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 maps

We have the data that we need to begin building the map for our report. Our approach will be the following steps:

  • Enhancing the elevation and base map images with filters

  • Blending the images together to provide a hillshaded OSM map

  • Creating a translucent layer to draw the street route

  • Blending the route layer with the hillshaded map

These tasks will all be accomplished using the PIL Image and ImageDraw modules:

# Convert the numpy array back to an image
relief = Image.fromarray(shaded).convert("L")

# Smooth the image several times so it's not pixelated
for i in range(10):
    relief = relief.filter(ImageFilter.SMOOTH_MORE)"Creating map image")

# Increase the hillshade contrast to make
# it stand out more
e = ImageEnhance.Contrast(relief)
relief = e.enhance(2)

# Crop the image to match the SRTM image. We lose
# 2 pixels during the hillshade process
base = + ".jpg").crop((0, 0, w-2, h-2))

# Enhance base map contrast before blending
e = ImageEnhance...