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

Working with utility functions

Next, we have several utility functions that are used several times throughout the program. All of these, except the functions related to time, have been used in the previous chapters in some form. The ll2m() function converts latitude and longitude to meters. The world2pixel() function converts geospatial coordinates to pixel coordinates on our output map image. Then, we have two functions named get_utc_epoch() and get_local_time() that convert the UTC time stored in the GPX file to the local time along the route. Finally, we have a haversine distance function and our simple wms function to retrieve the map images:

def ll2m(lat, lon):
    """Lat/lon to meters"""
    x = lon * 20037508.34 / 180.0
    y = math.log(math.tan((90.0 + lat) *
                 math.pi / 360.0)) / (math.pi / 180.0)
    y = y * 20037508.34 / 180.0
    return (x, y)

def world2pixel(x, y, w, h, bbox):
    """Converts world coordinates
    to image pixel coordinates"""
    # Bounding...