Learning Geospatial Analysis with Python-Second Edition: An effective guide to geographic information systems and remote sensing analysis using Python 3

This excellent book starts with a deep history of geospatial science and then dives into practical applications of the technology for common mapping problems. What I really like about this book is that it doesn't stop at "here's how to put a Google maps widget into your web application". This book goes deeper and explains how to use real geospatial analysis. In my personal experience, knowing how to use those analytic techniques opened up new possibilities in applications that greatly increased their usefulness.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.

DIY GIS via Python. Why spend gobs of money on ESRI ArcGIS or Matlab when you can do it yourself for free with Python? Power to the people (er, programmer)! Go Pythonistas! This book would make a fantastic undergraduate- and/or graduate-level textbook for an alternative Python-based GIS or scientific computing class.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.

Great introduction to the topic. Lots of useful examples.

The easiest way to justify my one star review is with an example: The code listing starting on page 207 and extending to page 209 uses turtle graphics to draw a histogram.