This dataset was originally created in 2012 by the Office of the Chief Technology Officer. OCTO staff used the Alcoholic Beverage and Cannabis Administration’s (ABCA) definition of Full-Service Grocery Stores which outlines criteria for a business to obtain licenses to sell beer, wine, and spirits. Visit abca.dc.gov for full definition.OCTO staff then reviewed the Office of Planning DC Food Policy’s 2018 Food System Assessment listing grocery stores in Appendix D, and comparing these to the ABCA definition. This led to additional locations that meet, or come very close to, the full-service grocery store criteria. The criteria in section one of ABCA’s full-service grocery store determined the initial locations included in this dataset. View the full assessment at dcfoodpolicycouncil.org.Since the initial creation of this dataset, OCTO and the Deputy Mayor for Planning and Economic Development (DMPED) staff confirm grocery store operations by comparing datasets from DLCP, media outlets, commercially licensed datasets, and onsite visits.Please review supplemental metadata for more details.

GapMaps curates up-to-date and high-quality GIS Data tracking store openings and closures for leading retail brands across Asia and MENA. Get the insights you need to make more accurate and informed business decisions.

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 coverage depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

Data Driven Detroit created the data by selecting locations from NETS and ESRI business data with proper NAICS codes, then adding and deleting though local knowledge and confirmation with Google Streetview. These locations are Grocery stores which primarily sell food and don't include convenience stores. Visual confirmation cues included the existence of the word "grocery" in the name, or the presence of shopping carts.

This item contains stores layer from OSM (OpenStreetMap) in India.OSM is a collaborative, open project to create a freely available and editable map of the world. Geographic information about streets, rivers, borders, points of interest and areas are collected worldwide and stored in a freely accessible database. Everyone can participate and contribute to OSM. The geographic information available on OSM relies entirely on volunteers or contributors.The attributes are given below:NameTypeCodeOSM IDThese map layers are offered by Esri India Content. If you have any questions or comments, please let us know via content@esri.in.

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

This specialized location dataset delivers detailed information about marina establishments. Maritime industry professionals, coastal planners, and tourism researchers can leverage precise location insights to understand maritime infrastructure, analyze recreational boating landscapes, and develop targeted strategies.

How Do We Create Polygons?

-All our polygons are manually crafted using advanced GIS tools like QGIS, ArcGIS, and similar applications. This involves leveraging aerial imagery, satellite data, and street-level views to ensure precision. -Beyond visual data, our expert GIS data engineers integrate venue layout/elevation plans sourced from official company websites to construct highly detailed polygons. This meticulous process ensures maximum accuracy and consistency. -We verify our polygons through multiple quality assurance checks, focusing on accuracy, relevance, and completeness.

What's More?

-Custom Polygon Creation: Our team can build polygons for any location or category based on your requirements. Whether it’s a new retail chain, transportation hub, or niche point of interest, we’ve got you covered. -Enhanced Customization: In addition to polygons, we capture critical details such as entry and exit points, parking areas, and adjacent pathways, adding greater context to your geospatial data. -Flexible Data Delivery Formats: We provide datasets in industry-standard GIS formats like WKT, GeoJSON, Shapefile, and GDB, making them compatible with various systems and tools. -Regular Data Updates: Stay ahead with our customizable refresh schedules, ensuring your polygon data is always up-to-date for evolving business needs.

Unlock the Power of POI and Geospatial Data

With our robust polygon datasets and point-of-interest data, you can: -Perform detailed market and location analyses to identify growth opportunities. -Pinpoint the ideal locations for your next store or business expansion. -Decode consumer behavior patterns using geospatial insights. -Execute location-based marketing campaigns for better ROI. -Gain an edge over competitors by leveraging geofencing and spatial intelligence.

Why Choose LocationsXYZ?

LocationsXYZ is trusted by leading brands to unlock actionable business insights with our accurate and comprehensive spatial data solutions. Join our growing network of successful clients who have scaled their operations with precise polygon and POI datasets. Request your free sample today and explore how we can help accelerate your business growth.

