Our Location Intelligence Data provides a detailed view of people’s movements across over 14 million physical locations worldwide. This aggregated and anonymized data is utilized to understand visit patterns and volumes at specific sites. Compiled from diverse global data sources, this information offers valuable context for analyzing foot traffic and location engagement.

Location Intelligence Data Reach

Our Location Intelligence Data delivers in-depth insights into Points of Interest (POIs), places, and Out-of-Home (OOH) advertising locations.By leveraging Factori's Mobility & People Graph data, which integrates information from numerous sources globally, we provide accurate foot-traffic attribution. For instance, to calculate foot traffic at a specific location, we combine attributes such as location ID, day of the week, and time of day, generating up to 40 distinct data records for each POI.

Data Export Methodology

We dynamically gather and update data, delivering the most current insights through methods tailored to your needs, whether daily, weekly, or monthly.

Use Cases

Our Location Intelligence Data is essential for credit scoring, retail analytics, market intelligence, and urban planning, offering businesses and organizations critical insights for strategic decision-making and planning.

GapMaps Foot Traffic 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 foot traffic 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?

Foot Traffic 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 Foot Traffic 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 be considered on a case to case basis.

Our world is more connected than ever. Our smartphone has become the most important device for surfing the Internet, checking the latest news and shopping online. In contrast, we also live in a world where we watch TV, listen to the Radio and cut out coupons from newspapers and flyers. Outside, we pass by Billboards and see screens with advertisements in elevators and supermarkets on a daily basis. This world seems far removed from the online world. It seems a challenge to bridge the gap between online and offline consumer behavior. But with the right tools and the right data, this is easier than you think. Mobile Location data connects the online and offline world.

Information which constitutes the geography or location of a land unit, farm, ranch or facility. This could include latitudinal/longitudinal points, boundaries, borders, addresses.

Xverum’s Point of Interest (POI) Data is a comprehensive dataset containing 230M+ verified locations across 5000 business categories. Our dataset delivers structured geographic data, business attributes, location intelligence, and mapping insights, making it an essential tool for GIS applications, market research, urban planning, and competitive analysis.

With regular updates and continuous POI discovery, Xverum ensures accurate, up-to-date information on businesses, landmarks, retail stores, and more. Delivered in bulk to S3 Bucket and cloud storage, our dataset integrates seamlessly into mapping, geographic information systems, and analytics platforms.

🔥 Key Features:

Extensive POI Coverage: ✅ 230M+ Points of Interest worldwide, covering 5000 business categories. ✅ Includes retail stores, restaurants, corporate offices, landmarks, and service providers.

Geographic & Location Intelligence Data: ✅ Latitude & longitude coordinates for mapping and navigation applications. ✅ Geographic classification, including country, state, city, and postal code. ✅ Business status tracking – Open, temporarily closed, or permanently closed.

Continuous Discovery & Regular Updates: ✅ New POIs continuously added through discovery processes. ✅ Regular updates ensure data accuracy, reflecting new openings and closures.

Rich Business Insights: ✅ Detailed business attributes, including company name, category, and subcategories. ✅ Contact details, including phone number and website (if available). ✅ Consumer review insights, including rating distribution and total number of reviews (additional feature). ✅ Operating hours where available.

Ideal for Mapping & Location Analytics: ✅ Supports geospatial analysis & GIS applications. ✅ Enhances mapping & navigation solutions with structured POI data. ✅ Provides location intelligence for site selection & business expansion strategies.

Bulk Data Delivery (NO API): ✅ Delivered in bulk via S3 Bucket or cloud storage. ✅ Available in structured format (.json) for seamless integration.

🏆Primary Use Cases:

Mapping & Geographic Analysis: 🔹 Power GIS platforms & navigation systems with precise POI data. 🔹 Enhance digital maps with accurate business locations & categories.

Retail Expansion & Market Research: 🔹 Identify key business locations & competitors for market analysis. 🔹 Assess brand presence across different industries & geographies.

Business Intelligence & Competitive Analysis: 🔹 Benchmark competitor locations & regional business density. 🔹 Analyze market trends through POI growth & closure tracking.

Smart City & Urban Planning: 🔹 Support public infrastructure projects with accurate POI data. 🔹 Improve accessibility & zoning decisions for government & businesses.

💡 Why Choose Xverum’s POI Data?

