Aurora:GeoStudio® is a premier geospatial analysis platform that excels in supporting foot traffic data through its sophisticated Population Dynamics® analytic. Foot traffic data encompasses information about the number of people visiting specific locations or establishments, providing deep insights into customer behavior, patterns, and trends. This data is crucial for businesses looking to understand their audience and make data-driven decisions.

Core Features:

  1.    Data Collection Methods:
  •    Passive Sensors: Aurora:GeoStudio® integrates data collected from passive sensors deployed at various locations. These devices count the number of visitors, track their movement paths, and record the duration of their visits.
  •    Mobile Devices: The platform also leverages data from mobile devices, providing additional insights into foot traffic patterns through location-based services and applications.
  2.    Population Dynamics® Analytic:
  •    Aurora:GeoStudio®’s Population Dynamics® analytic processes foot traffic data to deliver comprehensive insights. This analytic tool helps visualize and understand visitor behavior, peak visiting times, and movement trends within specific areas.
  3.    Visualization and Mapping:
  •    The platform offers advanced visualization capabilities, displaying foot traffic data on customizable maps from providers like Google, Esri, Open, and Stamen. These visualizations help users understand spatial patterns and relationships, facilitating informed decision-making.

Applications:

  1.    Customer Behavior Analysis:
  •    Businesses can analyze foot traffic data to understand customer behavior, such as the number of visitors, the duration of their visits, and the paths they take within an establishment. This information is crucial for tailoring services and improving customer satisfaction.
  2.    Store Layout Optimization:
  •    Foot traffic data helps businesses optimize store layouts by identifying high-traffic areas and bottlenecks. By understanding how customers move through a space, businesses can rearrange products and displays to enhance flow and maximize sales opportunities.
  3.    Marketing Strategy Enhancement:
  •    Aurora:GeoStudio® enables businesses to refine their marketing strategies by providing insights into peak visiting times and customer demographics. This data supports targeted marketing campaigns, ensuring promotions reach the right audience at the right time.
  4.    Operational Efficiency:
  •    Understanding foot traffic patterns allows businesses to optimize staffing levels, manage inventory more effectively, and improve overall operational efficiency. By aligning resources with actual customer demand, businesses can enhance service delivery and reduce costs.
  5.    Urban Planning and Public Spaces:
  •    Foot traffic data is invaluable for urban planners and managers of public spaces. It helps in designing public areas that accommodate pedestrian flow efficiently and ensures that amenities are accessible and well-placed.

Aurora:GeoStudio®’s support for foot traffic data through the Population Dynamics® analytic offers businesses and urban planners a powerful tool for understanding and optimizing visitor behavior. By leveraging data from sensors, cameras, and mobile devices, the platform provides detailed insights into customer movements and trends. These insights enable businesses to enhance their marketing strategies, optimize store layouts, and improve operational efficiency. For urban planners, foot traffic data facilitates the design of more effective and accessible public spaces. Aurora:GeoStudio®’s advanced features empower users to make informed decisions and achieve a comprehensive understanding of foot traffic dynamics, leading to better strategic outcomes.

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

GapMaps Mobility 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.

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 80 percent at Dollar Tree and over 45 percent at Dollar General, compared to the base month.

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 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).

Quadrant's location data contains 16 attributes, including standard attributes such as Latitude, Longitude, and Timestamp, and non-standard attributes such as Geohash. Our historical data spans as far back as 2019.

We conduct stringent evaluations on supplier feeds to ensure authenticity and quality. Our proprietary algorithms detect and cleanse corrupted and duplicated data points - allowing you to leverage our datasets rapidly with minimal data processing or cleaning.

Quadrant’s mobile location data is processed through a deduplicating algorithm that focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only pay for complete and unique datasets.

We actively identify overlapping values at the supply level to determine the value each supplier offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying suppliers based on unique data values rather than volumes alone – measures that provide significant benefits to our buyers.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a headstart on their analyses.

Quadrant’s Data Noise Algorithm weeds out events that occurred seven days before the data is received (unless historical data is requested). By filtering these outdated events, we ensure that the data we deliver to our customers is recent and relevant. Reducing latency also decreases file sizes, which results in more efficient data delivery.

Traffic-related data collected by the Boston Transportation Department, as well as other City departments and State agencies. Various types of counts: Turning Movement Counts, Automated Traffic Recordings, Pedestrian Counts, Delay Studies, and Gap Studies.

~_Turning Movement Counts (TMC)_ present the number of motor vehicles, pedestrians, and cyclists passing through the particular intersection. Specific movements and crossings are recorded for all street approaches involved with the intersection. This data is used in traffic signal retiming programs and for signal requests. Counts are typically conducted for 2-, 4-, 11-, and 12-Hr periods.

