What is H3?
H3 is an open-source geospatial indexing system developed by Uber that divides the Earth's surface into a hierarchical set of cells. Most of these cells are hexagonal, with a small number of pentagonal cells forming part of the global structure.
The system provides multiple spatial resolutions, from very large geographic areas to cells approaching metre-scale. A geographic location can therefore be represented by an H3 cell and its unique index, providing a consistent spatial reference that can be used for organizing and analysing geographic observations.
For researchers, the significance of H3 is less about the hexagon itself and more about the possibility of having a common, hierarchical spatial framework for working with geographic data.
Learn more: Official H3 documentation
What Does H3 Look Like?
At a global scale, H3 creates a hierarchical pattern of cells across the Earth. As the resolution becomes finer, those cells provide progressively more detailed representations of geographic space.
Figure 1. H3 represents geographic space through a hierarchy of cells at different spatial resolutions.
The H3 Cell Inspector provides an interactive way to explore individual H3 cells, their indexes, boundaries, neighbours, and resolutions.
Explore: H3 Cell Inspector
Why Might H3 Be Interesting for Researchers?
Research data rarely arrives in one common geographic format.
A study might combine:
GPS locations
census data
health records
environmental observations
transportation data
business locations
satellite-derived information
administrative boundaries
survey observations
These datasets may use different spatial units or have different geographic resolutions.
H3 offers one possible way of bringing many location-based observations into a common spatial indexing framework. Researchers can assign observations to H3 cells, aggregate information within those cells, and examine patterns at different spatial resolutions.
This can be particularly interesting for interdisciplinary research, where datasets from different fields need to be considered together.
Potential Research Applications
H3 can potentially support research across a wide range of disciplines.
Public Health and Health Geography
H3 could be used as a spatial framework for exploring:
healthcare accessibility
distribution of health services
population–service relationships
disease incidence and spatial patterns
environmental exposure
geographic health inequalities
For example, researchers could explore whether H3 cells provide a useful common spatial unit for combining population, health-service locations, mobility, and environmental data.
Explore further: H3 documentation
Urban Mobility and Transportation
Location-based mobility data can produce very large numbers of observations. H3 provides a way to organize those observations spatially.
Potential research questions include:
Where is transportation demand concentrated?
How does mobility vary between neighbourhoods?
Which areas have limited access to transportation?
How do mobility patterns change over time?
How does transportation activity relate to population or land use?
Environmental and Climate Research
Environmental observations can also be organized using H3 cells.
Potential applications include:
air-quality monitoring
pollution mapping
biodiversity observations
habitat monitoring
climate-data analysis
environmental change
land-use and land-cover research
The hierarchical nature of H3 can also allow researchers to investigate whether observed patterns persist at different geographic scales.
Agriculture and Rural Research
Potential applications include:
crop and agricultural monitoring
weather and climate exposure
agricultural risk assessment
water and irrigation analysis
agricultural service accessibility
spatial analysis of rural populations and services
H3 could potentially provide a common spatial framework for combining field observations with remotely sensed and environmental datasets.
Economics and Development
Researchers studying economic activity can potentially use H3 to examine:
business concentration
commercial activity
service accessibility
financial inclusion
development indicators
spatial patterns of economic activity
Because H3 is not based on national or municipal boundaries, the same indexing framework can potentially be applied across different regions.
Interdisciplinary Research
Perhaps one of the most interesting possibilities is the ability to bring different types of geographic information into a common spatial framework.
For example:
Population + health services + mobility + environment + economic activity
could potentially be represented using the same H3 spatial index.
Figure 2. Conceptual example of different geographic observations being organized into a common H3 spatial framework for interdisciplinary analysis.
This does not mean that H3 automatically makes different datasets comparable. Researchers still need to consider data quality, spatial scale, aggregation effects, privacy, and the meaning of the geographic units being used.
What Could You Do With H3?
For researchers exploring H3 for the first time, some possible research ideas include:
Health:
Could H3 provide a useful spatial unit for analysing healthcare accessibility?
Mobility:
Could mobility observations be aggregated into H3 cells to identify changing transportation patterns?
Environment:
Could environmental measurements be combined with population or health information using a common H3 index?
Agriculture:
Could H3 help integrate satellite observations, weather information, and field observations?
Urban research:
Could different urban datasets be compared without relying entirely on changing administrative boundaries?
Interdisciplinary research:
Could H3 provide a common spatial framework for combining datasets from different disciplines?
These are research questions rather than prescriptions. The usefulness of H3 depends on the specific geographic phenomenon, data, scale, and methodology involved.
H3 as a Research Resource
H3 is particularly interesting because it sits at the intersection of geography, spatial analysis, data science, and computing.
A researcher does not necessarily need to redesign an entire GIS workflow around H3. It can instead be considered as one additional spatial representation alongside points, polygons, rasters, administrative boundaries, and other geographic units.
For some research questions, H3 may provide a useful way to organize observations, compare locations, aggregate large datasets, or explore patterns across scales.
For others, a conventional geographic representation may remain more appropriate.
The value is therefore in having another spatial tool available for research.
Explore H3 Further
The following resources provide more detailed information for researchers who want to explore H3.
Official H3 documentation
https://h3geo.org
H3 Cell Inspector
https://h3geo.org/docs/tools/cell-inspector
H3 open-source repository
https://github.com/uber/h3
H3-Python
https://uber.github.io/h3-py/
H3 JavaScript
https://github.com/uber/h3-js
H3 for R
https://cran.r-project.org/web/packages/h3/
H3 discussions and community
https://github.com/uber/h3/discussions
Researchers can also search Google Scholar, arXiv, and other scholarly databases for “H3 geospatial”, “H3 hexagonal indexing”, or H3 combined with a particular research field such as health, mobility, environment, or urban studies.
Conclusion
H3 is more than a different way of drawing a map. It provides a hierarchical spatial indexing framework that researchers can potentially use to organize, aggregate, compare, and visualize geographic information.
Its potential becomes particularly interesting when research involves large volumes of location-based data, multiple spatial scales, or datasets originating from different disciplines.
For researchers interested in exploring new approaches to spatial analysis, H3 offers an accessible starting point—and a useful example of how modern geospatial infrastructure can connect geographic research with data science.
Start exploring: https://h3geo.org