▶Book 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.
▶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.
▶Key Features
⦁ 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.
▶Who This Book Is 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 this book covers
⦁ Chapter 1, Package Installation and Management, explains how to install and manage the code libraries used in the book.
⦁ Chapter 2, Introduction to Geospatial Code Libraries, covers the major code libraries used to process and analyze geospatial data.
⦁ Chapter 3, Introduction to Geospatial Databases, introduces the geospatial databases used for data storage and analysis.
⦁ Chapter 4, Data Types, Storage, and Conversion, focuses on the many different data types (both vector and raster) that exist within GIS.
⦁ Chapter 5, Vector Data Analysis, covers Python libraries such as Shapely, OGR, and GeoPandas. which are used for analyzing and processing vector data.
⦁ Chapter 6, Raster Data Processing, explores using GDAL and Rasterio to process raster datasets in order to perform geospatial analysis.
⦁ Chapter 7, Geoprocessing with Geodatabases, shows the readers how to use Spatial SQL to perform geoprocessing with database tables containing a spatial column.
⦁ Chapter 8, Automating QGIS Analysis, teaches the readers how to use PyQGIS to automate analysis within the QGIS mapping suite.
⦁ Chapter 9, ArcGIS API for Python and ArcGIS Online, introduces the ArcGIS API for Python, which enables users to interact with Esri's cloud platform, ArcGIS Online, using Python 3.
⦁ Chapter 10, Geoprocessing with a GPU Database, covers using Python tools to interact with cloud-based data to search and process data.
⦁ Chapter 11, Flask and GeoAlchemy2, describes how to use the Flask Python web framework and the GeoAlchemy ORM to perform spatial data queries.
⦁ Chapter 12, GeoDjango, covers using the Django Python web framework and the GeoDjango ORM to perform spatial data queries.
⦁ Chapter 13, Geospatial REST API, teaches the readers how to create a REST API for geospatial data.
⦁ Chapter 14, Cloud Geodatabase Analysis and Visualization, introduces the readers to the CARTOframes Python package, enabling the integration of Carto maps, analysis, and data services into data science workflows.
⦁ Chapter 15, Automating Cloud Cartography, covers a new location data visualization library for Jupyter Notebooks.
⦁ Chapter 16, Python Geoprocessing with Hadoop, explains how to perform geospatial analysis using distributed servers.
