@USGS in conjunction with @UCSD recently released LiDAR-derived Digital Elevation Model (DEM) and Digital Surface Model (DSM) datasets for the areas impacted by the devastating Palisades and Eaton fires. First we ingested each dataset into @heavy_ai (roughly 1.2 billion records each), and then pulled @OvertureMaps building footprint data for the area. We then joined the Overture building footprints to the the DEM data (essentially ground elevation) and DSM data (which includes the height of man-made structures) to get a map of which structures were destroyed by the fire, generally the purple structures on the map below, which are computed to have near-zero structure height post-fire. The entire query, with two 1B+ row geospatial joins between the elevation and buildings datasets, takes less than 100ms on an @nvidia Grace Hopper GH200 GPU on @Vultr cloud, meaning the outputs can be interactively visualized. We are currently diving deeper into the data, and augmenting it with imagery to try to build a burn risk model based on the location of vegetation relative to structures. Our hope is that approaches like this that can fuse multiple geospatial datasets can help mitigate risks of future wildfires.

I've been diving deeper into the @Foursquare Places dataset that was released two weeks ago, and am finding it great not only for accessing individual POIs, but also for performing large-scale analysis of geographic trends. Here I'm using @heavy_ai to join the Places dataset to the NASA Gridded Population of the World (GPW) dataset, containing population estimates per square km for the entire world, using Uber H3 hexagonal spatial indexes as join keys. This allows for easy calculation and visualization of the number of POIs by category per capita. See below for two visualizations comparing the number of places of worship with the number of bars per 1,000 people. You can easily see the Bible belt light up for the former category, but why the high prevalence of both places of worship and drinking establishments in the Upper Midwest?

Was super excited this morning to see @Foursquare had open sourced a dataset over 100M POIs. I immediately it into @heavy_ai and am having a lot of fun exploring the data, it looks fantastic both in terms of coverage and accuracy.

Check out the new @heavy_ai interactive 20 billion record AIS ship location demo running on a single GPU @nvidia Grace Hopper GH200 superchip on @Vultr cloud. Explore the impacts of the 2021 supply chain crisis, or look at seasonal patterns in sailboat usage in Boston Harbor.

#30DayMapChallenge Day 3: Polygons New @ORNL US buildings dataset, complete with addresses and height data. Here are building footprints in the Philadelphia area, colored by height in @heavy_ai.

#30DayMapChallenge Day 1: Points AIS transmissions from cargo ships and tankers loitering near the Port of Long Beach, showing the ship backlog during the supply chain crisis of 2021. Source: 20.4 billion AIS data points from Marine Cadastre, shown in @heavy_ai.

Come check out a webinar I'll be giving with @nvidia and @Vultr tomorrow about the revolutionary performance of the Grace Hopper Superchip. With fast NVLink between CPU and GPU, we are finally unchained from the "thin straw" of PCIe.

At @heavy_ai we recently benchmarked GPU-accelerated HeavyDB against an array of CPU data warehouses, including Snowflake, Databricks, BigQuery, and Redshift, seeing over 100X speedups in some cases. Included in our testing was the new @nvidia ARM-based Grace Hopper GH200 Superchip running in @Vultr Cloud, which is game-changing hardware due to its fast NVLink interconnect between CPU and GPU. Read more in the blog! heavy.ai/blog/speed-at-scale…