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Spatial Analytics with ArcGIS
Spatial Analytics with ArcGIS

Spatial Analytics with ArcGIS: Build powerful insights with spatial analytics

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Spatial Analytics with ArcGIS

Measuring Geographic Distributions with ArcGIS Tools

Obtaining basic spatial statistics about a dataset is often the first step in the analysis of geographic data. The Measuring Geographic Distributions toolset in the ArcGIS 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. Together, this toolset provides a set of basic statistical exploration tools.

In this chapter, you will learn how to use many of these tools to obtain basic spatial statistical information about a dataset, including the following topics:

  • Measuring geographic centrality with the Central Feature, Mean Center, and Median Center tools
  • Measuring the degree to which features are concentrated or dispersed around the geometric mean center with the Standard Distance...

Measuring geographic centrality

In this exercise, all three tools will be used to obtain descriptive spatial statistics about crime data for the city of Denver.

Preparation

Let's get prepared for obtaining spatial statistical information about a dataset using ArcGIS, as follows:

  1. In ArcMap, open the C:\GeospatialTraining\SpatialStats\DenverCrime.mxd file. You should see a point feature class called Crime, as shown in the following screenshot:
Point locations for all crimes for the city of Denver 2013
  1. The Crime feature class contains point locations for all crimes for the city of Denver in 2013. The first thing we need to do is isolate a type of crime for our analysis. Open the attribute table for the crime feature class.
  1. Use the Select by Attributes......

The Standard Distance and Directional Distribution tools

The Standard Distance tool measures the degree to which features are concentrated or dispersed around the geometric mean center. It shows the concentration or dispersal of data. The output circle created by this tool is defined by standard deviation and can include one, two, or three standard deviations. In general, the larger the circle, the more dispersed the data.

The Directional Distribution (Standard Deviational Ellipse) tool creates standard deviation ellipses to summarize the spatial characteristics of geographic features, including central tendency, dispersion, and directional trends. The ellipses are centered on the mean center. This tool calculates directionality, centrality, and dispersion.

In this exercise, you'll learn how to use both tools.

...

Summary

This chapter covered the use of a set of descriptive spatial statistics tools found in the Measuring Geographic Distributions toolset. These tools, including Central Feature, Mean Center, Median Center, Standard Distance, and Directional Distribution, return basic spatial statistical information about a dataset. The next chapter covers tools that analyze patterns in geographic datasets.

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Key benefits

  • 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

Description

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.

Who is this book 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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Apr 26, 2017
Length: 290 pages
Edition : 1st
Language : English
ISBN-13 : 9781787122581
Vendor :
ESRI
Category :
Languages :
Tools :

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Product Details

Publication date : Apr 26, 2017
Length: 290 pages
Edition : 1st
Language : English
ISBN-13 : 9781787122581
Vendor :
ESRI
Category :
Languages :
Tools :

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Table of Contents

10 Chapters
Introduction to Spatial Statistics in ArcGIS and R Chevron down icon Chevron up icon
Measuring Geographic Distributions with ArcGIS Tools Chevron down icon Chevron up icon
Analyzing Patterns with ArcGIS Tools Chevron down icon Chevron up icon
Mapping Clusters with ArcGIS Tools Chevron down icon Chevron up icon
Modeling Spatial Relationships with ArcGIS Tools Chevron down icon Chevron up icon
Working with the Utilities Toolset Chevron down icon Chevron up icon
Introduction to the R Programming Language Chevron down icon Chevron up icon
Creating Custom ArcGIS Tools with ArcGIS Bridge and R Chevron down icon Chevron up icon
Application of Spatial Statistics to Crime Analysis Chevron down icon Chevron up icon
Application of Spatial Statistics to Real Estate Analysis Chevron down icon Chevron up icon

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