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Pale Blue Dot: Visualization Challenge

Goal of the Competition

Our world is facing many urgent challenges, such as climate change, water insecurity, and food insecurity. Maintaining and improving quality of life around the world requires bringing together innovators across disciplines and countries to find creative solutions.

One critical tool for understanding and improving the urgent challenges facing our world is Earth observation data, meaning data that is gathered in outer space about life here on Earth! Earth observation data provides accurate and publicly accessible information on our atmosphere, oceans, ecosystems, land cover, and built environment. The United States and its partners have a long history of exploring outer space and making satellite, airborne, and in-situ sensor datasets openly available to all.

In this challenge, participants created visualizations using Earth observation data that advanced the Sustainable Development Goals of zero hunger, clean water, and climate action. The challenge was designed enable a broader, more diverse audience to engage with Earth observation data and take advantage of its potential.

What's in this Repository

This repository contains the winning submissions from the Pale Blue Dot DrivenData challenge. Code for all winning solutions are open source under the MIT License.

Winning code for other DrivenData competitions is available in the competition-winners repository.

Winning Submissions

Best Overall Prize

Team	Users	Summary of Submission
EE Frogs	<a href="https://www.drivendata.org/users/aln26/">aln26</a>, <a href="https://www.drivendata.org/users/Bastian6/">Bastian6</a>, and <a href="https://www.drivendata.org/users/tmulick/">tmulick</a>	Analyzes solar panel suitability on the Turks and Caicos Islands using environmental data from <a href="https://www.usgs.gov/landsat-missions">Landsat</a> and elevation data from <a href="https://cmr.earthdata.nasa.gov/search/concepts/C1711961296-LPCLOUD.html">ASTER</a>
H2plastic	<a href="https://www.drivendata.org/users/Marília/">Marília</a>, <a href="https://www.drivendata.org/users/ruvit/">ruvit</a>, <a href="https://www.drivendata.org/users/wesley_andradez/">wesley_andradez</a>, and <a href="https://www.drivendata.org/users/wtkellyy/">wtkellyy</a>	Tracks the concentration of microplastics in the ocean along Brazil's coast using <a href="https://podaac.jpl.nasa.gov/dataset/CYGNSS_L3_MICROPLASTIC_V1.0">CYGNSS satellite data</a>
Hunatek-Kalman	<a href="https://www.drivendata.org/users/bwalzer009/">bwalzer009</a>, <a href="https://www.drivendata.org/users/mnflabiano/">mnflabiano</a>, <a href="https://www.drivendata.org/users/nanderson/">nanderson</a>, and <a href="https://www.drivendata.org/users/rowanam/">rowanam</a>	Quantifies wildfire impacts in eastern North America by combining EPA air quality system <a href="https://www.epa.gov/aqs">data</a> with MODIS imagery of <a href="https://lpdaac.usgs.gov/products/mcd64a1v061/">burned area</a> and <a href="https://firms.modaps.eosdis.nasa.gov/active_fire/">active fires</a>
Spatial Clan	<a href="https://www.drivendata.org/users/Insights101/">Insights101</a> and <a href="https://www.drivendata.org/users/linusanari_/">linusanari_</a>	Maps food insecurity risk by combining drought reports (Kenya's National Drought Management Authority) and satellite-based rainfall data (<a href="https://disc.gsfc.nasa.gov/datasets/TRMM_3B42RT_7/summary">NASA</a>)
Viva Aqua	<a href="https://www.drivendata.org/users/AtomicTiger/">AtomicTiger</a>, <a href="https://www.drivendata.org/users/franfurey/">franfurey</a>, <a href="https://www.drivendata.org/users/Jaay/">Jaay</a>, and <a href="https://www.drivendata.org/users/Malenag/">Malenag</a>	Models groundwater level in The Gambia to identify locations for new water wells using <a href="https://www.earthdata.nasa.gov/sensors/modis">MODIS imagery</a>, groundwater data from <a href="https://ggis.un-igrac.org/">IGRAC</a> and the <a href="https://www2.bgs.ac.uk/groundwater/international/africangroundwater/mapsDownload.html">British Geological Survey</a>, and satellite-based precipitation models from NASA's <a href="https://gpm.nasa.gov/data/imerg">IMERG</a>

Full winning submissions are shared in the submission folder inside the directory for each team. Additional solution details can be found in the reports folder.

Community Code Bonus Prize

Katso Obotsang (username Katso) won the Community Code Bonus Prize for the post "Creating a visualization from your csv file in Python". Katso is based in Kweneng District, Botswana.

Honorable Mentions for Compelling Visuals

33 submissions received an Honorable Mention for Compelling Visuals. Check out the Honorable Mentions gallery page to see all of their visuals!

References

Winners Blog Post: Meet the winners of the Pale Blue Dot Challenge

Honorable Mentions for Compelling Visuals: Honorable Mentions gallery

Data Resources Blog Post: Open Earth Observation Data for the Pale Blue Dot: Visualization Challenge