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Mastering Geospatial Analysis with Python
Mastering Geospatial Analysis with Python

Mastering Geospatial Analysis with Python: Explore GIS processing and learn to work with GeoDjango, CARTOframes and MapboxGL-Jupyter

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Profile Icon Toms Profile Icon Eric van Rees Profile Icon Paul Crickard
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Mastering Geospatial Analysis with Python

Introduction to Geospatial Code Libraries

This chapter will introduce the major code libraries used to process and analyze geospatial data. You will learn the characteristics of each library, how they are related to each other, how to install them, where to find additional documentation, and typical use cases. These instructions assume that the user has a recent (2.7 or later) version of Python on their machine, and do not cover installing Python. Next, we'll discuss how all of these packages fit together and how they are covered in the rest of this book.

The following libraries will be covered in this chapter:

  • GDAL/OGR
  • GEOS
  • Shapely
  • Fiona
  • Python Shapefile Library (pyshp)
  • pyproj
  • Rasterio
  • GeoPandas

Geospatial Data Abstraction Library (GDAL) and the OGR Simple Features Library

The Geospatial Data Abstraction Library (GDAL)/OGR Simple Features Library combines two separate libraries that are generally downloaded together as a GDAL. This means that installing the GDAL package also gives access to OGR functionality, which is why they're covered together here. The reason GDAL is covered first is that other packages were written after GDAL, so chronologically, it comes first. As you will notice, some of the packages covered in this chapter extend GDAL's functionality or use it under the hood.

GDAL was created in the 1990s by Frank Warmerdam and saw its first release in June 2000. Later, the development of GDAL was transferred to the Open Source Geospatial Foundation (OSGeo). Technically, GDAL is a little different than your average Python package as the GDAL...

GEOS

The Geometry Engine Open Source (GEOS) is the C/C++ port of a subset of the Java Topology Suite (JTS) and selected functions. GEOS aims to contain the complete functionality of JTS in C++. It can be compiled on many platforms, including Python. As you will see later on, the Shapely library uses functions from the GEOS library. In fact, there are many applications using GEOS, including PostGIS and QGIS. GeoDjango, covered in Chapter 12GeoDjango, also uses GEOS, as well as GDAL, among other geospatial libraries. GEOS can also be compiled with GDAL, giving OGR all of its capabilities.

The JTS is an open source geospatial computational geometry library written in Java. It provides various functionalities, including a geometry model, geometric functions, spatial structures and algorithms, and i/o capabilities. Using GEOS, you have access to the following capabilities—...

Shapely

Shapely is a Python package for manipulation and analysis of planar features, using functions from the GEOS library (the engine of PostGIS) and a port of the JTS. Shapely is not concerned with data formats or coordinate systems but can be readily integrated with packages that are. Shapely only deals with analyzing geometries and offers no capabilities for reading and writing geospatial files. It was developed by Sean Gillies, who was also the person behind Fiona and Rasterio.

Shapely supports eight fundamental geometry types that are implemented as a class in the shapely.geometry module—points, multipoints, linestrings, multilinestrings, linearrings, multipolygons, polygons, and geometrycollections. Apart from representing these geometries, Shapely can be used to manipulate and analyze geometries through a number of methods and attributes.

Shapely has mainly the...

Fiona

Fiona is the API of OGR. It can be used for reading and writing data formats. The main reason for using it instead of OGR is that it's closer to Python than OGR as well as more dependable and less error-prone. It makes use of two markup languages, WKT and WKB, for representing spatial information with regards to vector data. As such, it can be combined well with other Python libraries such as Shapely, you would use Fiona for input and output, and Shapely for creating and manipulating geospatial data. 

While Fiona is Python compatible and our recommendation, users should also be aware of some of the disadvantages. It is more dependable than OGR because it uses Python objects for copying vector data instead of C pointers, which also means that they use more memory, which affects the performance. 

...

Python shapefile library (pyshp)

 The Python shapefile library (pyshp) is a pure Python library and is used to read and write shapefiles. The pyshp library's sole purpose is to work with shapefiles—it only uses the Python standard library. You cannot use it for geometric operations. If you're only working with shapefiles, this one-file-only library is simpler than using GDAL.

Installing pyshp

pyproj

The pyproj is a Python package that performs cartographic transformations and geodetic computations. It is a Cython wrapper to provide Python interfaces to PROJ.4 functions, meaning you can access an existing library of C code in Python.

PROJ.4 is a projection library that transforms data among many coordinate systems and is also available through GDAL and OGR. The reason that PROJ.4 is still popular and widely used is two-fold:

  • Firstly, because it supports so many different coordinate systems
  • Secondly, because of the routes it provides to do this—Rasterio and GeoPandas, two Python libraries covered next, both use pyproj and thus PROJ.4 functionality under the hood

The difference between using PROJ.4 separately instead of using it with a package such as GDAL is that it enables you to re-project individual points, and packages using PROJ.4 do not offer this...

Rasterio

Rasterio is a GDAL and NumPy-based Python library for raster data, written with the Python developer in mind instead of C, using Python language types, protocols, and idioms. Rasterio aims to make GIS data more accessible to Python programmers and helps GIS analysts learn important Python standards. Rasterio relies on concepts of Python rather than GIS.

Rasterio is an open source project from the satellite team of Mapbox, a provider of custom online maps for websites and applications. The name of this library should be pronounced as raster-i-o rather than ras-te-rio. Rasterio came into being as a result of a project called the Mapbox Cloudless Atlas, which aimed to create a pretty-looking basemap from satellite imagery.

One of the software requirements was to use open source software and a high-level language with handy multi-dimensional array syntax. Although GDAL offers...

