KNOWLEDGE BASE
The Ultimate Guide to Location Data
-
Understand location data sourcing & collection
-
Evaluating and finding the right data provider
-
Real world use cases, case studies & applications
.png?width=600&height=600&name=Untitled%20design%20(1).png "The Ultimate Guide to Location Data")
Location Data: The Ultimate Guide to Location Intelligence, Data Providers, Use Cases and Buying Location Data
Every time a mobile device moves through a city, visits a store, travels between two locations, or spends time at a particular place, it can generate a location signal. When these signals are collected responsibly, processed, validated, and analyzed at scale, they can reveal powerful patterns about how people move through the physical world.
Location data can help businesses understand where customers go, how they behave, which places they visit, how frequently they return, and how different locations perform. It can support everything from location based advertising and foot traffic analysis to retail site selection, transportation planning, tourism analytics, disaster management, market research, and artificial intelligence.
But not all location data is created equal.
The value of a location dataset depends on its accuracy, coverage, freshness, consistency, privacy practices, and the ability to tailor the data to a specific business requirement. Choosing the right location data provider is therefore just as important as choosing the right use case.
This guide explains everything you need to know about location data, including what it is, how it is collected, the different types of location data, where to buy location data, how location data providers differ from brokers and resellers, common challenges with location data, and the most important location data use cases across industries.
What is foot traffic data?
Location data is information that describes the geographic position of a device, object, place, or activity at a particular point in time.
In the context of mobile location data, it typically refers to geographic signals generated by smartphones and other connected devices. These signals can include latitude and longitude, timestamp, device identifiers, geographic accuracy, country, operating system, and other contextual information.
At its simplest, location data answers one fundamental question: Where is something, or where has something been?
A single location signal might indicate that a device was observed at a particular latitude and longitude at a specific time. A larger collection of signals can reveal movement patterns, visitation behavior, travel routes, dwell time, origin and destination patterns, and interactions with physical places.
This makes location data particularly valuable because it provides context that purely digital datasets cannot always capture.
-
Website analytics can tell a business that someone visited its website. Location data can help reveal whether people subsequently visited a physical store.
-
Customer databases can tell a retailer who purchased a product. Location data can help reveal where those customers live, work, shop, and spend their time.
-
Advertising platforms can report digital impressions. Location data can help measure whether audiences subsequently visited a physical location.
-
This connection between digital information and the physical world is what makes location data such a powerful component of location intelligence.
Location Data vs. Geospatial Data
Location data and geospatial data are closely related, but they are not exactly the same.
Location data generally refers to information about the geographic position or movement of a device, person, object, or event.
Geospatial data is a broader category that includes location data as well as geographic information such as maps, boundaries, roads, building footprints, satellite imagery, environmental information, demographic areas, and points of interest.
For example, a mobile device coordinate becomes much more useful when it can be associated with a shopping mall, restaurant, airport, neighborhood, road, city, or other point of interest.
Combining mobile location data with POI data, demographic data, transportation data, and other geographic datasets creates the foundation for location intelligence.
How is Location Data Collected?
GPS Location Data
GPS is one of the most widely used sources of precise mobile location data.
A GPS enabled device receives signals from satellites and uses them to calculate its geographic position. GPS can provide highly granular latitude and longitude information and is particularly useful when precise positioning is required.
GPS data can support applications such as navigation, mobility analytics, geofencing, foot traffic analysis, location based advertising, and movement analysis.
However, GPS accuracy can vary depending on factors such as user consent, network conditions, device hardware etc.
WiFi Location Data
WiFi based positioning can be used to estimate the location of a device based on nearby wireless networks.
This can be particularly useful in dense urban environments and indoor environments where GPS signals may be weaker.
WiFi signals can provide additional location context for applications such as indoor analytics, retail analysis, transportation hubs, and other environments where detailed positioning is required.
Cell Tower Data
Mobile devices communicate with cellular networks, which can provide information about the approximate geographic area in which a device is located.
