, similarity search, and least squares regression among others.
After an exhaustive look at the Spatial Statistics Tools toolbox, you will be introduced to the R programming language and you'll learn how to use ArcGIS Bridge to create custom R tools in ArcGIS Desktop.
In the final two chapters of the book, you’ll apply the new skills you’ve learned in the book to solve case studies. The first case study will apply spatial statistics tools and the R programming language to the analysis of crime data. The final chapter of the book will introduce you to the application of spatial statistics to the analysis of real estate data.
What this book covers
Chapter 1, Introduction to Spatial Statistics in ArcGIS and R, contains an introduction to spatial statistics, an overview to the Spatial Statistics Tools toolbox in ArcGIS, and an introduction to R and the R-ArcGIS Bridge.
Chapter 2, Measuring Geographic Distributions with ArcGIs Tools, covers the basic descriptive spatial statistics tools available through the Spatial Statistics Tools toolset, including the Mean and Median Feature, Central Feature, Linear Directional Distribution, Standard Distribution, and Directional Distribution tools.
Chapter 3, Analyzing Patterns with ArcGIS Tools, covers tools that evaluate whether features or the values associated with features form clustered, dispersed, or random spatial patterns. They also define the degree of clustering. These are inferential statistics that define the probability of how confident we are that the pattern is dispersed or clustered. The output is a single result for the entire dataset. Tools covered in this chapter include Average Nearest Neighbor, High/Low Clustering, Spatial Autocorrelation, Multi-Distance Spatial Cluster Analysis, and Spatial Autocorrelation.
Chapter 4, Mapping Clusters with ArcGIS Tools, covers the use of various clustering tools. Clustering tools are used to answer not only the question of Is there clustering? and Where is the clustering? but also Is the Clustering Statistically Significant? Tools covered in this chapter include Cluster and Outlier Analysis, Grouping Analysis, Hot Spot Analysis, Optimized Hot Spot Analysis, and Similarity Search.
Chapter 5, Modeling Spatial Relationships with ArcGIS Tools, shows how beyond analyzing spatial patterns, GIS analysis can be used to examine or quantify relationships among features. The Modeling Spatial Relationships tools construct spatial weights matrices or model spatial relationships using regression analyses. Tools covered in this chapter include Ordinary Least Squares (OLS), Geographically Weighted Regression, and Exploratory Regression.
Chapter 6, Working with the Utilities Toolset, covers the utility scripts that perform a variety of data conversion tasks. These tools can be used in conjunction with other tools in the Spatial Statistics Tools toolbox. Tools covered in this chapter include Calculate Areas, Calculate Distance Band from Neighbor Count, Collect Events, and Export Feature Attribute to ASCI.
Chapter 7, Introduction to the R Programming Language, covers the basics of the R programming language for performing spatial statistical programming. You will learn how to create variables and assign data to variables, create and use functions, work with data types and data classes, read and write data, load spatial data, and create basic plots.
Chapter 8, Creating Custom ArcGIS Tools with the ArcGIS Bridgeand R, covers the R-ArcGIS Bridge, which is a free, open source package that connects ArcGIS and R. Using the Bridge allows developers to create custom tools and toolboxes in ArcGIS that integrate R with ArcGIS to build spatial statistical tools. In this chapter, you will learn how to install the R-ArcGIS Bridge and build custom ArcGIS Tools using R.
Chapter 9, Application of Spatial Statistics to Crime Analysis, shows you how to apply the Spatial Statistics tools and R programming language to the analysis of crime data. After finding and downloading a crime dataset for a major U.S. city, you will perform a variety of spatial analysis techniques using ArcGIS and R.
Chapter 10, Application of Spatial Statistics to Real Estate Analysis, teaches you how to apply the Spatial Statistics tools and R programming language to the analysis of real estate data. After downloading a real estate dataset for a major U.S. city, you will perform a variety of spatial analysis techniques.
What you need for this book
To complete the exercises in this book, you will need to have installed ArcGIS for Desktop 10.2 or higher with the Basic, Standard, or Advanced license level. We recommend that you use ArcGIS Desktop 10.4 or 10.5. In addition to this, you will also need to install R. Instructions for installing R are provided in Chapter 7, Introduction to the R Programming Language.
Who this book is for
Spatial Analytics with ArcGIS is written for intermediate to advanced level GIS professionals who want to use spatial statistics to resolve complex geographic questions.
Reader feedback
Feedback from our readers is always welcome. Let us know what you think about this book-what you liked or disliked. Reader feedback is important for us as it helps us develop titles that you will really get the most out of. To send us general feedback, simply e-mail [email protected], and mention the book's title in the subject of your message. If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide at www.packtpub.com/authors.
