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
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Index
Index

Using Jupyter Notebook

We have covered the basic installation of Jupyter Notebook in Chapter 1, Package Installation and Management and in the previous chapter at various instances to run code and get the desired output.

Here, we will be using Jupyter Notebook for CARTOframes to connect to an account and analyze geospatial data and display it.

Connecting to an account

In the first code box, we will import the CARTOframes module, and pass the API key string along with the base URL, which is generated from your CARTO username as https://{username}.carto.com. In this case, the URL is https://lokiintelligent.carto.com:

In this code block, the API key and the URL are passed to the CartoContext class, and a CartoContext connect object is returned and assigned to the variable cc. With this object, we can now interact with the datasets associated with our account, load datasets into the account, and even generate maps directly in the Jupyter Notebook. 

Once the code has been entered into the section...

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