Red Flags When Choosing a Data Partner

As geospatial data and mobility intelligence become increasingly important across industries, organizations need scalable ways to organize, analyze, and visualize location data.

That’s where geospatial indexing systems like H3 and Geohash come in.

Both H3 and Geohash are methods for dividing the world into smaller geographic cells that make location data easier to process. These systems are foundational to modern geospatial analytics, powering everything from mobility intelligence and urban planning to logistics, mapping, advertising, and machine learning.

While H3 and Geohash solve similar problems, they work differently and are often optimized for different use cases. Choosing the right approach depends on how geographic data will be stored, queried, and analyzed.

In this article, we’ll break down:

• What H3 and Geohash are
• How they work
• The key differences between them
• When to use each system
• Why H3 has become increasingly popular for mobility and geospatial analytics

What Is a Spatial Index?

Before comparing H3 and Geohash, it’s important to understand the problem they solve.

Geospatial datasets are massive. Modern mobility datasets can contain billions of GPS signals, visits, trips, or movement events spread across the globe. Efficiently storing, querying, aggregating, and analyzing that data requires a way to organize geographic space into manageable units.

A spatial index is a system that divides the earth into smaller cells or grids so geographic coordinates can be grouped together efficiently. Instead of analyzing millions of raw latitude and longitude points individually, systems can aggregate data into spatial cells for:

• Faster queries
• More scalable analytics
• Improved visualization
• Pattern detection
• Spatial joins
• Geographic aggregation

This becomes especially important in mobility intelligence, where organizations analyze movement patterns, visitation trends, trade areas, dwell behavior, and population flows at scale.

What Is Geohash?

Geohash is a geographic encoding system that converts latitude and longitude coordinates into short alphanumeric strings.

Developed in the early 2000s, Geohash works by recursively dividing the world into rectangular grid cells. Each additional character in a Geohash represents a more precise geographic area.

One of Geohash’s biggest advantages is simplicity. Nearby locations often share similar prefixes, making Geohash useful for:

• Database indexing
• Location search
• Basic proximity queries
• Lightweight geospatial applications

Because Geohash outputs compact strings, it became widely adopted in early mapping and location systems. While Geohash is widely used for indexing and search, other frameworks may be better suited for certain analytical and mobility-focused workflows.

What Is H3?

H3 is a hierarchical hexagonal spatial indexing system originally developed by Uber Technologies for large-scale geospatial analysis.

Instead of rectangular cells, H3 divides the world into hexagons. This difference is important. Hexagons provide more consistent adjacency and distance relationships than squares or rectangles, making them useful for:

• Mobility analysis
• Spatial clustering
• Traffic analysis
• Routing
• Catchment modeling
• Movement visualization
• Machine learning applications

H3 is hierarchical, meaning each hexagon can be subdivided into smaller hexagons at increasing resolutions. This allows organizations to analyze data at multiple geographic scales while maintaining consistency across datasets.

Today, H3 is widely used across geospatial engineering, mobility intelligence, transportation, logistics, and urban analytics, particularly where spatial analysis and aggregation are important.

Geohash vs H3: Key Differences

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1. Cell Shape

The most obvious difference is the grid shape itself. Geohash uses rectangular cells, while H3 uses hexagonal cells. This matters because rectangular grids create uneven neighbor relationships. Distances between adjacent cells can vary depending on direction, especially near the poles.

Hexagons create more uniform adjacency:

• Each cell has more consistent neighboring relationships
• Distortion is reduced
• Movement modeling becomes more natural
• Spatial smoothing and clustering improve

For mobility and movement analysis, this often produces cleaner and more accurate results.

2. Spatial Consistency

H3 is often preferred for modeling geographic relationships because hexagons can represent movement and proximity patterns more consistently than rectangular grids.

This becomes especially important in:

• Human mobility analysis
• Trade area modeling
• Traffic flow analysis
• Route optimization
• Urban movement studies

Geohash is commonly used for indexing and lookup workflows, while H3 is frequently chosen for movement analysis and other spatial analytics applications.

3. Hierarchical Structure

Both systems support hierarchical indexing, but H3’s hierarchy is specifically optimized for scalable geospatial analysis. Each H3 resolution level nests consistently into larger parent cells, making aggregation and multi-resolution analysis easier.

For example:

• A city-wide mobility analysis may use larger hexagons
• Neighborhood-level analysis may use smaller hexagons
• Both datasets remain compatible within the same indexing framework

This flexibility is one reason H3 has become increasingly common in modern mobility intelligence platforms.

4. Visualization Quality

H3’s hexagonal structure typically creates more visually balanced maps and heatmaps.

Rectangular grids can introduce directional bias or visual artifacts, particularly when aggregating movement patterns.

