The demands on Spatial Data Scientists and Developers to produce ever more complex data visualizations and geospatial apps within shorter timeframes are constantly increasing. In response, here at CARTO we have been investing heavily to simplify the steps required to create spatial solutions that can extract meaningful location based insights as efficiently as possible.
Our latest developments unlock the unparalleled processing capabilities of cloud data warehouses and offer developers a unique set of tools to accelerate the spatial app development cycle for almost any analytical use case.
In a recent blog post, we outlined the key steps required to develop a dynamic geospatial application to visualize the progress of COVID-19 vaccinations across the US. At the heart of this application is our BigQuery Tiler, a resource efficient solution that offers almost limitless visualization capabilities of massive spatial datasets hosted in Google’s BigQuery.
We continue to streamline the BigQuery Tiler, simplifying the API to a minimum while ensuring the best possible result for the visualization of your dataset. The tiler optimizes the configuration for your spatial dataset and implements smart memory management techniques, completely removing any previous friction in the tileset creation process.
It’s now possible to create a tileset, such as the one in the above example, with the following simplified command:
CALL bqcarto.tiler.CREATE_TILESET( "`cartobq.maps.covid19_vaccinated_usa_blockgroups`", "`cartobq.maps.covid19_vaccination_usa_tileset`", NULL )
The CREATE_TILESET procedure only requires as inputs the table or query with the data to process, and the desired name for the resulting tileset. There is no need to explicitly set the zoom range, the list of features to encode or the maximum size allowed for your tiles. Everything is taken care of automatically.
BigQuery Tiler is accessible through our Spatial Extension, along with more than 60 advanced analytical functions that can be run natively in BigQuery, using simple SQL commands.
Our Data Observatory offers access to thousands of public and premium datasets. Given the size of some of these spatial datasets, either due to their global coverage, such as WorldPop or NASADEM, or their level of granularity, such as ACS Sociodemographics, building visualizations with them was a real challenge until now. By leveraging our BigQuery Tiler, we have simplified this process and it is now possible to easily create tilesets to visualize any of the available datasets. As examples of such visualizations, and building on our public data offering, we have created tilesets for some of the most popular public datasets in our Data Observatory and made them directly available in the following BigQuery project:
Here is an example visualization using a tileset from the NASADEM’s elevation dataset.
You can view the fully interactive visualizations for some of these spatial datasets by clicking on the links in the following gallery or visiting our Documentation page.
Reinforcing our mission to simplify the access and visualization of spatial data, these public data tilesets are offered in a “ready to use” format and can be visualized directly from your CARTO Dashboard or integrated into your custom spatial applications using CARTO’s module for deck.gl.
You can also create your own Data Observatory tilesets, either from any of our public datasets or from your existing premium data subscriptions. Simply find the location of your data subscription in BigQuery using the recently released “Access in BigQuery” functionality and run the Tiler from your BigQuery console.
With our latest enhancements, CARTO continues to enable Location Intelligence natively in the cloud, streamlining the steps required to visualize large spatial datasets and simplifying geospatial application creation for developers.
Want advanced geospatial analytics right inside Google BigQuery?Access the Spatial Extension for BigQuery
|This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 960401.|
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