Leverage the most reliable and compliant mobile device location/foot traffic dataset on the market. Veraset Movement (Mobile Device GPS / Foot Traffic Data) offers unparalleled insights into footfall traffic patterns across North America.

Covering the United States, Canada and Mexico, Veraset's Mobile Location Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement. Ideal for ad tech, planning, retail analysis, and transportation logistics, Veraset's Movement data helps in shaping strategy and making data-driven decisions.

Veraset’s North American Movement Panel: - United States: 768M Devices, 70B+ Pings - Canada: 55M+ Devices, 9B+ Pings - Mexico: 125M+ Devices, 14B+ Pings - MAU/Devices and Monthly Pings

Uses for Veraset's Mobile Location Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

Foot Traffic Data Market Outlook

According to our latest research, the global foot traffic data market size reached USD 5.9 billion in 2024, reflecting robust adoption across various industries. The market is poised for substantial growth, projected to expand at a CAGR of 14.2% from 2025 to 2033. By the end of 2033, the foot traffic data market is forecasted to achieve a value of USD 18.5 billion. This impressive growth trajectory is primarily driven by the increasing demand for advanced analytics and real-time insights into consumer behavior, propelling the adoption of foot traffic data solutions across retail, transportation, and smart city initiatives worldwide.

A key growth factor for the foot traffic data market is the escalating need for actionable business intelligence in brick-and-mortar environments. Retailers, shopping malls, and real estate developers are leveraging foot traffic data to optimize store layouts, enhance customer experiences, and drive sales conversion rates. The proliferation of omnichannel retail strategies has further intensified the necessity for precise in-store analytics, allowing businesses to align their physical and digital operations seamlessly. The integration of foot traffic data with artificial intelligence and machine learning algorithms enables predictive analytics, empowering organizations to anticipate consumer trends and personalize marketing efforts. As competition intensifies in the retail sector, the adoption of foot traffic analytics is becoming a strategic imperative, driving sustained market growth.

Another significant driver is the expansion of smart city initiatives and the growing emphasis on urban mobility solutions. Governments and municipal authorities are increasingly deploying advanced sensors, cameras, and wireless networks to monitor pedestrian movement, optimize public transportation routes, and enhance urban planning. The use of foot traffic data in urban environments facilitates efficient crowd management, improves public safety, and supports infrastructure development. Additionally, the rise of large-scale events, stadiums, and transportation hubs has necessitated the implementation of sophisticated foot traffic monitoring systems to manage crowd flow and ensure seamless visitor experiences. The convergence of IoT technologies with foot traffic analytics is unlocking new opportunities for data-driven decision-making in public and private sector applications.

The rapid adoption of mobile devices and the proliferation of connectivity technologies such as Wi-Fi and Bluetooth have transformed the way foot traffic data is collected and analyzed. Mobile applications and connected sensors enable real-time monitoring of pedestrian movement, providing granular insights into dwell times, footfall patterns, and peak hours. This technological evolution has significantly reduced the barriers to entry for organizations seeking to implement foot traffic analytics, democratizing access to valuable data for businesses of all sizes. The ongoing advancements in edge computing and cloud-based analytics platforms are further enhancing the scalability and flexibility of foot traffic data solutions, supporting their widespread adoption across diverse industry verticals.

The implementation of a Foot Traffic Heatmap Sensor Grid is revolutionizing how businesses and urban planners understand and utilize pedestrian data. By deploying a network of interconnected sensors, organizations can visualize foot traffic patterns in real-time, enabling more precise and dynamic decision-making. This technology is particularly beneficial in retail environments, where understanding customer flow can lead to optimized store layouts and enhanced shopping experiences. In urban settings, sensor grids contribute to improved public safety and efficient crowd management by providing detailed insights into pedestrian movement. As the demand for real-time analytics grows, the adoption of sensor grids is expected to become a standard practice in both commercial and public sectors, driving further innovation and integration with other smart technologies.

Regionally, North America continues to dominate the global foot traffic data market, driven by the presence of leading technology providers, a highly developed retail sector, and early adoption of smart city solutions. However, the Asia Pacific regio

GapMaps Foot Traffic Data by Azira provides actionable insights on consumer travel patterns at a global scale empowering Marketing and Operational Leaders to confidently reach, understand, and market to highly targeted audiences and optimize their business results.

According to Cognitive Market Research, the global Foot Traffic and Customer Location Intelligence Solution market size will be USD 7812.9 million in 2025. It will expand at a compound annual growth rate (CAGR) of 5.00% from 2025 to 2033.