Point geometry with attributes displaying quick stop type businesses in East Baton Rouge Parish, Louisiana.Metadata

As Esri’s commercial partner for parcel data, Regrid invites you to enjoy this free tile layer of parcel boundaries covering 100% of the United States. Complete parcel attributes are also available from an integrated Data Store."I think it’s fantastic that this layer exists. It's really helpful for my staff to see parcel boundaries in a quick and accessible layer."- Kate Berg, Geographic Information Systems (GIS) Manager | Department of Environment, Great Lakes, and EnergyVisit the Regrid Data Store for the ArcGIS User CommunityHassle-Free Parcel Data for Esri UsersWhen you click a parcel in the tile layer, you will see its address, size, and parcel ID number, along with a convenient link to purchase additional parcel attributes in The Regrid Data Store for the ArcGIS User Community. Once in the Data Store, you can purchase and download parcel files with attributes by the county and state for use in ArcGIS, as well as our add-on datasets like standardized zoning, matched building footprints, and matched secondary addresses.See regrid.com/esri for all of Regrid’s parcel products for the Esri ecosystem, including Feature Service delivery for ongoing parcel updates at scale.Key Features of Regrid's Parcel DataSourced & Standardized: Data combines authoritative public sources & third-party enrichments, aggregated, standardized, and matched by the Regrid team.158+ Million Parcel Records: Covering all 3,200+ US counties and territories.143+ Standardized Data Fields: Including geometry, ownership, buildings, secondary addresses, land use, and zoning.Universal Parcel ID & Placekey Location Identifier: Ensuring precise identification and integration.Detailed Attributes: Tax assessments, building counts, square footage, stacked parcels (condos), right-of-way, vacancy indicators and USPS deliverability. Comprehensive Coverage: 100% land parcel coverage across the US.Parcel Data Resources & DocumentationRegrid Data Dictionary / Parcel Data SchemaRegrid Coverage ReportParcel Data FAQsThank you to all the GIS professionals, state, county and federal officials, assessors, recorders, and public officials across the country who maintain the nation's parcel data and infrastructure.

Dataset Description: Washington Grocery Store Locations

This dataset contains detailed information about the locations and operational status of grocery stores in Washington, spanning multiple years. It includes both spatial and temporal data, offering a comprehensive view of how grocery stores are distributed and have evolved over time. Below is a breakdown of the columns included in the dataset:

1. X, Y: Geographic coordinates (latitude and longitude) representing the store's location in the dataset.

2. STORENAME: The name of the grocery store.

3. ADDRESS: The physical address of the grocery store.

4. ZIPCODE: The ZIP code of the store’s location.

5. PHONE: The contact phone number for the store.

6. WARD: The local government ward in which the store is located.

7. SSL: A unique identifier or code related to the store, possibly referring to specific data collection attributes.

8. NOTES: Additional comments or information about the store.

9. PRESENT: Temporal indicators showing the presence (likely open or closed) of each store across various years. These columns provide insights into the longevity and temporal trends of grocery store operations.

10. GIS_ID: A unique identifier for geographic information system (GIS) data.

11. XCOORD, YCOORD: Coordinates (likely more specific) used for spatial data analysis, providing the exact location of the store.

12. MAR_ID: A unique identifier for marketing or regional analysis purposes.

13. GLOBALID: A global unique identifier for the store data.

14. CREATOR: The individual or system that created the data entry.

15. CREATED: Timestamp showing when the data entry was created.

16. EDITOR: The individual or system that edited the data entry.

17. EDITED: Timestamp showing when the data entry was last edited.

18. SE_ANNO_CAD_DATA: Specific annotation or data related to CAD (computer-aided design), possibly linked to store location details.

