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Create GIS solutions using the new features introduced in Python 3.10

Explore a range of GIS tools and libraries, including PostGIS, QGIS, and PROJ

Identify the tools and resources that best align with your specific needs

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Description

Geospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. In this special 10th anniversary edition, you'll embark on an exhilarating geospatial analysis adventure using Python. This fourth edition starts with the fundamental concepts, enhancing your expertise in geospatial analysis processes with the help of illustrations, basic formulas, and pseudocode for real-world applications. As you progress, you’ll explore the vast and intricate geospatial technology ecosystem, featuring thousands of software libraries and packages, each offering unique capabilities and insights. This book also explores practical Python GIS geospatial applications, remote sensing data, elevation data, and the dynamic world of geospatial modeling. It emphasizes the predictive and decision-making potential of geospatial technology, allowing you to visualize complex natural world concepts, such as environmental conservation, urban planning, and disaster management to make informed choices. You’ll also learn how to leverage Python to process real-time data and create valuable information products. By the end of this book, you'll have acquired the knowledge and techniques needed to build a complete geospatial application that can generate a report and can be further customized for different purposes.

What you will learn

Automate geospatial analysis workflows using Python

Understand the different formats in which geospatial data is available

Unleash geospatial tech tools to create stunning visualizations

Create thematic maps with Python tools such as PyShp, OGR, and the Python Imaging Library

Build a geospatial Python toolbox for analysis and application development

Unlock remote sensing secrets, detect changes, and process imagery

Leverage ChatGPT for solving Python geospatial solutions

Apply geospatial analysis to real-time data tracking and storm chasing

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Frequently bought together

£33.99

Applied Geospatial Data Science with Python

£41.99

Learning Geospatial Analysis with Python

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Total £ 105.97

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John D Jan 19, 2024

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!

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Eniola Olakanmi Mar 12, 2024

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.

Dagoberto Orozco Feb 19, 2024

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.

matthew Apr 03, 2024

Overall I think this is a great resource as a jumping off point. If you are just getting in to geospatial processing with python I would happily point you to this. Specifically, the focus on the open-source stack in my opinion is the easiest way to get into this type of work, and lends itself well to scaling and parallelization down the line.The background and introductory chapters are surprisingly in-depth, and cover a lot of the history and philosophy of mapping, geospatial analysis, and remote sensing. I was delightedly surprised to see mention of the Great Nadar's pictures from hot air balloons included in this python book! (Anyone?, anyone?, oh well). In addition, I think it does a good job of showing how useful these type of analyses are.The examples throughout the book, though simple, are clear and set up the schema of how to think programmatically about thinking spatially. Not only are the walkthroughs clear, they also are good at deriving the logic behind some common spatial manipulations. However, because they are so explanatory, they do miss some opportunities to introduce shortcuts and useful packages that simplify workflows. I was quite surprised to see turtle graphics used to plot histograms, lol.The breadth of the topics covered is also impressive. From raster-based processes, to LiDAR processing, to GPS tracking in real-time, this book really does give you a taste of many different types of spatial analysis.That being said, I think the book does miss some good opportunities to encourage the use of more powerful libraries and tools. This introductory book glosses over how to ramp up processing of large datasets and how to account for dealing with big data. Specifically, I'd like to see things like GeoParquet mentioned for vector analysis, and libraries like xarray and dask for data-heavy raster processing. These days, geospatial analysis is big data analysis, and that should at least be recognized.In sum, I think this book provides a solid introduction to the field, and goes about it with the right mindset and baseline tool libraries. What can I say? I laughed, I cried, it was better than cats. I entreat you to check it out.

Gazal Agboola Mar 13, 2024

The media could not be loaded. This book is very essential for aspiring and current geospatial data analyst. I like the book for the following reasons:It is very informative and provided detail explanation of major available python packages for geospatial analysis.The use of python for raster data processing is also well explained.Working with LiDAR such as generation of DEM and classification using python.I am still enjoying reading the book.

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

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