Simplified tileset creation for streamlined app development

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.  

Visualize any dataset from our Data Observatory

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:

carto-do-public-tilesets

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.

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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.

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Developers can also benefit from the templates offered by CARTO for React to speed up the development cycle, as we showcased in a recent blog post.

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.