• 230M+ Verified POI Records – One of the largest & most detailed location datasets available.
• Global Coverage – POI data from 249+ countries, covering all major business sectors.
• Regular Updates – Ensuring accurate tracking of business openings & closures.
• Comprehensive Geographic & Business Data – Coordinates, addresses, categories, and more.
• Bulk Dataset Delivery – S3 Bucket & cloud storage delivery for full dataset access.
• 100% Compliant – Ethically sourced, privacy-compliant data.

Access Xverum’s 230M+ POI dataset for mapping, geographic analysis, and location intelligence. Request a free sample or contact us to customize your dataset today!

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers 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 processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm 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 receive complete and unique datasets.

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

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

According to a study conducted in March 2020, evaluating the use of location data among U.S. marketers, ** percent of respondents were of the opinion that improved return in investment was one of the leading benefits of using location data in their strategies. Another ** percent of marketing professionals said that location data helped them deliver relevant content to consumers.

Location information regarding samples taken for each individual. This data sheet is to be used in conjunction with the 'Convert' file.

AutoTrain Dataset for project: identifying-person-location-date

  Dataset Description

This dataset has been automatically processed by AutoTrain for project identifying-person-location-date.

  Languages

The BCP-47 code for the dataset's language is unk.

  Dataset Structure





  Data Instances

A sample from this dataset looks as follows: [ { "tokens": [ "I", "will", "be", "traveling", "to", "Tokyo"… See the full description on the dataset page: https://huggingface.co/datasets/PhaniManda/autotrain-data-identifying-person-location-date.

A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity.

The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions.

This update features the most recent geographic boundaries (2019 Census Block Groups) and new and expanded sources of data used to calculate variables. Entirely new variables have been added and the methods used to calculate some of the SLD variables have changed.

See https://www.epa.gov/smartgrowth/smart-location-mapping for more information.

Comprehensive park and ride polygon data covering transit parking locations across US and Canada. Includes commuter lot geofences, transportation hub boundaries, and public parking polygons. Ideal for transit planning, mobility analysis, and mapping transportation infrastructure.

The National Mine Map Repository (NMMR) maintains point locations for mines appearing on maps within its archive. This dataset is intended to help connect the Office of Surface Mining Reclamation and Enforcement, other federal, state, and local government agencies, private industry, and the general public with archived mine maps in the NMMR's collection. The coordinates for mine point locations represent the best information the NMMR has for the location of the mine. As much as possible, the NMMR strives to find precise locations for all historic mines appearing on mine maps. When this is not possible, another feature as close to the mine as is known is used. This information is reflected in the mine point symbols. However, the NMMR cannot guarantee the accuracy of mine point locations or any other information on or derived from mine maps. The NMMR is part of the United States Department of the Interior, Office of Surface Mining Reclamation and Enforcement (OSMRE). The mission of the NMMR is to preserve abandoned mine maps, to correlate those maps to the surface topography, and to provide the public with quality map products and services. It serves as a point of reference for maps and other information on surface and underground coal, metal, and non-metal mines from throughout the United States. It also serves as a location to retrieve mine maps in an emergency. Some of the information that can be found in the repository includes: Mine and company names, Mine plans including mains, rooms, and pillars, Man-ways, shafts, and mine surface openings. Geological information such as coal bed names, bed thicknesses, bed depths and elevations, bed outcrops, drill-hole data, cross-sections, stratigraphic columns, and mineral assays. Geographical information including historic railroad lines, roads, coal towns, surface facilities and structures, ponds, streams, and property survey lines, gas well and drill-hole locations. Please note: Map images are not available for download from this dataset. They can be requested by contacting NMMR staff and providing them with the desired Document Numbers. NMMR staff also have additional search capabilities and can fulfill more complex requests if necessary. See the NMMR website homepage for contact information: https://www.osmre.gov/programs/national-mine-map-repository. There is no charge for noncommercial use of the maps. Commercial uses will incur a $46/hour research fee for fulfilling requests.

The statistic shows the number of data centers in the United States by location and type in 2018. There were *** data center service providers in the United States.

