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Geospatial Analysis with SQL

You're reading from Geospatial Analysis with SQL A hands-on guide to performing geospatial analysis by unlocking the syntax of spatial SQL

Product type Paperback

Published in Oct 2023

Publisher Packt

ISBN-13 9781835083147

Length 234 pages

Edition 1st Edition

Languages

SQL

Tools

QGIS

Concepts

Geospatial Analysis

Author (1):

Bonny P McClain

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Table of Contents (13) Chapters

Preface

1. Section 1: Getting Started with Geospatial Analytics FREE CHAPTER

2. Chapter 1: Introducing the Fundamentals of Geospatial Analytics

3. Chapter 2: Conceptual Framework for SQL Spatial Data Science – Geometry Versus Geography

4. Chapter 3: Analyzing and Understanding Spatial Algorithms

5. Chapter 4: An Overview of Spatial Statistics

6. Section 2: SQL for Spatial Analytics

7. Chapter 5: Using SQL Functions – Spatial and Non-Spatial

8. Chapter 6: Building SQL Queries Visually in a Graphical Query Builder

9. Chapter 7: Exploring PostGIS for Geographic Analysis

10. Chapter 8: Integrating SQL with QGIS

11. Index

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12. Other Books You May Enjoy

Landscape Change Monitoring System

Let’s explore the Landscape Change Monitoring System (LCMS) Data Explorer (https://apps.fs.usda.gov/lcms-viewer/), shown in Figure 8.25. You can download data from different time periods and observe any changes. The latest data available here was for 2020. Although we can select specific dates from EarthExplorer, the concepts required to process and prepare for introducing spatial concepts in writing SQL queries would be beyond the scope of this book. For example, the workflow I would use professionally involves built-in QGIS raster functions or Python coding.

Figure 8.25 – LCMS Data Explorer (Puerto Rico)

Figure 8.25 – LCMS Data Explorer (Puerto Rico)

For importing raster data and exploring raster data, we can learn about a few SQL functions, but first, we need to locate the data and download it to our local computer. Download the years of interest available by scrolling in the lower-left panel. I selected 2017 and 2020 to explore how land use might...

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Authors (1)

Bonny P McClain

Bonny P McClain

Bonny P McClain applies advanced data analytics, including data engineering and geo-enrichment, to discussions of poverty, race, and gender. Racism, class exploitation, sexism, nationalism, and heterosexism all contribute to social inequality. Bonny redefines how we measure these attributes and how we can more accurately identify amenable interventions. Spatial data hosts a variety of physical and cultural features that reveal distribution patterns and help analysts and data professionals understand the underlying drivers of these patterns. Bonny is a popular conference keynote and workshop leader. Her professional goals include exploring large datasets and curating empathetic answers to larger questions, making a big world seem smaller.

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