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

Using spreadsheets


Spreadsheets such as Microsoft Office Excel and Open Office Calc are inexpensive (even free), ubiquitous, easy to use, and great for recording structured data. For these reasons, spreadsheets are widely used to collect data for entry into a GIS format. As an analyst, you will find yourself working with spreadsheets frequently. In previous chapters, we discussed the CSV format which is a text file with the same basic rows and columns data structure as a spreadsheet. For CSV files, you use Python's built-in csv module. But most of the time people don't bother exporting a true spreadsheet to a generic CSV file. That's where the pure Python xlrd module comes into play. The name xlrd is short for Excel Reader and is available from PyPI as it is accompanying the xlwt (Excel Writer) module. These two modules make reading and writing Excel spreadsheets as snap. Combine it with PyShp and you can move back and forth between spreadsheets and shapefiles with ease.

This example demonstrates...