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Geospatial Analysis with SQL
Geospatial Analysis with SQL

Geospatial Analysis with SQL: A hands-on guide to performing geospatial analysis by unlocking the syntax of spatial SQL

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Profile Icon Bonny P McClain
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€33.99
Paperback Oct 2023 234 pages 1st Edition
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€33.99
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Arrow left icon
Profile Icon Bonny P McClain
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€33.99
Paperback Oct 2023 234 pages 1st Edition
eBook
€17.99 €26.99
Paperback
€33.99
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Free Trial
Renews at €18.99p/m
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Paperback
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Geospatial Analysis with SQL

Introducing the Fundamentals of Geospatial Analytics

Understanding where something happened is often the key to understanding why it occurred in the first place. A flood wipes out a village in a remote country 1,000 miles away. If you are thinking geospatially, you are curious about climate forecasting, the demographics of the population, the nature of the soil, the topography of the land, and building footprints or structures. The ability to point to a spot on a map is only a small part of data collection. When you begin to think about roads and distances to impacted villages requiring disaster relief, you would be limited by point-to-point Cartesian geometry. This chapter will help you to understand why.

The process of detecting and quantifying patterns in datasets requires data exploration, visualization, data engineering, and the application of analysis and spatial techniques. At its core, geospatial technology provides an opportunity to explore location intelligence and how...

Technical requirements

The following are the technical requirements for this chapter:

Spatial databases

Geospatial data is vast and often resides in databases or data warehouses. Data stored in a filesystem is only accessible through your local computer and is limited to the speed and efficiency of your work space. Collecting and analyzing geographic information within storage systems of relationally structured data increases efficiency.

Spatial databases are optimized for storing and retrieving your data. Data stored in databases are accessed through client software. The client is a computer or hostname (where the database is located).

Next, the server listens for requests on a port. There are a variety of ports, but with PostgreSQL, the default is 5432—but more on that later. The final pieces of information are about security and accessibility. You will select a username and password to access the database. Now, the client has all it needs to access the database. The data within a database is formatted as tables and resides with the host either locally...

SRIDs

Each spatial instance or location has an SRID due to the earth being a non-standard ellipsoid. We talk about the surface of the earth, but what does this mean exactly? Do we plunge to the depths of the ocean floor or the highest mountains? The surface of the earth varies depending on where you are on the surface of the earth.

Spatial reference systems (SRS) allow you to compare different maps. If they are superimposable, they have the identical SRS. The European Petroleum Survey Group (EPSG) is the most popular.

Figure 1.1 visually demonstrates the discrepancy in mapping distances on the surface of the earth. Rotational forces at the poles flatten the earth, creating bulging at the equator. This phenomenon complicates the accurate calculation of the distance between two points located at different latitudes and longitudes on the Earth’s surface. The coordinate system helps to standardize spatial instances:

Figure 1.1 – Ellipsoid shape of the earth

Figure 1.1 – Ellipsoid...

Understanding geospatial data types

To understand how to work with geospatial data and the functions available to answer data questions, we will explore different data types. Geospatial geometries are represented by polygons, points, and lines that indicate features and attributes such as census blocks, subway station locations, and roadways. They are vector models as they are representative of real-world areas (polygons), locations (points), or linear data such as transportation networks or drainage networks (lines).

Collectively, we call these different geometries vector data. Think of two-dimensional space as x- and y-coordinates when thinking about a specific geometry. Geography represents data on a round-earth coordinate system as latitude and longitude. I tend to use geometry as the convention across the board but will clarify when relevant. You will see actual data in this chapter, but it is simply illustrative. In later chapters, you will use a data resource, but until...

Exploring SQL

Let’s get a feel for the capabilities and advantages of working with datasets in a spatial database. SQL is described as a declarative programming language. If you have experience in a coding environment, this might be a fairly big departure.

In a declarative programming language, you write what you want to do, and the program figures it out. SQL is a query language that sends queries to manipulate and retrieve data from the database. In an object-oriented programming (OOP) language such as Python, for example, you need to specify system calls incorporating libraries and various packages. Although Python’s syntax is readable, a familiarity with built-in data structures, powerful libraries, frameworks, and a relative commitment to scripting expertise is required. PostgreSQL includes extensions to write procedures in additional programming languages such as Python and R, for example, but the focus here is on SQL and spatial SQL.

In Figure 1.8, you will...

Summary

In this chapter, you were introduced to geospatial fundamentals to help understand the graphical syntax of spatial data science. You discovered a few SQL basic queries and set up your workflow for the chapters that follow. Important concepts about the properties and characteristics of vector and raster data were also introduced.

In Chapter 2, Conceptual Framework for SQL Spatial Data Science – Geometry Versus Geography, you will begin learning about SQL by working with the database you created in this chapter.

The fundamentals of a query-based syntax will become clearer as you discover spatial functions for discovering patterns and testing hypotheses in the datasets.

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Key benefits

  • Follow along with actionable instructions with this practical guide
  • Become well-versed in advanced spatial modeling and machine learning techniques
  • Learn best practices for performing spatial analysis from an expert spatial data analyst

Description

Geospatial analysis is industry agnostic and a powerful tool for answering location questions. Combined with the power of SQL, developers and analysts worldwide rely on database integration to solve real-world spatial problems. This book introduces skills to help you detect and quantify patterns in datasets through data exploration, visualization, data engineering, and the application of analysis and spatial techniques. You will begin by exploring the fundamentals of geospatial analysis where you’ll learn about the importance of geospatial analysis and how location information enhances data exploration. Walter Tobler’s second law of geography states, “the phenomenon external to a geographic area of interest affects what goes on inside.” This quote will be the framework of the geospatial questions we will explore. You’ll then observe the framework of geospatial analysis using SQL while learning to create spatial databases and SQL queries and functions. By the end of this book, you will have an expanded toolbox of analytic skills such as PostGIS and QGIS to explore data questions and analysis of spatial information.

Who is this book for?

This book is for anyone looking to leverage their SQL knowledge to perform geospatial analysis. GIS analysts, data analysts, and data scientists with a basic understanding of both geospatial analysis and SQL will find this book useful.

What you will learn

  • Understand geospatial fundamentals as a basis for learning spatial SQL
  • Generate point, line, and polygon data with SQL
  • Use spatial data types to abstract and encapsulate spatial structures
  • Work with open source GIS combined with plug-ins
  • Visualize spatial data and expand QGIS functionality with Postgres
  • Apply location data to leverage spatial analytics
  • Perform single-layer and multiple-layer spatial analyses
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Table of Contents

12 Chapters
Section 1: Getting Started with Geospatial Analytics Chevron down icon Chevron up icon
Chapter 1: Introducing the Fundamentals of Geospatial Analytics Chevron down icon Chevron up icon
Chapter 2: Conceptual Framework for SQL Spatial Data Science – Geometry Versus Geography Chevron down icon Chevron up icon
Chapter 3: Analyzing and Understanding Spatial Algorithms Chevron down icon Chevron up icon
Chapter 4: An Overview of Spatial Statistics Chevron down icon Chevron up icon
Section 2: SQL for Spatial Analytics Chevron down icon Chevron up icon
Chapter 5: Using SQL Functions – Spatial and Non-Spatial Chevron down icon Chevron up icon
Chapter 6: Building SQL Queries Visually in a Graphical Query Builder Chevron down icon Chevron up icon
Chapter 7: Exploring PostGIS for Geographic Analysis Chevron down icon Chevron up icon
Chapter 8: Integrating SQL with QGIS Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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