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”.

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.

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”.

Focusing on the type of location data French mobile users are willing to share with brands through geolocation, it appears that the majority of the respondents would not share their residence, workplace or current location. From the sample, ** percent mentioned that they were open to share their current position to receive personalized offers from brands, ** percent would share their area of residence and ** percent would share their workplace location.

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

Mobile phone location data have been extensively used to understand human mobility patterns through the employment of mobility indicators. The temporal sampling interval (TSI), which is measured by the temporal interval between consecutive records, determines how well such data can describe human activities and influence the values of human mobility indicators. However, systematic investigations of how the TSI affects human mobility indicators remain scarce, and characterizing those relationships is a fundamental research question for many related studies. This study uses a mobile phone location dataset containing 19,370 intensively sampled individual trajectories (TSI

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”.

Context

Full Database of city state country available in CSV format. All Countries, States & Cities are Covered & Populated with Different Combinations & Versions.

Each CSV has the 1. Longitude 2. Latitude

of each location, alongside other miscellaneous country data such as 3. Currency 4. State code 5. Phone country code

Content

Total Countries : 250 Total States/Regions/Municipalities : 4,963 Total Cities/Towns/Districts : 148,061

Last Updated On : 29th January 2022

Source

https://github.com/dr5hn/countries-states-cities-database

This dataset is sourced from the U.S. Department of Transportation Bureau of Transportation Statistics. All data and metadata is sourced from the page linked below. Metadata is not updated automatically; data updates weekly.

Source Data Link: https://data.bts.gov/Research-and-Statistics/Trips-by-Distance/w96p-f2qv

How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our new mobility statistics.

The Trips by Distance data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day.

Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air.

The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed.

These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

The Daily Mobility Statistics were derived from a data panel constructed from several mobile data providers, a step taken to address the reduce the risks of geographic and temporal sample bias that would result from using a single data source. In turn, the merged data panel only included data from those mobile devices whose anonymized location data met a set of data quality standards, e.g., temporal frequency and spatial accuracy of anonymized location point observations, device-level temporal coverage and representativeness, spatial distribution of data at the sample and county levels. After this filtering, final mobility estimate statistics were computed using a multi-level weighting method that employed both device- and trip-level weights, thus expanding the sample represented by the devices in the data panel to the at-large populations of each state and county in the US.

Data analysis was conducted at the aggregate national, state, and county levels. To assure confidentiality and support data quality, no data were reported for a county if it had fewer than 50 devices in the sample on any given day.

Trips were defined as movements that included a stay of longer than 10 minutes at an anonymized location away from home. A movement with multiple stays of longer than 10 minutes--before returning home--was counted as multiple trips.

The Daily Mobility Statistics data on this page, which cover the COVID and Post-COVID periods, are experimental. Experimental data products are created using novel or exploratory data sources or methodologies that benefit data users in the absence of other statistically rigorous products, and they not meet all BTS data quality standards.

Snapshot img

Location-Based Services (LBS) Market Size 2025-2029

The location-based services (lbs) market size is forecast to increase by USD 330 billion at a CAGR of 30.4% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for personal and enterprise navigation services. IoT technologies, such as radar sensors, RFID tags, and Wi-Fi access points, are being integrated into LBS to enhance accuracy and efficiency. Augmented reality (AR) and virtual reality (VR) technologies are also gaining popularity in LBS, providing good experiences for users. This trend is driven by the widespread adoption of smartphones and the integration of advanced location technologies, enabling real-time, contextually relevant information delivery. However, the market is not without challenges. Privacy and security concerns surrounding the collection, storage, and usage of location data pose significant hurdles. As users become more aware of the potential risks, companies must prioritize transparency, data protection, and user consent to build trust and maintain market position.
Effective data management and compliance with evolving regulations will be crucial for businesses seeking to capitalize on the opportunities presented by this dynamic market. Companies must navigate these challenges to deliver innovative, user-centric location-based solutions, meeting the growing demand for personalized experiences while addressing privacy and security concerns.

