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Скачать или смотреть How JupyterLab and widgets enable interactive analysis of the Earth's past, present, and future

  • O'Reilly
  • 2018-10-10
  • 1623
How JupyterLab and widgets enable interactive analysis of the Earth's past, present, and future
O'Reilly Media (Publisher)O'ReillyOReillyOReilly MediaJupyterLabwidgetsEarth'pastpresentand futureTyler EricksonGoogle
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Описание к видео How JupyterLab and widgets enable interactive analysis of the Earth's past, present, and future

How JupyterLab and widgets enable interactive analysis of the Earth's past, present, and future
Tyler Erickson (Google)

The Earth is rapidly changing, and this change has numerous important environmental and societal impacts. There is a strong need to understand these changes at a global scale in high resolution so that we might reverse or adapt to them.

Access to public Earth observation and model data is exploding, driven by recent satellite launches by government, intergovernmental agencies, and private organizations as well as Earth system model simulations run by institutions around the world. Given this deluge of data, the old paradigm of downloading raw data for local processing is highly limiting. New approaches are needed. To address this, many organizations are building cloud- or HPC-based platforms that allow filtering, aggregation, and complex processing prior to transferring data to users. Jupyter Project technologies are increasingly useful for providing access to these large Earth science data stores and analysis platforms.

Tyler Erickson highlights the use of JupyterLab and Jupyter widgets in analyzing complex high-dimensional datasets, providing insights into how our Earth is changing and what the future might look like.

Topics include:

Trends in adoption of Jupyter technologies at Earth science conferences data and by Earth science data and information providers
How to combine multiple Jupyter widgets in JupyterLab to interactively explore and analyze high-dimensional data stored on remote platforms
Case studies of using Jupyter widgets for Earth science data analysis, such as analyzing land-cover change models and exploring projections of future climate conditions
While the case studies will use Google’s Earth Engine platform, the techniques demonstrated are applicable to most remote geospatial platforms that provide API access.

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