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Learning Geospatial Analysis with Python
If you know Python and would like to use it for Geospatial Analysis this book is exactly what you've been looking for. With an organized, user-friendly approach it covers all the bases to give you the necessary skills and know-how. Overview In Detail Geospatial analysis is used in almost every field you can think of from medicine, to defense, to farming. It is an approach to use statistical analysis and other informational engineering to data which has a geographical or geospatial aspect. And this typically involves applications capable of geospatial display and processing to get a compiled and useful data. "Learning Geospatial Analysis with Python" uses the expressive and powerful Python programming language to guide you through geographic information systems, remote sensing, topography, and more. It explains how to use a framework in order to approach Geospatial analysis effectively, but on your own terms. "Learning Geospatial Analysis with Python" starts with a background of the field, a survey of the techniques and technology used, and then splits the field into its component speciality GIS, remote sensing, elevation data, advanced modelling, and real-time data. This book will teach you everything there is to know, from using a particular software package or API to using generic algorithms that can be applied to Geospatial analysis. This book focuses on pure Python whenever possible to minimize compiling platform-dependent binaries, so that you don't become bogged down in just getting ready to do analysis. "Learning Geospatial Analysis with Python" will round out your technical library with handy recipes and a good understanding of a field that supplements many a modern day human endeavors. What you will learn from this book Approach This is a tutorial-style book that helps you to perform Geospatial and GIS analysis with Python and its tools/libraries. This book will first introduce various Python-related tools/packages in the initial chapters before moving towards practical usage, examples, and implementation in specialized kinds of Geospatial data analysis. Who this book is written for This book is for anyone who wants to understand digital mapping and analysis and who uses Python or another scripting language for automation or crunching data manually.This book primarily targets Python developers, researchers, and analysts who want to perform Geospatial, modeling, and GIS analysis with Python.
- GenresComputer Science
346 pages, Paperback
Published October 25, 2013
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76 people want to read
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Displaying 1 - 10 of 10 reviews
June 14, 2019
A great reference book for a brief understanding of what python could do on Geospatial Analysis. Sample codes are all vivid and practical.
To all who interested in getting a startup with Geospatial Analysis in Python, it is highly recommended to read this book before searching any specific "how can i" question in StackOverflow
To all who interested in getting a startup with Geospatial Analysis in Python, it is highly recommended to read this book before searching any specific "how can i" question in StackOverflow
September 5, 2020
Learning Geospatial Analysis with Python is an excellent reference text, whether you are just starting out in this fascinating area of data analytics, or are a more seasoned pro. The examples are clear and practical, and the prose is extremely accessible. All around a real must-have book on the shelf for anyone interested in geospatial dynamics.
April 2, 2014
I'd like to start this review by addressing a few quick questions before I add my own thoughts.
First, who would be interested in this book?
Really, anyone who has ever had an interest in simply learning what Geospatial Analysis is will benefit from this book, the first chapters go through an excellent introduction into what the field of Geospatial Analysis even is and the topics that cover its range, such as Geographic Information Systems and Remote Sensing. That being said, once you get into the specific topics the focus really shifts to using Python to implement examples of these topics, and an individual who is completely unfamiliar with Python wouldn't benefit as much as someone who has a fairly good Python or programming background. Nevertheless eve if you never ran a single line of code, simply reading the book from front to back should give the reader a good foundation on what Geospatial Analysis is and how it is performed.
Second, I'd like to discuss if the book covers what it says it does in its description:
The description states it will offer a background in Geosptial Analysis, which it does a great job of in the beginning chapters. Before even jumping into the many Python modules you'll use, readers will gain an understanding of the topic they'll be learning.
The second big statement is that the book will teach you how do use Python to implement Geospatial Analysis, and I was pleasantly surprised that the author really strives to keep everything running in pure Python modules whenever possible. The most you'll stray from running pure Python modules is in the use of some file viewers. Another great addition is that the author really pushes the use of open source libraries, throughout the book I never felt like I needed to purchase any of the more intense Geographic Information System software packages that are on the market. I really felt like all I needed was my Python interpreter and an internet connection.
The next aspect that I found most enjoyable was that I simply liked reading the book. I initially sat down and just read the book from front to back, without following the code tutorials. I really enjoyed this approach because after I had read through, I simply went back and followed the tutorials that I found to be the most interesting to get a better understanding of how they were used. Another thing I highly enjoyed was seeing the use of some Python packages I have seen used in so many other scientific realms, most notably NumPy, as well as some modules that I'd never seen before, most of which are directly related to Geospatial Analysis.
The thing I learned the most from reading this book is that Geospatial Analysis is all about the data, and there is an extremely broad amount of data and data formats that can be used. A large portion of the text will cover what this data is, how it is used, and ways you can use Python to extract, alter, and use this data in creating some pretty cool Geospatial models. Even the very first map you draw depends on using this data. Thankfully you won't have to go searching for this, as the book provides plenty of data files for download.
