Book Image

Mastering Geospatial Analysis with Python

By : Silas Toms, Paul Crickard, Eric van Rees
Book Image

Mastering Geospatial Analysis with Python

By: Silas Toms, Paul Crickard, Eric van Rees

Overview of this book

Python comes with a host of open source libraries and tools that help you work on professional geoprocessing tasks without investing in expensive tools. This book will introduce Python developers, both new and experienced, to a variety of new code libraries that have been developed to perform geospatial analysis, statistical analysis, and data management. This book will use examples and code snippets that will help explain how Python 3 differs from Python 2, and how these new code libraries can be used to solve age-old problems in geospatial analysis. You will begin by understanding what geoprocessing is and explore the tools and libraries that Python 3 offers. You will then learn to use Python code libraries to read and write geospatial data. You will then learn to perform geospatial queries within databases and learn PyQGIS to automate analysis within the QGIS mapping suite. Moving forward, you will explore the newly released ArcGIS API for Python and ArcGIS Online to perform geospatial analysis and create ArcGIS Online web maps. Further, you will deep dive into Python Geospatial web frameworks and learn to create a geospatial REST API.

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Table of Contents (23 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
Packt Upsell
Packt Upsell
Contributors
Contributors
Preface
Preface
Free Chapter
1
Package Installation and Management
Package Installation and Management
Introducing Anaconda
Running a Jupyter Notebook
Managing Python packages
Python virtual environments
Summary
2
Introduction to Geospatial Code Libraries
Introduction to Geospatial Code Libraries
Geospatial Data Abstraction Library (GDAL) and the OGR Simple Features Library
GEOS
Shapely
Fiona
Python shapefile library (pyshp)
pyproj
Rasterio
GeoPandas
How it all works together
Summary
3
Introduction to Geospatial Databases
Introduction to Geospatial Databases
Installing PostgreSQL and PostGIS on Windows
Installing PostgreSQL and PostGIS on Mac
Working with PostgreSQL and PostGIS using Python
Summary
4
Data Types, Storage, and Conversion
Data Types, Storage, and Conversion
Raster and vector data
Raster data formats
Reading and writing vector data with GeoPandas
Reading and writing vector data with OGR
Reading and writing raster data with Rasterio
Reading and writing raster data using GDAL
Summary
5
Vector Data Analysis
Vector Data Analysis
OGR Simple Features Library
Shapely and Fiona
GeoPandas
Summary
6
Raster Data Processing
Raster Data Processing
Raster operations using GDAL
Raster operations using PostgreSQL
Summary
7
Geoprocessing with Geodatabases
Geoprocessing with Geodatabases
A crime dashboard
Summary
8
Automating QGIS Analysis
Automating QGIS Analysis
Working in the Python console
Writing custom toolboxes
Summary
9
ArcGIS API for Python and ArcGIS Online
ArcGIS API for Python and ArcGIS Online
Introducing the ArcGIS API for Python and ArcGIS Online
Summary
10
Geoprocessing with a GPU Database
Geoprocessing with a GPU Database
Cloud geodatabase solutions
Summary
11
Flask and GeoAlchemy2
Flask and GeoAlchemy2
Flask and its component modules
Summary
12
GeoDjango
GeoDjango
Installing and configuring Django and GeoDjango
Creating the application
Summary
13
Geospatial REST API
Geospatial REST API
Writing a REST API in Python
The REST API application
Deploying Flask to IIS
Summary
14
Cloud Geodatabase Analysis and Visualization
Cloud Geodatabase Analysis and Visualization
How to install CARTOframes
Signing up for a CARTO account
Virtual environments
Using Jupyter Notebook
Summary
15
Automating Cloud Cartography
Automating Cloud Cartography
All things cartographic
Mapbox Studio
Summary
16
Python Geoprocessing with Hadoop
Python Geoprocessing with Hadoop
What is Hadoop?
Installing the Hadoop framework
Hadoop basics
Esri GIS tools for Hadoop
HDFS and Hive in Python
Summary
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think
Index
Index

Summary

In this chapter, we learned how to use the Flask MVC web framework and some of the available component modules that add extra functionality. These modules include the SQLAlchemy ORM, the GeoAlchemy2 ORM for geospatial queries, WTForms for processing of web data, and the Jinja2 templating system for creating web page templates. We created database tables, added data tables and tables relationships, and created a web application that utilized geospatial and relationship queries to generate dynamic web pages.

A fun challenge, building on the code reviewed here, would be to explore adding editing capabilities to the Arena application, allowing the user to move the arenas into their correct location if the data is out of date. Explore the GeoAlchemy2 ORM documentation for more advanced capabilities.

In the next chapter, we will review a similar MVC web framework, Django, and its GeoDjango spatial component. With more batteries included philosophy, Django solves the same problems inherent...

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