Learn the core concepts of geospatial data analysis for building actionable and insightful GIS applications
Geospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. With this systematic guide, you'll get started with geographic information system (GIS) and remote sensing analysis using the latest features in Python.
This book will take you through GIS techniques, geodatabases, geospatial raster data, and much more using the latest built-in tools and libraries in Python 3.7. You'll learn everything you need to know about using software packages or APIs and generic algorithms that can be used for different situations. Furthermore, you'll learn how to apply simple Python GIS geospatial processes to a variety of problems, and work with remote sensing data.
By the end of the book, you'll be able to build a generic corporate system, which can be implemented in any organization to manage customer support requests and field support personnel.
This book is for Python developers, researchers, or analysts who want to perform geospatial modeling and GIS analysis with Python. Basic knowledge of digital mapping and analysis using Python or other scripting languages will be helpful.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Joel Lawhead is a PMI-certified Project Management Professional, a certified GIS Professional, and the Chief Information Officer of NVision Solutions Inc., an award-winning firm specializing in geospatial technology integration and sensor engineering for NASA, FEMA, NOAA, the US Navy, and many other commercial and non-profit organizations. Joel began using Python in 1997 and started combining it with geospatial software development in 2000. He has authored multiple editions of Learning Geospatial Analysis with Python and QGIS Python Programming Cookbook, both from Packt. He is also the developer of the open source Python Shapefile Library (PyShp) and maintains a geospatial technical blog, GeospatialPython, and Twitter feed, @SpatialPython.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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