Geospatial Data Abstraction Library (GDAL) and the OGR Simple Features Library


The Geospatial Data Abstraction Library (GDAL)/OGR Simple Features Library combines two separate libraries that are generally downloaded together as a GDAL. This means that installing the GDAL package also gives access to OGR functionality, which is why they're covered together here. The reason GDAL is covered first is that other packages were written after GDAL, so chronologically, it comes first. As you will notice, some of the packages covered in this chapter extend GDAL's functionality or use it under the hood.

GDAL was created in the 1990s by Frank Warmerdam and saw its first release in June 2000. Later, the development of GDAL was transferred to the Open Source Geospatial Foundation (OSGeo). Technically, GDAL is a little different than your average Python package as the GDAL package itself was written in C and C++, meaning that in order to be able to use it in Python, you need to compile GDAL and its associated...

GEOS


The Geometry Engine Open Source (GEOS) is the C/C++ port of a subset of the Java Topology Suite (JTS) and selected functions. GEOS aims to contain the complete functionality of JTS in C++. It can be compiled on many platforms, including Python. As you will see later on, the Shapely library uses functions from the GEOS library. In fact, there are many applications using GEOS, including PostGIS and QGIS. GeoDjango, covered in Chapter 12GeoDjango, also uses GEOS, as well as GDAL, among other geospatial libraries. GEOS can also be compiled with GDAL, giving OGR all of its capabilities.

The JTS is an open source geospatial computational geometry library written in Java. It provides various functionalities, including a geometry model, geometric functions, spatial structures and algorithms, and i/o capabilities. Using GEOS, you have access to the following capabilities—geospatial functions (such as within and contains), geospatial operations (union, intersection, and many more), spatial indexing...

Shapely


Shapely is a Python package for manipulation and analysis of planar features, using functions from the GEOS library (the engine of PostGIS) and a port of the JTS. Shapely is not concerned with data formats or coordinate systems but can be readily integrated with packages that are. Shapely only deals with analyzing geometries and offers no capabilities for reading and writing geospatial files. It was developed by Sean Gillies, who was also the person behind Fiona and Rasterio.

Shapely supports eight fundamental geometry types that are implemented as a class in the shapely.geometry module—points, multipoints, linestrings, multilinestrings, linearrings, multipolygons, polygons, and geometrycollections. Apart from representing these geometries, Shapely can be used to manipulate and analyze geometries through a number of methods and attributes.

Shapely has mainly the same classes and functions as OGR while dealing with geometries. The difference between Shapely and OGR is that Shapely has...

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Key benefits

  • Analyze and process geospatial data using Python libraries such as; Anaconda, GeoPandas
  • Leverage new ArcGIS API to process geospatial data for the cloud.
  • Explore various Python geospatial web and machine learning frameworks.

Description

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.

Who is this book for?

The audience for this book includes students, developers, and geospatial professionals who need a reference book that covers GIS data management, analysis, and automation techniques with code libraries built in Python 3.

What you will learn

  • Manage code libraries and abstract geospatial analysis techniques using Python 3.
  • Explore popular code libraries that perform specific tasks for geospatial analysis.
  • Utilize code libraries for data conversion, data management, web maps, and REST API creation.
  • Learn techniques related to processing geospatial data in the cloud.
  • Leverage features of Python 3 with geospatial databases such as PostGIS, SQL Server, and SpatiaLite.
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Table of Contents

17 Chapters
Package Installation and Management Chevron down icon Chevron up icon
Introduction to Geospatial Code Libraries Chevron down icon Chevron up icon
Introduction to Geospatial Databases Chevron down icon Chevron up icon
Data Types, Storage, and Conversion Chevron down icon Chevron up icon
Vector Data Analysis Chevron down icon Chevron up icon
Raster Data Processing Chevron down icon Chevron up icon
Geoprocessing with Geodatabases Chevron down icon Chevron up icon
Automating QGIS Analysis Chevron down icon Chevron up icon
ArcGIS API for Python and ArcGIS Online Chevron down icon Chevron up icon
Geoprocessing with a GPU Database Chevron down icon Chevron up icon
Flask and GeoAlchemy2 Chevron down icon Chevron up icon
GeoDjango Chevron down icon Chevron up icon
Geospatial REST API Chevron down icon Chevron up icon
Cloud Geodatabase Analysis and Visualization Chevron down icon Chevron up icon
Automating Cloud Cartography Chevron down icon Chevron up icon
Python Geoprocessing with Hadoop Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(4 Ratings)
5 star 50%
4 star 0%
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1 star 50%
Aurelio Pereira Mar 04, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
One of the best books to start to follow geospatial Analysis with python. The authors bring knowledge to a level that beginners and experienced users will benefit.
Amazon Verified review Amazon
zamiri Nov 28, 2021
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
The code in most sections doesn’t run at all. Its just a piece of garbage.
Amazon Verified review Amazon
Garas. Sep 14, 2018
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This book is HORRIBLE! ive been stuck on the same pages more several weeks of trying... Ive been forced to get help from the DISCORD Python community and stackoverflow. This book asks you to download packages/modules but they never actually run nor does the book have a section of common problems with importing these packages. The packages can NEVER be found by my programs and never download appropiately...The book SHOULD BE STAYED AWAY FROM! NOTHING IS TESTED NOTHING WORKS!Thank god for those that have helped me on discord and stackoverflow
Amazon Verified review Amazon
K K Aug 25, 2018
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Its a top-notch book for learning the current state of Python-related GIS database tools and analysis. It provides a overview of many packages available. For geospatial professionals as myself, it's an invaluable resource for getting 'caught up' on state of available software. Cheers to the authors!
Amazon Verified review Amazon
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