Python Geospatial Analysis Cookbook, by Michael Diener
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Python Geospatial Analysis Cookbook, by Michael Diener
Best PDF Ebook Python Geospatial Analysis Cookbook, by Michael Diener
Over 60 recipes to work with topology, overlays, indoor routing, and web application analysis with Python
About This Book
- Explore the practical process of using geospatial analysis to solve simple to complex problems with reusable recipes
- Concise step-by-step instructions to teach you about projections, vector, raster, overlay, indoor routing and topology analysis
- Create a basic indoor routing application with geodjango
Who This Book Is For
If you are a student, teacher, programmer, geospatial or IT administrator, GIS analyst, researcher, or scientist looking to do spatial analysis, then this book is for you. Anyone trying to answer simple to complex spatial analysis questions will get a working demonstration of the power of Python with real-world data. Some of you may be beginners with GIS, but most of you will probably have a basic understanding of geospatial analysis and programming.
What You Will Learn
- Discover the projection and coordinate system information of your data and learn how to transform that data into different projections
- Import or export your data into different data formats to prepare it for your application or spatial analysis
- Use the power of PostGIS with Python to take advantage of the powerful analysis functions
- Execute spatial analysis functions on vector data including clipping, spatial joins, measuring distances, areas, and combining data to new results
- Create your own set of topology rules to perform and ensure quality assurance rules in Python
- Find the shortest indoor path with network analysis functions in easy, extensible recipes revolving around all kinds of network analysis problems
- Visualize your data on a map using the visualization tools and methods available to create visually stunning results
- Build an indoor routing web application with GeoDjango to include your spatial analysis tools built from the previous recipes
In Detail
Geospatial development links your data to places on the Earth's surface. Its analysis is used in almost every industry to answer location type questions. Combined with the power of the Python programming language, which is becoming the de facto spatial scripting choice for developers and analysts worldwide, this technology will help you to solve real-world spatial problems.
This book begins by tackling the installation of the necessary software dependencies and libraries needed to perform spatial analysis with Python. From there, the next logical step is to prepare our data for analysis; we will do this by building up our tool box to deal with data preparation, transformations, and projections. Now that our data is ready for analysis, we will tackle the most common analysis methods for vector and raster data. To check or validate our results, we will explore how to use topology checks to ensure top-quality results. This is followed with network routing analysis focused on constructing indoor routes within buildings, over different levels.
Finally, we put several recipes together in a GeoDjango web application that demonstrates a working indoor routing spatial analysis application. The round trip will provide you all the pieces you need to accomplish your own spatial analysis application to suit your requirements.
Style and approach
Easy-to-follow, step-by-step recipes, explaining from start to finish how to accomplish real-world tasks.
Python Geospatial Analysis Cookbook, by Michael Diener- Amazon Sales Rank: #5845641 in Books
- Published on: 2015-11-30
- Released on: 2015-11-30
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .70" w x 7.50" l, 1.18 pounds
- Binding: Paperback
- 310 pages
About the Author
Michael Diener
Michael Diener graduated from Simon Fraser University, British Columbia, Canada, in 2001 with a bachelor of science degree in geography. He began working in 1995 with Environment Canada as a GIS (Geographic Information Systems) analyst and has continued to work with GIS technologies ever since. In 2008, he founded a company called GOMOGI that is focused on building web and mobile GIS application with open source tools. In 2011, the focus changed to indoor wayfinding and navigation solutions and building the indrz platform that Michael had envisioned. From time to time, Michael also holds seminars for organizations wanting to explore or discover the possibilities of how GIS can increase productivity and help better answer spatial questions. He is also the creative head of new product development in his company. His technical skills include working with Python to solve a wide range of spatial problems on a daily basis. Through the years, he has developed many spatial applications with Python, including indrz and golfgis, which are two of the products built by his company, GOMOGI. He is also lecturer of GIS at the Alpen Adria University, Klagenfurt, where he enjoys teaching students the wonderful powers of GIS and explaining how to solve spatial problems with open source GIS and Python.
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Most helpful customer reviews
1 of 1 people found the following review helpful. A lot of interesting recipes By Christian S. Chapter 1 does a good job in getting you up and running with Python and the main libraries used in the following chapters.Chapter 2 explains coordinate systems and introduces Shapefile and GeoJSON file formats. Chapter 3 continues explaining image formats such as raster and vector. Also we setup our PostgreSQL and PostGIS. The instruction are clear and easy to follow. An interesting example is the conversion of an OpenStreeMap to a Shapefile. In Chapter 4 the focus is on PostGIS.Chapter 5, 6 and 7 deal with vector and geometry analysis. There are examples to calculate intersections, distances, and operations between polygons.Chapter 8 explains network analysis. There are very interesting examples on how to calculate the shortest path and an example to calculate indoor route walk time. Definitely this was one of the most interesting chapters.Chapter 9 deals with topology and validations rules. It has several example algorithms to validate rules.Chapter 10 and 11 finalize the implementation of the previous chapters, dealing with the final presentation through visualizations for the web.In summary I would recommend this book, it has enough content to serve as a reference to find examples in which you can dig deeper. This is a more practical book in the sense that you will not find detailed explanations of the algorithms, and it uses several third party libraries in the examples to get the job done.One thing it could have been improved is the layout of the source code.
1 of 1 people found the following review helpful. Open your eyes to geospatial analysis with Python By Amazon Customer Great introduction to a variety of Python libraries available for geospatial analysis. Both vector and raster analysis is covered with interesting examples. Many of the recipes will act as a base for the reader to take snippets from and build upon to implement into their own workflows. Algorithms and code not heavily explained which might not be great for complete beginners. Overall I recommend this book as it will open your eyes to some of the geospatial analysis techniques that you may not have realised were easy to implement outside of a standard GIS software package.
0 of 0 people found the following review helpful. I found chapter 8 on network analysis very useful and interesting By DC_ The first chapter regarding getting everything setup is quite thorough regarding setting up the various Python libraries that are needed throughout the book. I found chapter 8 on network analysis very useful and interesting. This books gives a good grounding with code examples on which to build.
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