I recently had the pleasure of reading "Geospatial Analysis with SQL" by Bonny McClain, and it exceeded all my expectations. This book is an absolute gem for anyone interested in harnessing the power of Python for geospatial data analysis.The author's expertise shines through in the clarity of explanations and the well-structured content. From the very beginning, it is evident that McClain knows how to guide readers, whether they are beginners in geospatial analysis or seasoned professionals looking to expand their skills.What I appreciated most about this book is its practical approach. McClain does not just dive into theory; instead, the book is filled with hands-on examples and exercises that allow you to apply what you've learned immediately. The code samples are well-documented and easy to follow, making it accessible even for those who might be new to SQL

This is a great read for anyone looking to expand their skillset for GIS. I was having a hard time learning how to effectively query data specifically for GIS and this book has become such a great tool in learning to do so. Bonny does an excellent job expanding on fundamental topics but also shows tons of examples and photos of how to use SQL. As a visual learner I really appreciated that. She covers basic concepts to more advanced queries. There are tons of courses and online resources to learn SQL but finding one that solely focuses on tasks in Geospatial Analysis is a gem. I found the section on PostGIS especially useful.

As a technical consultant in the geospatial field, I’m always looking for resources to expand my knowledge and skills around SQL and other programming languages. This book offers a great review of what SQL is, how it works, and how it can be used for a variety of geospatial processes. I’ll be keeping this close by for sure!

I am pleased to recommend this book to whosoever is interested in developing spatial data analytics skills. You do not need prior knowledge to start from Chapter 1 (Introducing the Fundamentals of Geospatial Analytics), as the author made the learning process easy. For instance, despite instructing using Mac, I was able to follow along on Windows. For E.g., I posted the output of the step-by-step guide, which I did on Windows.One of the interesting things about this book is that all the tools and datasets used are open-source.This book is a hands-on guide that teaches you spatial skills and the framework behind them. The distinction between geometry and geography was explicitly explained in this book as it relates to geospatial datasets.The key takeaways from this hands-on guide:You will learn the following:1. PostGIS2. QGIS3. how to create a spatial database4. how to load your downloaded datasets to the spatial database schema5. how to build queries from the simplest to the most complex ones6. how to query your spatial datasets to show the spatial context of your subject matter (this is the strength of this hands-on guide)7. have access to the real-world dataset to reproduce all the analyses done by the author8. spatial statistics, including building prediction models9. how to construct spatial questions10. how to interact with spatial data types.Finally, I commend the author's logical thinking approach and how she articulated this step-by-step guide.As you read, if you have questions, your answers are in the next paragraphs or two to where you are because the author had probably thought about possible questions and responded in the next paragraph.NOTE:1. If your concentration shifts away from a paragraph, you might get lost. This step-by-step guide is so comprehensive that every sentence is a pathway to the next sentence.2. The links to the datasets in this step-by-step guide might be slightly different from what you will download because they are real-world datasets and are always updated.

Ms. McClain, the author, is well-known in the geospatial community, and one I follow - and I am therefore pleased to be asked to provide a review of this specific book.There is – you will not be surprised to hear – a LOT in this book, 230 pages or so intense (and excellent) information.I was fully prepared for the ‘grind’ you tend to get with so many dry technical books, circling back, clarifying as best you can to what is ‘scattered’ throughout such books, being ‘forced’ to take notes on the side, etc.I am very pleased to see that this is NOT the case with this volume. The organisation of the chapters leads you into the subject(s), not too simplistically like some ‘technical self-learning’ books, but also not dropping you in the proverbial ‘deep end’ – fundamentals, conceptual framework (geometry vs. geography), spatial algorithms, spatial statistics, functions, query building basics through to detailed methodologies and details, useage within PostGIS, and then integrating SQL with QGIS. Be sure that this book is very approachable and useful.We are all inherently visual creatures, and there is excellent use throughout of pertinent, attractive and clear graphics to effectively illustrate the text’s explanations – maps, database output views using colour formatted SQL for clarity, pgAdmin outputs, software dialogues and other screenshots, and much more; they will aid your learning.Many of us are also (at least in part) self-taught in terms of our ongoing education, including through books like these, and often to address a specific technical knowledge need; we may therefore be missing the finer points of some of the fundamental concepts. This book helps fill some of those holes in these areas as we work through it - projection information in PostGIS, nuances of a certain SQL approach, useage and constraints of spatial statistics, etc. – whilst retaining the focus of the learning.As part of that ongoing, self-taught learning, we have encountered unimaginative, dry working examples like MS Access’ NorthWind database (sorry MS, and yes, I am dating myself!) The examples datasets and their derivates used in this book are the opposite, being engaging and again well presented and integrated - and it is refreshing to encounter (see the note above about ‘grinding through’ a technical self-learning text.) Dare I say that it contributes as another factor making this book a ‘page turner’ (and I am not being (too) facetious…), we are working with data in a way that is relatable and hence helps keep our attention and concentrates our learning retention.The author’s writing style – and the excellent use of graphics and dataset examples as mentioned – makes this book an easy one to approach and engage with, its technical focus notwithstanding. They lead us into technical descriptions and explanations without being overwhelming, which is a rare and valuable skill. Their writing is also not the dry ‘Put tab A into slot B’ technical manual style we see so often (as above), but more a focused and eminently comprehensible narrative on the ‘What, Where, When, Why’ of the technical approach being described and how it fits into what has been learnt previously and is yet to be learnt.Although this book has excellent detail, it might have been nice to see some suggestions for other resources to help us continue our learning in specific areas and overall (being such a large & varied set of connected technical areas and skills to be acquired.) This is not a criticism per se, as ‘Google is my friend’, but it might be a way to further tap into the author’s knowledge of the subject if they had suggested ‘where next’ based on their obvious technical knowledge and experience.I got the e-book about a week ago and have started to work through it with dare I say glee and enthusiasm; I am looking forward very much to both ‘digging in’ and also using this as an ongoing reference as my primary (below) and related projects proceed, along with my own learning and technical use of this material. Further, I want to get hold of a hard copy – I am old-school I guess – and it will become a well-thumbed book (I am full of clichés today) referenced for years to come, with the coloured sticky notes for bookmarks, highlighted phrases, and pencilled marginalia that all my first-rate reference books have. As we mature technically, we realise we can’t learn and remember everything – but we can keep technical books like this close…I am part of a team (in the initial stages of the R&D and prototyping) that will be using PostgreSQL + PostGIS as the enterprise backbone of a multi-faceted system to analyse, add to and deliver tabular and spatial open data and products to the public. This will (primarily) not be using a GIS engine, will involve a LOT of data (is ‘big data’ passe these days?) and have integrated planned tasks and delivery systems. The reason I am going into such detail? With this immediate and likely decadal ongoing need, I need to add as much to my knowledge about SQL + spatial data manipulation as I can – and so a timely resource indeed![disclaimer(s) – (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.][brief bio - I am a spatial data scientist working in research areas, lately specifically related to hydrology at a US nationwide scale; my focus is being adept at using my scientific background and training, technical data familiarity, platform-agnostic software solutions prototyping skills, and varied analysis methods to help create robust applied science approaches - and then help get them to be production solutions.]#GIS #spatial #mapping #BookTwitter #bookreview #review #technicalbook #book #PostGIS #PostgreSQL #geospatial #analysis #SQL #opensource #gischat #spatialstatistics #QGIS #geography @datamongerbonny @PacktPublishing #PacktPublishing