What will be the Size of the Location-Based Services (LBS) Market during the forecast period?

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The market encompasses a range of technologies and applications that leverage mobile positioning, including Satellite-Based GPS, Enhanced Observed Time Difference (E-OTD), Observed Time Difference Of Arrival (OTDOA), Wireless-Assisted Global Navigation Satellite Systems (WAGNSS), and Hybrid Technologies. These positioning technologies are integral to various industries, from Smart City projects to 3D mapping applications, geolocation data, and Connected Devices. The market's growth is fueled by the increasing use of Global Positioning System (GPS) and Internet of Things (IoT) technologies, as well as the integration of Augmented Reality (AR) and Virtual Reality (VR) in navigation services.
Real-time data and positioning are essential for applications in logistics, transportation, and location-based social media. The integration of LBS with smartphone use and advanced technologies continues to expand its potential applications and market size.

How is this Location-Based Services (LBS) Industry segmented?

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

Component

  Hardware
  Software
  Services


Type

  Outdoor
  Indoor


Application

  Navigation and tracking
  GIS and mapping
  Geo marketing and advertising
  Social networking and entertainment
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Middle East and Africa

    UAE


  Rest of World

By Component Insights

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

Location-Based Services (LBS) involve real-time data and positioning technologies, such as Mobile Positioning through Satellite-based GPS, E-OTD, OTDOA, Wireless-assisted GNSS, A-GNSS, and Hybrid technologies, to provide navigation services and indoor location services. These technologies are integrated into various industries, including Smart city projects, 3D mapping applications, E-commerce, Mobile apps, Artificial intelligence, Real-time location tracking, Bluetooth beacons, Autonomous vehicles, Disaster information systems, and Geolocation Data. Hardware components, including passive and active RFID tags, beacons, sensors, and cameras, provide connectivity through Wi-Fi access points or require infrastructure upgrades for enterprise indoor location-based solutions. Companies like HPE offer hardware tags as part of their LBS offerings.

The Internet of Things (IoT) and 5G Infrastructure facilitate the growth of LBS, enabling applications such as Navigation and Tracking, Indoor Location Services, Transportation and Logistics, Healthcare, and Local Search. LBS also encompasses Augmented Reality (AR) and Virtual Reality (VR) technologies, enhancing user experiences in Food delivery services, Business Intelligence, Fleet Management, and Local Search.

Get a glance at the market report of share of various segments Request Free Sample

The Hardware segment was valued at USD 16.90 billion in 2019 and showed a gradual incre

Work in progress: data might be changed

The data set contains the locations of public roadside parking spaces in the northeastern part of Hanover Linden-Nord. As a sample data set, it explicitly does not provide a complete, accurate or correct representation of the conditions! It was collected and processed as part of the 5GAPS research project on September 22nd and October 6th 2022 as a basis for further analysis and in particular as input for simulation studies.

Vehicle Detections

Based on the mapping methodology of Bock et al. (2015) and processing of Leichter et al. (2021), the utilization was determined using vehicle detections in segmented 3D point clouds. The corresponding point clouds were collected by driving over the area on two half-days using a LiDAR mobile mapping system, resulting in several hours between observations. Accordingly, these are only a few sample observations. The trips are made in such a way that combined they cover a synthetic day from about 8-20 clock.

The collected point clouds were georeferenced, processed, and automatically segmented semantically (see Leichter et al., 2021). To automatically extract cars, those points with car labels were clustered by observation epoch and bounding boxes were estimated for the clusters as a representation of car instances. The boxes serve both to filter out unrealistically small and large objects, and to rudimentarily complete the vehicle footprint that may not be fully captured from all sides.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/807618b6-5c38-4456-88a1-cb47500081ff/download/detection_map.png" alt="Overview map of detected vehicles" title="Overview map of detected vehicles"> Figure 1: Overview map of detected vehicles

