Learning Geospatial Analysis with Python

Understand GIS fundamentals and perform remote sensing data analysis using Python 3.7, 3rd Edition

Joel Lawhead

• Publisher: Packt Publishing
• Year: 2019
• eBook ISBN: 9781789957938
• Topic: Computer Science
• Subtopic: Bioinformatics
• Index: Computer Science

Learn the core concepts of geospatial data analysis for building actionable and insightful GIS applications

Key Features

• Create GIS solutions using the new features introduced in Python 3.7
• Explore a range of GIS tools and libraries such as PostGIS, QGIS, and PROJ
• Learn to automate geospatial analysis workflows using Python and Jupyter

Book Description

Geospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. With this systematic guide, you'll get started with geographic information system (GIS) and remote sensing analysis using the latest features in Python.This book will take you through GIS techniques, geodatabases, geospatial raster data, and much more using the latest built-in tools and libraries in Python 3.7. You'll learn everything you need to know about using software packages or APIs and generic algorithms that can be used for different situations. Furthermore, you'll learn how to apply simple Python GIS geospatial processes to a variety of problems, and work with remote sensing data.By the end of the book, you'll be able to build a generic corporate system, which can be implemented in any organization to manage customer support requests and field support personnel.

What you will learn

• Automate geospatial analysis workflows using Python
• Code the simplest possible GIS in just 60 lines of Python
• Create thematic maps with Python tools such as PyShp, OGR, and the Python Imaging Library
• Understand the different formats that geospatial data comes in
• Produce elevation contours using Python tools
• Create flood inundation models
• Apply geospatial analysis to real-time data tracking and storm chasing

Who this book is for

This book is for Python developers, researchers, or analysts who want to perform geospatial modeling and GIS analysis with Python. Basic knowledge of digital mapping and analysis using Python or other scripting languages will be helpful.

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Section 1: The History and the Present of the Industry

This section starts by demonstrating common geospatial analysis processes using illustrations, basic formulas, simple code, and Python. Building on that, you'll learn how to play with geospatial data—acquiring data and preparing it for various analyses. After that, you'll gain an understanding of the various software packages and libraries used in the geospatial technology ecosystem. At the end of this section, you'll learn how to evaluate any geospatial tool.

This section includes the following chapters:

• Chapter 1, Learning about Geospatial Analysis with Python
• Chapter 2, Learning Geospatial Data
• Chapter 3, The Geospatial Technology Landscape

Learning about Geospatial Analysis with Python

Geospatial technology is currently impacting our world since it is changing our knowledge of human history. In this book, we will step through the history of geospatial analysis, which predates computers and even paper maps. Then, we will examine why you might want to learn about and use a programming language as a geospatial analyst as opposed to just using geographic information system (GIS) applications. This will help us understand the importance of making geospatial analysis as accessible as possible to as many people as possible.

In this chapter, we will be covering the following topics:

• Geospatial analysis and our world
• Dr. Sarah Parcak and archaeology
• Geographic information systems
• Remote sensing concepts
• Elevation data
• Computer-aided drafting
• Geospatial analysis and computer programming
• The importance of geospatial analysis
• Geographic information system concepts
• Common GIS processes
• Common remote sensing processes
• Common raster data concepts
• Creating the simplest possible Python GIS

Yes, you heard that right! We will be building the simplest possible GIS from scratch using Python, right from the start.

Technical requirements

This book assumes that you have some basic knowledge of the Python programming language, basic computer literacy, and at least an awareness of geospatial analysis. This chapter provides a foundation for geospatial analysis, which is needed to attack any subject in the areas of remote sensing and GIS, including the material in all the other chapters of this book.

The examples in this book are based on Python 3.4.3, which you can download here: https://www.python.org/downloads/release/python-343/.

Geospatial analysis and our world

In the 1880s, British explorers began applying scientific rigor to excavating ancient cultural sites. The field of archaeology is a frustrating, low, costly, and often dangerous endeavor requiring patience and a good bit of luck. The Earth is remarkably good at keeping secrets and erasing the story of human endeavors. Changing rivers, floods, volcanoes, dust storms, hurricanes, earthquakes, fires, and other events swallow entire cities into the surrounding landscape, and we lose them to the flow of time.

