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!

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.

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.

Irys specializes in delivering high-quality Location Data solutions for worldwide locations. Our unique data sourcing approach ensures superior data quality and volume. Highlight key features for target use cases in Mobility Data, Places Data, Footfall Data, and Foot Traffic Data.

Data Attributes:

• Raw GPS Data
• Hourly Cadence

Method: Real-Time

Experiment with various pricing strategies, offering transparency and flexibility to meet diverse needs.

Our commitment to privacy compliance is unwavering. All data is collected transparently with clear privacy notices. Opt-in/out management empowers users over data distribution. Customer testimonials speak to our reliability.

Experience the precision of our Location Data solutions—where quality meets privacy compliance.

To create this layer, OCTO staff used ABCA's definition of “Full-Service Grocery Stores” (https://abca.dc.gov/page/full-service-grocery-store#gsc.tab=0)– pulled from the Food System Assessment below), and using those criteria, determined locations that fulfilled the categories in section 1 of the definition.Then, staff reviewed the Office of Planning’s Food System Assessment (https://dcfoodpolicycouncilorg.files.wordpress.com/2019/06/2018-food-system-assessment-final-6.13.pdf) list in Appendix D, comparing that to the created from the ABCA definition, which led to the addition of a additional examples that meet, or come very close to, the full-service grocery store criteria. The explanation from Office of Planning regarding how the agency created their list:“To determine the number of grocery stores in the District, we analyzed existing business licenses in the Department of Consumer and Regulatory Affairs (2018) Business License Verification system (located at https://eservices.dcra.dc.gov/BBLV/Default.aspx). To distinguish grocery stores from convenience stores, we applied the Alcohol Beverage and Cannabis Administration’s (ABCA) definition of a full-service grocery store. This definition requires a store to be licensed as a grocery store, sell at least six different food categories, dedicate either 50% of the store’s total square feet or 6,000 square feet to selling food, and dedicate at least 5% of the selling area to each food category. This definition can be found at https://abca.dc.gov/page/full-service-grocery-store#gsc.tab=0. To distinguish small grocery stores from large grocery stores, we categorized large grocery stores as those 10,000 square feet or more. This analysis was conducted using data from the WDCEP’s Retail and Restaurants webpage (located at https://wdcep.com/dc-industries/retail/) and using ARCGIS Spatial Analysis tools when existing data was not available. Our final numbers differ slightly from existing reports like the DC Hunger Solutions’ Closing the Grocery Store Gap and WDCEP’s Grocery Store Opportunities Map; this difference likely comes from differences in our methodology and our exclusion of stores that have closed.”Staff also conducted a visual analysis of locations and relied on personal experience of visits to locations to determine whether they should be included in the list.

Cube Location

## Overview

Cube Location is a dataset for object detection tasks - it contains Cubeloc annotations for 497 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).

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.

This Location Data & Foot traffic dataset available for all countries include enriched raw 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 enduring night hours & day hours?
-What's the frequency of the visits partition by day of the week and hour of the day?

Extra insights -Visitors´ relative income Level. -Visitors´ preferences as derived by their visits to shopping, parks, sports facilities, churches, among others.

Overview & Key Concepts Each record corresponds to a ping from a mobile device, at a particular moment in time and at a particular latitude and longitude. 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 and process these massive datasets with a number of complex, computer-intensive calculations to make them easier to use in different 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 approximated location of where the device spends the night, which is usually their 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). Coverage: Worldwide.

