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In the latest edition of This Month In Wherobots the latest highlights from the Wherobots & Apache Sedona community include an overview of the Havasu spatial table format, a look back at Wherobots’ 2023 in review, analyzing Overture Maps and real estate data, finding the perfect Christmas tree, plus big data analytics for sustainable smart cities.
The Havasu spatial table format is an extension of Apache Iceberg that brings native spatial support to data lakes. This post introduces Havasu and some of its key features and technical insights such as support for primitive spatial data types and storage, spatial statistics, and spatial filter push down and indexing. Examples of creating and querying Havasu tables as well as how Havasu working with GeoParquet is also covered. If you’ve used the Wherobots open data catalog you may have already leveraged Havasu!
Read More About The Havasu Spatial Table Format
Our featured community member this month is Dr. Muhammed O?uzhan METE. Muhammed is an Assistant Professor at Istanbul Technical University in the Geomatics Engineering Department. His research areas of focus are land management, real estate management, GIS, machine learning, and big data analytics. He is also an AWS Community Builder. Muhammed presented “Geospatial Big Data Analytics For Sustainable Smart Cities” at the FOSS4G 2023 conference where he shared how Apache Sedona can be used for large scale spatial analytics.
Connect with Muhammed On LinkedIn
In this presentation from FOSS4G 2023 Dr. Muhammed Oguzhan Mete covers the need for large-scale geospatial data analysis for sustainable smart city project development. He covers some of the use cases for large scale data analysis relevant for smart cities such as how to make smart infrastructure decisions to meet goals for sustainability. He discusses how to work with geospatial big data in cloud computing environments for the purposes of analyzing energy performance of buildings at scale and how spatial joins and spatial clustering algorithms can be implemented using Apache Sedona and Dask GeoPandas to identify clusters of lower energy efficiency scores to inform policy decision making. Finally, he shows a benchmark comparing the performance of Apache Sedona and Dask GeoPandas for spatial joins and spatial clustering. You’ll have to watch the video to see the final results!
Watch the recording of “Geospatial Big Data Analytics For Sustainable Smart Cities”
2023 has been an exciting year for Wherobots and this post points out a handful of key moments for Wherobots and the Apache Sedona community. Highlights of the year included 130% growth of Apache Sedona usage, key hires for growing the Wherobots team, launching Wherobots Cloud and SedonaDB, and raising a $5.5M seed funding round.
Read More About Wherobots: 2023 Year In Review
In this article from Pranav Toggi we learn how to query and analyze the Overture Maps Places dataset using SedonaDB and Wherobots Cloud. After a review of the data schema we see how to filter for points of interest within New York City and explore businesses within walking distance of stadiums and event venues. Along the way we learn several ways to visualize the data and results of our analysis.
Read “Analyzing The Overture Maps Places Dataset Using SedonaDB, Wherobots Cloud, & GeoParquet
This tutorial walks through how to use US Forest Service road data and aerial imagery to find the perfect Christmas tree. It covers loading and querying USFS road data, annotating aerial imagery in QGIS, then combining them and querying using SQL to find routes in National Forests to the perfect tree stands.
Read “Finding The Perfect Christmas Tree With USFS Map Data, QGIS, & SedonaDB”
In a livestream tutorial on the Wherobots YouTube channel we worked through how to use data from Zillow to analyze real estate values in the US. We calculated the change in real estate values at the county level over the last 5 years and created choropleth maps to help visualize the results. We also wrote up a written tutorial so you can follow along in Wherobots Cloud.
Watch Analyzing Real Estate Data With SedonaDB
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Key takeaways
A spatial table format extending Apache Iceberg with primitive spatial types, spatial statistics, filter push-down, and indexing for vector and raster data lakehouses, including GeoParquet storage.
130% growth in Apache Sedona usage, team hires, launch of Wherobots Cloud and SedonaDB, and a $5.5 million seed funding round.
Analyzing Overture Maps Places, finding a Christmas tree with USFS roads and aerial imagery in QGIS + SedonaDB, and a livestream/write-up on Zillow county-level real-estate value change over five years.
No. It is a monthly roundup plus a short 2023 review; the Havasu and analysis details live in the linked posts and talks.
RasterFlow is now available in Public Preview
RasterFlow makes planetary-scale earth intelligence workflows easy and costs predictable. We are excited to announce that RasterFlow is now in Public Preview, opening up the power of planetary scale Earth Intelligence to all Wherobots Professional Edition customers! RasterFlow let’s you solve complex monitoring challenges with vision-language models or tailored models for specific use cases, without […]
Wherobots for QGIS: Cloud-Scale Spatial Data, from Wherobots Labs
Wherobots for QGIS is a new plugin that connects QGIS to Wherobots Cloud. From a panel inside QGIS, an analyst can run spatial SQL against WherobotsDB, load the results as a map layer, push a local layer up to a Wherobots Iceberg table, and pull raster data into the canvas. Why Wherobots for QGIS: Eliminating […]
How Wherobots builds with NVIDIA to let AI see the physical world
The world and what happens in it is digitized by petabytes of raw and derivative spatial datasets of various data types and scales, and the potential for applying AI to it is immense. But the AI models and agents we use every day need connectivity to tools that turn this data into usable insights and relationships. Wherobots gives AI the ability operate on and understand raw physical-world data, and NVIDIA GPUs are core to it. Architecturally here’s how this works at a high level.
El Niño 2026, atmospheric rivers, and California’s burn scars: one SQL engine, two data models
7,713 mapped building footprints sit inside or within 500 m (1,640 ft) of the 42 Eaton basins USGS rates high hazard for debris flows, and 388 km (241 mi) of mapped road and path cross the low ground below the scar. WherobotsDB finds both in SQL: a join to the USGS basins for the buildings, and a raster vector join over elevation for the roads.
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