GapMaps Live is an easy-to-use location intelligence platform available across 25 countries globally that allows you to visualise your own store data, combined with the latest demographic, economic and population movement intel right down to the micro level so you can make faster, smarter and surer decisions when planning your network growth strategy.

With one single login, you can access the latest estimates on resident and worker populations, census metrics (eg. age, income, ethnicity), consuming class, retail spend insights and point-of-interest data across a range of categories including fast food, cafe, fitness, supermarket/grocery and more.

Some of the world's biggest brands including McDonalds, Subway, Burger King, Anytime Fitness and Dominos use GapMaps Live as a vital strategic tool where business success relies on up-to-date, easy to understand, location intel that can power business case validation and drive rapid decision making.

Primary Use Cases for GapMaps Live includes:

1. Retail Site Selection - Identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers and where to find more of them.
3. Analyse your catchment areas at a granular grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
6. Customer Profiling
7. Target Marketing
8. Market Share Analysis

Some of features our clients love about GapMaps Live include: - View business locations, competitor locations, demographic, economic and social data around your business or selected location - Understand consumer visitation patterns (“where from” and “where to”), frequency of visits, dwell time of visits, profiles of consumers and much more. - Save searched locations and drop pins - Turn on/off all location listings by category - View and filter data by metadata tags, for example hours of operation, contact details, services provided - Combine public data in GapMaps with views of private data Layers - View data in layers to understand impact of different data Sources - Share maps with teams - Generate demographic reports and comparative analyses on different locations based on drive time, walk time or radius. - Access multiple countries and brands with a single logon - Access multiple brands under a parent login - Capture field data such as photos, notes and documents using GapMaps Connect and integrate with GapMaps Live to get detailed insights on existing and proposed store locations.

description: Multibeam backscatter imagery extracted from gridded bathymetry for selected U.S. locations in the Pacific. The backscatter datasets include data collected using the RESON 8101ER multibeam sonar, Kongsberg 300 kHz EM3002D multibeam sonar, and a Kongsberg 30 kHz EM300 multibeam sonar. Data are available in GeoTIFF and NetCDF format as well as composite maps (jpg or PDF) which show all available data for a region. Please see the individual metadata records for additional information about a specific location.; abstract: Multibeam backscatter imagery extracted from gridded bathymetry for selected U.S. locations in the Pacific. The backscatter datasets include data collected using the RESON 8101ER multibeam sonar, Kongsberg 300 kHz EM3002D multibeam sonar, and a Kongsberg 30 kHz EM300 multibeam sonar. Data are available in GeoTIFF and NetCDF format as well as composite maps (jpg or PDF) which show all available data for a region. Please see the individual metadata records for additional information about a specific location.

The MAPS Model Location Time Series (MOLTS) is one of the model output datasets provided in the Southern Great Plains - 1997 (SGP97). The full MAPS MOLTS dataset covers most of North America east of the Rocky Mountains (283 locations). MOLTS are hourly time series output at selected locations that contain values for various surface parameters and ‘sounding' profiles at MAPS model levels and are derived from the MAPS model output. The MOLTS output files were converted into Joint Office for Science Support (JOSS) Quality Control Format (QCF), the same format used for atmospheric rawinsonde soundings processed by JOSS. The MOLTS output provided by JOSS online includes only the initial analysis output (i.e. no forecast MOLTS) and only state parameters (pressure, altitude, temperature, humidity, and wind). The full output, including the forecast MOLTS and all output parameters, in its original format (Binary Universal Form for the Representation of meteorological data, or BUFR) is available from the National Center for Atmospheric Research (NCAR)/Scientific Computing Division. The Forecast Systems Laboratory (FSL) operates the MAPS model with a resolution of 40 km and 40 vertical levels. The MAPS analysis and forecast fields are generated every 3 hours at 0000, 0300, 0600, 0900, 1200, 1500, 1800, and 2100 UTC daily. MOLTS are hourly vertical profile and surface time series derived from the MAPS model output. The complete MOLTS output includes six informational items, 16 parameters for each level and 27 parameters at the surface. Output are available each hour beginning at the initial analysis (the only output available from JOSS) and ending at the 48 hour forecast. JOSS converts the raw format files into JOSS QCF format which is the same format used for atmospheric sounding data such as National Weather Service (NWS) soundings. JOSS calculated the total wind speed and direction from the u and v wind components. JOSS calculated the mixing ratio from the specific humidity (Pruppacher and Klett 1980) and the dew point from the mixing ratio (Wallace and Hobbs 1977). Then the relative humidity was calculated from the dew point (Bolton 1980). JOSS did not conduct any quality control on this output. The header records (15 total records) contain output type, project ID, the location of the nearest station to the MOLTS location (this can be a rawinsonde station, an Atmospheric Radiation Measurement (ARM)/Cloud and Radiation Testbed (CART) station, a wind profiler station, a surface station, or just the nearest town), the location of the MOLTS output, and the valid time for the MOLTS output. The five header lines contain information identifying the sounding, and have a rigidly defined form. The following 6 header lines are used for auxiliary information and comments about the sounding, and they vary significantly from dataset to dataset. The last 3 header records contain header information for the data columns. Line 13 holds the field names, line 14 the field units, and line 15 contains dashes ('-' characters) delineating the extent of the field. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/2ad09880-6439-440c-9829-c4653ec12a4f