Cellular positioning generally provides less precision than GPS, but it can provide broad geographic coverage because mobile devices connect to cellular networks across cities, regions, and rural areas.
Bluetooth & Beacons
Bluetooth beacons can provide highly localized proximity information.
They can be used in environments such as retail stores, airports, stadiums, museums, and other physical venues to understand movement within a specific area.
IP Based Location Data
An IP address can be associated with an approximate geographic area.
IP based location is generally less precise than GPS based location data, but it can be useful for website personalization, content localization, geographic restrictions, and other digital applications.
User Provided Data
Users may also voluntarily provide location information through applications, forms, check ins, addresses, or other interactions.
This information can be valuable because the location is directly associated with a user provided context, although it generally does not provide the continuous movement information available from mobile location signals.
Types of Location Data
Raw Location Data
Raw mobile location data consists of individual location events generated by devices.
A typical record can contain information such as device identifier, latitude, longitude, timestamp, horizontal accuracy, country, operating system, source, and consent information.
Raw location data provides maximum flexibility because organizations can apply their own analysis, geofences, models, and algorithms.
It is particularly valuable for data science teams, researchers, mapping companies, analytics platforms, and businesses developing their own location intelligence products.
Mobility Data
Mobility data focuses on how devices and populations move through geographic areas.
Rather than looking at isolated coordinates, mobility analysis can reveal movement patterns, visitation behavior, origin and destination flows, dwell time, frequency of visits, and changes in mobility over time.
Mobility data can be used to understand everything from consumer movement and commuting patterns to tourism, transportation demand, and regional economic activity.
Foot Traffic Data
Foot traffic data measures visitation to physical locations.
Businesses can use foot traffic data to understand how many people visit a location, when they visit, how long they stay, where visitors come from, and how visitation changes over time.
Retailers can use this information to compare stores. Advertisers can use it to measure physical visitation. Real estate companies can use it to evaluate potential sites.
Point of Interest Data
Point of Interest, or POI, data describes physical places.
Examples include: Retail stores, Restaurants, Hotels, Airports, Hospitals, Schools etc.
POI data can include attributes such as place name, category, address, latitude and longitude, brand, operating information, and other business attributes.
Mobile location data and POI data become particularly powerful when used together.
A location signal can tell you where a device was. POI data can help tell you what that location represents.
Geofence Data
A geofence is a virtual geographic boundary around a physical location.
Geofences can be circular, polygon based, or customized to match the shape of a real world area.
Businesses can use geofencing to analyze activity around stores, airports, events, competitor locations, shopping centers, advertising assets, and other places.
Quadrant supports geofence queries that can identify devices observed within customized geographic areas.
Origin and Destination Data
Origin and destination data helps explain where movement begins and where it ends.
For example, a transportation company can analyze movement between residential areas and employment centers. A retailer can analyze where customers travel from before visiting a store.
This type of analysis can help businesses understand catchment areas, commuting patterns, transportation demand, and regional mobility.
Understanding Location Data Coverage
Country Level
Country level location data provides insights into activity, movement, audiences, and geographic patterns across an entire country.
It is one of the most widely purchased types of location data, used by brands, analytics consultants, adtech agencies & market researchers.
Region and State Level
Regional location analysis provides more detail about differences between states, provinces, or other administrative areas.
This can support market expansion, advertising allocation, infrastructure planning, and regional business strategy.
City Level
City level location data is particularly useful for businesses operating across metropolitan areas.
It can help reveal differences in movement, visitation, customer behavior, and market activity between cities.
Neighborhood Level
Neighborhood level analysis can provide a much more detailed understanding of local activity.
Retailers, real estate companies, advertisers, and urban planners can use neighborhood level insights to identify opportunities and understand local movement patterns.
Point & Polygon Level
The most granular analysis can be performed around individual coordinates, points of interest, buildings, stores, venues, or custom polygons.
This enables applications such as geofencing, competitor analysis, store visit measurement, event analysis, and highly localized audience analysis.
Why is Location Data Important?
