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.
Free Chapter
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
Other Books You May Enjoy

Building prediction models

When beginning to analyze data spatially, there are a few practices that will make the endeavor run more smoothly. It is always important when bringing together more than one dataset to evaluate the geometry columns. Running the following code will give you a glimpse into the SRID and data type in Figure 4.20:

```SELECT Find_SRID('ch4','hisplat_la','geom');
SELECT * FROM geometry_columns```

Exploring the `below_poverty_censustract` data for Los Angeles County, I want to be able to isolate a tract and explore neighboring tracts. Location and distance might hold clues for exploring marginalized communities or populations living below the poverty line.