Published 3/2023MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 1.21 GB | Duration: 1h 57m

GeoAnalytics Desktop

What you'll learn

Learn about the hardware considerations for using large amounts of data in GIS

Learn about the software considerations for using large amounts of data in GIS

Learn about best practices for structuring data spatial and attribute data when working with large amounts of data in GIS

utilize GeoAnalytics Desktop to process large amounts of geospatial data in ArcGIS Pro

Requirements

Students should have some familiarity with GIS

Description

If you are moving into a territory in your GIS career where the amount of data you are using prevents you from doing your job effectively, this course is for you. We'll focus on the best practices for using large data sources and the new offering by Esri to parallelize geospatial tasks. Esri's GeoAnalytics Desktop tools provide a parallel processing framework for GIS analysis using your existing PC. Most PCs today have 8 or more processing cores (CPUs), and the use of Apache Spark in GeoAnalytics Desktop turns your ArcGIS system into a mini high performance computing lab. The tools are so well integrated in ArcGIS Pro that they operate in the same way as other geoprocessing tools in ArcGIS. While parallel processing tools exist, they may be severely ineffective unless you properly utilize the hardware, software, and data on your computer. This class will introduce you not only to the actual features in GeoAnalytics Desktop, but also some of the best practices when working with hardware, software, and data. Some of the topics we'll address include:hardware considerations for working with large spatial data.classes of databases to store large spatial data.working with different coordinate systems with large spatial data.indexing strats for improving the speed of database searches.formatting GIS data to improve spatial analysis.You will have an opportunity to not only learn about the theoretical topics of large spatial data analysis, but you'll perform hands on activities to test the processes yourself. This is the perfect course to get you ready for working with large amounts of spatial and non-spatial data.

Overview

Section 1: Introduction

Lecture 1 What the workshop is about

Lecture 2 A short demonstration on parallel processing

Lecture 3 The Three V's of data analytics

Section 2: Best Practices for Computing Resources

Lecture 4 Hardware considerations

Lecture 5 Local vs. distributed processing

Section 3: Overview of Geoanalytics Desktop Tools

Lecture 6 Available tools

Lecture 7 Hands on activity: Overlay in ArcGIS Pro with and without Geoanalytics Desktop

Section 4: Best Practices for Data Preparation

Lecture 8 Classes of databases

Lecture 9 Coordinate Systems

Lecture 10 Hands on activity: on-the-fly transformation vs. same coordinate system

Lecture 11 Attribute Data Indexing

Lecture 12 Hands on activity: search records on an indexed field vs. a non-indexed field

Lecture 13 Subdividing large polygons to increase efficiency: when more is less

Lecture 14 Access GeoAnalytics Desktop Tools: the GUI, Model Builder, and Python

Lecture 15 Bonus Lecture

Students who have a desire to learn about the best practices for handling large amounts of spatial and non spatial data in GIS processes.

HomePage:

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