Book Image

Learning Geospatial Analysis with Python

By : Joel Lawhead
Book Image

Learning Geospatial Analysis with Python

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. This book will guide you gently into this exciting and complex field. It walks you through the building blocks of geospatial analysis and how to apply them to influence decision making using the latest Python software. Learning Geospatial Analysis with Python, 2nd Edition uses the expressive and powerful Python 3 programming language to guide you through geographic information systems, remote sensing, topography, and more, while providing a framework for you to approach geospatial analysis effectively, but on your own terms. We start by giving you a little background on the field, and a survey of the techniques and technology used. We then split the field into its component specialty areas: GIS, remote sensing, elevation data, advanced modeling, and real-time data. This book will teach you everything you need to know about, Geospatial Analysis from using a particular software package or API to using generic algorithms that can be applied. 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. This book will round out your technical library through handy recipes that will give you a good understanding of a field that supplements many a modern day human endeavors.
Table of Contents (17 chapters)
Learning Geospatial Analysis with Python Second Edition
About the Author
About the Reviewers

Creating a color hillshade

In this example, we'll combine the previous techniques in order to combine our terrain hillshade from Chapter 7, Python and Elevation Data, with the color classification that we used on the LIDAR. For this example, we'll need the ASCII Grid DEMs named dem.asc and relief.asc that we used in the previous chapter. We'll create a colorized DEM and a hillshade and then use PIL in order to blend them together for an enhanced elevation visualization. The code comments will guide you through the example as many of these steps are already familiar to you:

import gdal_array as gd
    import Image
    from PIL import Image

relief = "relief.asc"
dem = "dem.asc"
target = "hillshade.tif"

# Load the relief as the background image
bg = gd.numpy.loadtxt(relief, skiprows=6)

# Load the DEM into a numpy array as the foreground image
fg = gd.numpy.loadtxt(dem, skiprows=6)[:-2, :-2]

# Create a blank 3-band image to colorize the DEM
rgb = gd.numpy.zeros((3, len(fg), len...