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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

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781835083147
Length 234 pages
Edition 1st Edition
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Author (1):
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Bonny P McClain Bonny P McClain
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Bonny P McClain
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Table of Contents (13) Chapters Close

Preface 1. Section 1: Getting Started with Geospatial Analytics
2. Chapter 1: Introducing the Fundamentals of Geospatial Analytics FREE CHAPTER 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 12. Other Books You May Enjoy

Exploring census data in the SQL Query Builder

For the remaining chapter, let’s explore the population dynamics in Los Angeles County—specifically, declines or increases in the Hispanic population. The folder download included a JSON file. We need to identify populations of interest from the dense JSON file you see in Figure 6.19:

Figure 6.19 – Demographic metadata JSON file Hispanic/non-Hispanic aged over 18

Figure 6.19 – Demographic metadata JSON file Hispanic/non-Hispanic aged over 18

The area highlighted in Figure 6.19 is the percent change in population from the 2010 census to the 2020 census. In addition, I captured the total population and total population: Hispanic Latino for 2020, "P0040001_2020": "P4-1: Total (2020)", "P0040002_2020": "P4-2: Hispanic or Latino (2020)".

Renaming the columns in census tables is quite a task. The codes identify data products available from the census. For example, B in the column heading in Figure 6.20 is used for detailed estimates...

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