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Mastering Geospatial Analysis with Python

By : Toms, Paul Crickard, Eric van Rees

3 (4)

Buy this Book

Mastering Geospatial Analysis with Python

3 (4)

By: Toms, Paul Crickard, Eric van Rees

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Package Installation and Management

Introducing Anaconda

Running a Jupyter Notebook

Managing Python packages

Python virtual environments

Summary

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

Introduction to Geospatial Databases

Installing PostgreSQL and PostGIS on Windows

Installing PostgreSQL and PostGIS on Mac

Working with PostgreSQL and PostGIS using Python

Summary

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

Vector Data Analysis

OGR Simple Features Library

Shapely and Fiona

GeoPandas

Summary

Raster Data Processing

Raster operations using GDAL

Raster operations using PostgreSQL

Summary

Geoprocessing with Geodatabases

A crime dashboard

Summary

Automating QGIS Analysis

Working in the Python console

Writing custom toolboxes

Summary

ArcGIS API for Python and ArcGIS Online

Introducing the ArcGIS API for Python and ArcGIS Online

Summary

Geoprocessing with a GPU Database

Cloud geodatabase solutions

Summary

Flask and GeoAlchemy2

Flask and its component modules

Summary

GeoDjango

Installing and configuring Django and GeoDjango

Creating the application

Summary

Geospatial REST API

Writing a REST API in Python

The REST API application

Deploying Flask to IIS

Summary

Cloud Geodatabase Analysis and Visualization

How to install CARTOframes

Signing up for a CARTO account

Virtual environments

Using Jupyter Notebook

Summary

Automating Cloud Cartography

All things cartographic

Mapbox Studio

Summary

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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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...