- Paperback: 394 pages
- Publisher: Packt Publishing; 2 edition (December 31, 2015)
- Language: English
- ISBN-10: 1783552425
- ISBN-13: 978-1783552429
- Product Dimensions: 7.5 x 0.9 x 9.2 inches
- Shipping Weight: 1.8 pounds (View shipping rates and policies)
- Average Customer Review: 5 customer reviews
- Amazon Best Sellers Rank: #725,823 in Books (See Top 100 in Books)
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Learning Geospatial Analysis with Python - Second Edition 2nd Edition
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About the Author
Joel Lawhead is a project management institute-certified Project Management Professional (PMP), certified GIS Professional (GISP), and the Chief Information Officer (CIO) of NVision Solutions Inc., an award-winning firm that specializes in geospatial technology integration and sensor engineering. Joel began using Python in 1997 and started combining it with geospatial software development in 2000. He is the author of the first edition of Learning Geospatial Analysis with Python and QGIS Python Programming Cookbook, both by Packt Publishing. His Python cookbook recipes were featured in two editions of Python Cookbook, O'Reilly Media. He is also the developer of the widely-used, open source Python Shapefile Library (PyShp). He maintains the geospatial technical blog http://geospatialpython.com/ and the Twitter feed, @SpatialPython, which discusses the use of the Python programming language in the geospatial industry. In 2011, Joel reverse-engineered and published the undocumented shapefile spatial indexing format and assisted fellow geospatial Python developer, Marc Pfister, in reversing the algorithm used, allowing developers around the world to create better-integrated and more robust geospatial applications. Joel serves as the lead architect, project manager, and co-developer for geospatial applications used by U.S. government agencies, including NASA, FEMA, NOAA, the U.S. Navy, and many other commercial and non-profit organizations. In 2002, he received the international Esri Special Achievement in GIS award for his work on the Real-Time Emergency Action Coordination Tool (REACT), for emergency management using geospatial analysis.
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A lot of material is covered in this book and it would serve as an excellent primer to anyone who's been handed a mapping project but doesn't know where to start. GIS can be a bit overwhelming at first because it's really its own little world. In order to build a working app you usually wind up using several different software libraries, special file formats like shapefile, and spatial databases. This book gives an excellent overview of each and shows how to practically apply these new skills to solve real problems.
I also recommend this book if you're a GIS pro but you've spent most of your career looking at the world through an Esri lens. The open source software covered is worth knowing and you may find yourself turning to it where you would normally reach for Arc*. It's amazing how simple and lightweight some of the applications are.
Not only does this book lay the groundwork to sufficiently educate the reader on geospatial analysis (its history, basic terminology, etc.), it has relevant, fun examples, plentiful screenshots throughout, and an enthusiastic and inspiring tone. It provides a crystal clear breakdown of key GIS concepts, giving equal weight to both vector and raster data sources. Motivates the reader to use programming for geospatial analysis as opposed to just traditional "point-and-click" GIS software. With programming, the possibilities are practically limitless: utilities can be coded to meet specific needs rather than being limited to whatever canned set of functions a particular software package provides.
Already we get our hands dirty building a simple Python GIS example in chapter 1.
Chapter 2 provides a useful survey of data formats, which can otherwise feel like a dizzying and overwhelming array of options for the novitiate. The chapter helps boil this down into the various types, categorized by vector or raster, human-readable or binary. Even in a book focused primarily on Python, it is important to lay this kind of groundwork so that the reader isn’t later bogged down or confused simply by the chosen data format of a given example, etc. The author also covers Open Geospatial Consortium (OGC) web services, web mapping, and GeoJSON.
Chapter 3 highlights many of the big players in today’s geotech industry in an organized fashion that helps make sense out of the chaos: an invaluable overview for newbies to bring them up to speed on a diverse technology stack. While it is not Python per se, the chapter brings the focus back to Python at the end.
Chapter 4 surveys many of the important Python geo-modules with good examples of how to use them.
Chapter 5 delves more deeply into a few Python modules for greater enrichment. The reader learns a whole slew of handy tricks and tips and could already easily adapt the numerous examples provided to solve many real-world GIS problems. Highlights include Shapefile manipulation, data visualization, reading Excel spreadsheets, geocoding, and parsing GPS data.
While chapter 5 focuses on aspects of vector data, chapter 6 spreads the love to raster data, accomplishing many of the more common (though complicated) remote sensing (i.e. satellite data) tasks.
Chapter 7 illustrates the complexities and advantages of elevation (DEM) datasets and how Python can be leveraged to process and visualize them—-a useful follow-on to the previous vector and raster chapters since DEMs are part both (and more). With contours, shaded relief maps, and color-coded rasters, there are unique ways of analyzing elevation that differ from other datasets. Another highlight for me was the inclusion of LiDAR data.
Chapter 8 showcases the kind of “heavy lifting” that can be achieved with all of the skills accrued earlier in the book, including terrain routing, street routing, flood inundation models, and vegetation analysis (via NDVI). It shows off the capabilities and flexibility inherent to a Python GIS approach.
Chapter 9 goes beyond the more "static" GIS paradigm and addresses time. It puts near real-time information (tracking bus locations) onto maps, both static maps and interactive ones (via Leaflet). This chapter introduces readers to a typical Web GIS workflow using REST, Web Map Service (WMS), XML, and OpenStreetMaps.
The closing chapter (chapter 10) does a good job of combining lots of the previous lessons into one “grand finale”, including vector, raster, hillshades, and real time data using state of the art examples, also incorporating Google Charts and producing PDF reports in the finished product.
As the proverb goes: "Give a man a fish and you feed him for a day; teach a man to fish and you feed him for a lifetime". In a similar spirit, giving the "GIS" analyst a GUI-based software package solves a limited set of problems and leaves them hungry for the next upgrade and new buttons to press (often blindly); teach them to program, however, and they can solve any problem themselves. And they will also likely gain a better understanding of the processing that is involved along the way.