Our Location Intelligence Data connects people's movements to over 14M physical locations globally. These are aggregated and anonymized data that are only used to offer context for the volume and patterns of visits to certain locations. This data feed is compiled from different data sources around the world.

Location Intelligence Data Reach: Location Intelligence data brings the POI/Place/OOH level insights calculated based on Factori’s Mobility & People Graph data aggregated from multiple data sources globally. To achieve the desired foot-traffic attribution, specific attributes are combined to bring forward the desired reach data. For instance, to calculate the foot traffic for a specific location, a combination of location ID, day of the week, and part of the day can be combined to give specific location intelligence data. There can be a maximum of 40 data records possible for one POI based on the combination of these attributes.

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method at a suitable interval (daily/weekly/monthly).

Use Case: Retail Analytics Platform: Location intelligence to analyze foot traffic patterns around retail stores, combining this data with customer profiles to gain insights into visitor demographics. These insights optimize store layouts, staffing, and product placements Marketing Campaign Optimization: Utilize location intelligence to analyze consumer behavior and preferences using geographical and demographic data for more effective audience segmentation and targeting. Emergency Response Planning Tool: To identify high-risk areas for natural disasters or emergencies and profiles to assess vulnerability and evacuation needs across different population segments Smart City Mobility Solution: Provide city planners and transportation authorities with insights to optimize transportation systems, alleviate congestion, and improve urban mobility for residents Event Planning and Venue Selection: Assists planners in selecting suitable venues that match the demographic profile and preferences of their audience

Data Attributes Included: Location ID n_visitors day_of_week distance_from_home do_date month part_of_day travelled_countries Visitor_country_origin Visitor_home_origin Visitor_work_origin year Carrier Brand Visited Place _Categories Geo _ behaviour make model OS_versions ratio_age_18_24 ratio_age_25_34 ratio_age_35_44 ratio_age_45_54 ratio_age_55_64 ratio_age_65 ratio_female ratio_male ratio_residents ratio_workers ratio_others

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

Location Intelligence Data Reach

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

Data Export Methodology

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

Use Cases

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

This dataset contains instantaneous measurements of discharge, as measured at 4 locations: East Canal, West Canal, Center Ditch, and Sodhouse Canal. Dataset is considered provisional, preliminary, and subject to revision.

SafeGraph Places provides baseline information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of places ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).

SafeGraph Places is a point of interest (POI) data offering with varying location data coverage depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

SafeGraph provides clean and accurate geospatial datasets on 51M+ physical places/points of interest (POI) globally. Hundreds of industry leaders like Mapbox, Verizon, Clear Channel, and Esri already rely on SafeGraph POI data to unlock business insights and drive innovation.

Abstract

This dataset is derived from GA TOPO 250K Series 3 features clipped to the BA_SYD and environs extent for the purpose of providing geographic context in BA_SYD report map images. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Selected features currently include:

Lakes

PlaceNames*

PopulatedPlaces

Railways

Roads

WatercourseLines

additional features may be included as required (relevant feature classes asterisked).

Currently the only addition has been to PlaceNames with the addition of Census Spring (see Lineage).

Purpose

providing geographic context in BA_SYD report map images.

Dataset History

A rectangular mask polygon feature was manually drawn around the BA_SYD (ie NSB+SSB) boundary extending approximately 100km beyond the BA_SYD extent. This mask is included in the dataset (SYD_clip).

Selected features from the national GEODATA TOPO 250K series 3 were overlaid with the mask and intersecting features extracted.