Our knowledge of human history is based on glimpses into ancient cultures through archaeological excavation and the study of sites we have been lucky enough to stumble across through educated guesses or trial and error. There used to be no success in archaeology unless a team excavated a site, found something, and correctly identified it. Predictions on where to look were based on a handful of major factors such as proximity to water that was needed to support agriculture, previously discovered sites, accounts by early explorers, and other broad clues.

In 2007, archeologist Dr. Sarah Parcak, from the University of Alabama, Birmingham, began to coax our stubborn Earth into revealing its secrets about where humans have been and what they've done. Since then, her approach has revolutionized the field of archaeology.

In a few short years, Dr. Parcak and her team found traces of 17 pyramids, more than 1,000 tombs, and the footprints of 3,000 ancient settlements in Egypt, including the city grid of the famous lost city of Tanis. She identified significant archaeological sites in Romania, the Nabataean Kingdom, and Tunisia. She located an arena at the well-excavated ancient Roman harbor of Portus, as well as its lighthouse and canal leading to Rome near the Tiber river.

How did she find so many hidden treasures that eluded detection for almost two centuries? She looked at the bigger picture. Dr. Parcak perfected the art of using satellite imagery to locate ancient sites from almost 400 miles above the Earth. Her career happened to coincide with the advent of readily-available, high-resolution satellite imagery that had a 10-inch pixel resolution or less, thereby providing the detail that was needed to detect subtle changes in the landscape, thus indicating ancient sites.

Despite the volume and significance of her finds, locating cultural heritage sites from space requires a tremendous amount of work. Space archaeologists first research old maps and historical accounts. Then, they look at modern digital maps of existing sites. They also look at digital terrain models to locate subtle rises in the land where ancient people would build to avoid floods. Then, they use multispectral imagery, including infrared, which can expose changes in vegetation or soil when processed due to imported stone and other materials buried underground that bubble up to the surface. This discoloration, which is represented by false colors, allows us to differentiate between the bandwidths of sunlight reflected from sites that are completely invisible on the ground, or even from the air, to the naked eye, which suddenly stand out in sharp contrast, showing precise locations on a satellite image.

Ancient cultural sites are often invisible to the naked eye from the ground. For example, the following photograph shows a well-preserved Native American burial mound near Lewiston, Illinois, USA, which has survived for thousands of years due to its location and is easily visible:

However, in areas with harsher weather conditions, sites can be partially destroyed, and so they are difficult to find. The following photograph shows an area of marsh in Louisiana, which is full of ancient Native American burial mounds that have eroded over the centuries and are now nearly impossible to detect without satellite images:

The following processed satellite image, from NASA scientist Dr. Marco Giardino, is in the same marsh area as the previous photograph and shows the remains of four distinct burial mounds that aren't visible from the ground. Even though this site is hundreds of years old, the vegetation species and their health are different compared to the surrounding marsh. Although archaeologists researched dozens of similar sites in the area, this project was the first to determine that the mound builders often used a pattern of placing the mounds in the four cardinal directions (north, south, west, east), which is highly visible from space but difficult to realize on the ground:

As quick as space archaeologists are at locating ancient sites, they now find themselves battling more than geological and meteorological elements. Looting has always been a threat to archaeology, but due to warfare and black market artifacts, it has become even more of a problem. Modern construction can also destroy valuable sites. However, determined archaeologists are using the same technology they used to find the sites...

Table of contents

• Title Page
• Copyright and Credits
• Dedication
• About Packt
• Contributors
• Preface
• Section 1: The History and the Present of the Industry
• Learning about Geospatial Analysis with Python
• Learning Geospatial Data
• The Geospatial Technology Landscape
• Section 2: Geospatial Analysis Concepts
• Geospatial Python Toolbox
• Python and Geographic Information Systems
• Python and Remote Sensing
• Python and Elevation Data
• Section 3: Practical Geospatial Processing Techniques
• Advanced Geospatial Python Modeling
• Real-Time Data
• Putting It All Together
• Other Books You May Enjoy