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Learning Geospatial Analysis with Python-Second Edition

You're reading from   Learning Geospatial Analysis with Python-Second Edition An effective guide to geographic information systems and remote sensing analysis using Python 3

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Product type Paperback
Published in Dec 2015
Publisher Packt
ISBN-13 9781783552429
Length 394 pages
Edition 1st Edition
Languages
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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 (12) Chapters Close

Preface 1. Learning Geospatial Analysis with Python FREE CHAPTER 2. Geospatial Data 3. The Geospatial Technology Landscape 4. Geospatial Python Toolbox 5. Python and Geographic Information Systems 6. Python and Remote Sensing 7. Python and Elevation Data 8. Advanced Geospatial Python Modeling 9. Real-Time Data 10. Putting It All Together Index

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. However, there is another type of GPS data that pre-dates XML and GPX called National Marine Electronics Association (NMEA). These data are ASCII text sentences 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 to communicate 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.

Take a look at the following small sample of NMEA sentences:

$GPRMC,012417.859,V,1856.599,N,15145.602,W,12.0,7.27,020713,,E*4F
$GPGGA,012418.859,1856.599,N,15145.602,W,0,00,,,M,,M,,*54
$GPGLL...
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