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, 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 ESS-DIVE location metadata reporting format provides instructions and templates for reporting a minimum set of metadata for discrete point locations in geographic space represented by x, y, and z coordinates. This format was created based on a need for earth and environmental science researchers to more consistently provide metadata about locations where they conduct studies. To create the format, we incorporated elements from ESS-DIVE’s community reporting formats as well as 12 additional data standards or other data resources (e.g., databases, data systems, or repositories). In the template, we ask researchers to indicate unique locations using Location IDs and indicate hierarchies of locations through parent location IDs. We also provide additional optional fields for researchers to indicate how they measured the point location and the date and time that the location was first used as a research site This dataset contains support documentation for the reporting format (README.md and instructions.md), a terminology guide (guide.md), a crosswalk indicating how this reporting format relates to existing standards and data resources (Location_metadata_crosswalk.csv), a data dictionary (dd.csv), file-level metadata (flmd.csv), and the location metadata templates in both CSV (Location_metadata_template.csv) and Excel formats (Location_metadata_template.xlsx).

The Report Includes Europe Location Intelligence Companies and the Market is Segmented by End-User Industry (Telecom, BFSI, Healthcare, Manufacturing, Retail), by Country (United Kingdom, Germany, France, Spain, Italy, and the Rest of Europe). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

This is the dataset of our Mobicom 2023 paper titled "The Wisdom of 1,170 Teams: Lessons and Experiences from a Large Indoor Localization Competition". We organized an indoor location competition in 2021. 1446 contestants from more than 60 countries making up 1170 teams participated in this unique global event. In this competition, a first-of-its-kind large-scale indoor location benchmark dataset (60 GB) was released. The dataset for this competition consists of dense indoor signatures of WiFi, geomagnetic field, iBeacons etc. as well as ground truth locations collected from hundreds of buildings in Chinese cities. Here we upload a sample data to Zenodo, and the whole dataset can be found at https://www.kaggle.com/c/indoor-location-navigation.

The ESS-DIVE location metadata reporting format provides instructions and templates for reporting a minimum set of metadata for discrete point locations in geographic space represented by x, y, and z coordinates. This format was created based on a need for earth and environmental science researchers to more consistently provide metadata about locations where they conduct studies. To create the format, we incorporated elements from ESS-DIVE’s community reporting formats as well as 12 additional data standards or other data resources (e.g., databases, data systems, or repositories). In the template, we ask researchers to indicate unique locations using Location IDs and indicate hierarchies of locations through parent location IDs. We also provide additional optional fields for researchers to indicate how they measured the point location and the date and time that the location was first used as a research site This dataset contains support documentation for the reporting format (README.md and instructions.md), a terminology guide (guide.md), a crosswalk indicating how this reporting format relates to existing standards and data resources (Location_metadata_crosswalk.csv), a data dictionary (dd.csv), file-level metadata (flmd.csv), and the location metadata templates in both CSV (Location_metadata_template.csv) and Excel formats (Location_metadata_template.xlsx).

