As of March 2021, Waze was the mobile GPN navigation app found to collect the largest amount of data from global iOS users, with 21 data points collected across all examined segments. Maps.me collected a total of 20 data points from its users, including five data points on contact information. Hiking and trail GPS map Gaia followed, with 13 data points, respectively.

As of October 2020, the average amount of mobile data used by Apple Maps per 20 minutes was 1.83 MB, while Google maps used only 0.73 MB. Waze, which is also owned by Google, used the least amount at 0.23 MB per 20 minutes.

This Mobile Location Data product provides extensive coverage of North America with precise timestamped GPS coordinates from opted in mobile devices. Data is available both in real time and historically, enabling detailed analysis of movement patterns, foot traffic trends and location based behavior.

The dataset is sourced via partnerships with established app publishers, ensuring accuracy, scale and full privacy compliance. Each record contains latitude, longitude, event timestamp and optional device metadata, making it adaptable for operational monitoring and strategic market research.

Included attributes: Latitude & Longitude coordinates Event timestamp (epoch & date) Mobile Advertising ID (IDFA/GAID) Horizontal accuracy (~85% fill rate) Country code (ISO3) Optional metadata: IP address, carrier, device model Access & Delivery Delivered via API with polygon queries (up to 10,000 tiles)

Formats: JSON, CSV, Parquet Supports API, AWS S3, or Google Cloud Storage delivery Hourly or daily refresh options Historical coverage starting September 2024 Flexible, credit-based query pricing Privacy & Compliance Fully compliant with GDPR and CCPA Clear privacy notices with every data source Robust opt-in/opt-out user controls

Use Cases Retail expansion & site selection Audience segmentation & behavioral analysis Urban mobility planning & infrastructure optimization DOOH / OOH campaign performance measurement Geofencing for targeted marketing campaigns Tourism & event attendance mapping

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.

This dataset includes geospatial files providing an updated habitat classification map covering wetland and upland coastal habitats throughout Mobile and Baldwin counties in Alabama (approximately 3,671 square miles).

The Mobile Mapping System Market Report is Segmented by Component (Hardware, Software, and Services), Mounting Type (Vehicle Mounted, Railway Mounted, and More), Application (Imaging Services, Aerial Mobile Mapping, and More), End-User Verticals (Government, Oil and Gas, Mining, Military, and More), and Geography (North America, Europe, South America, and More). The Market Forecasts are Provided in Terms of Value (USD).

Abstract This research investigates subjective user preference for using Floor Plans and Schematic Maps in an indoor environment, and how users locate and orient themselves when using these representations. We sought to verify the efficiency of these two kinds of digital maps and evaluate which elements found in physical environments and which elements found in the representations influence the user spatial orientation process. Users answered questions and performed orientation tasks which indicated their level of familiarity with the area being studied, their understanding of the symbology used, and their identification of Points of Interest (POI) in the environment. The initial results indicated a preference for the Schematic Map, because users thought that the symbology used on the map adopted was easy to understand.

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

Maps are increasingly read on mobile devices. Mobile maps necessitate specific design considerations to improve readability and user experience. Little research has focused on how to design mobile thematic maps, in contrast to reference maps. Data journalism represents a common way that the public encounters mobile thematic maps. This paper characterizes the design techniques and challenges associated with mobile thematic cartography in the context of data journalism. Through interviews with 18 expert news cartographers, I show that teams of data journalists are increasingly aware of mobile users, but face numerous constraints when designing for these users. They face time constraints, the need to design for both desktop and mobile, and must reach vast general audiences, meaning they often practice simultaneous design over mobile-first design. News cartographers have also reduced their use of interactivity, which reduces complexity related to designing for both desktop and mobile. This work shows that news cartographers solve mobile thematic map design challenges through iterative design processes that draw from years of expertise, not a strict set of guidelines. News cartographers currently design mobile thematic maps based on generalized best practices, but are uncertain what choices do and do not work for their readersMany news cartographers design maps simultaneously for desktop and mobile, rather than prioritizing one over the otherNews cartographers are decreasing their use of interactive maps, given that they expect news readers want to consume information as fast as possibleNews maps are produced under time constraints that can be limiting on creativity and novelty, and without time for user testing News cartographers currently design mobile thematic maps based on generalized best practices, but are uncertain what choices do and do not work for their readers Many news cartographers design maps simultaneously for desktop and mobile, rather than prioritizing one over the other News cartographers are decreasing their use of interactive maps, given that they expect news readers want to consume information as fast as possible News maps are produced under time constraints that can be limiting on creativity and novelty, and without time for user testing

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent GIS data at 150m grid levels across Asia and MENA. Understand who lives in a catchment, where they work and their spending potential.

This map data product delivers accurate, real-time, and historical GPS event data from across Africa, including major cities, rural regions, and transit corridors. The dataset is built for mapping, spatial analysis, mobility research, and commercial decision-making.

Data Attributes Latitude & longitude coordinates Timestamp (epoch & human-readable date) Device ID (MAID: IDFA/GAID) Country code (ISO3) Horizontal accuracy (85% fill rate)

Optional: IP address, mobile carrier, device model

Access & Delivery Data is available via API with polygon-based querying (up to 10,000 tiles) for precise POI or region targeting. Delivery options include hourly or daily updates in JSON, CSV, or Parquet formats, through AWS S3, Google Cloud, or direct API access. Historical coverage extends back to September 2024, and 95% of events are available within 3 days for near-real-time analysis.

Compliance & Customization GDPR & CCPA compliant sourcing Credit-based pricing for scalable usage Custom schema mapping & folder structures on request Applications Base mapping and geospatial visualization Infrastructure planning and asset tracking Retail site selection and catchment analysis Transport route optimization Urban mobility and zoning analysis Risk and environmental planning

A map used in the Vision Zero application to enlist feedback from bikers, drivers, and pedestrians using public streets.

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

1. HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.
2. HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.
3. HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.
4. target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

1. YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.
2. H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59.
   The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License
Creative Commons Attribution 4.0 International.

Related datasets

• Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612
• Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

184 views (3 recent) Dataset extent Map data © OpenStreetMap contributors. What is mobile sorting? It is a trailer fitted out and towed by a vehicle. Once on the site, the trailer unfolds to allow the public to be received and a ramp gives access to a sorting platform. This system allows residents to deposit their small bulky items in the specially designed boxes and crates adapted for the different types of waste, in order to encourage sorting and recycling.

These data were automated to provide an accurate high-resolution historical shoreline of Mobile Bay, Alabama suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attri...

we extract human trips from Call Records Detail data. Combining traffic analysis zone dataset

Mobile City Limit Boundary of as November 14, 2024.

This dataset includes the name and location of active mobile home parks operating in New York State. Active mobile home parks include only parks that were categorized as active (i.e., operating with accommodations for the placement of five or more mobile or manufactured homes) on the date the data was downloaded from a Department of Health database. This data also includes the date of the last inspection and violations of Part 17 of the New York State Code of Rules and Regulations that were identified during that inspection. Additionally, the data includes the park owner-operator, the number of sites within the park, the type of on-site water source and sewage disposal system serving the mobile home park, and whether a pool or beach is operated as part of the mobile home park. The location of the mobile home park includes its street address, city, state, zip code, municipality, and county.

These data provide an accurate high-resolution shoreline compiled from imagery of MOBILE BAY, BON SECOUR BAY TO FAIRHOPE, AL . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartogr...

The mobile map market is booming, projected to reach $XX million by 2033 with a CAGR of 18.41%. Explore key drivers, trends, and regional insights in this comprehensive market analysis. Discover leading companies and competitive strategies shaping this dynamic sector. Learn more!