Location data provides something many other datasets cannot provide: context about the physical world.
It can help answer questions such as:
-
Where do customers go?
-
Which stores do they visit?
-
How often do they return?
-
Where do visitors come from?
-
How long do they stay?
-
Which competitors attract the same audiences?
-
Which areas are growing?
-
How do people move between locations?
-
How does visitation change over time?
-
Which physical locations are generating the most activity?
Understand Real World Consumer Behavior
Location data provides behavioral signals based on physical movement.
Businesses can analyze visitation patterns to understand where consumers spend time, which places they visit, and how frequently they return.
This can complement demographic and transactional information to create a more complete understanding of consumers.
Improve Marketing Performance
Location data can make marketing more relevant by connecting audiences with physical locations.
Businesses can create location based audiences, target people near relevant locations, analyze competitor visitation, and measure whether campaigns contribute to physical store visits.
Measure Store Visits
Store visit attribution connects marketing activity with physical visitation.
Instead of measuring only impressions or clicks, businesses can analyze whether exposed audiences subsequently visited a physical location.
Improve Site Selection
Location data can help businesses evaluate potential sites before making significant investments.
Retailers can analyze foot traffic, surrounding businesses, competitor locations, visitor origins, demographics, and mobility patterns to identify potentially attractive locations.
Understand Markets and Competition
Location intelligence can reveal how businesses perform within specific markets.
Companies can compare visitation patterns across their own locations and competitors, identify underserved areas, and understand changes in local market activity.
Where to Buy Location Data?
Businesses looking to buy location data have several options.
The right choice depends on the type of location data required, the geographic markets involved, the required level of accuracy, the desired delivery method, and how much customization is needed.
Before purchasing location data, buyers should understand the different types of companies operating in the market.
Location Data Technology Companies
Location data technology companies build infrastructure around the collection, validation, processing, management, and delivery of location data.
They typically work with data sources and technology partners, apply quality controls, and provide datasets through APIs, cloud delivery, files, or other technical interfaces.
A strong location data technology company should give buyers visibility into data quality, collection methodology, coverage, privacy practices, and available customization.
Quadrant operates as a location data technology company, providing privacy first mobile location data and POI data for enterprises, technology companies, researchers, and other organizations. Its datasets are designed to support analytics, AI, location based products, and real world decision making.
Location Data Brokers
A location data broker generally sits between data sources and buyers.
Brokers may aggregate datasets from multiple sources and package them for specific customers or use cases.
The advantage can be convenience, particularly for buyers that want access to multiple datasets without establishing relationships with individual data suppliers.
However, buyers should understand exactly where the data originates, how it has been processed, whether multiple sources have been combined, and what quality controls have been applied.
Location Data Resellers
Resellers purchase or license data from another provider and then sell it to customers.
This can introduce additional layers between the original data source and the end buyer.
For a buyer, this makes transparency particularly important. Understanding who originally collected the data, what permissions were obtained, what transformations occurred, and how quality was assessed can be difficult when there are several intermediaries.
The distinction matters because buying location data is not simply about obtaining the largest possible dataset.
The goal is to obtain data that is fit for purpose.
| Provider type | Primary role | Typical approach | What buyers should assess |
| Location data broker | Aggregates and packages data | Combines data from multiple sources , combines it and sells as is | Source transparency, quality, privacy |
| Location data reseller | Purchases or licenses data and sells it onward | Adds a commercial layer between source and buyer, sells the same dataset over and over | Original source, provenance, flexibility |
| Location data technology company | Builds technology and processes around location data | Collects, validates, manages, enriches and delivers data while managing end-to-end privacy and compliance | Data quality, coverage, technology, customization |
Problems With Location Data Brokers and Resellers
Not every broker or reseller provides a poor experience. However, buyers should be aware of several common challenges when purchasing location data through intermediaries.
Selling Data in Bulk
Some providers focus heavily on selling large quantities of data rather than helping buyers identify the data they actually need.
A large volume of location signals does not automatically mean better location intelligence.
