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


Pandas is a high-performance Python data analysis library, which can handle large datasets that are tabular (similar to a database), ordered/unordered, labeled matrices, or unlabeled statistical data. GeoPandas is simply a geospatial extension to Pandas that builds upon Shapely, Fiona, PyProj, matplotlib, and Descartes, all of which must be installed. It enables you to easily perform operations in Python, which would otherwise require a spatial database such as PostGIS. You can download a wheel file for GeoPandas from

The following script opens a shapefile and dumps it to GeoJSON; it then creates a map with matplotlib:

>>> import geopandas
>>> import matplotlib.pyplot as plt
>>> gdf = geopandas.GeoDataFrame
>>> census = gdf.from_file("GIS_CensusTract_poly.shp")
>>> census.plot()

The following image is the resulting map plot of the previous commands: