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.

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Learning Geospatial Analysis with Python - Third Edition
Joel Lawhead
Sep, 2019 | 456 pages
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Table of Contents (15 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: The History and the Present of the Industry
Section 1: The History and the Present of the Industry
2
Learning about Geospatial Analysis with Python
Learning about Geospatial Analysis with Python
Technical requirements
Geospatial analysis and our world
History of geospatial analysis
GIS
Remote sensing
Elevation data
Computer-aided drafting
Geospatial analysis and computer programming
The importance of geospatial analysis
GIS concepts
Remote sensing concepts
Common vector GIS concepts
Common raster data concepts
Creating the simplest possible Python GIS
Summary
Further reading
3
Learning Geospatial Data
Learning Geospatial Data
Getting an overview of common data formats
Understanding data structures
Understanding spatial indexing
What are overviews?
What is metadata?
Understanding the file structure
Knowing the most widely used vector data types
Understanding raster data types
What is point cloud data?
What are web services?
Understanding geospatial databases
Summary
Further reading
4
The Geospatial Technology Landscape
The Geospatial Technology Landscape
Technical requirements
Understanding data access
Understanding computational geometry
Understanding desktop tools (including visualization)
Understanding metadata management
Summary
Further reading
5
Section 2: Geospatial Analysis Concepts
Section 2: Geospatial Analysis Concepts
6
Geospatial Python Toolbox
Geospatial Python Toolbox
Technical requirements
Installing third-party Python modules
Python virtualenv
Conda
Installing GDAL
Python networking libraries for acquiring data
ZIP and TAR files
Python markup and tag-based parsers
Python JSON libraries
OGR
PyShp
dbfpy
Shapely
Fiona
ESRI shapefile
GDAL
NumPy
PIL
PNGCanvas
GeoPandas
PyMySQL
PyFPDF
Geospatial PDF
Rasterio
OSMnx
Jupyter
Conda
Summary
Further reading
7
Python and Geographic Information Systems
Python and Geographic Information Systems
Technical requirements
Measuring distance
Calculating line direction
Understanding coordinate conversion
Understanding reprojection
Understanding coordinate format conversion
Calculating the area of a polygon
Editing shapefiles
Aggregating geometry
Creating images for visualization
Using GPS data
Geocoding
Multiprocessing
Summary
8
Python and Remote Sensing
Python and Remote Sensing
Technical requirements
Swapping image bands
Creating histograms
Performing a histogram stretch
Clipping images
Classifying images
Extracting features from images
Understanding change detection
Summary
Further reading
9
Python and Elevation Data
Python and Elevation Data
Accessing ASCII Grid files
Creating a shaded relief
Creating elevation contours
Working with LIDAR data
Summary
Further reading
10
Section 3: Practical Geospatial Processing Techniques
Section 3: Practical Geospatial Processing Techniques
11
Advanced Geospatial Python Modeling
Advanced Geospatial Python Modeling
Technical requirements
Creating a normalized difference vegetative index
Creating a flood inundation model
Creating a color hillshade
Performing least cost path analysis
Converting the route to a shapefile
Routing along streets
Geolocating photos
Calculating satellite image cloud cover
Summary
12
Real-Time Data
Real-Time Data
Technical requirements
Limitations of real-time data
Using real-time data
Tracking vehicles
Storm chasing
Reports from the field
Summary
13
Putting It All Together
Putting It All Together
Technical requirements
Understanding a typical GPS report
Building a GPS reporting tool
Summary
Further reading
14
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dbfpy

Both OGR and PyShp read and write the .dbf files because they are part of the shapefile specification. The .dbf files contain the attributes and fields for the shapefiles. However, both libraries have very basic .dbf support. Occasionally, you will need to do some heavy-duty DBF work. The dbfpy3 module is a pure Python module dedicated to working with .dbf files. It is currently hosted on GitHub. You can force easy_install to find the download by specifying the download file:

easy_install -f
 https://github.com/GeospatialPython/dbfpy3/archive/master.zip

If you are using pip to install packages, use the following command:

pip install
 https://github.com/GeospatialPython/dbfpy3/archive/master.zip

The following shapefile has over 600 .dbf records representing US Census Bureau tracts, which make it a good sample for trying out dbfpy: https://github.com/GeospatialPython/Learn/raw...

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