Book Image

Learning Geospatial Analysis with Python

By : Joel Lawhead
Book Image

Learning Geospatial Analysis with Python

By: Joel Lawhead

Overview of this book

Geospatial Analysis is used in almost every field you can think of from medicine, to defense, to farming. This book will guide you gently into this exciting and complex field. It walks you through the building blocks of geospatial analysis and how to apply them to influence decision making using the latest Python software. Learning Geospatial Analysis with Python, 2nd Edition uses the expressive and powerful Python 3 programming language to guide you through geographic information systems, remote sensing, topography, and more, while providing a framework for you to approach geospatial analysis effectively, but on your own terms. We start by giving you a little background on the field, and a survey of the techniques and technology used. We then split the field into its component specialty areas: GIS, remote sensing, elevation data, advanced modeling, and real-time data. This book will teach you everything you need to know about, Geospatial Analysis from using a particular software package or API to using generic algorithms that can be applied. This book focuses on pure Python whenever possible to minimize compiling platform-dependent binaries, so that you don’t become bogged down in just getting ready to do analysis. This book will round out your technical library through handy recipes that will give you a good understanding of a field that supplements many a modern day human endeavors.
Table of Contents (17 chapters)
Learning Geospatial Analysis with Python Second Edition
About the Author
About the Reviewers

Geospatial analysis and our world

On March 25, 2014, the world awoke to news from the United Nations World Health Organization (WHO) announcing the early stages of a deadly virus outbreak in West Africa. The fast-moving Ebola virus would spread rapidly over the coming summer months resulting in cases in six countries on three continents, including the United States and Europe.

Government and humanitarian agencies faced a vicious race against time to contain the outbreak. Patients without treatment died in as little as six days after symptoms appeared. The most critical piece of information was the location of new cases relative to the existing cases. The challenge they faced required reporting these new cases in mostly rural areas with limited infrastructure. Knowing where the virus existed in humans provided the foundation for all of the decisions that response agencies needed for containment. The location of cases defined the extent of the outbreak. It allowed governments to prioritize the distribution of containment resources and medical supplies. It allowed them to trace the disease to the first victim. It ultimately allowed them to see if they were making progress in slowing the disease.

This map is a relative heat map of the affected countries based on the number of cases documented and their location:

Unfortunately, the rural conditions and limited number of response personnel at the beginning of the outbreak resulted in a five-day reporting cycle to the Liberian Ministry of Health who initially tracked the virus. Authorities needed better information to bring the outbreak under control as new cases grew exponentially.

The solution came from a Liberian student using open source software from a non-profit Kenyan technology start-up called Ushahidi. Ushahidi is the Swahili word for testimony or witness. A team of developers in Kenya originally developed the system in 2008 to track reports of violence after the disputed presidential election there. Kpetermeni Siakor set up the system in Liberia in 2011 following a similarly controversial election. When the epidemic hit Liberia, Siakor turned Ushahidi into a disease-monitoring platform.

Siakor created a dispatch team of volunteers who received phone calls from the public reporting possible Ebola cases. The details were entered into the Ushahidi database, which was available on a web-based map almost instantly. The Liberian Ministry of Health and other humanitarian organizations could access the website, track the spread of the disease, and properly distribute supplies at health centers. This effort, amplified by the international response, would ultimately contain the epidemic globally. In 2015, cases are receding as the world monitors West African cases in anticipation of the last patient recovering. The following screenshot shows the latest Liberian public Ushahidi map as of April, 2015:

Relief workers also used the Ushahidi disaster mapping system to respond to the 2010 Haiti earthquake. Maps have always been used in disaster relief; however, the modern evolution of GPS-enabled mobile phones, web technology, and open source geospatial software have created a revolution in humanitarian efforts globally.


The Ushahidi API has a Python library that you can find at

Beyond disasters

The application of geospatial modeling to disaster relief is one of the most recent and visible case studies. However, the use of geospatial analysis has been increasing steadily over the last 15 years. In 2004, the U.S. Department of Labor declared the geospatial industry as one of 13 high-growth industries in the United States expected to create millions of jobs in the coming decades.

Geospatial analysis can be found in almost every industry, including real estate, oil and gas, agriculture, defense, politics, health, transportation, and oceanography, to name a few. For a good overview of how geospatial analysis is used in dozens of different industries, visit