For many use cases, a smaller dataset with better accuracy, coverage, freshness, and consistency can be more valuable than billions of low quality signals.
Inflexible Commercial Models
Due to lack of in-house data engineering abilities to process and customize data, some providers structure location data around lengthy contracts, fixed minimum commitments, or large bulk purchases.
This can be difficult for companies that want to test a dataset before committing to a larger deployment.
A more flexible approach allows businesses to validate the data against their use case before scaling.
Volume Over Quality
Location data quality depends on much more than the number of records.
Important quality indicators include accuracy, completeness, frequency, consistency, device activity, geographic coverage, latency, and the presence of anomalous or duplicate signals.
Limited Coverage in Rural and Remote Areas
A dataset can appear impressive at a global or national level while still having significant gaps in specific regions.
Urban areas typically generate large volumes of mobile data. Rural and remote locations can be much harder to cover consistently.
This matters for companies operating outside major cities.
Unclear Data Provenance
Buyers should know where location data comes from.
A provider should be able to explain its collection methodology, source relationships, consent framework, processing practices, and quality controls.
Without this transparency, it becomes difficult to evaluate whether a dataset is appropriate for a particular business application.
Duplicate and Overlapping Data
When datasets are aggregated from multiple sources, the same device or event can potentially appear multiple times.
Without appropriate quality controls, duplicate signals can inflate volumes and distort analysis.
Data deduplication and normalization are therefore important parts of enterprise location data processing.
Inconsistent Data Schemas
Different providers can use different schemas, identifiers, geographic formats, timestamps, and definitions.
Combining multiple feeds can create substantial engineering work.
A technology focused provider can reduce this complexity by providing standardized datasets and consistent delivery formats.
Fluctuating Data Volumes
Location data volumes can change over time because of changes in applications, permissions, device behavior, operating systems, collection methods, and geographic events.
Businesses relying on location data should understand how stable the underlying dataset is and how changes will be communicated.
Limited Customization
Some providers offer only predefined datasets.
This can create problems when a buyer needs a specific geographic area, accuracy threshold, field selection, device frequency, or filtering requirement.
Lack of Technical Support
Location data can be complex to implement and analyze.
Buyers may need help with schemas, APIs, geographic queries, data quality, filtering, geofencing, and integration.
A provider that offers technical support can significantly reduce the time between purchasing data and generating useful insights.
How to Evaluate Location Data Before You Buy
The most important mistake a buyer can make is choosing location data based only on price or volume. A proper evaluation should consider coverage, freshness, completeness, horizontal accuracy, frequency, consistency, privacy, delivery options, and customization.
Coverage
How much of the geography and device population does the dataset represent? Consider coverage across countries, regions, cities, rural areas, and relevant device populations.
Broad coverage is important, but consistent representation across different markets is equally important.
Freshness
There's no such thing as real-time data in location data. The right approach is to ask - How recently was the data generated?
The level of freshness you need depends on your use case. Historical analysis may work with older data, while applications such as mobility analysis, audience activation, and real time insights may require more recent observations.
Completeness
Are the key data fields consistently populated and usable?
Important attributes such as latitude, longitude, timestamp, and horizontal accuracy should be available at the level required for your analysis. Missing or inconsistent fields can significantly affect the usefulness of a dataset.
Horizontal Accuracy
How close is each reported location to the actual device position?
Horizontal accuracy indicates the estimated radius of uncertainty around a reported location. The level of accuracy required depends on the use case, with more precise applications generally requiring higher quality GPS based observations.
Frequency
How often are devices observed over time?
Higher observation frequency can provide a more detailed view of movement and behavioral patterns, while lower frequency data may be sufficient for broader geographic analysis.
Consistency
Does the dataset maintain comparable quality across different markets, time periods, and device populations?
Consistent data makes it easier to compare locations and identify meaningful changes without results being affected by variations in data quality.
Privacy & Compliance
How was the data collected, processed, and made available?
Consider whether appropriate consent and privacy requirements were addressed, whether opt out mechanisms are supported, and whether the data is handled appropriately for sensitive locations and applicable regulations.
