January 2023 Summaries

The modern data stack, specifically data warehouses, have emerged as a scalable and serverless solution for geospatial analysis. They provide a cost-effective manner of storing large amounts of geospatial data, separate computational power and storage resources, allowing for efficient querying, scaling, and security. Data warehouses support geospatial functions, enabling the ingestion and querying of geospatial data, and can even create machine learning models within them. The partnership between companies like CARTO and cloud providers offers tools such as Spatial SQL, map tiles, geocoding, trade area creation, and visualization in a dashboard. This enables teams to analyze and visualize geospatial data quickly, securely, and at scale, providing insights for business decisions.

As a geospatial professional, it's essential to evangelize your work to others, particularly those in management positions who may not fully understand the value of geospatial data and technology. To do this effectively, focus on the business outcomes and benefits of geospatial, such as increased efficiency, cost savings, and competitive advantage. Use statistics and figures to support your arguments, and appeal to both commercial and deliverable interests. Additionally, present geospatial as a tool that can be used by everyone in an organization, regardless of their technical expertise, and use plain language to explain complex concepts. By doing so, you can "sell" geospatial internally and externally, and ultimately get your colleagues and clients excited about the power of location intelligence.

Visualizing geospatial data is complex due to high zoom scales, browser limitations, and the need for databases. Map tiles are used to efficiently visualize such data by breaking maps into smaller parts and compressing them. The most commonly used tools for creating map tiles are Mapbox or Google Maps with tippecanoe. However, these solutions have limitations in handling changing data and large tile files. CARTO offers an alternative solution that works directly within databases and data warehouses, allowing users to create tilesets without writing code. It supports three methods of visualization: raw GeoJSON, dynamic tiles, and pregenerated tiles. Additionally, CARTO now supports native tiles with spatial indexes like H3 and Quadbins for faster rendering and query times.

CARTO Workflows is a no code spatial analysis tool that enables analysts of all skill levels and backgrounds to leverage geospatial data and spatial analysis using the power of geo-enabled data warehouses and databases. It allows users to access geospatial analytics running in a cloud-native environment for the first time, giving them control and power similar to writing SQL themselves through a visual interface. The tool can be used with various data sources, including remote files that update each time the workflow is run, allowing users to visualize data and results at each node along the way. Workflows offers a wide range of analytical and data processing functionality, including geospatial functions such as spatial joins & filters, clustering and creating isolines, and tools to leverage Spatial Indexes like H3 and Quadbin. The tool is now available in public beta, with support for popular databases and data warehouses like CARTO Data Warehouse, Google Cloud BigQuery, Amazon Redshift, Snowflake, and PostgreSQL/PostGIS.

The CARTO platform has released several new features and enhancements in the final quarter of 2022, including CARTO Workflows, which enables users to create repeatable analysis using spatial SQL and advanced spatial analytics tools in a familiar visual interface. Users can now leverage spatial indexes more easily, with improved aggregation resolution and options for creating better and more insightful visualizations. Additionally, Admin Users can install the Analytics Toolbox in their Snowflake account through a simplified process, and customers relying on PostgreSQL and PostGIS can create and execute spatial analyses directly from Builder. The platform also supports importing geospatial files through a PostgreSQL connection, generating spatial index tilesets based on H3 natively in Databricks, and provides a set of Python packages to work with the platform from within Python notebooks. Furthermore, CARTO has launched a pre-built spatial application for CPG Customer Segmentation, which integrates powerful map visualization, 3rd party spatial data, and analytical functions to drive key CPG route-to-market strategies.