North America held the major market share for more than 40% of the global revenue with a market size of USD 2890.77 million in 2025 and will grow at a compound annual growth rate (CAGR) of 3.8% from 2025 to 2033.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 2265.74 million.
APAC held a market share of around 23% of the global revenue with a market size of USD 1875.10 million in 2025 and will grow at a compound annual growth rate (CAGR) of 7.5% from 2025 to 2033.
South America has a market share of more than 5% of the global revenue with a market size of USD 296.89 million in 2025 and will grow at a compound annual growth rate (CAGR) of 5.3% from 2025 to 2033.
The Middle East had a market share of around 2% of the global revenue and was estimated at a market size of USD 312.52 million in 2025 and will grow at a compound annual growth rate (CAGR) of 5.5% from 2025 to 2033.
Africa had a market share of around 1% of the global revenue and was estimated at a market size of USD 171.88 million in 2025 and will grow at a compound annual growth rate (CAGR) of 4.7% from 2025 to 2033.
Hardware category is the fastest growing segment of the Foot Traffic and Customer Location Intelligence Solution industry

Market Dynamics of Foot Traffic and Customer Location Intelligence Solution Market

Key Drivers for Foot Traffic and Customer Location Intelligence Solution Market

Rise in Demand for Personalized Consumer Experiences to Boost Market Growth

As businesses increasingly prioritize delivering personalized experiences, the demand for foot traffic and customer location intelligence solutions is growing. By tracking and analyzing customer movements, businesses can gain real-time insights into consumer behaviour and preferences. These solutions help retailers, malls, and other businesses tailor their marketing efforts, promotional strategies, and product placements to meet specific consumer needs. For example, stores can use data to send personalized offers or promotions based on a customer’s location within a store or mall. This enhances customer engagement, increases sales opportunities, and improves the overall shopping experience. In an era where customer satisfaction is a key competitive advantage, businesses are increasingly adopting location-based intelligence tools to enhance customer loyalty and drive revenue.

Growth of Omnichannel Retail Strategies To Boost Market Growth

The growth of omnichannel retail strategies is another key driving factor for the market of foot traffic and customer location intelligence solutions. Modern retailers and service providers are striving to create seamless experiences for customers across multiple touchpoints, including physical stores, websites, and mobile apps. Location intelligence solutions allow businesses to integrate data from different channels, enhancing both in-store and online interactions. For instance, retailers can track foot traffic in physical stores and combine this with online shopping data to understand consumer preferences, predict demand, and optimize inventory. By leveraging location-based insights, retailers can drive more effective cross-channel strategies, improve customer retention, and better allocate resources.

Restraint Factor for the Foot Traffic and Customer Location Intelligence Solution Market

High Data Privacy and Security Concerns Will Limit Market Growth

Data privacy and security remain significant concerns for businesses and consumers in the Foot Traffic and Customer Location Intelligence (FTCLIS) market. These solutions rely heavily on the collection and analysis of location data, often obtained from mobile devices and other tracking technologies. While this data provides valuable insights into customer behaviour, it raises questions about the safety and privacy of personal information. Governments worldwide are implementing stricter regulations like the GDPR in Europe and CCPA in California to protect consumers' data, creating challenges for companies in terms of compliance. Businesses may face high costs to ensure their systems adhere to privacy laws and safeguard against data breaches. Additionally, consum...

This foot traffic dataset provides GPS-based mobile movement signals from across South America. It is ideal for retailers, city agencies, advertisers, and real estate professionals seeking insights into how people move through physical locations and urban spaces.

Each record includes:

Device ID (IDFA or GAID) Timestamps (in milliseconds and readable format) GPS coordinates (lat/lon) Country code Horizontal accuracy (85%) Optional IP address, mobile carrier, and device model

Access the data via polygon queries (up to 10,000 tiles), and receive files in CSV, JSON, or Parquet, delivered hourly or daily via API, AWS S3, or Google Cloud. Data freshness is strong (95% delivered within 3 days), with full historical backfill available from September 2024.

This solution supports flexible credit-based pricing and is privacy-compliant under GDPR and CCPA.

Key Attributes:

Custom POI or polygon query capability Backfilled GPS traffic available across LATAM High-resolution movement with daily/hourly cadence GDPR/CCPA-aligned with opt-out handling Delivery via API or major cloud platforms

Use Cases:

Competitive benchmarking across malls or stores Transport and infrastructure planning Advertising attribution for outdoor/DOOH campaigns Footfall modeling for commercial leases City zoning, tourism, and planning investments Telecom & tower planning across developing corridors

In September 2024, Walgreens was the drug store with the highest number of average visits in the United States, at over ** thousand. CVS was the second most visited, with about ** thousand visits.

