Python Geospatial Analysis Cookbook

More Information
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
About

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.

Features
  • 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
Page Count 310
Course Length 9 hours 18 minutes
ISBN 9781783555079
Date Of Publication 30 Nov 2015

Authors

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.