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information: https://catalog.data.gov/dataset/smart-location-database7 If you have questions about the underlying data stored here, please contact Thomas John (thomas.john@epa.gov). If you have questions or recommendations related to this metadata entry and extracted data, please contact the CAFE Data Management team at: climatecafe@bu.edu. "The Smart Location Database is a nationwide geographic data resource for measuring location efficiency. It includes more than 90 attributes summarizing characteristics, such as housing density, diversity of land use, neighborhood design, destination accessibility, transit service, employment and demographics. Most attributes are available for every census block group in the United States. A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity. The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions. EPA first released a beta version of the Smart Location Database in 2011. The initial full version was released in 2013, and the database was updated to its current version in 2021." Quote from https://www.epa.gov/smartgrowth/smart-location-mapping and https://catalog.data.gov/dataset/smart-location-database7

A GIS compiled locational database in Microsoft Access of ~15,000 mines with uranium occurrence or production, primarily in the western United States. The metadata was cooperatively compiled from Federal and State agency data sets and enables the user to conduct geographic and analytical studies on mine impacts on the public and environment.

GapMaps Crime Risk Location data sourced from Applied Geographic Solutions (AGS) includes the latest crime risk indexes and projections available at census block level. Understand the relative crime risk across any location across the USA and Canada so you can make more informed business decisions.

Provides details on the sites selected for each study, including various attributes to allow for comparison across sites. ------------------------------------------ The City of Seattle Department of Transportation (SDOT) is providing data from the public life studies it has conducted since 2017. These studies consist of measuring the number of people using public space and the types of activities present on select sidewalks across the city, as well as several parks and plazas. The data set is continually updated as SDOT and other parties conduct public life studies using Gehl Institute’s Public Life Data Protocol. This dataset consists of four component spreadsheets and a GeoJSON file, which provide public life data as well as information about the study design and study locations: 1 Public Life Study: provides details on the different studies that have been conducted, including project information. https://data.seattle.gov/Transportation/Public-Life-Data-Study/7qru-sdcp 2 Public Life Location: provides details on the sites selected for each study, including various attributes to allow for comparison across sites. 3 Public Life People Moving: provides data on people moving through space, including total number observed, gender breakdown, group size, and age groups. https://data.seattle.gov/Transportation/Public-Life-Data-People-Moving/7rx6-5pgd 4 Public Life People Staying: provides data on people staying still in the space, including total number observed, demographic data, group size, postures, and activities. https://data.seattle.gov/Transportation/Public-Life-Data-People-Staying/5mzj-4rtf 5 Public Life Geography: A GeoJSON file with polygons of every location studied. https://data.seattle.gov/Transportation/Public-Life-Data-Geography/v4q3-5hvp Please download and refer to the Public Life metadata document - in the attachment section below - for comprehensive information about all of the Public Life datasets.

Weigh-in-motion sensors capture vehicle weights, number of axles and weight distribution of vehicles as they travel over the sensor location. This information is used to inform activities including pavement and bridge design, network maintenance and research. TII currently has 6 such locations collecting data on the National Road Network.

GapMaps premium Business Location Data for Asia includes the most up-to-date view of store locations for over 850 leading retail brands covering 700k+ locations across Asia and MENA including Indonesia, India, Philippines, Malaysia, Singapore and Saudi Arabia.

Business Location Data categories include Fast Food, Cafe, Fitness, Supermarket/grocery sectors which are updated monthly. Brands in other sectors are updated quarterly.

Detailed attributes provided for each Business Location Data location include:

• Category
• Business Name
• Postal code
• Latitude and longitude
• Street address, city, and state
• Location Status (open or closed)
• Location Updated Date
• Location Validated Date

Business Location Data datasets will be supplied as CSV file.

From 20 October 2023, COVID-19 datasets will no longer be updated. Detailed information is available in the fortnightly NSW Respiratory Surveillance Report: https://www.health.nsw.gov.au/Infectious/covid-19/Pages/reports.aspx.
Latest national COVID-19 spread, vaccination and treatment metrics are available on the Australian Government Health website: https://www.health.gov.au/topics/covid-19/reporting?language=und

COVID-19 cases by notification date and postcode, local health district, and local government area. The dataset is updated weekly on Fridays.

The data is for confirmed COVID-19 cases only based on location of usual residence, not necessarily where the virus was contracted.

Case counts reported by NSW Health for a particular notification date may vary over time due to ongoing investigations and the outcome of cases under review thus this dataset and any historical data contained within is subject to change on a daily basis.

The underlying dataset was assessed to measure the risk of identifying an individual and the level of sensitivity of the information gained if it was known that an individual was in the dataset. The dataset was then treated to mitigate these risks, including suppressing and aggregating data.

This dataset does not include cases with missing location information.