Parking Areas

The public parking areas were digitized manually using aerial images and the detected vehicles in order to exclude irregular parking spaces as far as possible. They were also tagged as to whether they were aligned parallel to the road and assigned to a use at the time of recording, as some are used for construction sites or outdoor catering, for example. Depending on the intended use, they can be filtered individually.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/16b14c61-d1d6-4eda-891d-176bdd787bf5/download/parking_area_example.png" alt="Example parking area occupation pattern" title="Visualization of example parking areas on top of an aerial image [by LGLN]"> Figure 2: Visualization of example parking areas on top of an aerial image [by LGLN]

Parking Occupancy

For modelling the parking occupancy, single slots are sampled as center points every 5 m from the parking areas. In this way, they can be integrated into a street/routing graph, for example, as prepared in Wage et al. (2023). Own representations can be generated from the parking area and vehicle detections. Those parking points were intersected with the vehicle boxes to identify occupancy at the respective epochs.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/ca0b97c8-2542-479e-83d7-74adb2fc47c0/download/datenpub-bays.png" alt="Overview map of parking slots' average load" title="Overview map of parking slots' average load"> Figure 3: Overview map of average parking lot load

However, unoccupied spaces cannot be determined quite as trivially the other way around, since no detected vehicle can result just as from no measurement/observation. Therefore, a parking space is only recorded as unoccupied if a vehicle was detected at the same time in the neighborhood on the same parking lane and therefore it can be assumed that there is a measurement.

To close temporal gaps, interpolations were made by hour for each parking slot, assuming that between two consecutive observations with an occupancy the space was also occupied in between - or if both times free also free in between. If there was a change, this is indicated by a proportional value. To close spatial gaps, unobserved spaces in the area are drawn randomly from the ten closest occupation patterns around.

This results in an exemplary occupancy pattern of a synthetic day. Depending on the application, the value could be interpreted as occupancy probability or occupancy share.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/184a1f75-79ab-4d0e-bb1b-8ed170678280/download/occupation_example.png" alt="Example parking area occupation pattern" title="Example parking area occupation pattern"> Figure 4: Example parking area occupation pattern

References

• F. Bock, D. Eggert and M. Sester (2015): On-street Parking Statistics Using LiDAR Mobile Mapping, 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain, 2015, pp. 2812-2818. https://doi.org/10.1109/ITSC.2015.452
• A. Leichter, U. Feuerhake, and M. Sester (2021): Determination of Parking Space and its Concurrent Usage Over Time Using Semantically Segmented Mobile Mapping Data, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 185–192. https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-185-2021
• O. Wage, M. Heumann, and L. Bienzeisler (2023): Modeling and Calibration of Last-Mile Logistics to Study Smart-City Dynamic Space Management Scenarios. In 1st ACM SIGSPATIAL International Workshop on Sustainable Mobility (SuMob ’23), November 13, 2023, Hamburg, Germany. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3615899.3627930

Phone apps that use your location are a big part of modern life. Location-based services (LBS) are phone software that use the Global Positioning System (GPS) and various other services to determine your position on our globe. Those services provide the geolocation data to your navigation app, for example, to calculate a route to your destination. In fact, a majority of the apps on your phone use location data in some way.But before we start talking about how your smart phone works, we'll discuss a brief history of the technology and infrastructure that got us here.

Snapshot img

Geofencing Market Size 2025-2029

The geofencing market size is forecast to increase by USD 6.21 billion at a CAGR of 27.4% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing adoption of location-based marketing strategies. Businesses across various industries are recognizing the value of delivering targeted, contextually relevant promotions and offers to consumers based on their physical location. This trend is further fueled by the expanding applications of geofencing technology, which goes beyond marketing to include areas such as asset tracking, employee safety, and logistics optimization. However, the market also faces challenges that could hinder its growth. One of the most notable obstacles is the high initial setup costs and capital investments required for implementing geofencing solutions. This can be a significant barrier for small and medium-sized businesses, limiting their ability to enter the market and compete with larger players.
Despite these challenges, companies seeking to capitalize on the opportunities presented by the market must navigate this landscape effectively to stay ahead of the competition and maximize their return on investment.

