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Python for geospatial data analysis : theory, tools, and practice for location intelligence / Bonny P. McClain.
Material type:
TextPublisher: Sebastopol, CA : O'Reilly Media, Inc., 2022Copyright date: ©2023Edition: First editionDescription: xv, 262 pages : illustrations (chiefly color), maps ; 24 cmContent type: - text
- still image
- unmediated
- volume
- 9781098104795
- 109810479X
- 910.285 23
- QA76.73.P98 M395 2022
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RLKU Library & Information Resource Center | 005 MCC (Browse shelf(Opens below)) | Available | 13757 | |
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RLKU Library & Information Resource Center | 005 MCC (Browse shelf(Opens below)) | Available | 13758 | |
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Includes bibliographical references (pages 249-250) and index.
Introduction to geospatial analytics -- Essential facilities for spatial analysis -- QGIS: exploring PyQGIS and native algorithms for spatial analytics -- Geospatial analytics in the cloud: Google Earth Engine and other tools -- OpenStreetMap: accessing geospatial data with OSMnx -- The ArcGIS Python API -- GeoPandas and spatial statistics -- Data cleaning -- Exploring the Geospatial Data Abstraction Library (GDAL) -- Using Python to measure climate data.
"Tobler's first law of geography states, "Everything is related to everything else, but near things are more related than distant things." But if you look at Tobler's second law, "Phenomena external to a geographic area of interest affect what goes on inside it," you can see why a geographer and data analyst brings the science of location into data stories and large-scale research projects. With this practical book, geospatial professionals, data scientists, geographers, geologists, and others familiar with data analysis and visualization will learn the fundamentals of spatial data analysis." -- Page 4 of cover.
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