Delivery Options
How can the data be accessed and integrated into your systems?
Common options include APIs, Batch Delivery to cloud storage and other technical integrations. The right delivery method depends on your infrastructure, data volume, and intended use.
Customization
Can the dataset be tailored to your specific requirements?
Look for options to filter data by geography, attributes, frequency, device population, accuracy, or other parameters. The ability to customize the data can help ensure you are paying for the information that is actually relevant to your use case.
Location Data Privacy and Compliance
Location data can provide highly valuable insights, but it must be handled responsibly.
Because location signals can reveal patterns about movement and behavior, organizations must consider privacy requirements throughout the collection, processing, analysis, and use of location data.
A responsible location data strategy should consider:
- User consent
- Opt out mechanisms
- Data minimization
- Anonymization and pseudonymization
- Sensitive location controls
- Data security
- Regulatory requirements
- Appropriate use policies
- Transparency around collection and usage
Regulations such as GDPR, CCPA, and PDPA have increased the importance of privacy compliant data collection and processing.
Quadrant describes its mobile location data as privacy first and states that its data collection follows applicable consent and opt out requirements, including GDPR, CCPA, DROP, and PDPA. Quadrant's location data schema also includes a consent field, while its documentation distinguishes personally identifiable information from device level location attributes.
Location Data Use Cases by Industry
Retail
Retailers use location data to understand customer behavior and optimize physical locations, compare visitation across stores, understand where visitors come from, identify competitive locations, and evaluate potential new sites and more.
- Foot traffic analysis
- Store visit measurement
- Retail site selection
- Competitor analysis
- Trade area analysis
- Customer catchment analysis
- Store performance benchmarking
- New market identification
- Audience segmentation
- Advertising measurement
- Shopping behavior analysis
- Cross shopping analysis
Advertising & Marketing
Location data enables marketers to connect audiences with the physical world and move beyond digital engagement metrics and understand how campaigns influence real world behavior.
- Geotargeting
- Geofencing
- Proximity marketing
- Competitor analysis
- Audience segmentation
- Store visit attribution
- Campaign measurement
- OOH advertising measurement
- Event targeting
- Audience planning
- Trade area analysis
- Catchment analysis
Real Estate
Real estate companies and investors can use location intelligence to understand the performance and potential of physical locations. Location data can provide an additional layer of evidence when evaluating potential investments and development opportunities.
- Property analysis
- Site selection
- Market expansion
- Foot traffic analysis
- Neighborhood analysis
- Tenant analysis
- Competitive analysis
- Development planning
- Investment research
- Property performance benchmarking
Transport & Mobility
Transportation companies and mobility providers can use location data to understand how people move through cities and regions. Location data can help organizations understand movement at both local and regional scales.
- Origin and destination analysis
- Route analysis
- Commuter flow analysis
- Transportation demand
- Mobility pattern analysis
- Congestion analysis
- Infrastructure planning
- Public transportation planning
- Fleet optimization
- Mobility market analysis
- Economic disparity analysis
.png)
Tourism & Hospitality
Tourism organizations, city administration, governments and brands can use location data to understand visitor flows and travel behavior. Understanding where visitors come from and which locations they visit can help destinations improve planning, marketing, and visitor experiences.
- Visitor origin analysis
- Tourist movement analysis
- Attraction visitation
- Hotel market analysis
- Destination analysis
- Event impact measurement
- Tourism demand analysis
- Visitor flow analysis
- Regional tourism planning
Financial Services
Financial institutions can use location intelligence to support market analysis, risk assessment, and branch strategy. Location data can provide additional context when evaluating geographic markets and customer populations.
- Branch site selection
- POI comparison
- Market expansion
- Customer catchment analysis
- Competitive analysis
- Economic activity analysis
- Location based risk analysis
- Market opportunity identification
- Financial service accessibility analysis
Public Sector
Government agencies can use location data to understand population movement to improve infrastructure and public services, fast track disaster response, improve urban planning etc.