Overall I would highly recommend this book to anyone that has a love of Python and has a real interest in getting a starting background in understanding Geospatial Analysis. I would offer a slight recommendation for those users that are simply looking for a text book about Geospatial Analysis, as the real fun of the book comes from seeing the models and images being manipulated from the solid set of functions that the author gives you in Python. It is obvious that the author cares a great deal about the topic, and the enthusiasm is a definite benefit to the flow of the book. A fun, good read in my opinion as someone who initially knew very little about the topic.
First, who would be interested in this book?
Really, anyone who has ever had an interest in simply learning what Geospatial Analysis is will benefit from this book, the first chapters go through an excellent introduction into what the field of Geospatial Analysis even is and the topics that cover its range, such as Geographic Information Systems and Remote Sensing. That being said, once you get into the specific topics the focus really shifts to using Python to implement examples of these topics, and an individual who is completely unfamiliar with Python wouldn't benefit as much as someone who has a fairly good Python or programming background. Nevertheless eve if you never ran a single line of code, simply reading the book from front to back should give the reader a good foundation on what Geospatial Analysis is and how it is performed.
Second, I'd like to discuss if the book covers what it says it does in its description:
The description states it will offer a background in Geosptial Analysis, which it does a great job of in the beginning chapters. Before even jumping into the many Python modules you'll use, readers will gain an understanding of the topic they'll be learning.
The second big statement is that the book will teach you how do use Python to implement Geospatial Analysis, and I was pleasantly surprised that the author really strives to keep everything running in pure Python modules whenever possible. The most you'll stray from running pure Python modules is in the use of some file viewers. Another great addition is that the author really pushes the use of open source libraries, throughout the book I never felt like I needed to purchase any of the more intense Geographic Information System software packages that are on the market. I really felt like all I needed was my Python interpreter and an internet connection.
The next aspect that I found most enjoyable was that I simply liked reading the book. I initially sat down and just read the book from front to back, without following the code tutorials. I really enjoyed this approach because after I had read through, I simply went back and followed the tutorials that I found to be the most interesting to get a better understanding of how they were used. Another thing I highly enjoyed was seeing the use of some Python packages I have seen used in so many other scientific realms, most notably NumPy, as well as some modules that I'd never seen before, most of which are directly related to Geospatial Analysis.
The thing I learned the most from reading this book is that Geospatial Analysis is all about the data, and there is an extremely broad amount of data and data formats that can be used. A large portion of the text will cover what this data is, how it is used, and ways you can use Python to extract, alter, and use this data in creating some pretty cool Geospatial models. Even the very first map you draw depends on using this data. Thankfully you won't have to go searching for this, as the book provides plenty of data files for download.
Overall I would highly recommend this book to anyone that has a love of Python and has a real interest in getting a starting background in understanding Geospatial Analysis. I would offer a slight recommendation for those users that are simply looking for a text book about Geospatial Analysis, as the real fun of the book comes from seeing the models and images being manipulated from the solid set of functions that the author gives you in Python. It is obvious that the author cares a great deal about the topic, and the enthusiasm is a definite benefit to the flow of the book. A fun, good read in my opinion as someone who initially knew very little about the topic.
December 15, 2013
I decided to read this book since I've been doing maps using R. Hence it is better to learn the literature and science behind mapping and how to do a proper analysis on it. In addition, I would like to see what Python can offer in this discipline.
The book has 10 chapters contained in a 364 pages. The first three chapters was a long reading, not much on coding, but rather on discussions of introduction to Geospatial Analysis.
Impression:
I like the idea that the author spent three chapters talking about the overall story (I would say) of Geospatial Analysis. Just a preview, the first chapter is of course the introduction; second is the data types, which surprisingly has a variety of formats; and third is all about the libraries and packages used in the said study. I am familiar with ArcGIS and QGIS, but this book lets you aware with other tools as well.
The simple illustration that complements the discussion is very helpful in telling the overall story of the subject. And the step-by-step tutorials are easy to follow (just what the book's description suggest), this is one thing that Packt books never frustrates me.
The real action begins in chapter 4, where you start to know the first few tools below,
Python Version 2.x (minimum 2.5)
GDAL/OGR Version 1.7.1 or later
GEOS Version 3.2.2 or later
PyShp 1.1.6 or later
Shapely Version 1.2 or later
Proj Version 4.7 or later
PyProj Version 1.8.6 or later
NumPy
PNGCanvas
Python Imaging Library (IPL)
This unordered list enumerates the tools required for the readers.
The fun commenced in chapter 5, then all the way to chapter 10. Because here, you get to play with maps. When it comes to topics discussed in this book, I can't say more. Everything is just cool, the techniques shared by the author are undeniably worth to study.
Suggestion:
I know some readers may disagree on me, but I still find it easy to refer if the installation of the modules were placed in one chapter (like appendix). By the way, the procedures for unloading and installing these tools are covered in the relevant chapters of the book as needed.
Conclusion:
In conclusion, I would say the book is a useful reference for Geospatial Analysis, especially if you prefer to do it in an open-source program with full controls on every manipulation you want. I encourage you to grab a copy, the author has a remarkable experience in this field.
