Book Image

Geospatial Analysis with SQL

By : Bonny P McClain
Book Image

Geospatial Analysis with SQL

By: Bonny P McClain

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)
Free Chapter
Section 1: Getting Started with Geospatial Analytics
Section 2: SQL for Spatial Analytics

Analyzing spatial relationships

Locating datasets is a necessary step for analysis. We have uploaded several datasets from NYC Open Data. The Department of Health and Mental Hygiene (DOHMH) dataset reports environmental complaints logged by the DOHMH.

How can we find out where the type and frequency of these complaints are located? Are there areas where the complaints are handled more efficiently?

Let’s learn how to join tables and analyze data based on additional columns and data types. Run the following code to use INNER JOIN on the two tables:

SELECT * FROM ch2."nyc_neighborhoods" JOIN ch2."DOHMH" ON name = "NTA";
FROM ch2."nyc_neighborhoods",ch2."DOHMH"
WHERE name = "NTA";

Although tables are not visually informative in the same way as maps, they can be instrumental in formulating questions and bringing datasets together. Figure 2.16 shows a join of tables in pgAdmin:

Figure 2.16 – Join of tables in pgAdmin