Dekart

EV Charging Analytics Maps

This curated collection of interactive maps—built with SQL in BigQuery and Snowflake—helps analysts, planners, and EV operators uncover high-opportunity locations, optimize deployments, and benchmark market presence.

How to Use These Maps

To use these maps, you can:

  • Open the provided links to view the maps in Dekart.
  • Fork the maps to your own Dekart workspace.
  • Customize the underlying SQL queries to fit your specific data needs.

Electric Vehicle Ownership Affinity Index

Dataset provided by Echo Analytics.

View map and data

This map visualizes electric vehicle (EV) adoption in France using the EV Affinity Index, which shows how concentrated EV ownership is in each postcode compared to national and regional averages.

  • Darker green = Above-average adoption
  • Lighter green/white = Below-average adoption

Data sources (Google BigQuery):

• EV Ownership – Echo Analytics dekart-data-samples.echo_analytics.electric_vehicle_owners – postcode, affinity_index_nation, affinity_index_region

• Geolocation – Overture Maps bigquery-public-data.overture_maps.address – postcode, geometry

Joining process:

  1. Filter relevant postcodes from EV dataset
  2. Get coordinates from Overture Maps
  3. Join on postcode
  4. Sample 4% for performance using RAND() <= 0.04

How to interpret the map:

• Affinity Index > 1.19 → High EV adoption (ideal for expanding charging networks) • 0.75 – 1.19 → Average adoption • < 0.75 → Low adoption (could signal access or infrastructure challenges)

Use cases:

• Plan EV charging infrastructure • Target high-affinity areas for EV marketing • Guide policy or incentives in low-adoption regions

EV Charging Demand vs Supply

Dataset provided by Data Appeal.

View map and data

This map reveals the spatial gap between charging demand and existing EV charger locations in central Paris, using POI-based demand signals from Data Appeal.

  • Orange points = EV charging stations (vehicle_charging_station)
  • Cyan points = All other POIs (for density and land-use context)
  • Extruded hexagons = Aggregated EV-relevant demand, based on: ev_relevance_popularity = popularity * category_weight (Weights reflect how likely a POI type supports charging — e.g., higher for gyms/cafés, lower for banks.)

Hexagon colors show the demand-to-supply ratio: relevance_per_charger = SUM(ev_relevance_popularity) / (COUNT(chargers) + 1) This highlights areas with high EV activity but few existing chargers.

Why this matters:

  • Combines human behavior (POI popularity) with real charger locations
  • Highlights “opportunity charging zones” — where people dwell and could charge
  • Uses H3 hexagons (~100m) for granular urban planning
  • Category weights are based on actual EV user behavior and EU mobility studies

Data sources:

  • dekart-data-samples.datappeal_2.poi_data
  • dekart-data-samples.datappeal_2.poi_characterization
  • POI category weights (custom, based on research)
  • H3 indexing via bqcarto.h3.LONGLAT_ASH3

Use cases:

  • Identify underserved high-demand areas for new chargers
  • Model demand from urban amenities
  • Layer with EV adoption or grid data for planning
  • Track shifts in demand/supply over time

EV Charging Competition Analysis

Dataset provided by MyTraffic.

View map and data

This map shows the spatial distribution of EV chargers in Paris using MyTraffic data, visualized with H3 hexagons to analyze charger density and brand dominance.

  • Darker hexagons = Higher charger density
  • Point symbols = Individual stations by brand, size, or power

Layer descriptions:

  • Charger Density (H3) – Charging points per km²
  • Top Brand (H3) – Most common brand in each hex
  • By Brand (Points) – Station locations grouped by provider
  • By Count (Points) – Sized by number of ports
  • By Power (Points) – Sized by maximum charging power
  • Paris (GeoJSON) – City boundary reference

Key insights:

  • High charger density in central Paris
  • Brand competition varies by area, showing localized leadership
  • Peripheral areas show gaps with potential for expansion

Data sources:

  • MyTraffic EV charging data
  • H3 processing via Snowflake

Use cases:

  • Spot underserved zones for network growth
  • Benchmark charger brands by geography
  • Guide investment or partnership strategies

EV Charger Proximity Analysis (UK Highways)

Dataset from Overture Maps.

View map and data

This map shows EV charger density along major UK roads, highlighting how many charging stations are within 50 km of each motorway or trunk segment.

Darker lines = More nearby chargers Lighter lines = Fewer or none

What’s measured: Each road segment is scored by the number of EV charging stations located within a 50 km radius.

How it works:

  • Defines the UK boundary using Overture Maps divisions
  • Selects motorways and trunk roads only
  • Filters POIs categorized as EV charging stations
  • Counts stations within 50 km of each road segment

Data sources:

  • OVERTURE_MAPS__DIVISIONS.CARTO.DIVISION_AREA – UK boundary
  • OVERTURE_MAPS__TRANSPORTATION.CARTO.SEGMENT – Road geometry and metadata
  • OVERTURE_MAPS__PLACES.CARTO.PLACE – Charging station locations

Use cases:

  • Identify highway segments underserved by EV infrastructure
  • Guide national charging network expansion
  • Support EV readiness assessments across transport corridors

EV Charging Density – by Country (Overture Maps + H3)

Dataset from Overture Maps.

View map and data

This map shows the distribution of EV charging stations within a selected country using H3 hexagons for spatial aggregation.

Taller hexagons = More EV charging stations Darker stroke colors = Country boundary for geographic context

What’s measured: EV charging stations are counted within H3 hexagons (resolution 7 ≈ 1 km²) based on POI data. Results are sorted by station density.

How it works:

  • Filters country boundaries from overture_maps.division_area using a country code
  • Selects POIs categorized as EV charging stations
  • Applies ST_WITHIN() to include only stations inside the country
  • Aggregates station counts into H3 cells via bqcarto.h3.ST_ASH3()
  • Orders by density for analysis or visualization

Data sources:

  • bigquery-public-data.overture_maps.division_area – Country geometry
  • bigquery-public-data.overture_maps.place – Charging station locations
  • H3 spatial indexing via bqcarto.h3.ST_ASH3

Use cases:

  • Compare charging infrastructure density across regions
  • Identify high- and low-coverage areas within a country
  • Support infrastructure planning and investment decisions

To run for another country: Replace {{country}} with a valid 2-letter ISO code (e.g., DE, FR, IT)

Want to build similar maps?

Book a free demo

Book a free demo: we’ll walk you through the process, help customize your data, and show how to spin off maps in minutes.

Edit this page on GitHub

← Build from Source
Reusable Map Templates →