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Learning Geospatial Analysis with Python

Learning Geospatial Analysis with Python

By : Joel Lawhead
4.1 (8)
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Learning Geospatial Analysis with Python

Learning Geospatial Analysis with Python

4.1 (8)
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 (12 chapters)
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11
Index

Python markup and tag-based parsers


Tag-based data, particularly different XML dialects, have become a very popular way to distribute geospatial data. Formats which are both machine and human readable are generally easy to work with though they sacrifice storage efficiency for usability. These formats can become unmanageable for very large data sets but work very well in most cases.

While most formats are some form of XML (such as KML or GML), there is a notable exception. The Well-Known Text (WKT) format is fairly common but uses external markers and square brackets ("[]") to surround data instead of tags in angled brackets around data like XML does.

Python has standard library support for XML as well as some excellent third-party libraries available. Proper XML formats all follow the same structure so you can use a generic XML library to read it. Because XML is text-based, it is often easy to write it as a string instead of using an XML library. The vast majority of applications which output...

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