Why Teams Are Moving from Isochrones to H3 (And What It Unlocks)

Polygon isochrones are a single-line boundary. They are 'time maps' that replace distance radii and are perfect for delivering a clear yes/no, in/out visual.

For years, isochrones have been the go-to for visualising locations within a given timeframe, foundational for spatial analysis, trade analysis, territory planning, accessibility visualisations, and much more.

But as spatial analytics scales across thousands of locations and millions of data points, teams are increasingly moving from polygon-only workflows to H3.

Because teams demand more structure, higher performance at scale, and more flexibility — and that's exactly what H3 delivers.

What is TravelTime H3?

H3 takes a different approach.

It divides the world into a consistent grid of hexagonal cells. And instead of returning a single-boundary shape, TravelTime H3 returns the travel time information indexed to every single H3 cell inside that area.

TravelTime H3 helps answer questions like:

• How far is every customer from every site?
• Where do service areas overlap?
• Which areas are underserved?
• How would coverage change with more / fewer sites?
• How does accessibility change by transport mode?
• How does it change by time of day?

Each H3 cell is a consistent unit, so time-based analysis becomes something you can layer with other demographics, filter, group, compare, and reuse — all while reducing processing time by up to 99%.

This transforms travel time analysis from a visual map output into a structured data layer.

H3 Performance at Scale

As spatial analytics teams face increasingly complex demands across millions of data points, performance becomes critical — particularly when analysing catchment overlap, accessibility gaps, enriching locations with other data, or performing repeated analysis at huge scales.

TravelTime isochrones are already highly performant compared to providers like TomTom, HERE, and Mapbox. But even when generating polygons is fast, many workflows slow down in the steps that follow, for example when filtering, joining, and comparing results across large datasets.

TravelTime H3 introduces a new approach for teams who need more from their location technology.

"With TravelTime H3, we went from rendering complex polygons to a simple, elegant hex grid that clicks with users. It's easier to process, easier to filter, and way faster to display."

Daniel Cooper

Co-Founder and CTO, Jitty

With results indexed to hexagonal cells:

• Spatial querying becomes simpler: Instead of repeatedly testing whether points fall inside complex shapes, teams can work with cell IDs that are easy to store and filter.
• Large-scale modelling becomes more efficient: Teams can run consistent workflows across thousands of origins and large geographies without workflows becoming brittle or slow.
• Overlap becomes measurable by default: When each cell is unique, overlap is immediately visible and quantifiable — enabling cannibalisation and density analysis at scale.
• Consistency: H3 grids provide a standardised unit for analysis across the globe, removing the ambiguity of irregular shapes.
• Fast, seamless joins: With time data already indexed to H3, joining it with other H3-indexed datasets, like demographics, human mobility, or financial data, is fast and simple at scale.

This is exactly why modern location intelligence platforms are adopting H3.

CARTO recently shared that customers report a 96–99% reduction in processing time across data preparation, spatial enrichment, and large-scale analysis with TravelTime H3 — without sacrificing accuracy.

"As spatial analytics teams face increasingly complex demands across millions of data points, the need for scalable, high-performance tools is critical. TravelTime was engineered to provide uncompromising accuracy at any scale."

Charlie Davies

CEO and Co-founder, TravelTime

H3 provides the scalable spatial structure, TravelTime provides real-world journey time accuracy. And together, we enable travel time analysis that works at huge scale, built so you don't have to compromise.

TravelTime H3 Use Cases

H3 isn't tied to one industry, use case, or workflow. It's a flexible foundation for modern spatial systems.

Location intelligence platforms

Location intelligence platforms like CARTO are integrating TravelTime H3 to expand their spatial analytics capabilities. By combining real-world travel time insights with H3 spatial indexing, platforms can offer scalable analysis for:

• Trade-area and catchment modelling
• Accessibility and reachability analysis
• Overlap and cannibalisation measurement
• Large-scale enrichment workflows

The result is a more flexible, high-performance foundation for modern location intelligence.

Try our catchment area demo app here.

Territory and Logistics Planning

Imagine managing a network of thousands of depots, retail locations, healthcare facilities, or sales territories. Creating polygons for each location quickly results in overlapping shapes that require heavy compute and are difficult to analyse at scale.

With H3-based analysis, teams can:

• Identify coverage gaps: Discover gaps in your provision or coverage by analysing how people really travel across regions
• Quantify overlap: TravelTime H3 reduces processing time for data preparation and analysis, so you can easily layer datasets to discover potential cannibalisation
• Site placement: Test the effectiveness of different site scenarios to prioritise new site placement
• Assign customers: Accurately match customers or users to the most accessible location, using a variety of transport modes

Using TravelTime H3, teams can work with indexed cells that can be grouped, compared, and analysed efficiently across huge national or international datasets.

Try our Voronoi Diagram demo app here.

Marketplace Location Search

In location-based platforms and online marketplaces — like property portal Jitty — speed and relevance are everything.

TravelTime H3 enables interactive, high-performance travel-time search experiences, including:

• Faster filtering at scale: H3 indexing makes it easier to filter large datasets and return results quickly, even when users change search parameters.
• Clear, intuitive visualisation: Hex cells form a uniform grid that's easy to interpret. Shaded gradients communicate travel-time ranges at a glance.
• Real-time interactivity: With hover or click interactions, users can explore the map and get instant feedback for specific locations.
• Flexible search experiences: Commute time can be combined with other datasets and filters — without slowing the experience down.
• Developer-friendly integration: TravelTime H3 can be implemented quickly via SDKs, enabling teams to ship faster and iterate confidently.

Network Mapping and Coverage Analysis

For organisations managing service networks — utilities, healthcare, mobility, logistics — H3 supports scalable coverage analysis powered by real-world travel times.

Overlap becomes measurable, accessibility gaps and underserved areas become clear, and coverage becomes comparable across regions. Real-world, evidence-based accessibility becomes quantifiable, not just visible.

This allows teams to move to repeatable, data-driven coverage planning and improve outcomes for customers or users.

How to Use TravelTime H3

TravelTime H3 is flexible by design.

Across industries and use cases, it enables teams to perform spatial analysis, for example:

• Accessibility heatmaps
• Time-based catchments
• Voronoi-style allocation by travel time
• Overlap and cannibalisation analysis
• Coverage gap identification

Try our H3 demos to see what you can build.

Getting Started with TravelTime H3

Moving from polygons to H3 isn't about replacing visual boundaries. It's about moving faster at scale, while maintaining accuracy, in modern spatial analytics systems.

As spatial datasets grow and business questions become more complex, teams need tools that are scalable, accurate, repeatable, compatible with modern analytics stacks, and high-performance across millions of data points.

H3 provides the structure. TravelTime provides real-world journey time accuracy. Together, they unlock a new generation of spatial analysis for real-world decision-making at scale.

Chat to us to explore how your business can use TravelTime, or get your free trial API key.