- Urban planning
- Transportation planning
- Infrastructure development
- Emergency response
- Disaster response
- Evacuation planning
- Population movement analysis
- Public service planning
- Economic development
- Tourism planning
Technology & AI
Location data is increasingly being incorporated into technology products and AI systems. High quality location data can provide AI systems with a richer understanding of the physical world by connecting people, places, movement, and geographic context.
- Training AI models
- Geospatial AI
- Mapping applications
- Location based applications
- Mobility platforms
- Consumer intelligence
- Recommendation systems
- Location based search
- Real world behavior modeling
- Predictive analytics
Academic Research
Researchers use location data to study human behavior at geographic scale. Aggregated and privacy conscious location datasets can provide researchers with a powerful source of behavioral information without requiring traditional surveys for every analysis.
- Human mobility research
- Urban studies
- Consumer behavior
- Migration analysis
- Transportation research
- Economic research
- Tourism research
- Population movement
- Social science research
Why Choose Quadrant for Location Data?
Choosing a location data provider is about more than finding the largest dataset. The right provider should give you high quality location data, reliable technology, flexibility, transparency, and the support you need to turn data into meaningful insights.
Quadrant is a location data technology company with deep experience in the location data industry, an in house data engineering team, rigorous data quality processes, and dedicated technological and post sales support. We work with businesses, researchers, academic institutions, and technology companies to provide location data that is built around their specific requirements rather than forcing every customer into the same data product.
Built by Location Data Experts
Quadrant has been working with location data for over a decade, building technology and expertise around the collection, processing, validation, and delivery of geospatial data.
Our in house data engineering team develops and maintains the systems and processes that turn raw location signals into reliable, usable datasets. This gives us direct control over how data is processed, evaluated, filtered, and delivered.
Because location data is our core business, we understand that different applications require different data characteristics. A dataset that works for advertising measurement may not be suitable for mobility research, AI development, or site selection.
Our approach starts with understanding the use case and then providing the data that best fits it.
Rigorous Location Data Quality Assurance
Location data quality cannot be measured by volume alone.
Quadrant has in house data quality assurance processes designed to identify and address common problems in raw location data before it reaches customers.
Our quality processes can identify issues including:
- Duplicate location signals
- One time pings and inactive devices
- Teleportation and implausible device movement
- Unreliable or anomalous location signals
- Inconsistent data patterns
- Incomplete records
- Poor horizontal accuracy
- Other signals that may affect downstream analysis
We continuously evaluate data quality so customers can work with location data that is more consistent, reliable, and fit for purpose.
Data That Fits Your Use Case
There is no universal definition of the “right” location dataset.
The data requirements for a global mobility study can be very different from those for a single store analysis. An advertising measurement project may require a different geographic area, time period, frequency, and accuracy threshold than an academic research project.
Quadrant provides the flexibility to define the data around your requirements.
You can choose the geographic area, time period, data fields, and other requirements that are relevant to your use case.
Whether you need data covering an entire country or a much smaller geographic area, historical data or a specific time period, we work to provide the data that fits your project rather than requiring you to purchase a predefined package.
No Rigid Contracts or Data Commitments
Location data requirements can change as a project develops.
Quadrant does not believe customers should be locked into rigid data commitments that do not reflect their actual needs.
We offer flexible data arrangements without forcing customers into specific contract structures, fixed geographic requirements, or unnecessary data volumes.
-
Need data for a specific period?
-
Need data for a smaller geographic area?
-
Need to test the data before scaling?
-
Need a dataset customized for a particular research or commercial application?
We can work with you to determine the right approach.
Global Coverage, Including Hard to Reach Areas
Location data is only useful where it has sufficient coverage.
Quadrant provides location data across global markets, including coverage beyond the largest metropolitan areas. This is particularly important for organizations conducting research or analysis across rural, remote, or less densely populated regions.
Rather than evaluating coverage only at a global level, we work with customers to understand whether the data available in their specific geographic area is appropriate for their intended use.
