Learning Geospatial Analysis with Python: Unleash the power of Python 3 with practical techniques for learning GIS and remote sensing , Fourth Edition

[disclaimers – (i) a publisher’s representative solicited a review of this book and provided an e-book version for that purpose but no recompense, (ii) this is my impartial, personal review - and hence is not an endorsement by my employer, implicit or otherwise.]--Chapters:Learning Geospatial Analysis with PythonLearning About Geospatial DataThe Geospatial Technology LandscapeGeospatial Python ToolboxPython And Geospatial AlgorithmsCreating And Editing GIS DataPython And Remote SensingPython And Elevation DataAdvanced Geospatial ModelingWorking With Real-Time DataPutting It All TogetherAssessments--Anecdotal I know: in a previous role, I wrote, tested and applied code for ‘geospatial analysis with Python’, applied hydrologic science. With a new role – which I am grateful for – I don’t get to do this often. When the publisher asked me to look at this new (4th Edition) of this book and provide my impression, I was therefore very happy to do so, a nudge to get me back into some old tasks in a guided way, that will help my current role in a technologies platform agnostic way.My initial look over this book has, frankly, got me excited, as cliché as that might sound! The chapters are well laid out, leading you into your exploration and learning, the writing is far from dry, the description of options and different possible approaches is excellent and so much more.The first three chapters bring you into the space, and are wonderful refreshers for those more experienced, with some reminders that GIS, spatial analysis and applied science, business, technology, etc is for a purpose, to help solve problems, ‘better, faster, stronger’ – helping tale away through code and association automation some of the mundanity of spatial data processing, analysis and results presentation and implementation in pragmatic ways – with associated reduction in errors, increase in accuracy, etc. That all sounds a little ‘fluffy I know, but I think it is the core of what we do as spatial professionals, especially in research and/or bringing systems to a production environment.The chapters following are where you get to ‘dig in’, with outstanding descriptions of the what, how, why of the various software and libraries that form the foundation of the book and are used throughout, descriptions of many spatial algorithms and how to apply them in a Python setting, ditto with the large volume of readily available and powerful remote sensing (open) data, ditto the strengths and what to be careful with in terms of elevation data.As the author states, “geospatial data editing and processing helps use understand the world as it is”, and this is the thread of the chapter on geospatial modeling, and for me the most powerful chapter in the book, as the applied scientist that I like to consider myself.Working with real-time data is – frankly – something I have not had a lot of experience in and so I look forward to the exposure and grounding that this chapter will bring, as I can see myself wanting if not needing to use it ongoing.The final chapter is one that, through my initial look, will be the most powerful & worthwhile for me. For those of you old enough, the dry-as-dust ‘worked examples’ like the Northwind database and other such are not in evidence here. I used to teach a little (and also for some of my mentees) the one thing I tried to get across is to work with real-world data and problem-solving as soon and as often as you can in your learning processes, to experience all of its warts, missing info, non-clean contributing datasets, and more – and this chapter appears to do just that! (as you will expect from a self-help book like this, especially one of its evident quality and usefulness, there are example code and data files to download – and my initial appraisal is that they are excellent, and I know that I am using that word a lot in this review.)In my opinion, you will like this book, get value from it, not get bored or ‘stuck’, and will come out the other side a better spatial professional, and I certainly expect to do so as I work through it over the coming months.

I recently had the pleasure of diving into 'Geospatial Analysis with Python - Fourth Edition,' and it has been an enlightening experience. This book masterfully unravels the complexities of geospatial analysis, making it accessible and engaging. The authors do an excellent job of blending theoretical knowledge with practical applications, providing readers with the tools and confidence to tackle their own geospatial projects. The updated content in this edition, including the latest Python libraries and techniques, has been particularly valuable. Whether you're a beginner or looking to sharpen your skills, this book is a must-have for anyone interested in unlocking the power of Python for geospatial analysis. Highly recommended!

This is not your typical geospatial and python book! While seemingly geared towards beginners it has content that is interesting even for seasoned professionals. Some of the key aspects I enjoyed most about this book are:1. Full examples of common geospatial algorithms written in python2. The comprehensive layout of the FOSS geospatial ecosystem3. Useful real-world code for accessing and cleaning data4. The background history of GIS and remote sensingMy main critique would be that for a book about analysis, I found the example analytics to be a little basic. There are also a couple of examples of using ChatGPT to help you write code which I think would have been better as an aside rather than an example.All said though, it's a great book, and I will be referencing it in the future!

As a geologist learning Python I found this book very helpful since it uses practical examples that I can apply my the job. Moreover it was useful to refresh some of the subjects related to remote sensing and to understand what other tools for geospatial analysis are out there. The examples used in the book are easy to follow, well explain and realistic.

This book is well packaged with good illustrations for anyone interested in Geospatial analysis with python. I love the fact that it covered the geospatial analysis tools including remote sensing and GIS. I am sure this is well detailed for both beginners and professional. I found chapters 5 and 6 very interesting. I'm still using the book anyways but I highly recommend.