What will be the Size of the Geofencing Market during the forecast period?

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The market continues to evolve, with dynamic applications across various sectors. Geospatial analytics plays a pivotal role in this technology, enabling businesses to leverage location data for customer engagement and targeted marketing. Virtual fences, powered by RFID tags and GPS tracking, facilitate asset tracking and fleet management. Geofencing software and APIs enable real-time location services, enhancing contextual marketing and IoT applications. Privacy concerns persist, necessitating permission-based marketing and data security measures. Indoor positioning and spatial data analysis expand geofencing's reach, while location intelligence fuels smart city development.
Mobile app development and mapping software further enhance geofencing capabilities, enabling hyperlocal marketing and proximity marketing. Geospatial databases and location services provide the foundation for these applications, ensuring accurate and timely data. Geofencing's continuous evolution underscores its potential in enhancing customer experience, optimizing operations, and driving growth across industries.

How is this Geofencing Industry segmented?

The geofencing 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.

Product

  Fixed
  Mobile


Component

  Services
  Solutions


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

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

Geofencing, a location-based technology, enables virtual fences around specific areas or fixed objects. When breached, this fence triggers alerts and reports on intruders' entry or exit, along with the time spent. Applications span across industries, including transportation and logistics, retail, healthcare, hospitality, and industrial manufacturing. Geospatial data, GIS mapping, and GPS tracking fuel geofencing, while wearable technology and RFID tags enhance indoor positioning. Geofencing software and APIs facilitate asset tracking and fleet management. Retail analytics and hyperlocal marketing leverage geofencing for contextual customer engagement. IoT applications, real-time location services, and spatial data fuel location intelligence. Mobile app development and mapping software integrate geofencing, while geospatial analytics ensure data security and privacy.

Proximity marketing and mobile payments further expand geofencing's reach. Geofencing platforms offer permission-based marketing and location-based advertising solutions. Smart cities and industrial manufacturing harness geofencing for infrastructure monitoring and optimization. Despite privacy concerns, the market continues to grow, driven by customer segmentation, geospatial analysis, and location awareness.

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The Fixed segment was valued at USD 790.00 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 37% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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In the dynamic business landscape of 2024, the market experiences significant growth,

The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

Introduction

The 802.11 standard includes several management features and corresponding frame types. One of them are Probe Requests (PR), which are sent by mobile devices in an unassociated state to scan the nearby area for existing wireless networks. The frame part of PRs consists of variable-length fields, called Information Elements (IE), which represent the capabilities of a mobile device, such as supported data rates.

This dataset contains PRs collected over a seven-day period by four gateway devices in an uncontrolled urban environment in the city of Catania.

It can be used for various use cases, e.g., analyzing MAC randomization, determining the number of people in a given location at a given time or in different time periods, analyzing trends in population movement (streets, shopping malls, etc.) in different time periods, etc.

Related dataset

Same authors also produced the Labeled dataset of IEEE 802.11 probe requests with same data layout and recording equipment.

Measurement setup

The system for collecting PRs consists of a Raspberry Pi 4 (RPi) with an additional WiFi dongle to capture WiFi signal traffic in monitoring mode (gateway device). Passive PR monitoring is performed by listening to 802.11 traffic and filtering out PR packets on a single WiFi channel.

The following information about each received PR is collected: - MAC address - Supported data rates - extended supported rates - HT capabilities - extended capabilities - data under extended tag and vendor specific tag - interworking - VHT capabilities - RSSI - SSID - timestamp when PR was received.

The collected data was forwarded to a remote database via a secure VPN connection. A Python script was written using the Pyshark package to collect, preprocess, and transmit the data.