The book has 10 chapters contained in a 364 pages. The first three chapters was a long reading, not much on coding, but rather on discussions of introduction to Geospatial Analysis.
Impression:
I like the idea that the author spent three chapters talking about the overall story (I would say) of Geospatial Analysis. Just a preview, the first chapter is of course the introduction; second is the data types, which surprisingly has a variety of formats; and third is all about the libraries and packages used in the said study. I am familiar with ArcGIS and QGIS, but this book lets you aware with other tools as well.
The simple illustration that complements the discussion is very helpful in telling the overall story of the subject. And the step-by-step tutorials are easy to follow (just what the book's description suggest), this is one thing that Packt books never frustrates me.
The real action begins in chapter 4, where you start to know the first few tools below,
Python Version 2.x (minimum 2.5)
GDAL/OGR Version 1.7.1 or later
GEOS Version 3.2.2 or later
PyShp 1.1.6 or later
Shapely Version 1.2 or later
Proj Version 4.7 or later
PyProj Version 1.8.6 or later
NumPy
PNGCanvas
Python Imaging Library (IPL)
This unordered list enumerates the tools required for the readers.
The fun commenced in chapter 5, then all the way to chapter 10. Because here, you get to play with maps. When it comes to topics discussed in this book, I can't say more. Everything is just cool, the techniques shared by the author are undeniably worth to study.
Suggestion:
I know some readers may disagree on me, but I still find it easy to refer if the installation of the modules were placed in one chapter (like appendix). By the way, the procedures for unloading and installing these tools are covered in the relevant chapters of the book as needed.
Conclusion:
In conclusion, I would say the book is a useful reference for Geospatial Analysis, especially if you prefer to do it in an open-source program with full controls on every manipulation you want. I encourage you to grab a copy, the author has a remarkable experience in this field.
February 22, 2014
I had wondered if the book was for me since the book is intended for people with strong Python skills who would like to understand digital mapping. I have a Masters in GIS and a number of years experience as a GIS Analyst and have a decent grasp of Python.
This book ended up being very useful for me as I try to get away from only being able to use esri's products for all my GIS needs. Learning Geospatial Analysis with Python did cover a lot of basics about GIS but it goes beyond the basics with a lot of good information on how to manipulate and analyze spatial data, mostly using pure Python but also only a very few additional libraries. I highly recommend this book to anyone who loves GIS and Python and would like to get away from only using arcpy methods for their analysis.
I now have confidence in moving towards using more open source software for my GIS analysis knowing that it's fairly easy to fall back to pure Python to fill in any automation or analysis gaps. The examples in the book have also given me an idea on how to fix elevation data and add weather data to my GPS tracks with a simple script.
This book ended up being very useful for me as I try to get away from only being able to use esri's products for all my GIS needs. Learning Geospatial Analysis with Python did cover a lot of basics about GIS but it goes beyond the basics with a lot of good information on how to manipulate and analyze spatial data, mostly using pure Python but also only a very few additional libraries. I highly recommend this book to anyone who loves GIS and Python and would like to get away from only using arcpy methods for their analysis.
I now have confidence in moving towards using more open source software for my GIS analysis knowing that it's fairly easy to fall back to pure Python to fill in any automation or analysis gaps. The examples in the book have also given me an idea on how to fix elevation data and add weather data to my GPS tracks with a simple script.
February 3, 2014
The first third of this book gives a good historical oveview of geospatial analysis with focus on data, software and libraries. There are also some simple Python examples how Python can be used. The main part of the book deals more in depth with Python tools and how they work i GIS analysis, remote sensing, elevation, and modeling. The final chapter combines all previous parts of the book in a realistic example.
I think this is a good step by step introduction how to use Python in geospatial analysis, from a basic start to a quite advanced and complicated level.
I think this is a good step by step introduction how to use Python in geospatial analysis, from a basic start to a quite advanced and complicated level.
May 13, 2014
I recommend this book to all those interested in getting a startup with background in understanding Geospatial Analysis.There are few simple Python examples also that says how Python can be used.I think this is a tutorial-style book that helps you how to use Python in geospatial analysis, from a basic start to a quite advanced and complicated level.
Read More Here: http://www.packtpub.com/learning-geos...
Read More Here: http://www.packtpub.com/learning-geos...
May 13, 2014
I really learnt a lot about the book. As in the description it say "This book is for anyone who wants to understand digital mapping and analysis and who uses Python or another scripting language for automation or crunching data manually" the book fullfills it. I recommend it to people with strong Python skills who would like to understand digital mapping..Want to read more: http://www.packtpub.com/learning-geos...
September 22, 2015
The perfect book to pick up for brushing up on my geospatial python skills after starting a new job.
Lots of hands on examples. It's interesting to re-implement lots of things from scratch, but showing how much better using some libraries are would have been good too.
Lots of hands on examples. It's interesting to re-implement lots of things from scratch, but showing how much better using some libraries are would have been good too.
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August 9, 2018Location: ND6 IRC
Accession No: DL030053
Accession No: DL030053
Displaying 1 - 10 of 10 reviews