#xarray — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #xarray, aggregated by home.social.
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RE: https://wisskomm.social/@ioer/115899330915763542
I really took a deep dive into #datashader with this map: Locals & Tourists in Germany, as derived from 67 Million Geo-Social Media Posts (2007-2022) in Germany. The data includes public shared posts from Instagram, Flickr, Twitter and iNaturalist.
I always wanted to create such a map, following the footsteps of Eric Fisher's Locals & Tourists dataset from 2011 [1].
I shared the code for producing this map here [2]. The repository is available here [3]. This includes some neat methods for various #geospatial processing tasks in #Python, such as exporting a datashader map to a #GeoTiff [4] with the help of #Xarray and #Rasterio.
Finally, all of this was created in a privacy-preserving way using #HyperLogLog, which allowed me to share the code and abstracted data publicly for full reproducibility and transparency. [6] #FAIR
Below you'll find the link to the (quite succinct) publication in Natur und Landschaft in Karten (#NuL).
[1]: https://www.flickr.com/photos/walkingsf/albums/72157624209158632
[2]: https://code.ad.ioer.info/wip/digital_traces_map/html/03_visualization.html
[3]: https://gitlab.hrz.tu-chemnitz.de/ad/digital_traces_map/
[4]: https://gitlab.hrz.tu-chemnitz.de/s7398234--tu-dresden.de/base_modules/-/blob/main/raster.py?ref_type=heads#L78
[5]: https://www.nul-online.de/article-7301410-1111/landschaft-und-natur-in-karten-.html
[6]: https://doi.org/10.71830/VDMUWW -
RE: https://wisskomm.social/@ioer/115899330915763542
I really took a deep dive into #datashader with this map: Locals & Tourists in Germany, as derived from 67 Million Geo-Social Media Posts (2007-2022) in Germany. The data includes public shared posts from Instagram, Flickr, Twitter and iNaturalist.
I always wanted to create such a map, following the footsteps of Eric Fisher's Locals & Tourists dataset from 2011 [1].
I shared the code for producing this map here [2]. The repository is available here [3]. This includes some neat methods for various #geospatial processing tasks in #Python, such as exporting a datashader map to a #GeoTiff [4] with the help of #Xarray and #Rasterio.
Finally, all of this was created in a privacy-preserving way using #HyperLogLog, which allowed me to share the code and abstracted data publicly for full reproducibility and transparency. [6] #FAIR
Below you'll find the link to the (quite succinct) publication in Natur und Landschaft in Karten (#NuL).
[1]: https://www.flickr.com/photos/walkingsf/albums/72157624209158632
[2]: https://code.ad.ioer.info/wip/digital_traces_map/html/03_visualization.html
[3]: https://gitlab.hrz.tu-chemnitz.de/ad/digital_traces_map/
[4]: https://gitlab.hrz.tu-chemnitz.de/s7398234--tu-dresden.de/base_modules/-/blob/main/raster.py?ref_type=heads#L78
[5]: https://www.nul-online.de/article-7301410-1111/landschaft-und-natur-in-karten-.html
[6]: https://doi.org/10.71830/VDMUWW -
I am really looking forward to a time when scientific data analysis is less of a constant fuckaround and fight with technical bullshit. I'd *really* like
- #netCDF natively supporting complex numbers
- #Python #xarray and #pandas to natively support physical units (#pint is great on its own but the integrations leave a LOT to be desired)
- #Jupyter notebooks to suck less (crashes, glitches, widget plots not saved statically, an effing BUILTIN formatter, etc.)
- proper data pipeline systems
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GSPy - A New Toolbox And Data Standard For Geophysical Datasets
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https://doi.org/10.3389/feart.2022.907614 <-- shared paper
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https://doi.org/10.5066/P9XNQVGQ | https://code.usgs.gov/g3sc/gspy <-- shared code repository
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[an older paper, but code is in active and ongoing development/evolution]
#GIS #spatial #mapping #geophysics #geophysical #NetCDF #datatypes #code #opensource #library #dataformats #standardisation #standardization #openstandard #portable #metadata #Python #package #GSPy #methods #workflows #xarray #CRS #opendata #architecture #toolbox -
Py3DEP [hydroclimate analysis]
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https://pypi.org/project/py3dep/ <-- link to app / resources
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“Py3DEP is a part of [PyGeoUtils] HyRiver software stack that is designed to aid in hydroclimate analysis through web services. This package provides access to the 3DEP database which is a part of the National Map services. The 3DEP service has multi-resolution sources and depending on the user-provided resolution, the data is resampled on the server-side based on all the available data sources. Py3DEP returns the requests as xarray dataset…”
#GIS #spatial #mapping #Py3DEP #PyGeoUtils #HyRiver #softwarestack #elevation #3DEP #USGS #NationalMap #opendata #processing #spatialanalysis #hydroclimate #water #hydrology #webservices #xarray
@HyRiver @USGS
