Finding the Next EV Charging Hotspots with PlacePulse Embeddings

CARTO’s PlacePulse Embeddings, developed with Applied Geographic Solutions, represent each U.S. neighborhood as a 256-dimensional vector encompassing demographic, economic, commercial, environmental, and built-environment characteristics. The approach is applied to EV charging planning by training a regression model on these embeddings and public charging-station data from the U.S. Department of Energy’s Alternative Fuels Data Center to estimate how many chargers a location would be expected to support. Comparing estimated and observed charger counts produces an opportunity score, identifying potentially underserved areas whose characteristics resemble established charging markets as well as locations that may be saturated. CARTO Workflows can run the process from modeling through mapping in the cloud using its Composite Score Supervised component, and the same reusable embeddings can support analysis of other place-dependent outcomes, including broadband coverage, retail performance, health outcomes, and infrastructure demand.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.