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Learning Geospatial Analysis with Python

You're reading from   Learning Geospatial Analysis with Python Understand GIS fundamentals and perform remote sensing data analysis using Python 3.7

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Product type Paperback
Published in Sep 2019
Publisher
ISBN-13 9781789959277
Length 456 pages
Edition 3rd Edition
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Author (1):
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Joel Lawhead Joel Lawhead
Author Profile Icon Joel Lawhead
Joel Lawhead
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Table of Contents (15) Chapters Close

Preface 1. Section 1: The History and the Present of the Industry
2. Learning about Geospatial Analysis with Python FREE CHAPTER 3. Learning Geospatial Data 4. The Geospatial Technology Landscape 5. Section 2: Geospatial Analysis Concepts
6. Geospatial Python Toolbox 7. Python and Geographic Information Systems 8. Python and Remote Sensing 9. Python and Elevation Data 10. Section 3: Practical Geospatial Processing Techniques
11. Advanced Geospatial Python Modeling 12. Real-Time Data 13. Putting It All Together 14. Other Books You May Enjoy

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...

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