Hexagons tend to:

• Reduce edge effects
• Improve spatial smoothing
• Produce more natural-looking density visualizations
• Better represent continuous movement patterns

This is particularly valuable when visualizing visitation trends, audience movement, or transportation flows.

5. Performance and Scalability

Both systems are highly scalable, though they were designed with different priorities in mind. Geohash is often used for efficient indexing and search, while H3 was built for large-scale geospatial analysis.

Organizations working with the following often prefer H3 because of its efficient indexing and analytical flexibility:

• Billions of GPS signals
• Real-time movement analysis
• Large mobility datasets
• Distributed geospatial systems

As geospatial datasets continue growing, scalable spatial indexing frameworks are becoming increasingly important for modern data infrastructure.

When Should You Use Geohash?

Geohash may be a strong fit when:

• Simplicity is important
• Lightweight indexing is sufficient
• Applications rely on string-based lookup systems
• Basic geographic search is the primary use case

It remains widely used across:

• Databases
• Mapping systems
• Search applications
• Lightweight geospatial tooling

Reach for Geohash when: 

• You need a simple, lightweight way to index and search geographic data
• Your workflows rely on database lookups or proximity searches
• Interoperability and broad ecosystem support are priorities
• You want a straightforward hierarchical structure for location data

When Should You Use H3?

H3 is often better suited for:

• Mobility intelligence
• Human movement analysis
• Spatial modeling
• Large-scale analytics
• Trade area analysis
• Transportation analytics
• Audience intelligence
• Machine learning workflows
• Geospatial visualization

Reach for H3 when:

• You're analyzing movement, visitation, or geographic relationships
• You need to aggregate data across multiple geographic scales
• Visualization quality and spatial consistency are important
• You're working with large-scale analytics or mobility datasets

Because H3 handles adjacency and aggregation more naturally, it has become increasingly common in advanced geospatial and mobility applications.

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Why H3 Is Commonly Used in Mobility Analytics

Modern mobility datasets are fundamentally about movement:

• Where people travel
• How populations flow between regions
• How visitation changes over time
• How audiences move through physical space

Analyzing these behaviors requires spatial frameworks that can handle:

• Scale
• Resolution changes
• Proximity relationships
• Clustering
• Geographic aggregation

Because of its approach to aggregation, adjacency, and multi-resolution analysis, H3 has become a popular choice for many mobility intelligence and geospatial analytics workflows.

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What we use at Veraset

At Veraset, we use both H3 and Geohash because each serves a different purpose within location data workflows. Rather than viewing one as better than the other, we apply each where it provides the most value.

• Geohash supports efficient indexing, spatial matching, and data delivery workflows.
• H3 is provided alongside movement data to support spatial analysis, aggregation, and mobility use cases.
• Different resolutions are used depending on the level of geographic detail required, from broad regional analysis to highly granular movement patterns.
• Together, H3 and Geohash help balance efficient data processing with flexible geospatial analytics.

Final Thoughts

Both H3 and Geohash are powerful spatial indexing systems, and each has valuable use cases.

Geohash remains a lightweight and effective option for many geographic search and indexing workflows.

But as mobility intelligence, geospatial analytics, and large-scale movement analysis continue to evolve, H3 has emerged as a preferred framework for many modern geospatial applications due to its flexibility, scalability, and spatial consistency.

As organizations increasingly rely on location intelligence to power analytics, decision-making, and operational workflows, understanding the differences between these spatial indexing systems becomes increasingly important.

Whether you're building mobility models, analyzing visitation trends, optimizing logistics, or powering geospatial applications, choosing the right spatial framework can significantly improve how effectively geographic data is organized and analyzed.

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Mobile location data powers many of the digital experiences people use every day, like navigation apps, weather forecasts and online food ordering. 

But despite how common location-enabled applications have become, there’s still significant misunderstanding around how mobile location data is actually collected and used. One of the biggest misconceptions is that mobile devices are continuously tracking and transmitting a perfectly complete “breadcrumb trail” of a person’s movement every second of the day.

In reality, modern mobility data works very differently.

In this article, we’ll explain:

• What is mobile location data
• How mobile location data is collected
• What GPS signals actually represent
• The difference between foreground and background collection
• Why location data is not “always on”
• How raw signals become mobility insights

What Is Mobile Location Data?

Mobile location data refers to geographic signals generated by mobile devices, typically smartphones, when apps or operating systems access location services.

These signals may include:

• GPS coordinates
• Wi-Fi positioning
• Bluetooth proximity
• Cell tower triangulation
• Timestamp data
• Device movement signals

When combined and processed at scale, these signals help organizations understand broader movement patterns, visitation trends, audience behavior, transportation flows, and geographic activity.

