#### Overview of this book

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.
Table of Contents (13 chapters)
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Section 1: Getting Started with Geospatial Analytics
Chapter 1: Introducing the Fundamentals of Geospatial Analytics
Chapter 2: Conceptual Framework for SQL Spatial Data Science – Geometry Versus Geography
Chapter 3: Analyzing and Understanding Spatial Algorithms
Chapter 4: An Overview of Spatial Statistics
Section 2: SQL for Spatial Analytics
Chapter 5: Using SQL Functions – Spatial and Non-Spatial
Chapter 6: Building SQL Queries Visually in a Graphical Query Builder
Chapter 7: Exploring PostGIS for Geographic Analysis
Chapter 8: Integrating SQL with QGIS
Index
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# 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...