Python for Geospatial Data Analysis: Theory, Tools, and Practice for Location Intelligence
by Bonny McClain(Author)
Publisher finelybook 出版社: O’Reilly Media; (November 29, 2022)
Language 语言: English
Print Length 页数: 200 pages
ISBN-10: 109810479X
ISBN-13: 9781098104795
Book Description
In spatial data science, things in closer proximity to one another likely have more in common than things that are farther apart. With this practical book, geospatial professionals, data scientists, business analysts, geographers, geologists, and others familiar with data analysis and visualization will learn the fundamentals of spatial data analysis to gain a deeper understanding of their data questions.
Author Bonny P. McClain demonstrates why detecting and quantifying patterns in geospatial data is vital. Both proprietary and open source platforms allow you to process and visualize spatial information. This book is for people familiar with data analysis or visualization who are eager to explore geospatial integration with Python.
This book helps you:
Understand the importance of applying spatial relationships in data science
Select and apply data layering of both raster and vector graphics
Apply location data to leverage spatial analytics
Design informative and accurate maps
Automate geographic data with Python scripts
Explore Python packages for additional functionality
Work with atypical data types such as polygons, shape files, and projections
Understand the graphical syntax of spatial data science to stimulate curiosity
From the Preface
Who Is This Book For?
My vision for Python for Geospatial Data Analysis presented me with a conundrum: how do you write a book for newly minted geospatial professionals who know Python and for newly minted Python programmers who are well versed in geospatial analytics? I decided simply to make it interesting. My goal isn’t to grant you professional expertise at either end of the spectrum but to bring us all together to learn tools and best practices.
By the end of this book, I want all of you to feel proficient and confident enough to go out and explore geospatial analytics on your own. To that end, as I teach each tool and technique, I ask you to follow along, installing the tools as needed and using a Jupyter or google Colab notebook to run code. But I don’t want you to stop there, so I also provide a host of different experiences that invite you to continue to explore.
How This Book Works
We will begin with a quick level set introducing you to a few key GIS concepts. As we advance, I’ll slowly integrate Python learning. I do not assume expertise in a coding language or in geospatial analytics.
The resources presented here are open source: their source code is distributed freely by the developers and usually incorporates contributions from community members. Most make use of Python. To the best of my ability, I’ve ensured that this book features resources available without burdensome subscription services. Any costs, however modest, are highlighted so that you can make informed decisions. My focus on open source doesn’t mean that I don’t support enterprise solutions; it means I want to lower the barriers to conducting meaningful analysis around big questions.
This book covers a wide swath of open source tools and data and looks at a variety of datasets, some of which you perhaps don’t have access to in your current professional role. Python for Geospatial Data Analysis is not linear like typical books about technology (or, for that matter, about Python). There are multiple ways to explore data problems. Perhaps you can draw inspiration from working in an integrated development environment (IDE) for the first time. Maybe you’re curious about working in a terminal or console.
It is impossible to walk through the granular details of each Python package or library in a single book. If you’re familiar with a particular tool or library, you probably have favorite features that I haven’t included here. That’s fine—I just want to give you a feel for each one. From there, you can continue discovering what they have to offer.
Review
“I love how comprehensive this is!” g.iablonovski
“This is amazing! I’m so glad you wrote this thank you so much! ” Ryan Schaner
“Beautiful book!” Pablo Moreno
“In the expanding market of Geospatial analytics I am sure this is a very useful resource. I read through the already available content and I found it very illustrative.” Ezequiel Garcia Hernandez
Excellent book. Great production quality to go along with the fantastic content on #LocationIntelligence.–Kirk Borne, Chief Science Officer
From the Author
In both python and geospatial resources I found a gap initially difficult for me to cross. The beginner resources were too fundamental but when I dropped into the specific modules, datasets, or open-source tools covering the topics of interest — I felt like I missed something. Even navigating the user documentation it seemed there were important concepts that were time consuming to have to learn independently before moving onto the project or deeper analyses.
I decided to write Python for Geospatial analysis in an attempt to fill the void. Ten information packed chapters covering the foundational tools for your learning journey. These topics were picked by me to reflect a workflow that can help you on your journey. It isn’t one size fits all but it is definitely a choose your own adventure that reflects the skills available for you to integrate the power of open-source tools.
