Book Image

Learning Geospatial Analysis with Python - Third Edition

By : Joel Lawhead
Book Image

Learning Geospatial Analysis with Python - Third Edition

By: Joel Lawhead

Overview of this book

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.
Table of Contents (15 chapters)
Free Chapter
Section 1: The History and the Present of the Industry
Section 2: Geospatial Analysis Concepts
Section 3: Practical Geospatial Processing Techniques

Using GPS data

The most common type of GPS data these days is the Garmin GPX format. We covered this XML format in Chapter 4, Geospatial Python Toolbox, which has become an unofficial industry standard. Because it is an XML format, all of the well-documented rules of XML apply to it. However, there is another type of GPS data that pre-dates XML and GPX, called the National Marine Electronics Association (NMEA). This data is ASCII text sentences that are designed to be streamed.

You occasionally bump into this format from time to time because even though it is older and esoteric, it is still very much alive and well, especially for communicating ship locations via the Automated Identification System (AIS), which tracks ships globally. But as usual, you have a good option in pure Python. The pynmea module is available on PyPI. The following code is a small sample of NMEA sentences...