Data preprocessing

The gateway collects PRs for each successive predefined scan interval (10 seconds). During this interval, the data is preprocessed before being transmitted to the database. For each detected PR in the scan interval, the IEs fields are saved in the following JSON structure:

PR_IE_data = { 'DATA_RTS': {'SUPP': DATA_supp , 'EXT': DATA_ext}, 'HT_CAP': DATA_htcap, 'EXT_CAP': {'length': DATA_len, 'data': DATA_extcap}, 'VHT_CAP': DATA_vhtcap, 'INTERWORKING': DATA_inter, 'EXT_TAG': {'ID_1': DATA_1_ext, 'ID_2': DATA_2_ext ...}, 'VENDOR_SPEC': {VENDOR_1:{ 'ID_1': DATA_1_vendor1, 'ID_2': DATA_2_vendor1 ...}, VENDOR_2:{ 'ID_1': DATA_1_vendor2, 'ID_2': DATA_2_vendor2 ...} ...} }

Supported data rates and extended supported rates are represented as arrays of values that encode information about the rates supported by a mobile device. The rest of the IEs data is represented in hexadecimal format. Vendor Specific Tag is structured differently than the other IEs. This field can contain multiple vendor IDs with multiple data IDs with corresponding data. Similarly, the extended tag can contain multiple data IDs with corresponding data.
Missing IE fields in the captured PR are not included in PR_IE_DATA.

When a new MAC address is detected in the current scan time interval, the data from PR is stored in the following structure:

{'MAC': MAC_address, 'SSIDs': [ SSID ], 'PROBE_REQs': [PR_data] },

where PR_data is structured as follows:

{ 'TIME': [ DATA_time ], 'RSSI': [ DATA_rssi ], 'DATA': PR_IE_data }.

This data structure allows to store only 'TOA' and 'RSSI' for all PRs originating from the same MAC address and containing the same 'PR_IE_data'. All SSIDs from the same MAC address are also stored. The data of the newly detected PR is compared with the already stored data of the same MAC in the current scan time interval. If identical PR's IE data from the same MAC address is already stored, only data for the keys 'TIME' and 'RSSI' are appended. If identical PR's IE data from the same MAC address has not yet been received, then the PR_data structure of the new PR for that MAC address is appended to the 'PROBE_REQs' key. The preprocessing procedure is shown in Figure ./Figures/Preprocessing_procedure.png

At the end of each scan time interval, all processed data is sent to the database along with additional metadata about the collected data, such as the serial number of the wireless gateway and the timestamps for the start and end of the scan. For an example of a single PR capture, see the Single_PR_capture_example.json file.

Folder structure

For ease of processing of the data, the dataset is divided into 7 folders, each containing a 24-hour period. Each folder contains four files, each containing samples from that device.

The folders are named after the start and end time (in UTC). For example, the folder 2022-09-22T22-00-00_2022-09-23T22-00-00 contains samples collected between 23th of September 2022 00:00 local time, until 24th of September 2022 00:00 local time.

Files representing their location via mapping: - 1.json -> location 1 - 2.json -> location 2 - 3.json -> location 3 - 4.json -> location 4

Environments description

The measurements were carried out in the city of Catania, in Piazza Università and Piazza del Duomo The gateway devices (rPIs with WiFi dongle) were set up and gathering data before the start time of this dataset. As of September 23, 2022, the devices were placed in their final configuration and personally checked for correctness of installation and data status of the entire data collection system. Devices were connected either to a nearby Ethernet outlet or via WiFi to the access point provided.

Four Raspbery Pi-s were used: - location 1 -> Piazza del Duomo - Chierici building (balcony near Fontana dell’Amenano) - location 2 -> southernmost window in the building of Via Etnea near Piazza del Duomo - location 3 -> nothernmost window in the building of Via Etnea near Piazza Università - location 4 -> first window top the right of the entrance of the University of Catania

Locations were suggested by the authors and adjusted during deployment based on physical constraints (locations of electrical outlets or internet access) Under ideal circumstances, the locations of the devices and their coverage area would cover both squares and the part of Via Etna between them, with a partial overlap of signal detection. The locations of the gateways are shown in Figure ./Figures/catania.png.