Mobility data is used across industries including:

• Advertising and media
• Retail and QSR
• Urban planning
• Transportation
• Research and academia
• Real estate
• Tourism and economic development

How Mobile Devices Generate Location Signals

Location signals are typically generated when a mobile application accesses a device’s location services. Additionally the user has to provide permissions for the application to use their location. 

This can happen during activities such as:

• Opening a weather app
• Requesting directions
• Searching nearby businesses
• Using rideshare or delivery apps
• Checking in at locations
• Interacting with map-enabled applications

Importantly, location data collection is generally event-driven and not constant. Mobile devices do not continuously transmit a perfect second-by-second record of movement.

Instead, signals are generated intermittently based on:

• App usage
• Device settings
• Permission settings
• Operating system behavior
• Battery optimization
• Signal availability

This means mobility datasets represent samples of movement activity, not uninterrupted tracking streams.

Foreground vs Background Location Collection

A key concept in understanding mobile location data is the difference between foreground and background collection.

Foreground Collection

Foreground collection occurs when a user is actively interacting with an application.

For example:

• Using a navigation app for directions
• Looking up current temperatures in a weather app
• Searching within a retail app for stores nearby

These interactions often produce higher-frequency and more precise location signals because the app is actively requesting location updates.

Background Collection

Background collection occurs when an app receives permission to access location data while running in the background, beyond the times when an app is opened.

However, even background collection is not continuous or unlimited.

Modern mobile operating systems increasingly restrict:

• Frequency of updates
• Background refresh behavior
• Battery usage
• Permission access

As a result, location data availability varies significantly across devices, apps, and operating environments. This is one reason mobility data should not be interpreted as a complete “always-on” movement record.

Why Mobile Location Data Is Not “Always On”

One of the most common misconceptions about mobility data is the idea that smartphones are constantly transmitting exact user locations every moment of the day.

In reality, location signal generation is highly dependent on:

• User behavior
• App engagement
• Permission settings
• Device operating systems
• Signal availability
• Battery optimization controls

This creates natural gaps between observed signals.

For example:

• A device may generate multiple signals during active navigation
• Then generate very few signals while idle
• Or temporarily stop transmitting location updates altogether

As a result, mobility data is best understood as a series of observed location events — not a perfectly continuous tracking feed. Understanding this distinction is important when analyzing movement behavior, visitation patterns, and geographic trends at scale.

From Raw Signals to Mobility Insights

Raw GPS points alone are not inherently useful. To transform raw signals into actionable mobility intelligence, data providers apply additional processing and analysis.

This may include:

• Filtering noisy or inaccurate signals
• Removing duplicate observations
• Mapping coordinates to points of interest (POIs)
• Identifying visits and dwell behavior
• Aggregating movement patterns
• Applying spatial indexing frameworks
• Validating signal quality

This processing helps convert billions of fragmented location observations into structured datasets that can support analysis and decision-making.

For example, processed mobility data can help organizations understand:

• Store visitation trends
• Trade areas
• Tourism flows
• Audience movement patterns
• Transportation behavior
• Event impact
• Regional activity changes

Why Data Quality Matters

Not all mobility datasets are created equal.

The usefulness of mobility intelligence depends heavily on:

• Signal quality
• Coverage consistency
• Data processing methodologies
• POI accuracy
• Timestamp precision
• Spatial resolution
• Privacy and consent frameworks

High-quality mobility datasets require sophisticated processing pipelines to improve accuracy and reduce noise before analysis occurs.

This is especially important for organizations using mobility data for:

• Research
• Measurement
• Planning
• Audience development
• Modeling
• Operational decision-making

Final Thoughts

Mobile location data has become foundational to modern geospatial analysis and mobility intelligence. Understanding how location data actually works is critical for interpreting it correctly.

Mobile devices do not continuously broadcast perfect movement trails. Instead, mobility datasets are built from intermittent location observations generated through app activity, device behavior, and operating system permissions. Transforming those raw signals into useful mobility intelligence requires significant processing, validation, and geospatial analysis.

As organizations increasingly rely on location intelligence to understand real-world behavior, having a strong understanding of how mobility data is collected, processed, and interpreted becomes increasingly important.

At Veraset, mobility intelligence is built on transforming large-scale location signals into structured, actionable geospatial insights that help organizations better understand movement, visitation, and real-world behavior at scale.

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What is location data?

Location data, also known as human mobility data, refers to the information gathered from devices such as smartphones, GPS devices, and Internet of Things (IoT) sensors that can track the geographical location of individuals or objects. This data provides insights into the movement patterns, behaviors, and preferences of individuals in various contexts.

What are use cases of location data?

Different industries leverage mobility data to improve decision making and produce insights.

Real Estate

Location data plays a crucial role in the real estate industry. By analyzing the movement patterns and preferences of individuals, real estate professionals can gain insights into market trends, demand for specific locations, and property values. This information helps them make informed decisions regarding property investments and development projects, ultimately benefiting both buyers and sellers.

