Description: Mastering Geospatial Analysis With Python by Silas Toms, Eric Van Rees, Paul Crickard Python comes with many libraries and tools that help you work on geoprocessing tasks without investing in expensive tools. This book introduces you to new libraries that perform geospatial and statistical analysis and data management. It uses examples that explain how Python v3 differs from v2, and solve age-old problems in geospatial analysis. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description Explore GIS processing and learn to work with various tools and libraries in Python. 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. 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. 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. Author Biography Author Bio: Silas Toms Silas Toms is a geographer and geospatial developer from California. Over the last decade, Silas has become an expert in the use of Python programming for geospatial analysis, publishing two books on the use of ArcPy. Now, as a President of Loki Intelligent Corporation, Silas develops ETL automation tools, interactive web maps, enterprise GIS, and location data for businesses and governments. Silas teaches classes on programming for GIS with BayGeo, and co-hosts The Mappyist Hour podcast. Author Bio: Eric van Rees Eric van Rees was first introduced to Geographical Information Systems (GIS) when studying Human Geography in the Netherlands. For 9 years, he was the editor-in-chief of GeoInformatics, an international GIS, surveying, and mapping publication and a contributing editor of GIS Magazine. During that tenure, he visited many geospatial user conferences, trade fairs, and industry meetings. He focuses on producing technical content, such as software tutorials, tech blogs, and innovative new use cases in the mapping industry. Author Bio: Paul Crickard Paul Crickard authored a book on the Leaflet JavaScript module. He has been programming for over 15 years and has focused on GIS and geospatial programming for 7 years. He spent 3 years working as a planner at an architecture firm, where he combined GIS with Building Information Modeling (BIM) and CAD. Currently, he is the CIO at the 2nd Judicial District Attorneys Office in New Mexico. Table of Contents Table of Contents Package installation and management Introduction to geospatial code libraries Introduction to geospatial databases Data types, storage and conversion Vector data analysis Raster data processing Geoprocessing with geodatabases Automating QGIS analysis ArcGIS API for Python and ArcGIS Online Geoprocessing with a GPU Database Flask and GeoAlchemy GeoDjango Creating a geospatial REST API Cloud Geodatabase Analysis and Visualization Automating Cloud Cartography Python geoprocessing with Hadoop Long Description Explore GIS processing and learn to work with various tools and libraries in Python. 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. 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. 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. Details ISBN1788293339 Author Paul Crickard Publisher Packt Publishing Limited Year 2018 ISBN-10 1788293339 ISBN-13 9781788293334 Format Paperback Pages 440 Publication Date 2018-04-27 Short Title Mastering Geospatial Analysis with Python Language English Subtitle Explore GIS Processing and Learn to Work with GeoDjango, CARTOframes and MapboxGL-Jupyter Audience General/Trade UK Release Date 2018-04-27 Imprint Packt Publishing Limited Place of Publication Birmingham Country of Publication United Kingdom AU Release Date 2023-04-02 NZ Release Date 2023-04-02 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:118585961;
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ISBN-13: 9781788293334
Book Title: Mastering Geospatial Analysis With Python
Item Height: 93 mm
Item Width: 75 mm
Author: Silas Toms, Eric Van Rees, Paul Crickard
Publication Name: Mastering Geospatial Analysis with Python: Explore GIS processing and learn to work with GeoDjango, CARTOframes and MapboxGL-Jupyter
Format: Paperback
Language: English
Publisher: Packt Publishing Limited
Subject: Geography & Geosciences, Computer Science
Publication Year: 2018
Type: Textbook
Number of Pages: 440 Pages
