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

Choosing your geospatial data source


If you need to obtain map data, images, elevations, or place names for use in your geospatial applications, the sources we have covered should give you everything you need. Of course, this is not an exhaustive list—other sources of data are available, and can be found online using a search engine or sites such as http://freegis.org.

The following table lists the various requirements you may have for geospatial data in your application development and which data source(s) may be most appropriate in each case:

Requirement

Suitable data sources

Simple base map

World Borders Dataset

Shaded relief (pseudo-3D) maps

GLOBE or NED data processed using gdaldem; Natural Earth raster images

Street map

OpenStreetMap

City outlines

TIGER (US); Natural Earth urban areas

Detailed country outlines

GSHHG Level 1

Photorealistic images of the Earth

Landsat

List of names of cities and places

GNIS (US) or Geonet Names Server (elsewhere)