~_Automated Traffic Recordings (ATR)_ record the volume of motor vehicles traveling along a particular road, measures of travel speeds, and approximations of the class of the vehicles (motorcycle, 2-axle, large box truck, bus, etc). This type of count is conducted only along a street link/corridor, to gather data between two intersections or points of interest. This data is used in travel studies, as well as to review concerns about street use, speeding, and capacity. Counts are typically conducted for 12- & 24-Hr periods.

~_Pedestrian Counts (PED)_ record the volume of individual persons crossing a given street, whether at an existing intersection or a mid-block crossing. This data is used to review concerns about crossing safety, as well as for access analysis for points of interest. Counts are typically conducted for 2-, 4-, 11-, and 12-Hr periods.

~_Delay Studies (DEL)_ measure the delay experienced by motor vehicles due to the effects of congestion. Counts are typically conducted for a 1-Hr period at a given intersection or point of intersecting vehicular traffic.

~_Gap Studies (GAP)_ record the number of gaps which are typically present between groups of vehicles traveling through an intersection or past a point on a street. This data is used to assess opportunities for pedestrians to cross the street and for analyses on vehicular “platooning”. Counts are typically conducted for a specific 1-Hr period at a single point of crossing.

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

Veraset Movement (GPS Mobility Data) offers unparalleled insights into footfall traffic patterns across nearly four dozen countries in Africa.

Covering 46+ countries, Veraset's Mobility 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 (Mobility data) helps shape strategy and make impactful data-driven decisions.

Veraset’s Africa Movement Panel includes the following countries: - algeria-DZ - angola-AO - benin-BJ - botswana-BW - burkina faso-BF - burundi-BI - cameroon-CM - central african republic-CF - chad-TD - comoros-KM - congo-brazzaville-CG - congo-kinshasa-CD - djibouti-DJ - egypt-EG - eritrea-ER - ethiopia-ET - gabon-GA - gambia-GM - ghana-GH - guinea-bissau-GW - kenya-KE - lesotho-LS - liberia-LR - libya-LY - madagascar-MG - malawi-MW - mali-ML - mauritius-MU - morocco-MA - mozambique-MZ - namibia-NA - nigeria-NG - rwanda-RW - senegal-SN - seychelles-SC - sierra leone-SL - somalia-SO - south africa-ZA - south sudan-SS - tanzania-TZ - togo-TG - tunisia-TN - uganda-UG - zambia-ZM - zimbabwe-ZW

Companies use Veraset's Mobility Data for: - 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

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

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.

This dataset contains a line network representing pedestrian walking routes throughout the City of Melbourne.
It features links to additional land subdivisions and incorporates additional elements/attributes. The network has been created with the intent to assist interested stakeholder in multiple modelling tasks including catchment analysis and route analysis.

The network emulates pedestrian behaviour, by allowing free movement in low traffic areas while penalising busy crossings with traffic lights.

The network also connects to the City of Melbourne property layer through property centroids and connectors for all the different properties in the dataset.

The download is a zip file containing compressed .json files. Please see the metadata attached for further information.

The 3D people counter market is experiencing robust growth, driven by the increasing need for accurate and real-time foot traffic analysis across various sectors. Retail businesses leverage this technology to optimize store layouts, staffing levels, and marketing campaigns based on precise customer flow data. Similarly, commercial real estate utilizes 3D people counters to understand space utilization and improve operational efficiency. The market's expansion is fueled by advancements in sensor technology, providing more accurate and reliable data, and the integration of people counting systems with other analytics platforms for comprehensive insights. Wireless systems are gaining traction due to their ease of installation and flexibility, while wired systems continue to be prevalent in high-security environments demanding reliable data transmission. The market is segmented by application (residential, commercial) and type (wired, wireless), with the commercial segment currently dominating due to higher adoption rates. North America and Europe represent significant market shares, driven by early adoption and technological advancements. However, Asia-Pacific is expected to witness substantial growth in the coming years, fueled by rising urbanization and increasing retail infrastructure development. While the initial investment cost can be a restraint, the long-term return on investment (ROI) through improved operational efficiency and data-driven decision-making makes 3D people counters an attractive proposition for businesses across diverse sectors. Competition among established players and emerging companies is driving innovation and offering various feature sets and pricing models. The forecast period (2025-2033) anticipates a sustained upward trajectory, with the market expected to be significantly larger by 2033. This growth is further underpinned by the increasing adoption of advanced analytics and the integration of 3D people counters with other business intelligence tools. The convergence of technologies, such as AI and machine learning, will further enhance the capabilities of 3D people counters, resulting in more insightful data and improved decision-making processes. Challenges remain in addressing concerns regarding data privacy and ensuring accurate counting in high-traffic environments. However, advancements in technology and robust data security measures are steadily mitigating these challenges, paving the way for widespread adoption across a broader range of industries. The market’s success depends on continued innovation, affordable pricing, and effective marketing strategies that highlight the benefits of using this technology to gain competitive advantages.

