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

Geolocating photos

Photos taken with GPS-enabled cameras including smartphones store location information in the header of the file in a format called exchangeable image file format (EXIF) tags. These tags are based largely on the same header tags used by the TIFF image standard. In this example, we'll use those tags to create a shapefile with point locations for the photos and file paths to the photos as attributes.

We'll use the Python Imaging Library in this example because it has the ability to extract EXIF data. Most photos taken with smartphones are geotagged images; however, you can download the set used in this example from the following URL:

First, we'll import the libraries we need including PIL for the image metadata and PyShp for the shapefiles:

import glob
import os
    import Image
    import ImageDraw
    from PIL import Image
    from PIL.ExifTags import TAGS
import shapefile

Now, we'll need three functions. The first extracts the EXIF data...