Book Image

Python Geospatial Development - Second Edition - Second Edition

By : Erik Westra
Book Image

Python Geospatial Development - Second Edition - Second Edition

By: Erik Westra

Overview of this book

Geospatial development links your data to places on the Earth's surface. Writing geospatial programs involves tasks such as grouping data by location, storing and analyzing large amounts of spatial information, performing complex geospatial calculations, and drawing colorful interactive maps. In order to do this well, you'll need appropriate tools and techniques, as well as a thorough understanding of geospatial concepts such as map projections, datums and coordinate systems. Python Geospatial Development - Second Edition teaches you everything you need to know about writing geospatial applications using Python. No prior knowledge of geospatial concepts, tools or techniques is required. The book guides you through the process of installing and using various toolkits, obtaining geospatial data for use in your programs, and building complete and sophisticated geospatial applications in Python. Python Geospatial Development teaches you everything you need to know about writing geospatial applications using Python. No prior knowledge of geospatial concepts, tools or techniques is required. The book guides you through the process of installing and using various toolkits, obtaining geospatial data for use in your programs, and building complete and sophisticated geospatial applications in Python. This book provides an overview of the major geospatial concepts, data sources and toolkits. It teaches you how to store and access spatial data using Python, how to perform a range of spatial calculations, and how to store spatial data in a database. Because maps are such an important aspect of geospatial programming, the book teaches you how to build your own “slippy map” interface within a web application, and finishes with the detailed construction of a geospatial data editor using Geodjango. Whether you want to write quick utilities to solve spatial problems, or develop sophisticated web applications based around maps and geospatial data, this book includes everything you need to know.
Table of Contents (18 chapters)
Python Geospatial Development
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Summary


In this chapter, we briefly introduced the Python programming language and the main concepts behind geospatial development. We have seen:

  • That Python is a very high-level language eminently suited to the task of geospatial development.

  • That there are a number of libraries which can be downloaded to make it easier to perform geospatial development work in Python.

  • That the term "geospatial data" refers to information that is located on the earth's surface using coordinates.

  • That the term "geospatial development" refers to the process of writing computer programs that can access, manipulate, and display geospatial data.

  • That the process of accessing geospatial data is non-trivial, thanks to differing file formats and data standards.

  • What types of questions can be answered by analyzing geospatial data.

  • How geospatial data can be used for visualization.

  • How mash-ups can be used to combine data (often geospatial data) in useful and interesting ways.

  • How Google Maps, Google Earth, and the development of cheap and portable GPS units have "democratized" geospatial development.

  • The influence the open source software movement has had on the availability of high quality, freely-available tools for geospatial development.

  • How various standards organizations have defined formats and protocols for sharing and storing geospatial data.

  • The increasing use of geolocation to capture and work with geospatial data in surprising and useful ways.

In the next chapter, we will look in more detail at traditional GIS, including a number of important concepts which you need to understand in order to work with geospatial data. Different geospatial formats will be examined, and we will finish by using Python to perform various calculations using geospatial data.