Retail

In the retail sector, location data enables businesses to better understand consumer behavior and tailor their marketing strategies accordingly. By analyzing the places individuals visit and the routes they take, retailers can optimize store locations, personalize advertisements, and create targeted promotions. This leads to improved customer experiences and increased sales.

Academic Research

Location data has immense potential for academic research. By examining human mobility patterns, researchers can gain insights into various aspects of society, such as migration patterns and trends, transportation systems, urban planning, and the spread of diseases. This data can contribute to evidence-based decision-making and the development of more efficient and sustainable solutions.

Finance

Location data can also benefit the finance industry. By analyzing the movement patterns and spending behaviors of individuals, financial institutions can enhance fraud detection and prevention measures

Traffic flow and transportation systems

Another compelling use case for location data is optimizing traffic flow. With the help of real-time location data, traffic managers can gain valuable insights into the movement patterns of vehicles on the road.

This data enables them to identify congestion hotspots, determine optimal routes, and implement proactive measures to alleviate traffic congestion. By making data-driven decisions, traffic flow can be streamlined, reducing travel time and enhancing the overall driving experience for commuters.

Location data and intelligence are essential for assessing the demand and usage of both private and public transport systems. By analyzing location data, transportation authorities can identify areas with high demand for public transportation and allocate resources accordingly. This helps in optimizing routes, adjusting schedules, and improving the accessibility and efficiency of public transportation services.

Ultimately, this leads to enhanced commuter experiences, increased ridership, and reduced congestion on the roads.

Advertising

Location data has become invaluable in the world of advertising. Leveraging this data, companies can better understand and target their audiences, leading to more effective marketing campaigns.

One of the most compelling use cases of location data in the advertising industry is targeted advertising. By analyzing location data, marketers can gain insights into consumer behavior and preferences. They can identify where their target audience spends their time and tailor their advertising campaigns accordingly.

For example, a retailer can use location data to display targeted ads to customers when they are near their stores, increasing the chances of attracting them to visit and make a purchase.

Another exciting use case is location-based promotions. With location data, advertisers can create promotions that are specific to certain geographic areas.

For instance, a restaurant chain can send mobile coupons to customers who are in close proximity to their locations, enticing them to come in for a meal. This personalized approach can significantly improve the effectiveness of promotions, as it targets consumers at the right time and place.

Location data also plays a crucial role in measuring the effectiveness of advertising campaigns. Marketers can use this data to gauge the impact of location-targeted campaigns by analyzing footfall and conversion rates in different areas. By understanding which locations perform well and which ones don’t, advertisers can better tailor their message.

How is privacy protected in location data?

As location data becomes increasingly integral to various industries, ensuring the privacy and security of such data is paramount. Here are some recent updates in privacy measures concerning location data:

Consent

• Implementation of stricter consent mechanisms where users have to explicitly agree to share their location data, often through granular consent forms.
• Introduction of just-in-time notices that inform users exactly when the location data is being collected and for what purpose.
• Mandating periodic re-consent, where users are asked to renew their consent to share location data at regular intervals.

Anonymization

• Adoption of advanced anonymization techniques such as differential privacy, which adds noise to datasets to prevent identification of individual records.
• Implementing stricter controls on the storage and sharing of raw location data to minimize the risk of re-identification.
• Usage of pseudonymization to replace identifying fields within data records with artificial identifiers or pseudonyms.

Aggregation

• Increased use of data aggregation, where location data is combined into groups or sets that are large enough to prevent the identification of individuals.
• Setting minimum group sizes for aggregated data to ensure individual data points cannot be isolated.
• Combining location data with other types of data in a way that it cannot be used to identify individual patterns or behaviors.

Safeguarding

• Employing encryption both for data in transit and data at rest to ensure that location data remains secure.
• Regular auditing and monitoring of access to location data to detect and prevent unauthorized access.
• Implementation of data minimization principles, ensuring that only the data that is absolutely necessary is collected.

Opt-out

• Facilitating easier mechanisms for users to opt-out of location data collection, such as intuitive toggles within app settings.
• Providing users with the ability to not only opt-out of current data collection but also to request the deletion of historical location data.
• Increasing transparency by informing users of the implications of opting out and how it might affect their experience with the service.

As the applications of location data continue to expand, it becomes essential to find a balance between leveraging this data for its immense utility and protecting the privacy of individuals. Recent advancements in privacy-preserving techniques such as anonymization and aggregation, coupled with regulatory changes emphasizing consent and opt-out options, have paved the way for a more responsible handling of location data.

Through collective efforts, location data can continue to contribute positively to various sectors while respecting the fundamental human right to privacy.