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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

Raster data

Raster data consists of rows and columns of cells or pixels, with each cell representing a single value. The easiest way to think of raster data is as images, which is how they are typically represented by software. But raster data sets are not necessarily stored as images. They can also be ASCII text files or Binary Large Objects (BLOBs) in databases.

Another difference between geospatial raster data and regular digital images is resolution. Digital images express resolution as dots-per-inch if printed at full size. Resolution can also be expressed or the total number of pixels in the image defined as megapixels. However, geospatial raster data uses the ground distance each cell represents. For example, a raster data set with two-foot resolution means that a single cell represents two feet on the ground, which also means only objects larger than two feet can be identified visually in the data set.

Raster data sets may contain multiple bands, meaning that different wavelengths...

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