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Learning Geospatial Analysis with Python Paperback – October 25, 2013
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About the Author
Joel Lawhead is a PMI-certified Project Management Professional (PMP) and the Chief Information Officer (CIO) for NVisionSolutions.com, an award-winning firm specializing in geospatial technology integration and sensor engineering.
He began using Python in 1997 and began combining it with geospatial software development in 2000. He has been published in two editions of the Python Cookbook by O'Reilly. He is also the developer of the widely used open source Python Shapefile Library (PyShp) and maintains the geospatial technical blog GeospatialPython.com and Twitter feed @SpatialPython discussing the use of the Python programming language within the geospatial industry.
In 2011, he 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 involving shapefiles.
He has served as the lead architect, project manager, and co-developer for geospatial applications used by US government agencies including NASA, FEMA, NOAA, the US Navy, as well as many commercial and non-profit organizations. In 2002, he received the international "Esri Special Achievment in GIS" award for work on the Real-time Emergency Action Coordination Tool (REACT) for emergency management using geospatial analysis.
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If you are interested in learning geospatial analysis with python, DO NOT get this book.
First, who would be interested in this book?
Really, anyone who has ever had an interest in simply learning what Geospatial Analysis is will benefit from this book, the first chapters go through an excellent introduction into what the field of Geospatial Analysis even is and the topics that cover its range, such as Geographic Information Systems and Remote Sensing. That being said, once you get into the specific topics the focus really shifts to using Python to implement examples of these topics, and an individual who is completely unfamiliar with Python wouldn't benefit as much as someone who has a fairly good Python or programming background. Nevertheless eve if you never ran a single line of code, simply reading the book from front to back should give the reader a good foundation on what Geospatial Analysis is and how it is performed.
Second, I'd like to discuss if the book covers what it says it does in its description:
The description states it will offer a background in Geosptial Analysis, which it does a great job of in the beginning chapters. Before even jumping into the many Python modules you'll use, readers will gain an understanding of the topic they'll be learning.
The second big statement is that the book will teach you how do use Python to implement Geospatial Analysis, and I was pleasantly surprised that the author really strives to keep everything running in pure Python modules whenever possible. The most you'll stray from running pure Python modules is in the use of some file viewers. Another great addition is that the author really pushes the use of open source libraries, throughout the book I never felt like I needed to purchase any of the more intense Geographic Information System software packages that are on the market. I really felt like all I needed was my Python interpreter and an internet connection.
The next aspect that I found most enjoyable was that I simply liked reading the book. I initially sat down and just read the book from front to back, without following the code tutorials. I really enjoyed this approach because after I had read through, I simply went back and followed the tutorials that I found to be the most interesting to get a better understanding of how they were used. Another thing I highly enjoyed was seeing the use of some Python packages I have seen used in so many other scientific realms, most notably NumPy, as well as some modules that I'd never seen before, most of which are directly related to Geospatial Analysis.
The thing I learned the most from reading this book is that Geospatial Analysis is all about the data, and there is an extremely broad amount of data and data formats that can be used. A large portion of the text will cover what this data is, how it is used, and ways you can use Python to extract, alter, and use this data in creating some pretty cool Geospatial models. Even the very first map you draw depends on using this data. Thankfully you won't have to go searching for this, as the book provides plenty of data files for download.
Overall I would highly recommend this book to anyone that has a love of Python and has a real interest in getting a starting background in understanding Geospatial Analysis. I would offer a slight recommendation for those users that are simply looking for a text book about Geospatial Analysis, as the real fun of the book comes from seeing the models and images being manipulated from the solid set of functions that the author gives you in Python. It is obvious that the author cares a great deal about the topic, and the enthusiasm is a definite benefit to the flow of the book. A fun, good read in my opinion as someone who initially knew very little about the topic.
The book starts with some interesting history and the underlying theory behind Geospatial systems. But you won't get bored, because right from the start (at the end of chapter 1) you learn how to implement a simple Geospatial system in Python.
It then continues with a description of all the tools involved with doing Geospatial analysis, showing important concepts such as how to: read erroneous XML-based Geospatial data, measure distances, create histograms, etc.
A minus is the image quality. If you read on a Kindle that doesn't support image colors it's impossible to understand the author's descriptions.