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We’re big fans of Topi Tjukanov’s annual 30 Day Map Challenge. We especially love how he and the geospatial sciences community encourage folks to share and learn from each other. Never one to back down from a fun challenge, my colleagues and I jumped in to see what we could do with OmniSci as our base geospatial platform, for querying and visualizing massive geospatial datasets.
We’ve collected some of our favorite OmniSci mapping features, capabilities, and submissions here to highlight what is possible with our platform, and provide more detail on the datasets and processing we performed to make these visualizations possible. You can go to Twitter to see the breadth of submissions across the entire geospatial sciences community by searching #30DayMapChallenge.
Day 3: Polygons
Microsoft's Open Sourced Australia Building Footprints (data - Tweet - LinkedIn)
Microsoft recently open sourced 11,334,866 computer generated building footprints derived using Bing Maps algorithms on satellite imagery. We mapped the polygons using the Choropleth Chart in OmniSci’s Immerse, gave them a light blue color with mild opacity, and overlaid them on Mapbox’s Odyssey basemap. It's a simple map that underlines the intricate urban landscape of Sydney Harbour.
Day 7: Green
Fishing boat movement patterns off the coast of Vancouver from 2009 to 2015 filtered down from 11.6 billion records (data - Tweet - Demo)
Day 9: Monochrome
Southern California Digital Elevation via Shuttle Radar Topography Mission (SRTM) (data - Tweet - LinkedIn)
Southern California digital elevation data offered at a resolution of 1 arc-second (30 meters) was vectorized using a combination of QGIS and GDAL before being ingested into OmniSci. We symbolized the 207 million pixels with a monochrome white to black color ramp and paired the Choropleth Map Chart with a 10-bin elevation Histogram that allows the user to quickly explore different elevation profiles throughout the region.
Day 13: Raster
Chlorophyll Concentration (1 Month - AQUA/MODIS) September 2020 (data - Tweet - LinkedIn)
The MODIS Chlorophyll-a data product provides an estimate of the near-surface concentration of chlorophyll calculated using an empirical relationship derived from in situ measurements of chlorophyll and remote sensing reflectances (Rrs) in the blue-to-green region of the visible spectrum. We symbolized the 1.35 million pixels with a white to dark green color ramp and paired the Choropleth Map Chart with a 10-bin concentration Histogram that makes interrogating various Chlorophyll densities easy and efficient.
Day 17: Historical Map
Historical North Atlantic and Eastern North Pacific Tropical Cyclone Tracks 1851 through 2008 (data - Tweet - LinkedIn)
This next map depicts approximately 45 thousand historical North Atlantic and Eastern North Pacific Tropical Cyclone Tracks for all subtropical depressions and storms, extratropical storms, tropical lows, waves, disturbances, depressions, storms, and all hurricanes from 1851 through 2008. After ingesting the Homeland Infrastructure Foundation Level Data product, we mapped the tracks using Immerse’s Linemap Chart, a diverging blue to yellow color ramp, a density gradient, and the wildly useful line autosize feature to ensure we’d maintain our understanding of storm hotspots at any zoom scale.
Day 22: Movement
Internal Mexican Migration Flow (data - Tweet - LinkedIn)
This map represents the 2015 WHO estimated internal human migration flows between Mexican subnational administrative units due to the malaria endemic. A thousand lines between the administrative units were generated in QGIS and ingested into OmniSci where five discrete line color categories were calculated using the SQL Editor. We used the thinnest, non-autosizing line width with a diverging blue to red color ramp that represents the lowest to highest levels of human migration.
Day 27: Big or Small Data
Zooming in from 11.6 billion records to a few thousand, showing the Deep Water Horizon oil spill on April 20, 2010 (data - Tweet - Demo)
Day 30: A Map
Global Bathymetry Contours (data - Tweet - LinkedIn)
Nested global contour lines for each depth level created from SRTM Plus and transformed by GDAL for ingestion into OmniSci. The 1.4 million contour lines were mapped using a Linemap Chart with a custom continuous color ramp created in the color palette section of Immerse’s UI settings to resemble classic contour and isochrone symbology. The vibrant red lines show the oceans’ deepest locations and contrast with the dark blue lines of coastal waters.
As a relatively new member of the OmniSci family this challenge gave me the opportunity to realize our platform’s place within the growing and powerful OSGeo stack. OmniSci’s ability to query and visualize massive geospatial data, create data cohorts, and integrate with JupyterLab, the most commonly used spatial data science development environment, makes it an indispensable part of any exploratory geosciences workflow.
If you have any questions or would like to see any of these datasets in action let me know, I’d be happy to deliver a live demo. You can reach me on Twitter @AFCotroneo or LinkedIn.