Book Image

Learning Geospatial Analysis with Python - Third Edition

By : Joel Lawhead
Book Image

Learning Geospatial Analysis with Python - Third Edition

By: Joel Lawhead

Overview of this book

Geospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. With this systematic guide, you'll get started with geographic information system (GIS) and remote sensing analysis using the latest features in Python. This book will take you through GIS techniques, geodatabases, geospatial raster data, and much more using the latest built-in tools and libraries in Python 3.7. You'll learn everything you need to know about using software packages or APIs and generic algorithms that can be used for different situations. Furthermore, you'll learn how to apply simple Python GIS geospatial processes to a variety of problems, and work with remote sensing data. By the end of the book, you'll be able to build a generic corporate system, which can be implemented in any organization to manage customer support requests and field support personnel.
Table of Contents (15 chapters)
Free Chapter
Section 1: The History and the Present of the Industry
Section 2: Geospatial Analysis Concepts
Section 3: Practical Geospatial Processing Techniques


The osmnx library combines Open Street Map (OSM) and the powerful NetworkX library to manage street networks used for routing. This library has dozens of dependencies which it rolls up to do all of the complex steps of downloading, analyzing, and visualizing street networks.

You can try to install osmnx using pip:

pip install osmnx

However, you may run into some installation issues due to the dependencies. In that case, it's easier to use the Conda system, which we'll introduce later in this chapter.

The following example uses osmnx to download street data from OSM for a city, creates a street network from it, and calculates some basic statistics:

>>> import osmnx as ox
>>> G = ox.graph_from_place('Bay Saint Louis, MS , USA', network_type='drive')
>>> stats = ox.basic_stats(G)
>>> stats["street_length_avg...