Known dataset shortcomings

Due to technical and physical limitations, the dataset contains some identified deficiencies.

PRs are collected and transmitted in 10-second chunks. Due to the limited capabilites of the recording devices, some time (in the range of seconds) may not be accounted for between chunks if the transmission of the previous packet took too long or an unexpected error occurred.

Every 20 minutes the service is restarted on the recording device. This is a workaround for undefined behavior of the USB WiFi dongle, which can no longer respond. For this reason, up to 20 seconds of data will not be recorded in each 20-minute period.

The devices had a scheduled reboot at 4:00 each day which is shown as missing data of up to a few minutes.

 Location 1 - Piazza del Duomo - Chierici

The gateway device (rPi) is located on the second floor balcony and is hardwired to the Ethernet port. This device appears to function stably throughout the data collection period. Its location is constant and is not disturbed, dataset seems to have complete coverage.

 Location 2 - Via Etnea - Piazza del Duomo

The device is located inside the building. During working hours (approximately 9:00-17:00), the device was placed on the windowsill. However, the movement of the device cannot be confirmed. As the device was moved back and forth, power outages and internet connection issues occurred. The last three days in the record contain no PRs from this location.

 Location 3 - Via Etnea - Piazza Università

Similar to Location 2, the device is placed on the windowsill and moved around by people working in the building. Similar behavior is also observed, e.g., it is placed on the windowsill and moved inside a thick wall when no people are present. This device appears to have been collecting data throughout the whole dataset period.

 Location 4 - Piazza Università

This location is wirelessly connected to the access point. The device was placed statically on a windowsill overlooking the square. Due to physical limitations, the device had lost power several times during the deployment. The internet connection was also interrupted sporadically.

Recognitions

The data was collected within the scope of Resiloc project with the help of City of Catania and project partners.

This dataset contains data of mobile network coverage in India.

Data has been sourced from https://opencellid.org/ The world's largest Open Database of Cell Towers Locate devices without GPS, explore Mobile Operator coverage and more!

Radio: The generation of broadband cellular network technology (Eg. LTE, GSM)

MCC: Mobile country code.

MNC: Mobile network code.

LAC/TAC/NID: Location Area Code

CID: This is a unique number used to identify each Base transceiver station or sector of BTS

Longitude:This is a geographic coordinate that specifies the east-west position of a point on the Earth's surface

Latitude:This is a geographic coordinate that specifies the north–south position of a point on the Earth's surface.

Range: Approximate area within which the cell could be. (In meters)

Samples: Number of measures processed to get a particular data point

Changeable=1: The location is determined by processing samples

Changeable=0: The location is directly obtained from the telecom firm

Created: When a particular cell was first added to database (UNIX timestamp)

Updated: When a particular cell was last seen (UNIX timestamp)

AverageSignal: To get the positions of cells, OpenCelliD processes measurements from data contributors. Each measurement includes GPS location of device + Scanned cell identifier (MCC-MNC-LAC-CID) + Other device properties (Signal strength). In this process, signal strength of the device is averaged. Most ‘averageSignal’ values are 0 because OpenCelliD simply didn’t receive signal strength values.

Since 2019, most biases are in the range of [-0.071, 0.034].

Context

Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.3k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 66k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query-by-example search over UIs.

Content

Rico was built by mining Android apps at runtime via human-powered and programmatic exploration. Like its predecessor ERICA, Rico’s app mining infrastructure requires no access to — or modification of — an app’s source code. Apps are downloaded from the Google Play Store and served to crowd workers through a web interface. When crowd workers use an app, the system records a user interaction trace that captures the UIs visited and the interactions performed on them. Then, an automated agent replays the trace to warm up a new copy of the app and continues the exploration programmatically, leveraging a content-agnostic similarity heuristic to efficiently discover new UI states. By combining crowdsourcing and automation, Rico can achieve higher coverage over an app’s UI states than either crawling strategy alone. In total, 13 workers recruited on UpWork spent 2,450 hours using apps on the platform over five months, producing 10,811 user interaction traces. After collecting a user trace for an app, we ran the automated crawler on the app for one hour.

