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PRODID:-//pretalx//cfp.pydata.org//pydataglobal2025//speaker//EBDUZK
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SUMMARY:Engineering Large-scale geospatial raster processing with xarray a
 nd dask - CLINTON OYOGO DAVID
DTSTART:20251211T123000Z
DTEND:20251211T130000Z
DTSTAMP:20261006T025913Z
UID:pretalx-pydataglobal2025-YN7DYP@cfp.pydata.org
DESCRIPTION:Geospatial analysis often involves harmonizing and processing 
 raster datasets from diverse sources with varying resolutions\, coordinate
  systems\, and data formats. This talk demonstrates how you can build effi
 cient\, scalable pipelines for zonal statistics extraction using Python’
 s scientific computing stack\, xarray\, and dask to handle rasters that wo
 uld otherwise overwhelm traditional processing approaches. \nThrough a rea
 l-world case study of processing multi-source geospatial data for small-ar
 ea estimation of poverty\, we’ll explore practical strategies for memory
 -efficient raster harmonization\, parallel computing workflows\, and autom
 ated statistical aggregation across administrative boundaries.
LOCATION:Data Engineering & Infrastructure
URL:https://cfp.pydata.org/pydataglobal2025/talk/YN7DYP/
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