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Introduction to spatial and spatiotemporal data in Python

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Introduction to spatial and spatiotemporal data in Python
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57
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CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Production PlaceWageningen

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Abstract
Software requirements: opengeohub/py-geo docker image (gdal, rasterio, geopandas, eumap) This tutorial explores the basics of spatial referencing, reading/writing raster datasets and vector datasets. It also shows how to access the datasets produced by the Geo-harmonizer with eumap, working with time-series datasets, and finally how to visualize the results.
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