Red Flags When Choosing a Data Partner

Choosing a mobility data provider isn't just about comparing datasets.

Whether you're building audiences, measuring campaign performance, selecting new retail locations, or powering research and AI models, the quality of your decisions depends on choosing the right data, and the right partner.

Yet many evaluation processes focus on the wrong criteria. Buyers compare scale, coverage, or pricing before they've clearly defined their use case or goals.

After years of working with agencies, retailers, researchers, and technology partners, we've seen the same evaluation mistakes surface time and again.

Here are five of the most common mistakes (and how to avoid them).

Mistake #1: Starting with the Data Instead of the Business Problem

One of the biggest mistakes buyers make is evaluating providers before they've clearly defined what they're trying to accomplish.

Different use cases require different strengths.

For example:

• Audience creation may prioritize scale and consistency.
• Retail site selection may depend on geographic precision and long-term visitation trends.
• Attribution requires reliable methodology and confidence in movement patterns.
• AI applications often demand flexible access to large, well-structured datasets.

Without first defining your objectives, it's easy to optimize for the wrong criteria — choosing a provider based on the largest dataset, the lowest price, or the longest feature list, rather than the one that's best suited to your specific use case.

Before evaluating vendors, ask yourself:

• What business question am I trying to answer or solve?
• Who will use this data, and how?
• What does success look like?
• Which capabilities are essential and which are nice to have?

Once that answer is clear, evaluating providers becomes much more straightforward. Instead of searching for the "best" mobility data provider, you'll be looking for the provider that's best equipped to help you achieve your goals.

Mistake #2: Assuming All Mobility Data Is Created Equal

Not all mobility data is collected, processed, or delivered in the same way, and those differences can have a meaningful impact on the insights you generate.

Providers may offer: 

• Raw location observations
• Processed or modeled datasets
• Aggregated insights
• Specialized products built for specific workflows

None of these approaches is inherently better than another. The right choice depends on what you're trying to accomplish, who will be using the data, and how much flexibility or customization your team requires.

As you evaluate providers, consider questions like:

• How is the data collected and sourced?
• What processing or modeling has been applied?
• How frequently is the data refreshed?
• What quality assurance and validation processes are in place?
• Does the provider offer the data in a format that fits my workflow—whether that's raw data, APIs, or ready-to-use insights?

Understanding these differences will help ensure you're evaluating providers based on what matters most for your business, rather than assuming all mobility data delivers the same value.

Mistake #3: Confusing More Data with Better Data

Bigger is not always better. A larger dataset isn't valuable if it lacks the precision, consistency, or quality required for your application.

If you're building national audiences, training AI models, or conducting broad market analysis, having a large, representative dataset can be a significant advantage. In these scenarios, greater scale often translates into more robust insights and better statistical confidence.

But there are many cases where simply having more data won't improve your results.

For applications like visitation analysis, site selection, attribution, or localized market intelligence, factors such as precision, consistency, and data quality can be just as important — if not more so — than the total number of devices.

Instead of asking, "Who has the biggest dataset?" consider questions like:

• Is the dataset representative of the population I'm trying to understand?
• Is coverage consistent across the geographies that matter most to me?
• How is data quality monitored and maintained?
• How frequently is the data refreshed?
• Does the provider have the scale my specific use case requires?

The best mobility data provider for you is the one with the right combination of scale, quality, and consistency for your business objectives.

Mistake #4: Treating Privacy as a Checkbox

Privacy has become one of the most important considerations when evaluating mobility data, but it's about much more than checking a compliance box.

As regulations continue to evolve and consumer expectations around data transparency increase, organizations need confidence that the data they're using has been collected and managed responsibly. A provider's approach to privacy doesn't just impact legal compliance—it can also influence data quality, business continuity, and long-term trust.

When evaluating mobility data providers, ask questions such as:

• How is user consent obtained and respected?
• How is data collected and sourced?
• What governance and quality controls are in place?
• How does the provider adapt to changing privacy regulations?
• What processes exist to ensure responsible data stewardship over time?

The strongest providers view privacy as an ongoing commitment. They invest in transparent practices, continuously monitor the evolving regulatory landscape, and build privacy considerations into the way they collect, manage, and deliver data.

Ultimately, a thoughtful approach to privacy benefits everyone. It helps organizations reduce risk, maintain confidence in their data strategy, and build solutions that are sustainable as the industry continues to evolve.

Mistake #5: Assuming There's Only One Way to Access Mobility Data

One of the most overlooked aspects of evaluating a mobility data provider is how you'll actually access and use the data.

Organizations have different technical capabilities, workflows, and business needs. A data science team building custom models has very different requirements than a marketing team looking for audience insights or an analyst exploring visitation trends.

That's why the best providers offer multiple ways to work with their data.

For example, at Veraset, customers can choose the approach that best fits their organization:

• Flat Files for organizations that want direct access to raw data for custom analysis, modeling, and integration into their own environments.
• APIs for teams that need real-time or on-demand access to mobility data within existing applications and workflows.
• Self-Service Platform for business users who want to explore mobility insights, visualize trends, and answer questions without relying on engineering resources.

No single delivery method is inherently better than another. The right choice depends on your team's technical expertise, your existing technology stack, and the business problems you're trying to solve.

As you evaluate providers, consider questions like:

• Does this provider support the way my team prefers to work?
• Can different teams access the data in the ways they need?
• Will this delivery model scale as our use cases evolve?
• If our needs change, will we have to switch providers or simply use a different product?

The best mobility data partner isn't just one that delivers high-quality data. It's one that can deliver that data in the format that creates the most value for your organization today and as your business grows.

What Great Evaluations Have in Common

Successful buyers don't begin by comparing vendors; they begin by defining success. They ask thoughtful questions about their business objectives, data quality requirements, privacy expectations, and operational workflows before narrowing their list of providers.

That approach consistently leads to stronger partnerships and better business outcomes.

If you're evaluating mobility data providers, we'd be happy to help you think through the process. Whether you're comparing vendors, validating an existing solution, or exploring a new use case, our team can help you ask the right questions before making a decision.

Get in touch to discuss your use case.

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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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