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

GapMaps Mobile Location 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.

GapMaps Mobility Data uses location data on mobile phones sourced by Azira which is collected from smartphone apps when the users have given their permission to track their location. It can shed light on consumer visitation patterns (“where from” and “where to”), frequency of visits, profiles of consumers and much more.

Businesses can utilise Mobility data to answer key questions including: - What is the demographic profile of customers visiting my locations? - What is my primary catchment? And where within that catchment do most of my customers travel from to reach my locations? - What points of interest drive customers to my locations (ie. work, shopping, recreation, hotel or education facilities that are in the area) ? - How far do customers travel to visit my locations? - Where are the potential gaps in my store network for new developments?
- What is the sales impact on an existing store if a new store is opened nearby? - Is my marketing strategy targeted to the right audience? - Where are my competitor's customers coming from?

Mobility Data provides a range of benefits that make it a valuable addition to location intelligence services including: - Real-time - Low-cost at high scale - Accurate - Flexible - Non-proprietary - Empirical

Azira have created robust screening methods to evaluate the quality of Mobility data collected from multiple sources to ensure that their data lake contains only the highest-quality mobile location data.

This includes partnering with trusted location SDK providers that get proper end user consent to track their location when they download an application, can detect device movement/visits and use GPS to determine location co-ordinates.

Data received from partners is put through Azira's data quality algorithm discarding data points that receive a low quality score.

Use cases in Europe will require approval on a case by case basis to ensure compliance with GDPR.

Leading dollar stores in the United States, Dollar General and Dollar Tree, have sustained foot traffic growth during most months over the past four years, compared to January 2020. Each December, visits surged and, in 2023, they increased by over ** percent at Dollar Tree and over ** percent at Dollar General, compared to the base month.

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

We provide unmatched data curation, with a detailed view of location activity over time. Our solution analyzes consumer visits before and after your POI, determining your store's reach. Gain a 360-degree view without PII data for optimal site selection, lease negotiations, and market intelligence.

The dataset features SafeGraph data that measures foot-traffic mobility changes around Open Streets in New York City during Covid-19. In addition to the raw counts of visitors to each POI during the week. It contains weekly pattern data collected between May 2nd, 2020, to July 28th , 2021. The point-level POI data is aggregated to census block group neighborhood-level data to maintain a standard level of resolution for all data used for this study. The Open Streets have been manually geocoded in Google Earth and imported the KMZ data as a shapefile into ArcGIS. Once in ArcGIS, the locations of the Open Streets were matched to CBGs, which either bound or intersect with the Open Streets. Since the Open Streets vary in opening dates, we consider the week that a street first opens as an Open Street as Week 0 for each street. For each observation, we consider the time series data three weeks before the week of opening date (Week 0) and six weeks after as our observation period. To create a control sample, we draw a 1 mile buffer area around each Open Street in ArcGIS to minimize spillover effects, and randomly select a CBG that sits outside this buffer area and pair it with each observation. The buffer takes into account the spatial effects an Open Street is likely to have on surrounding neighborhoods, such that a neighborhood that is within a 15-20 minute walk of an Open Street may see increase in walking behaviors after the introduction of the Open Streets Program, even if the Open Street is not located directly within the CBG.

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).

At Echo, our dedication to data curation is unmatched; we focus on providing our clients with an in-depth picture of a physical location based on activity in and around a point of interest over time. Our dataset empowers you to explore the “what” by allowing you to dig deeper into customer movement behaviors, eliminate gaps in your trade area and discover untapped potential. Leverage Echo's Activity datasets to identify new growth opportunities and gain a competitive advantage.

This sample of our Area Activity data provides you insights into the estimated total unique visitors and visits in an area. This helps you understand frequentation dynamics over time, identify emerging trends in people movements and measure the impact of external factors on how people move across a city.

Additional Information: - Understand the actual movement patterns of consumers without using PII data, gaining a 360-degree consumer view. Complement your online behavior knowledge with actual offline actions, and better attribute intent based on real-world behaviors. - Echo collects, cleans and updates its footfall on a daily basis. Normalization of the data occurs on a monthly basis. - We provide data aggregation on a weekly, monthly and quarterly basis. - Information about our country offering and data schema can be found here:

1) Data Schema: https://docs.echo-analytics.com/activity/data-schema
2) Country Availability: https://docs.echo-analytics.com/activity/country-coverage
3) Methodology: https://docs.echo-analytics.com/activity/methodology

Echo's commitment to customer service is evident in our exceptional data quality and dedicated team, providing 360° support throughout your location intelligence journey. We handle the complex tasks to deliver analysis-ready datasets to you.

Business Needs: 1. Site Selection: Leverage footfall data to identify the best location to open a new store. By analyzing areas with high footfall you can select sites that are likely to attract more customers. 2. Urban Planning Development: City planners can use footfall data to optimize the layout and infrastructure of urban areas, guide the development of commercial areas by indicating where pedestrian traffic is heaviest, and aid in traffic management and safety measures. 3. Real Estate Investment: Leverage footfall data to identify lucrative investment opportunities and optimize property management by analyzing pedestrian traffic patterns.