Location Intelligence Market size was valued at USD 18.5 Billion in 2023 and is projected to reach USD 63.15 Billion by 2030, growing at a CAGR of 15.63% during the forecasted period 2024 to 2030.Global Location Intelligence Market DriversThe growth and development of the Location Intelligence Market drivers. These factors have a big impact on how Location Intelligence are demanded and adopted in different sectors. Several of the major market forces are as follows:Proliferation of Spatial Data: A rich source of data for location intelligence and analytics is made possible by the exponential increase of spatial data produced by sources including GPS-enabled devices, Internet of Things sensors, and geographic information systems (GIS). In order to extract meaningful insights, there is a growing need for sophisticated tools and technologies due to the volume and diversity of spatial data.Location-Based Services (LBS) are Growing: The demand for location intelligence and analytics solutions is fueled by the widespread use of location-based services including ride-sharing services, navigation apps, and location-based marketing. Companies use location data to target services based on local context, optimize operations, and improve customer experiences.Need for Real-time information: To make wise judgments swiftly in the hectic business world of today, businesses need to have real-time access to location-based information. Businesses may increase agility and responsiveness by using location intelligence and analytics solutions to monitor events, identify patterns, and react to changes in real-time.The amalgamation of location: intelligence and analytics with nascent technologies such as artificial intelligence (AI) and the Internet of Things (IoT) amplifies their potential and value proposition. Through the integration of sensor data, AI algorithms, and location data, enterprises may gain more profound understanding, anticipate future patterns, and streamline their decision-making procedures.Urbanization and Smart City Initiatives: The use of location intelligence and analytics solutions is fueled by the global trend toward urbanization and the growth of smart city initiatives. These technologies help municipalities, urban planners, and government agencies create sustainable and effective urban environments by optimizing infrastructure development, city planning, and service delivery.Cross-Industry Applications: Location analytics and intelligence are useful in a variety of industries, such as banking, logistics, healthcare, and retail. Businesses use location-based data to increase risk management, streamline supply chains, target customers more effectively, and increase operational efficiency across a range of company operations.Regulatory Compliance and Risk Management: The use of location intelligence and analytics solutions for regulatory compliance and risk management is influenced by compliance requirements relating to location-based data, such as privacy laws and geospatial standards. These products are purchased by organizations to guarantee data governance, reduce risks, and prove compliance with legal and regulatory obligations.The need for location-based: marketing is growing as companies use location analytics and intelligence to create more focused advertising and marketing campaigns. Organizations may increase customer engagement and conversion rates by providing tailored offers, promotions, and content depending on the geographic context of their customers by evaluating location data and consumer activity patterns.Emergence of Digital Twin Technology: This technology opens up new possibilities for location intelligence and analytics by building virtual versions of real assets or environments. Organizations can improve decision-making processes in a variety of fields, such as manufacturing, infrastructure management, and urban planning, by incorporating location data into digital twin models and simulating scenarios.

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

Snapshot img

Real-Time Location Systems (RTLS) Market Size 2025-2029

The real-time location systems (rtls) market size is forecast to increase by USD 45.5 billion, at a CAGR of 42.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasingly low cost of Radio Frequency Identification (RFID) tags and the adoption of Ultra-Wideband (UWB) technology. RFID tags, a key component of RTLS, have seen a notable decrease in price, making them more accessible and cost-effective for businesses seeking to implement location tracking systems. UWB RTLS technology, known for its high accuracy and ability to provide real-time location data, is gaining traction in various industries, including healthcare, manufacturing, and logistics. However, the market faces challenges as well.
One major obstacle is the high implementation costs associated with deploying RTLS solutions. This includes the expense of hardware, software, and installation services. Additionally, ensuring interoperability between different RTLS systems and integrating them with existing IT infrastructure can add to the financial burden. Companies must carefully weigh the benefits of implementing RTLS against these costs to make informed strategic decisions.

What will be the Size of the Real-Time Location Systems (RTLS) Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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Real-Time Location Systems (RTLS) continue to evolve and unfold in diverse applications across various sectors, driving market dynamics with unrelenting momentum. Healthcare monitoring and patient tracking systems utilize RTLS for improved care delivery and enhanced safety. In emergency response situations, RTLS enables quick identification and location of individuals in need. Energy efficiency gains are achieved through RTLS-enabled power consumption monitoring and optimization. Support services benefit from RTLS for streamlined workflow automation and improved productivity. Network infrastructure and cost reduction are enhanced through wireless communication and deployment services. Data visualization tools offer valuable insights through data aggregation and real-time alerts.

RTLS technology is integrated with RFID tags, ultrasonic sensor fusion, and positioning algorithms for proximity detection and outdoor positioning. Security protocols ensure data encryption and error reduction, while API integrations facilitate seamless system integration. The ongoing development of RTLS technology encompasses the deployment of mobile apps, cloud platforms, and web applications for real-time data access. Indoor positioning systems, such as those utilizing Ultra-Wideband (UWB) technology, expand the capabilities of RTLS to previously uncharted territories. Continuous innovation in RTLS technology is shaping the future of industries, from healthcare and emergency response to logistics and security management. The integration of real-time location tracking, Wi-Fi positioning, and data analytics is revolutionizing the way businesses operate, offering unprecedented levels of efficiency, productivity, and safety.

