Book Image

Learning Geospatial Analysis with Python

By : Joel Lawhead
4 (1)
Book Image

Learning Geospatial Analysis with Python

4 (1)
By: Joel Lawhead

Overview of this book

Geospatial analysis is used in almost every field you can think of from medicine, to defense, to farming. It is an approach to use statistical analysis and other informational engineering to data which has a geographical or geospatial aspect. And this typically involves applications capable of geospatial display and processing to get a compiled and useful data. "Learning Geospatial Analysis with Python" uses the expressive and powerful Python programming language to guide you through geographic information systems, remote sensing, topography, and more. It explains how to use a framework in order to approach Geospatial analysis effectively, but on your own terms. "Learning Geospatial Analysis with Python" starts with a background of the field, a survey of the techniques and technology used, and then splits the field into its component speciality areas: GIS, remote sensing, elevation data, advanced modelling, and real-time data. This book will teach you everything there is to know, from using a particular software package or API to using generic algorithms that can be applied to Geospatial analysis. This book focuses on pure Python whenever possible to minimize compiling platform-dependent binaries, so that you don't become bogged down in just getting ready to do analysis. "Learning Geospatial Analysis with Python" will round out your technical library with handy recipes and a good understanding of a field that supplements many a modern day human endeavors.
Table of Contents (17 chapters)
Learning Geospatial Analysis with Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Chapter 9. Real-Time Data

A common saying among geospatial analysts is, "A map is out-dated as soon as it's created". This saying reflects the fact that the Earth and everything on it is constantly changing. For most of the history of geospatial analysis and through most of this book, geospatial products are relatively static. Raw data sets are typically updated anywhere from a few months to a few years. Data currency has traditionally not been the primary focus because of the time and expense needed to collect data.

Web mapping, wireless cellular modems, and low-cost GPS antennas have changed that focus. It is now logistically feasible and even quite affordable to monitor a rapidly changing object or system and broadcast those changes to millions of people online. This change is revolutionizing geospatial technology and taking it in new directions. The most direct evidence of this revolution is web-mapping mash-ups using systems such as Google Maps or OpenLayers and web-accessible data formats...