Technology and Post Sales Support
Buying location data is only the beginning.
Organizations often need technical support to understand schemas, integrate APIs, process large datasets, configure geographic queries, interpret data quality, or adapt the data to an existing technology stack.
Quadrant has an in house data engineering team and provides dedicated technological and post sales support to help customers throughout the data lifecycle.
Our team can help customers understand the data, work through technical requirements, troubleshoot integrations, and identify the most appropriate way to use the dataset for their specific application.
Supporting Academic and Research Projects
Location data can provide valuable insights for academic research and global studies, but data costs can sometimes prevent researchers from accessing the datasets they need.
Quadrant actively supports academic and research projects and offers special discounts for qualifying research initiatives.
This makes high quality location data more accessible to researchers working on projects that can contribute to a broader understanding of mobility, human behavior, cities, transportation, tourism, and other aspects of the physical world.
Privacy First and Responsible Data Practices
Location data needs to be collected and processed responsibly.
Quadrant takes a privacy first approach to location data, with processes designed around consent, privacy, compliance, and responsible data use.
Our data quality and engineering processes are designed not only to improve the usability of location data but also to support responsible handling throughout the data lifecycle.
For organizations evaluating location data providers, understanding how data is sourced, processed, quality controlled, and delivered is just as important as understanding the size of the dataset.
Frequently Asked Questions
What is location data?
Location data is information that identifies the geographic position of a device, object, place, or activity. Mobile location data typically consists of geographic signals generated by smartphones and other connected devices.
What is mobile location data?
Mobile location data is geographic information generated by mobile devices. It can be used to understand device movement, visitation, mobility patterns, foot traffic, and interactions with physical locations.
What is location intelligence?
Location intelligence is the process of analyzing location and geospatial data to generate insights that support business and operational decisions.
Where can I buy location data?
Businesses can buy location data from location data technology companies, data brokers, and data resellers. When evaluating a provider, buyers should consider accuracy, coverage, freshness, privacy, data provenance, flexibility, and delivery options.
What should I look for in a location data provider?
Look for a provider that can demonstrate strong geographic coverage, high data quality, transparent sourcing, privacy compliant collection, reliable data delivery, and the ability to customize datasets according to your requirements.
What is mobility data?
Mobility data describes how devices or populations move through geographic areas over time. It can be used to understand visitation, travel patterns, origin and destination flows, and changes in human mobility.
What is foot traffic data?
Foot traffic data measures visitation to physical locations. It can help businesses understand visitor volumes, visit frequency, dwell time, visitor origins, and changes in physical activity.
What should I look for in a mobile location data provider?
Key considerations include device coverage, event volumes, horizontal accuracy, data completeness, duplicate rates, consistency over time, geographic coverage, privacy practices, delivery options, flexibility and technical support.
What industries use mobile location data?
Mobile location data supports applications across retail, transportation, logistics, real estate, advertising, urban planning, market research and other location driven industries.
Can raw location data be used for foot traffic analytics?
Yes. Raw mobile location data can be analyzed against Points of Interest, geographic boundaries and other datasets to derive foot traffic and visitation insights. Organizations can perform this analysis internally or work with analytics and location intelligence partners.
Location Data in Action
Plan Smarter Retail Expansion with Location Intelligence
Place Intelligence used mobile location data to analyze visitation across more than 12,500 ski resorts, hiking trails, and marinas across the US.
Using Location Intelligence to Improve Supply Chain
Predik Data-Driven a leading market intelligence and research company uses Quadrant's mobile location data to help retail, FMCG, and logistics businesses improve supply chain efficiency.
How Brisbane's transport grid handles massive influx of people
BizziRex uses mobility data to map attendee origins and movements during Brisbane’s 2026 NRL Magic Round, revealing where visitors came from.
Request More Info
Got a question we haven't answered? Please fill out the form to get in touch with our team. We strive to respond to every request as quickly as possible. If you're ready to have advanced discussions about data pricing etc, you can talk to Tim.