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.

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.

The global real-time footfall counting analysis system market is experiencing robust growth, projected to reach $335.4 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 15.0% from 2025 to 2033. This expansion is driven by the increasing need for businesses across various sectors – including transportation, retail, commercial spaces, and education – to gain actionable insights into customer traffic patterns. Real-time data allows for optimized resource allocation, improved operational efficiency, and enhanced customer experience. The market is segmented by technology (IR beam, thermal imaging, video-based, and others) and application, with the retail and commercial sectors currently dominating. Technological advancements in image processing, AI-powered analytics, and the integration of IoT devices are key factors fueling market growth. Furthermore, the rising adoption of sophisticated analytics tools that provide detailed customer behavior insights is significantly contributing to market expansion. The increasing focus on data-driven decision-making within businesses is further propelling the demand for these systems. Competition within the market is relatively high, with several established players including ShopperTrak, RetailNext, FLIR Systems, and others vying for market share. However, the market also presents opportunities for smaller, specialized firms focusing on niche applications or advanced technological solutions. Despite the overall positive outlook, factors such as high initial investment costs and the potential for data privacy concerns might act as restraints to some degree, though these challenges are likely to be mitigated through technological innovation and improved data security measures. The continued development of more affordable and user-friendly systems, coupled with a growing awareness of the business benefits of real-time footfall analysis, are likely to drive further market expansion in the coming years.

Echo’s Customer Journey dataset reveals where visitors go before and after visiting a specific POI — empowering brands with a dynamic view of consumer behavior.

Focused on the EU market, this GDPR-compliant, non-PII dataset uncovers brand and category visitation patterns around a location, helping businesses map influence zones, identify co-visited brands, and refine their location strategy.

Key data points include: - Pre- and post-visit brand/category behaviors - Customer journey paths linked to POIs - Weekly, monthly, and quarterly aggregations - Cleaned, normalized, non-PII mobility data - Major EU country coverage with real-world behavioral insights

Ideal for retail, real estate, and strategy teams aiming to optimize site selection, improve customer experience, and outsmart competition with movement-based intelligence.

In September 2024, Walgreens was the drug store with the highest number of average visits in the United States, at over 29 thousand. CVS was the second most visited, with about 28 thousand visits.

This Mobility & Foot traffic dataset includes enriched mobility data and visitation at POIs to answer questions such as: -How often do people visit a location? (daily, monthly, absolute, and averages). -What type of places do they visit? (parks, schools, hospitals, etc) -Which social characteristics do people have in a certain POI? - Breakdown by type: residents, workers, visitors. -What's their mobility like during night hours & day hours?
-What's the frequency of the visits by day of the week and hour of the day? Extra insights -Visitors´ relative Income Level. -Visitors´ preferences as derived from their visits to shopping, parks, sports facilities, and churches, among others. - Footfall measurement in all types of establishments (shopping malls, stand-alone stores, etc). -Visitors´ preferences as derived from their visits to shopping, parks, sports facilities, and churches, among others. - Origin/Destiny matrix. - Vehicular traffic, measurement of speed, types of vehicles, among other insights. Overview & Key Concepts Each record corresponds to a ping from a mobile device, at a particular moment in time, and at a particular lat and long. We procure this data from reliable technology partners, which obtain it through partnerships with location-aware apps. All the process is compliant with applicable privacy laws. We clean, process and enrich these massive datasets with a number of complex, computer-intensive calculations to make them easier to use in different tailor-made solutions for companies and also data science and machine learning applications, especially those related to understanding customer behavior. Featured attributes of the data Device speed: based on the distance between each observation and the previous one, we estimate the speed at which the device is moving. This is particularly useful to differentiate between vehicles, pedestrians, and stationery observations. Night base of the device: we calculate the approximate location of where the device spends the night, which is usually its home neighborhood. Day base of the device: we calculate the most common daylight location during weekdays, which is usually their work location. Income level: we use the night neighborhood of the device, and intersect it with available socioeconomic data, to infer the device’s income level. Depending on the country, and the availability of good census data, this figure ranges from a relative wealth index to a currency-calculated income. POI visited: we intersect each observation with a number of POI databases, to estimate check-ins to different locations. POI databases can vary significantly, in scope and depth, between countries. Category of visited POI: for each observation that can be attributable to a POI, we also include a standardized location category (park, hospital, among others). Delivery schemas We can deliver the data in three different formats: Full dataset: one record per mobile ping. These datasets are very large, and should only be consumed by experienced teams with large computing budgets. Visitation stream: one record per attributable visit. This dataset is considerably smaller than the full one but retains most of the more valuable elements in the dataset. This helps understand who visited a specific POI, and characterize and understand the consumer's behavior. Audience profiles: one record per mobile device in a given period of time (usually monthly). All the visitation stream is aggregated by category. This is the most condensed version of the dataset and is very useful to quickly understand the types of consumers in a particular area and to create cohorts of users.