How is this Real-Time Location Systems (RTLS) Industry segmented?

The real-time location systems (rtls) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Healthcare
  Transportation and logistics
  Retail
  Government
  Others


Solution

  Systems
  Tags


Technology

  Active RFID
  Passive RFID
  Others


Management

  Inventory/asset tracking and management
  Access control and security
  Environmental monitoring
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    The Netherlands
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Application Insights

The healthcare segment is estimated to witness significant growth during the forecast period.

The market is experiencing notable growth across various industries, with a particular focus on healthcare. In this sector, RTLS solutions are revolutionizing patient care and asset management. The advantages of real-time tracking and cost savings have led to increased adoption in hospitals. Indoor Location Based Services (LBS) are a key application, integrating RTLS with clinical systems for enhanced analytics. RTLS technology facilitates improved operational efficiency, security, and safety. Positioning algorithms, such as Wi-Fi positioning and Ultra-Wideband (UWB), enable accurate indoor positioning.

Bluetooth beacons and RFID tags are essential components, supporting proximity detection and asset tracking. Integration wi

Timeseries data from 'Approx Location' (boem_ahmd_approx_location)

Information which details the location of a facility or specific areas of usage within a facility. For example bins within a storage facility or floor plan layouts of office buildings.

Leverage advanced location data from high-quality geospatial data covering patterns, behaviours, and trends across diverse industries. With accurate insights from multiple sources, our solutions empower businesses in retail, logistics, real estate, finance, and urban planning to optimize operations, enhance decision-making, and drive strategic growth.

Key use cases where Location Data has helped businesses : 1. Optimize Logistics & Route Planning : Streamline delivery routes, reduce transit times, and enhance operational efficiency with precise location intelligence. 2. Enhance Market Positioning & Competitor Insights : Identify high-traffic zones, analyse competitor locations, and fine-tune business strategies to maximize market presence. 3. Transform Navigation & EV Infrastructure : Power navigation systems, real-time travel recommendations, and EV charging station mapping for seamless location-based services. 4. Enhance Urban & Retail Site Selection : Identify optimal locations for stores, warehouses, and infrastructure investments with in-depth spatial data and demographic insights. 5. Strengthen Spatial Analysis & Risk Management : Leverage advanced geospatial insights for disaster preparedness, public health initiatives, and land-use optimization.

The global spatial location services market is experiencing robust growth, driven by increasing demand for precise location intelligence across diverse sectors. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors. Firstly, the proliferation of smart devices and the Internet of Things (IoT) is generating massive location data, which fuels the need for sophisticated spatial analysis and location-based services. Secondly, advancements in technologies like GPS, GIS, and machine learning are enhancing the accuracy and capabilities of location services, enabling innovative applications in various industries. Thirdly, the growing adoption of location-based marketing and advertising strategies is creating lucrative opportunities for businesses to engage with customers more effectively. Finally, government initiatives focusing on infrastructure development and smart city projects are further propelling market growth. The market is segmented by application (commercial, municipal, military, others) and type (indoor, outdoor positioning). Commercial applications currently dominate, but the municipal and military segments are expected to witness significant growth in the coming years due to increasing investments in smart city infrastructure and defense modernization programs. The competitive landscape is characterized by a mix of established technology providers, GIS specialists, and consulting firms. Major players like Google Cloud, Oracle, IBM, and HERE Technologies are leveraging their extensive data resources and technological expertise to gain a strong foothold. However, smaller, specialized firms are also thriving by offering niche solutions and innovative applications. Regional variations exist, with North America and Europe currently dominating the market due to higher technology adoption rates and well-established infrastructure. However, the Asia-Pacific region is poised for rapid expansion, driven by increasing smartphone penetration and government support for digitalization initiatives. The market faces challenges such as data privacy concerns, cybersecurity risks, and the need for seamless integration of diverse location data sources. Nevertheless, the overall outlook remains highly positive, indicating substantial growth potential for spatial location services in the years to come.