Extracted feature classes have the same names as the source features.

The additional feature of "Census Spring" was added to place names. It's approximate location was sourced from

Fig 4, p172 of the document :

Duralie Coal (2013) Duralie Coal Mine - Water Management Plan (Document No. WAMP-R02-D) Appendix 3 - Groundwater Management Plan . September 2013 Document No. GWMP-R02-C (00519574) . Fig4 pp13

http://www.gloucestercoal.com.au/documents/community_environment/duralie/Duralie_Coal_Mine_Water_Management_Plan.pdf

Dataset Citation

Bioregional Assessment Programme (2014) BA SYD selected GA TOPO 250K data plus added map features. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/ba5feac2-b35a-4611-82da-5b6213777069.

Dataset Ancestors

• Derived From GEODATA TOPO 250K Series 3

Geographical location of the languages selected

The U.S. Geological Survey Dakota Water Science Center, in cooperation with the Lower Brule Sioux Tribe, have been collecting data to document shoreline erosion along Lake Sharpe and within the Lower Brule Reservation. This data release includes data collected and compiled from four efforts: shorelines were digitized from existing available maps and aerial imagery to record shoreline locations over time, trail cameras were used to automatically collect photos of the shoreline over time and create time-lapse videos, bank location surveys were completed at intervals to record changes in the location of the top of bank over time, and an unmanned aerial system (drone) was used to collect imagery that was used to generate a basemap of the Lake Sharpe shoreline near Lower Brule, South Dakota. These datasets and their associated metadata are being made available to the public through this data release.

Use the Nearby template to guides your app users to places of interest close to an address. This template helps users find focused types of locations (such as schools) within a search distance of an address, their current location, or other place they specify. They can adjust distance values to change the search radius and get directions to locations they select. For users who are searching, you can set a range for the distance slider so users can define their search buffer or pan the map to see results from the map view. Include directions to help users navigate to locations within a defined search radius. Include the export tool to allow users to capture images of the map along with results from the search. Examples: Create a store locator app that allows customers to input a location, find a nearby store, and navigate to it. Create an app for finding health care facilities within a specified distance of a searched address. Provide users with directions and information for election polling locations. Build an app where users can find nearby trails and view an elevation profile of each result. Data requirements The Nearby template requires a feature layer to take full advantage of its capabilities. Key app capabilities Distance slider - Set a minimum and maximum search radius for finding results. Map extent result - Show all the results in the map view. Panel options - Customize result panel location information with feature attributes from a configured pop-up. Results-focused layout - Keep the map out of the app to maintain focus on the search and results. Attribute filter - Configure map filter options that are available to app users. Export - Print or export the search results or selected features as a .pdf, .jpg, or .png file that includes the pop-up content of returned features and an option to include the map. Alternatively, download the search results as a .csv file. Directions - Provide directions from a searched location to a result location. Elevation profile - Generate an elevation profile graph across an input line feature that can be selected in the scene or from drawing a single or multisegment line using the tool. Language switcher - Provide translations for custom text and create a multilingual app. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

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

Xverum’s Location Data is a highly structured dataset of 230M+ verified locations, covering businesses, landmarks, and points of interest (POI) across 5000 industry categories. With accurate geographic coordinates, business metadata, and mapping attributes, our dataset is optimized for GIS applications, real estate analysis, market research, and urban planning.

With continuous discovery of new locations and regular updates, Xverum ensures that your location intelligence solutions have the most current data on business openings, closures, and POI movements. Delivered in bulk via S3 Bucket or cloud storage, our dataset integrates seamlessly into mapping, navigation, and geographic analysis platforms.

🔥 Key Features:

Comprehensive Location Coverage: ✅ 230M+ locations worldwide, spanning 5000 business categories. ✅ Includes retail stores, corporate offices, landmarks, service providers & more.

Geographic & Mapping Data: ✅ Latitude & longitude coordinates for precise location tracking. ✅ Country, state, city, and postal code classifications. ✅ Business status tracking – Open, temporarily closed, permanently closed.

Continuous Discovery & Regular Updates: ✅ New locations added frequently to ensure fresh data. ✅ Updated business metadata, reflecting new openings, closures & status changes.

Detailed Business & Address Metadata: ✅ Company name, category, & subcategories for industry segmentation. ✅ Business contact details, including phone number & website (if available). ✅ Operating hours for businesses with scheduling data.