The global visitor counting solutions market, valued at $499 million in 2025, is projected to experience robust growth, driven by the increasing adoption of advanced analytics in retail, hospitality, and other sectors. The Compound Annual Growth Rate (CAGR) of 10.2% from 2025 to 2033 indicates a significant expansion of this market over the forecast period. This growth is fueled by several key factors. Businesses are increasingly recognizing the importance of accurate foot traffic data for optimizing operational efficiency, enhancing customer experience, and making data-driven decisions regarding staffing, inventory management, and marketing campaigns. The shift towards sophisticated, AI-powered solutions offering real-time insights and predictive analytics further contributes to market expansion. Moreover, the rising demand for contactless solutions, particularly post-pandemic, is accelerating the adoption of advanced technologies like computer vision and sensor-based systems. Competition is fierce, with established players like ShopperTrak and RetailNext alongside emerging technology providers constantly innovating to meet evolving market needs. The market is segmented by technology (e.g., video analytics, infrared sensors, Wi-Fi analytics), deployment (cloud-based, on-premise), and end-user industry (retail, hospitality, transportation). While precise segment data isn't available, it's likely that retail currently holds the largest market share due to its high dependence on customer traffic data for sales optimization. However, the hospitality and transportation sectors are showing rapid growth as they leverage visitor counting solutions for improved resource allocation and customer service. Challenges remain, including concerns regarding data privacy and the integration of disparate data sources. Nonetheless, the overall market outlook remains positive, driven by continuous technological advancements and increasing awareness of the benefits of data-driven decision-making across various industries. The market will likely see further consolidation as larger companies acquire smaller firms and integrated solutions become more prevalent.

Our dataset gives access to the most precise data thanks to the power of our advanced algorithms. We use massive, precise and representative geolocation data from mobile applications that we aggregate, standardize and couple with manual counts to offer the most reliable analysis. This data product contains footfall data as well as shopping center names, city, postal code and geographies for shopping centers in Belgium / England / France / Germany / Italy / Netherlands / Spain, over the past several years. Use Cases: Foot Traffic Analytics Foot Traffic Analytics Territory Planning Gain detailed insights into pedestrian traffic across diverse locations, such as addresses, shopping centers, and shopping areas, to make strategic decisions for your location strategy. Identify high-traffic areas to optimize site selection and expansion plans. Competition Analytics Benchmark footfall within the shopping centers of your competitors, enabling informed business decisions. Understand competitor performance and identify opportunities for market share growth by analyzing comparative traffic patterns. Marketing Targeting Enhance your marketing strategies by analyzing footfall data to identify high-traffic areas and peak times. Target your marketing and promotional efforts more effectively by understanding where and when to reach your audience, maximizing engagement and conversion rates.. Urban and Infrastructure Planning Support urban and infrastructure planning by providing data on pedestrian traffic flows. Help city planners and developers design more efficient public spaces, transportation hubs, and commercial areas by understanding how people move through different environments.

In week 11 (March 9 to 15) of 2020, foot traffic in Walmart stores was up by 16.86 percent when compared to the equivalent period in 2019. As of March, 2020, 28 percent of Americans reported that they were stockpiling food because of the coronavirus. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

The high Fidelity Data feed is aggregated from multiple data sources and is delivered as a daily feed to a location of your choice. All data is collected in anonymized and explicitly opted-in data where clear consent and terms of usage are dictated. High-Fidelity Mobility Data Reach: Our reach data represents the total number of data counts available within various categories and comprises attributes such as user demographics, anonymous id, device details, location, affluence, interests, traveled countries, and so on.

Use CasesConsumer Insights:Gain a comprehensive 360-degree perspective of the customer to spot behavioral changes, analyze trends and predict business outcomesAdvertising:Create campaigns and customize your messaging depending on your target audience's online and offline activity.Retail Analytics:Analyze footfall trends in various locations and gain understanding of customer personas.

In the industry of QSRs, data-driven decisions are the key to staying ahead. dataplor's Global QSR Locations Dataset offers an in-depth view of the worldwide QSR landscape using foot traffic patterns and location intelligence that empower businesses with the insights needed to thrive.

Data Points for Precision:

• Brand Profiles: Detailed information on both independent QSRs and multinational chains, including official names, unique identifiers, and specializations.

• Business Classification: Precise categorization by cuisine type (e.g., snack bar, sandwich shop, pizza restaurant) to ensure granular insights.

• Location Precision: Exact street addresses and geographic coordinates for pinpoint mapping and analysis.

• Store Attributes: Comprehensive details such as open/close status to gauge market presence.

Empowering Use Cases:

• Market Entry and Expansion: Identify high-potential markets with unmet demand, and pinpoint optimal locations for new restaurant openings or franchise expansions.

• Competitive Benchmarking: Gain deep insights into competitor strategies, QSR offerings, and geographic trends to inform your own business decisions.

• Targeted Marketing and Promotions: Develop hyper-targeted campaigns based on location demographics, competitor proximity, and local cuisine.

• Supply Chain Optimization: Streamline distribution logistics by understanding restaurant locations, demand fluctuations, and local preferences.

• Investment and Risk Analysis: Evaluate potential investment opportunities in the QSR sector by assessing market saturation, growth potential, and risk factors associated with specific locations and cuisine types.