DESCRIPTION

Geospatial data is hard to ignore. Nearly every car, phone, or camera has a GPS sensor, and aerial photos, satellite imagery, and data representing political boundaries, roads, rivers, and streams are available for free download from many websites. Geoprocessing is the science of reading, analyzing, and presenting geospatial data programmatically. The Python language, along with dozens of open source libraries and tools, makes it possible to take on professional geoprocessing tasks without investing in expensive proprietary packages like ArcGIS and MapInfo.

 

Geoprocessing with Python teaches how to use the Python programming language along with free and open source tools to read, write, and process geospatial data. It shows how to access available data sets to make maps or perform analyses using free and open source tools like the GDAL, Shapely, and Fiona Python modules. Readers will master core practices like handling multiple vector file formats, editing and manipulating geometries, applying spatial and attribute filters, working with projections, and performing basic analyses on vector data. They’ll also learn how to create geospatial data, rather than just consuming it. The book also covers how to manipulate and analyze raster data, such as aerial photographs, satellite images, and digital elevation models.

 

KEY SELLING POINTS

 

Geoprocessing from the ground up

Shows how to create custom geoprocessing tools

Takes advantage of free and open source tools

 

AUDIENCE

Readers need a basic knowledge of Python or a similar programming language. No previous experience with geoprocessing or geospatial data required. Perfect for readers who have used Python with Esri ArcGIS and want to apply their Python skills elsewhere.

 

ABOUT THE TECHNOLOGY

Geoprocessing is the science of reading, analyzing, and presenting geospatial data programmatically. The Python language, along with dozens of open source libraries and tools, makes it possible to take on professional geoprocessing tasks without investing in expensive proprietary packages like ArcGIS and MapInfo.