Optimized for Mapping & Location Intelligence: ✅ Supports GIS, real estate analysis & smart city planning. ✅ Enhances navigation & mapping solutions with structured geographic data. ✅ Helps businesses optimize site selection & expansion strategies.

Bulk Data Delivery (NO API): ✅ Delivered via S3 Bucket or cloud storage for full dataset access. ✅ Available in a structured format (.json) for easy integration.

🏆 Primary Use Cases:

Location Intelligence & Mapping: 🔹 Power GIS platforms & digital maps with structured geographic data. 🔹 Integrate accurate location insights into real estate, logistics & market analysis.

Retail Expansion & Business Planning: 🔹 Identify high-traffic locations & competitors for strategic site selection. 🔹 Analyze brand distribution & presence across different industries & regions.

Market Research & Competitive Analysis: 🔹 Track openings, closures & business density to assess industry trends. 🔹 Benchmark competitors based on location data & geographic presence.

Smart City & Infrastructure Planning: 🔹 Optimize city development projects with accurate POI & business location data. 🔹 Support public & commercial zoning strategies with real-world business insights.

💡 Why Choose Xverum’s Location Data? - 230M+ Verified Locations – One of the largest & most structured location datasets available. - Global Coverage – Spanning 249+ countries, with diverse business & industry data. - Regular Updates – Continuous discovery & refresh cycles ensure data accuracy. - Comprehensive Geographic & Business Metadata – Coordinates, addresses, industry categories & more. - Bulk Dataset Delivery (NO API) – Seamless access via S3 Bucket or cloud storage. - 100% Compliant – Ethically sourced & legally compliant.

Access Xverum’s 230M+ Location Data for mapping, geographic analysis & business intelligence. Request a free sample or contact us to customize your dataset today!

Carbon-14 data in this file were compiled by W.F. Ruddiman and

 staff at the Lamont-Doherty Earth Observatory of Columbia University. Data include

 974 carbon-14 dates from 309 ocean sediment cores, covering the period from 40,000

 years bp to the present worldwide. Estimated error range is given. Also included are

 core identifier, latitude/longitude, water depth, depth in core, institutional

 source, laboratory number, and sediment fraction analyzed (total, bulk, or fine

 fraction). The LDEO C14 data were originally submitted to NOAA's National Geophysical

 Data Center (NGDC) for archive, but were subsequently transferred to NOAA's National

 Climatic Data Center (NCDC) Paleoclimatology Group for stewardship. The data are available for direct download from NCDC's Web

 server.

Across most of the countries and regions analyzed, slow city chargers were the most common charging points. Norway and Italy were the notable exceptions, as the share of slow chargers in locations besides cities, urban areas, and highways was greater than the share of slow chargers in cities for these two countries.

Companies in Hong Kong with Parent Companies Located outside Hong Kong - Table 325-43011A : Number of regional headquarters by selected location of parent company

This statistic displays the types of destinations tourists from four leading travel markets would visit when on holiday in England according to a survey conducted in 2017. London was the most popular destination for all the markets, although ** percent of German tourists would be interesting in visiting coastal or beach locations when on holiday in England.

This dataset (packaged as the zip file history_share.zip) accompanies the article titled "Location- and feature-based selection histories make independent, qualitatively distinct contributions to urgent visuomotor performance" by EE Oor, E Salinas, and TR Stanford which is available as a preprint in bioRxiv. The experimental results in the article are based on behavioral data collected from 2 monkey subjects during performance of a visuomotor task (the compelled oddball task), as described in the text. This dataset contains the trial-by-trial behavioral results collected for each subject and upon which all subsequent analyses were based.

In addition to the trial-wise data arrays (stored in the files dataC.csv, dataN.csv, and dataCN.csv), the package includes Matlab functions and scripts (*.m files) used to analyze the data and recreate the results and figures in the article. Instructions and specifics are detailed in the README file.

This statistic shows the public wireless internet usage in selected locations according to internet users worldwide as of June 2015, by device. During the survey period, 20 percent of internet users reported to accessing the public Wi-Fi in shops or shopping centers on their smartphone.

This data file lists approximate locations of Amazon Web Services (AWS) data centers around the world. Some of this was collected manually by searching local news articles on real estate purchases by Amazon in each region, and other information was obtained from https://www.datacenterdynamics.com/. Note that in most regions AWS has multiple data centers, and so the selected location may only reflect one of them in that region.

This data is helpful for AWS users to quickly view where their assets are housed across the world and help them ensure that they are meeting information privacy guidelines.