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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

NumPy


NumPy is an extremely fast, multidimensional Python array processor designed specifically for Python and scientific computing but written in C. It is available via PyPI and installs easily. In addition to its amazing speed, the magic of NumPy includes its interaction with other libraries. NumPy can exchange data with GDAL, Shapely, the Python Imaging Library (PIL), and many other scientific computing Python libraries in other fields.

As a quick example of NumPy's ability, we'll combine it with GDAL to read in our sample satellite image and create a histogram of it. The interface between GDAL and NumPy is a GDAL module called gdalnumeric which has NumPy as a dependency. Numeric is the legacy name of the NumPy module. The gdalnumeric module imports NumPy.

In this example, we'll use gdalnumeric, which imports NumPy, to read the image in as an array, grab the first band, and save it back out as a JPEG image:

>>> from osgeo import gdalnumeric
>>> srcArray = gdalnumeric.LoadFile...

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