Leading dollar stores in the United States, Dollar General and Dollar Tree, have sustained foot traffic growth during most months over the past two years, compared to July 2022. Each December and May, visits surged and, in July 2024, they increased by over ** percent at Dollar General and over ***percent at Dollar Tree, compared to the base month.

Foot Traffic Data Market Outlook

According to our latest research, the global foot traffic data market size in 2024 stands at USD 5.9 billion, reflecting robust adoption across retail, real estate, and smart city applications. The market is experiencing a strong growth momentum, with a projected CAGR of 12.6% from 2025 to 2033, leading to a forecasted market value of USD 17.2 billion by 2033. This remarkable growth is primarily fueled by the increasing emphasis on data-driven decision-making, the proliferation of IoT devices, and the growing need for real-time analytics in both public and private sectors.

A primary growth driver for the foot traffic data market is the retail sector’s rapid digital transformation and the intensifying competition among brick-and-mortar stores and e-commerce platforms. Retailers are leveraging foot traffic analytics to optimize store layouts, personalize marketing campaigns, and enhance overall customer experiences. The integration of advanced technologies such as AI, machine learning, and cloud computing has empowered businesses to gain deeper insights into consumer behavior, dwell times, and conversion rates. Moreover, the rise of omnichannel retail strategies necessitates the synchronization of online and offline customer journeys, further boosting demand for comprehensive foot traffic data solutions. As retailers continue to invest in smart infrastructure, the need for actionable, real-time data to drive operational efficiency and profitability will only escalate, ensuring sustained market growth.

Another significant growth factor is the increasing adoption of smart city initiatives and the deployment of IoT infrastructure across urban centers. Municipalities and public authorities are turning to foot traffic data to optimize urban planning, improve public safety, and enhance transportation systems. By analyzing pedestrian flows and congestion points, city planners can make informed decisions regarding infrastructure investments, event management, and emergency response strategies. The integration of foot traffic data with other urban datasets, such as traffic, weather, and public transport usage, enables the creation of holistic, data-driven solutions that improve the quality of urban life. This trend is particularly strong in regions with high urbanization rates, such as Asia Pacific and parts of Europe, where governments are prioritizing smart city development as part of their digital transformation agendas.

The proliferation of mobile devices and advancements in sensor technologies are further accelerating the growth of the foot traffic data market. With the widespread adoption of smartphones, Wi-Fi, Bluetooth beacons, and camera-based systems, businesses and public entities can now collect granular, location-based data at an unprecedented scale. This technological convergence has significantly lowered the barriers to entry for organizations seeking to implement foot traffic analytics, enabling even small and medium-sized enterprises to harness the power of location intelligence. Furthermore, the growing availability of cloud-based analytics platforms ensures that data can be processed and visualized in real-time, supporting agile decision-making and rapid response to changing footfall patterns. As sensor technologies continue to evolve, offering greater accuracy and lower power consumption, the market is expected to witness further innovation and expansion.

From a regional perspective, North America currently leads the global foot traffic data market, driven by early technology adoption, the presence of major market players, and significant investments in smart infrastructure. However, the Asia Pacific region is emerging as a high-growth market, fueled by rapid urbanization, expanding retail sectors, and ambitious smart city projects in countries such as China, Japan, and India. Europe also demonstrates strong demand, particularly in retail analytics and transportation management, while the Middle East & Africa and Latin America are gradually catching up, supported by digital transformation initiatives and increasing awareness of the benefits of foot traffic analytics. As market penetration deepens across these regions, the competitive landscape is expected to intensify, with local and global players vying for market share through product innovation and strategic partnerships.

Component Analysis

The component segment o

Leverage the most reliable and compliant mobile device location/foot traffic dataset on the market!

Veraset Movement (GPS Mobility Data) offers unparalleled insights into foot traffic patterns for dozens of countries across the Middle East.

Covering 14+ countries for the Middle East alone, Veraset's foot traffic Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement. Ideal for ad tech, planning, retail, and transportation logistics, Veraset's Movement data (footfall) helps shape strategy and make impactful data-driven decisions.

Veraset’s Africa Footfall Panel includes the following countries: - bahrain-BH - iran-IR - iraq-IQ - israel-IL - jordan-JO - kuwait-KW - lebanon-LB - oman-OM - palestinian territories-PS - qatar-QA - saudi arabia-SA - syria-SY - united arab emirates-AE - yemen-YE

Common Use Cases of Veraset's Foot Traffic Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

The average monthly footfall in physical stores steadily grew over 2021, peaking in July, with an increase of almost ** percent compared to January of that year. In January 2022 growth dropped to just ** percent, but by March 2022 it had recovered to over ** percent.