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

Information layer of Topographic DataBase — DBT 2012 of the Municipality of Milan, flight March 2012, nominal return scale 1:1000. Area intended for the circulation of pedestrians, it includes all portions of the road platform that, within urban areas, are reserved for the transit of pedestrians, i.e. sidewalks, as well as all the pedestrian passages or parking areas such as porches or underpasses, pedestrian passages with or without steps, savers, etc... The CLASS A010102 belongs to the TEMA 0101 (roads) that makes up the STRATE 01 (Viability, mobility and transport). Download possible for the STRATO 01 series and for the DBT 2012 series.

The AI people counter market is experiencing robust growth, driven by the increasing need for accurate and real-time foot traffic analytics across diverse sectors. The market's expansion is fueled by several key factors. Firstly, the rising adoption of AI-powered solutions in retail, office buildings, and public transportation offers businesses valuable insights into customer behavior, optimizing operational efficiency and enhancing the overall customer experience. Secondly, advancements in computer vision and infrared technology are leading to more sophisticated and reliable people counting systems, improving accuracy and reducing errors associated with traditional methods. Finally, the increasing affordability and accessibility of AI-based solutions are making them attractive to a wider range of businesses, regardless of size. We estimate the 2025 market size to be approximately $800 million, considering the prevalent growth in related technologies and the expanding adoption across various sectors. A conservative Compound Annual Growth Rate (CAGR) of 15% over the forecast period (2025-2033) is projected, leading to significant market expansion by 2033. Despite the positive outlook, certain restraints could impede market growth. Data privacy concerns surrounding the collection and usage of foot traffic data remain a challenge. The high initial investment costs associated with implementing AI people counting systems might deter smaller businesses. Furthermore, the accuracy of AI-based systems can be impacted by factors such as lighting conditions, obstructions, and crowd density, demanding continuous improvements in technology. Market segmentation reveals a strong demand for computer vision-based systems, due to their versatility and advanced analytical capabilities. The retail and office building sectors are leading adopters, followed by public transportation and other applications. Key players like V-Count, Hikvision, and others are driving innovation and competition in this dynamic landscape. Geographical expansion is also observed, with North America and Europe currently dominating the market share, while Asia Pacific is projected to experience significant growth due to rapid technological adoption and increasing urbanization.

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.

Irys specializes in collecting and curating high-quality geolocation signals from millions of connected devices across the globe. Our real-time and historical foot traffic data, categorized under Map Data, is sourced through partnerships with tier-1 mobile applications and app developers. The advanced aggregated location data covers the entire world, providing valuable insights for diverse use-cases related to Transport and Logistic Data, Mobile Location Data, Mobility Data, and IP Address Data.

Our commitment to privacy compliance is paramount. We ensure that all data is collected in accordance with privacy regulations, accompanied by clear and compliant privacy notices. Our opt-in/out management allows for transparent control over data collection, use, and distribution to third parties.

Discover the power of foot traffic data with Irys – where precision meets privacy.

In-Store Analytics Market Valuation – 2024-2031

In-Store Analytics Market was valued at USD 1532.7 Million in 2024 and is projected to reach USD 5213.2 Billion By 2031, growing at a CAGR of 18.24% during the forecast period 2024 to 2031.

In-Store Analytics Market: Definition/ Overview

In-store analytics refers to the collection, measurement, and analysis of data related to customer behavior and store operations within a retail environment. Utilizing technologies such as sensors, cameras, and data analytics platforms, it provides insights into how customers navigate the store, their interaction with products, and overall shopping patterns. This data helps retailers understand shopper preferences, optimize store layouts, and enhance the shopping experience.

In-store analytics is applied to various aspects of retail operations. For example, it can optimize store layouts by analyzing foot traffic patterns to place high-demand products in strategic locations. Retailers also use it to monitor real-time inventory levels, ensuring popular items are stocked appropriately and reducing out-of-stock scenarios. Additionally, the data helps in personalizing marketing efforts by tracking customer behavior and tailoring promotions to increase engagement and sales.

Our unparalleled combination of points-of-interest (POI) data enriched with Sentiment and Foot Traffic Data KPIs will empower your decision making. We provide the most accurate and comprehensive POI / Location data, enriched with Business Listings Data and customer insights.