Customer support
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Downloading the example code
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Downloading the color images of this book
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Errata
Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you find a mistake in one of our books-maybe a mistake in the text or the code-we would be grateful if you could report this to us. By doing so, you can save other readers from frustration and help us improve subsequent versions of this book. If you find any errata, please report them by visiting http://www.packtpub.com/submit-errata, selecting your book, clicking on the Errata Submission Form link, and entering the details of your errata. Once your errata are verified, your submission will be accepted and the errata will be uploaded to our website or added to any list of existing errata under the Errata section of that title.
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Piracy
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Questions
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Introduction to Spatial Statistics in ArcGIS and R
Spatial statistics are a set of exploratory techniques for describing and modeling spatial distributions, patterns, processes, and relationships. Although spatial statistics are similar to traditional statistics, they also integrate spatial relationships into the calculations. In spatial statistics, proximity is important. Things that are closer together are more related.
ArcGIS includes the Spatial Statistics Tools toolbox available for all license levels of its desktop software. Included with this toolbox are a number of toolsets that help analyze spatial distributions, patterns, clustering, and relationships in GIS datasets. This book will cover each of the toolsets provided with the Spatial Statistics Tools toolbox in ArcGIS to provide a comprehensive survey of the spatial statistics tools available to ArcGIS users.
The R platform for data analysis is a programming language and software platform for statistical computing and graphics, and it is supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data analysts for developing statistical software and data analysis. In addition, R can be used for spatial statistical analysis and can also be integrated with ArcGIS through the R-ArcGIS Bridge.
This book also contains an introductory chapter for the R programming language as well as a chapter that covers the installation of the R-ArcGIS Bridge and the creation of custom ArcGIS script tools written with R.
In this chapter, we will cover the following topics:
Introduction to spatial statistics
An overview of the Spatial Statistics Tools toolbox in ArcGIS
An overview of the integration between R and ArcGIS
Introduction to spatial statistics
Let's start with a definition of spatial statistics. The GIS dictionary (http://gisgeography.com/gis-dictionary-definition-glossary/) defines spatial statistics as the field of study concerning statistical methods that use space and spatial relationships (such as distance, area, volume, length, height, orientation, centrality, and/or other spatial characteristics of data) directly in their mathematical computations. Spatial statistics are used for a variety of different types of analyses, including pattern analysis, shape analysis, surface modeling and surface prediction, spatial regression, statistical comparisons of spatial datasets, statistical modeling and prediction of spatial interaction, and more. The many types of spatial statistics include descriptive, inferential, exploratory, geostatistical, and econometric statistics.
Spatial statistics are applicable across a wide range of environmental disciplines, including agriculture, geology, soil science, hydrology, ecology, oceanography, forestry, meteorology, and climatology, among others. Many socio-economic disciplines including epidemiology, crime analysis, real estate, planning, and others also benefit from spatial statistical analysis.
Spatial statistics can give answers to the following questions:
How are the features distributed?
What is the pattern created by the features?
Which are the clusters?
How do patterns and clusters of different variables compare to one another?
What is the relationship between sets of features or values?
An overview of the Spatial Statistics Tools toolbox in ArcGIS
The ArcGIS Spatial Statistics Tools toolbox is available for all license levels of ArcGIS Desktop, including basic, standard, and advanced. The toolbox includes a number of toolsets, which are as follows:
The
Analyzing Patterns
toolset
The
Mapping Clusters
toolset
The
Measuring Geographic Distributions
toolset
The
Modeling Spatial Relationships
toolset
The Measuring Geographic Distributions toolset
The Measuring Geographic Distributions toolset in the Spatial Statistics Tools toolbox contains a set of tools that provide descriptive geographic statistics, including the Central Feature, Directional Distribution, Linear Directional Mean, Mean Center, Median Center, and Standard Distance tools. Together, this toolset provides a set of basic statistical exploration tools. These basic descriptive statistics are used only as a starting point in the analysis process. The following screenshot displays the output from the Directional Distribution tool for an analysis of crime data:
The Central Feature, Mean Center, and Median Center tools all provide similar functionality. Each creates a feature class containing a single feature that represents the centrality of a geographic dataset.
The Linear Directional Mean tool identifies the mean direction, length, and geographic center for a set of lines. The output of this tool is a feature class with a single linear feature.
The Standard Distance and Directional Distribution tools are similar, in that they both measure the degree to which features are concentrated or dispersed around the geometric center, but the Directional Distribution tool, also known as the Standard Deviational Ellipse, is superior as it also provides a measure of directionality in the dataset.
The Analyzing Patterns toolset
The Analyzing Patterns toolset in the Spatial Statistics Tools toolbox contains a series of tools that help evaluate whether features or the values associated with features form a clustered, dispersed, or random spatial pattern. These tools generate a single result for the entire dataset in question. In addition, the result does not take the form of a map, but rather statistical output, as shown in the following screenshot:
Tools in this category generate what is known as inferential statistics or the probability of how confident we are that the pattern is either dispersed or clustered. Let's examine the following tools found in the Analyzing Patterns toolset:
Average Nearest Neighbor