Set is updated hourly. Data includes:Occupancy numbers of three spaces at Chapel Hill Public Library. Data is collected by people counter sensors and calculated to display occupancy of the building on an hourly basis. The three spaces:Total Building (inside the entire Library building)Children's RoomLobbyCumulative counts from four sensors, of "Ins" and "Outs" at four particular locations in the Library building, separated into two groups "Adult" and "Child". The four sensor locations:Main Entrance (entrance to the building by the Traffic Circle)Lower Entrance (entrance to the building on the Lower Level)Children's Entrance (primary entrance to the interior Kids Room)Security Gate (interior archway that separates the Lobby from the rest of the Library)As of May 24, 2019: For the purposes of data analysis, a person with a minimum height of 4'9" is counted as an "Adult" and a person with a maximum height of 4'9" is counted as an "Child". This height was chosen based on the Centers for Disease Control and Prevention's Clinical Growth Rate charts - considering the median height for children age 11. Previous to May 24, 2019, the height distinction was 4'3".

For the week ended May 9, 2020, aggregate consumer foot traffic in the United States was up by **** percent when compared to the previous week. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

This dataset provides detailed insights and metrics related to foot traffic optimization for businesses in downtown Colorado Springs, Colorado. The data covers peak traffic hours, effective storefront strategies, primary pedestrian corridors, weather impact, budget-friendly tactics, and measurement techniques to help businesses maximize visibility and customer engagement in this thriving commercial district.

We provide foot traffic data across mobile apps with 30+ enriched attributes, including demographics, devices, app activity, intent and foot traffic insights. We help marketers, agencies, and platforms build precise foot traffic segments, optimize foot traffic targeting, attribute locations, and understand cross-device journeys. Our continuously updated foot traffic datasets deliver real-time foot traffic insights that power smarter location-based campaigns and future-ready strategies.

Leverage our foot traffic data solutions for the following use cases: - Foot Traffic Data Validation & Model Building - Cultural & Seasonal Foot Traffic Insights - Targeted, Data-Driven Foot Traffic Advertising - Foot Traffic & Location-Based Targeting - Trial & Partnership Transparency

With AdPreference, expect the following key benefits through our partnership: - Augment Foot Traffic Data Attributes - Enrich CRM - Personalize Foot Traffic Audiences - Fraud Prevention - Foot Traffic Audience Curation

Access the largest and most customizable foot traffic data segments with AdPreference today. Supercharge your needs with unique and enriched foot traffic data not found anywhere else.

For more information, please visit https://www.adpreference.co/

CAMPUS FOOT TRAFFIC INTELLIGENCE

## Overview

CAMPUS FOOT TRAFFIC INTELLIGENCE is a dataset for object detection tasks - it contains People annotations for 400 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset contains hourly pedestrian counts since 2009 from pedestrian sensor devices located across the city. The data is updated on a monthly basis and can be used to determine variations in pedestrian activity throughout the day.The sensor_id column can be used to merge the data with the Pedestrian Counting System - Sensor Locations dataset which details the location, status and directional readings of sensors. Any changes to sensor locations are important to consider when analysing and interpreting pedestrian counts over time.Importants notes about this dataset:• Where no pedestrians have passed underneath a sensor during an hour, a count of zero will be shown for the sensor for that hour.• Directional readings are not included, though we hope to make this available later in the year. Directional readings are provided in the Pedestrian Counting System – Past Hour (counts per minute) dataset.The Pedestrian Counting System helps to understand how people use different city locations at different times of day to better inform decision-making and plan for the future. A representation of pedestrian volume which compares each location on any given day and time can be found in our Online Visualisation.Related datasets:Pedestrian Counting System – Past Hour (counts per minute)Pedestrian Counting System - Sensor Locations

Our Location Intelligence Data connects people's movements to over 14M physical locations globally. These are aggregated and anonymized data that are only used to offer context for the volume and patterns of visits to certain locations. This data feed is compiled from different data sources around the world. Location Intelligence Data Reach: Location Intelligence data brings the POI/Place/OOH level insights calculated on the basis of Factori’s Mobility & People Graph data aggregated from multiple data sources globally. In order to achieve the desired foot-traffic attribution, specific attributes are combined to bring forward the desired reach data.For instance, in order to calculate the foot-traffic for a specific location, a combination of location ID, day of the week and part of the day can be combined to give specific location intelligence data. There can be a maximum of 40 data records possible for one POI based on the combination of these attributes.

In December 2024, Dollar Tree was the discounter with the highest number of average visits in the United States, at over ** thousand. Dollar General and Family Dollar came close in second and third place, with about ** and ** thousand visits, respectively.

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

This dataset gives the numbers of foot traffic counts by branches in a given quarter. Updated annually.