Acknowledgements

UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN https://interactionmining.org/rico

Inspiration

The Rico dataset is large enough to support deep learning applications. We trained an autoencoder to learn an embedding for UI layouts, and used it to annotate each UI with a 64-dimensional vector representation encoding visual layout. This vector representation can be used to compute structurally — and often semantically — similar UIs, supporting example-based search over the dataset. To create training inputs for the autoencoder that embed layout information, we constructed a new image for each UI capturing the bounding box regions of all leaf elements in its view hierarchy, differentiating between text and non-text elements. Rico’s view hierarchies obviate the need for noisy image processing or OCR techniques to create these inputs.

Geospatial Data Clean-Room Market Outlook

According to our latest research, the global geospatial data clean-room market size in 2024 stands at USD 1.4 billion, driven by the surging need for secure and collaborative geospatial data environments across multiple industries. The market is projected to expand at a robust CAGR of 18.2% from 2025 to 2033, reaching a forecasted market size of USD 6.3 billion by 2033. This remarkable growth is fueled by increasing concerns over data privacy, the proliferation of location-based services, and the mounting regulatory requirements for secure data collaboration and analytics.

One of the primary growth factors for the geospatial data clean-room market is the exponential increase in the volume and variety of geospatial data generated by IoT devices, drones, satellites, and mobile applications. Organizations across sectors such as transportation, urban planning, and logistics are leveraging this data to derive actionable insights. However, the sensitive nature of location data and the need to comply with global privacy regulations such as GDPR and CCPA necessitate secure environments for data aggregation and analysis. Geospatial data clean-rooms provide a controlled and compliant infrastructure for multiple parties to collaborate on sensitive datasets without exposing raw data, thus unlocking value while minimizing risk.

Another significant driver is the digital transformation initiatives undertaken by governments and enterprises worldwide. As smart city projects and digital twin technologies gain traction, the demand for secure, scalable, and interoperable platforms to process and analyze geospatial data is surging. Clean-room solutions offer advanced capabilities such as federated analytics, privacy-preserving computation, and policy-driven data governance. These features are particularly crucial for sectors like healthcare, BFSI, and defense, where the confidentiality of location data is paramount. Additionally, the integration of artificial intelligence and machine learning algorithms within clean-room platforms is enhancing the accuracy and utility of geospatial analytics, further accelerating market adoption.

The geospatial data clean-room market is also benefiting from the evolving landscape of data monetization and data sharing partnerships. Companies are increasingly seeking ways to collaborate with external partners, suppliers, or governmental organizations to unlock new revenue streams and improve operational efficiency. Clean-rooms act as a trusted intermediary, enabling secure, permissioned access to geospatial datasets while preserving data sovereignty and intellectual property rights. This collaborative approach is fostering innovation across industries such as retail, energy, and utilities, where location intelligence can drive targeted marketing, resource optimization, and risk management.

From a regional perspective, North America currently dominates the geospatial data clean-room market, accounting for the largest revenue share, followed by Europe and the Asia Pacific. The presence of leading technology providers, stringent regulatory frameworks, and early adoption of advanced analytics solutions are key factors contributing to North America's leadership. Meanwhile, the Asia Pacific region is expected to witness the fastest growth over the forecast period, propelled by rapid urbanization, government investments in smart infrastructure, and the burgeoning digital economy. Europe remains a critical market due to its strong emphasis on data privacy and cross-border data collaboration initiatives.

Component Analysis

The component segment of the geospatial data clean-room market is categorized into software, services, and hardware. Software solutions form the backbone of clean-room platforms, offering functionalities such as data ingestion, anonymization, access control, and analytics. The software segment holds the largest market share, primarily due t