Book Image

Python Geospatial Development - Third Edition

By : Erik Westra
Book Image

Python Geospatial Development - Third Edition

By: Erik Westra

Overview of this book

Geospatial development links your data to locations on the surface of the Earth. 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. This book provides an overview of the major geospatial concepts, data sources, and toolkits. It starts by showing 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. Further on, 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 the GeoDjango framework. By the end of this book, you will be able to confidently use Python to write your own geospatial applications ranging from quick, one-off utilities to sophisticated web-based applications using maps and other geospatial data.
Table of Contents (20 chapters)
Python Geospatial Development Third Edition
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Summary


In this chapter, we surveyed a number of sources of freely-available geospatial data. For vector-format data, we looked at OpenStreetMap, a collaborative site where people can create and edit vector maps worldwide; TIGER, which is a service of the US Census Bureau; the Natural Earth Data web site; the GSHHG high-resolution shoreline database; and the simple but effective World Borders Dataset.

For geospatial data in raster format, we looked at Landsat imagery, the GLOBE digital elevation model, and the high-resolution National Elevation Dataset for the US and its protectorates.

We then looked at two sources of place name data: the GEOnet Names Server, which provides information about official place names for every country other than the US and Antarctica, and GNIS, which provides official place names for the United States.

This completes our survey of geospatial data sources. In the next chapter, we will use the Python toolkits described in Chapter 3, Python Libraries for Geospatial...