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.
