Spatial Analytics
with ArcGIS
Use the spatial statistics tools provided by ArcGIS and build your own to perform complex geographic analysis
Eric Pimpler
شرح التحليلات المكانية داخل برنامج ArcGIS
Preface 1
Chapter 1: Introduction to Spatial Statistics in ArcGIS and R 7
Introduction to spatial statistics 8
An overview of the Spatial Statistics Tools toolbox in ArcGIS 8
The Measuring Geographic Distributions toolset 8
The Analyzing Patterns toolset 10
The Mapping Clusters toolset 11
The Modeling Spatial Relationships toolset 14
Integrating R with ArcGIS 18
Summary 19
Chapter 2: Measuring Geographic Distributions with ArcGIS Tools 20
Measuring geographic centrality 20
Preparation 20
Running the Central Feature tool 24
Running the Mean Center tool 26
Running the Median Center tool 28
The Standard Distance and Directional Distribution tools 31
Preparation 31
Running the Standard Distance tool 33
Running the Directional Distribution tool 36
Summary 37
Chapter 3: Analyzing Patterns with ArcGIS Tools 38
The Analyzing Patterns toolset 38
Understanding the null hypothesis 39
P-values 39
Z-scores and standard deviation 40
Using the Average Nearest Neighbor tool 41
Preparation 41
Running the Average Nearest Neighbor tool 43
Examining the HTML report 45
Using Spatial Autocorrelation to analyze patterns 46
Preparation 47
Running the Spatial Autocorrelation tool 51
Examining the HTML report 53
Using the Multi-Distance Spatial Cluster Analysis tool to determine clustering or dispersion 54
Preparation 55
Running the Multi-Distance Spatial Cluster Analysis tool 57
Examining the output 59
Summary 61
Chapter 4: Mapping Clusters with ArcGIS Tools 62
Using the Similarity Search tool 63
Preparation 65
Running the Similarity Search tool 66
Interpreting the results 68
Using the Grouping Analysis tool 69
Preparation 71
Running the Grouping Analysis tool 72
Interpreting the results 74
Analysing real estate sales with the Hot Spot Analysis tool 78
Explanation 79
Preparation 81
Running the Hot Spot Analysis tool 88
Using the Optimized Hot Spot Analysis tool in real estate sales 93
Preparation 94
Running the Optimized Hot Spot Analysis tool 94
Interpreting the results 97
Creating Hot Spot maps from point data using the Optimized Hot Spot Analysis tool 100
Preparation 100
Running the Optimized Hot Spot Analysis tool 100
Finding outliers in real estate sales activity using the Cluster and Outlier Analysis tool 105
Preparation 105
Running the Cluster and Outlier Analysis tool 105
Interpreting the results 107
Summary 109
Chapter 5: Modeling Spatial Relationships with ArcGIS Tools 110
The basics of Regression Analysis 111
Why use Regression Analysis? 111
Regression Analysis terms and concepts 111
Linear regression with the Ordinary Least Squares (OLS) tool 113
Running the Ordinary Least Squares tool 115
Examining the output generated by the tool 118
Using the Exploratory Regression tool 132
Running the Exploratory Regression tool 133
Examining the output generated by the tool 135
Using the Geographically Weighted Regression tool 138
Running the Geographically Weighted Regression tool 139
Examining the output generated by the tool 140
Summary 147
Chapter 6: Working with the Utilities Toolset 148
The Calculate Distance Band from Neighbor Count tool 149
Running the Calculate Distance Band from Neighbor Count tool 149
Using the maximum distance as the distance band in the Hot Spot Analysis tool 151
The Collect Events tool 153
Data preparation 153
Executing the Collect Events tool 155
Using the Collect Events results in the Hot Spot Analysis tool 158
The Export Feature Attribute to ASCII tool 159
Exporting a feature class 160
Summary 161
Chapter 7: Introduction to the R Programming Language 162
Installing R and the R interface 163
Variables and assignment 165
R data types 169
Vectors 170
Matrices 172
Data frames 174
Factors 177
Lists 179
Reading, writing, loading, and saving data 179
Additional R study options 187
Summary 187
Chapter 8: Creating Custom ArcGIS Tools with ArcGIS Bridge and R 188
Installing the R-ArcGIS Bridge package 189
Building custom ArcGIS tools with R 193
Introduction to the arcgisbinding package 193
The arcgisbinding package functionality - checking for licenses 194
The arcgisbinding package functionality - accessing ArcGIS format data 194
The arcgisbinding package functionality - shape classes 195
The arcgisbinding package functionality - progress bar 195
Introduction to custom script tools in ArcGIS 196
The tool_exec() function 196
Creating the custom toolbox and tool 198
Exercise - creating a custom ArcGIS script tool with R 198
Summary 207
Chapter 9: Application of Spatial Statistics to Crime Analysis 208
Obtaining the crime dataset 209
Data preparation 216
Getting descriptive spatial statistics about the crime dataset 221
Using the Analyzing Patterns tool in the crime dataset 223
Using the Mapping Clusters tool in vehicle theft data 225
Modeling vehicle theft with Regression Analysis 228
Data preparation 228
Spatial Statistical Analysis 235
Summary 236
Chapter 10: Application of Spatial Statistics to Real Estate Analysis 237
Obtaining the Zillow real estate datasets 238
Data preparation 239
Finding similar neighborhoods 243
The Similarity Search tool 244
The Grouping Analysis tool 249
Finding areas of high real estate sales activity 252
Running the Hot Spot Analysis tool 252
Recommendations for the client 265
Summary 269
Index 270
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 Bridge and 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 RArcGIS 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
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