19. OBJECTID: A unique identifier for the object or record within the dataset.

Insights We Can Extract:

• Geographic Distribution: By analyzing the X and Y coordinates along with ZIP codes and wards, we can identify where grocery stores are concentrated and map areas with high or low store density.
• Temporal Trends: The data in the "PRESENT" columns helps us track the opening and closure patterns of grocery stores over time, providing insights into market trends and store longevity.
• Service Gaps: We can identify areas with no grocery stores, possibly indicating food deserts or underserved communities, by mapping the stores and comparing coverage across ZIP codes and wards.
• Operational Trends: By analyzing the temporal data and comparing store turnover, we can uncover patterns in the longevity or turnover of grocery stores.
• Urban Planning and Accessibility: This dataset could help us assess whether the location of grocery stores aligns with urban infrastructure like transportation routes or population density, which could inform policy decisions to improve grocery access.

This dataset is invaluable for urban planners, policymakers, and business stakeholders looking to improve food access and urban infrastructure.

GIS Data Management Market Outlook

The global GIS Data Management market size is projected to grow from USD 12.5 billion in 2023 to USD 25.6 billion by 2032, exhibiting a CAGR of 8.4% during the forecast period. This impressive growth is driven by the increasing adoption of geographic information systems (GIS) across various sectors such as urban planning, disaster management, and agriculture. The rising need for effective data management systems to handle the vast amounts of spatial data generated daily also significantly contributes to the market's expansion.

One of the primary growth factors for the GIS Data Management market is the burgeoning demand for spatial data analytics. Businesses and governments are increasingly leveraging GIS data to make informed decisions and strategize operational efficiencies. With the rapid urbanization and industrialization worldwide, there's an unprecedented need to manage and analyze geographic data to plan infrastructure, monitor environmental changes, and optimize resource allocation. Consequently, the integration of GIS with advanced technologies like artificial intelligence and machine learning is becoming more prominent, further fueling market growth.

Another significant factor propelling the market is the advancement in GIS technology itself. The development of sophisticated software and hardware solutions for GIS data management is making it easier for organizations to capture, store, analyze, and visualize geographic data. Innovations such as 3D GIS, real-time data processing, and cloud-based GIS solutions are transforming the landscape of geographic data management. These advancements are not only enhancing the capabilities of GIS systems but also making them more accessible to a broader range of users, from small enterprises to large governmental agencies.

The growing implementation of GIS in disaster management and emergency response activities is also a critical factor driving market growth. GIS systems play a crucial role in disaster preparedness, response, and recovery by providing accurate and timely geographic data. This data helps in assessing risks, coordinating response activities, and planning resource deployment. With the increasing frequency and intensity of natural disasters, the reliance on GIS data management systems is expected to grow, resulting in higher demand for GIS solutions across the globe.

Geospatial Solutions are becoming increasingly integral to the GIS Data Management landscape, offering enhanced capabilities for spatial data analysis and visualization. These solutions provide a comprehensive framework for integrating various data sources, enabling users to gain deeper insights into geographic patterns and trends. As organizations strive to optimize their operations and decision-making processes, the demand for robust geospatial solutions is on the rise. These solutions not only facilitate the efficient management of spatial data but also support advanced analytics and real-time data processing. By leveraging geospatial solutions, businesses and governments can improve their strategic planning, resource allocation, and environmental monitoring efforts, thereby driving the overall growth of the GIS Data Management market.

Regionally, North America holds a significant share of the GIS Data Management market, driven by high technology adoption rates and substantial investments in GIS technologies by government and private sectors. However, Asia Pacific is anticipated to witness the highest growth rate during the forecast period. The rapid urbanization, economic development, and increasing adoption of advanced technologies in countries like China and India are major contributors to this growth. Governments in this region are also focusing on smart city projects and infrastructure development, which further boosts the demand for GIS data management solutions.

Component Analysis

The GIS Data Management market is segmented by component into software, hardware, and services. The software segment is the largest and fastest-growing segment, driven by the continuous advancements in GIS software capabilities. GIS software applications enable users to analyze spatial data, create maps, and manage geographic information efficiently. The integration of GIS software with other enterprise systems and the development of user-friendly interfaces are key factors propelling the growth of this segment. Furthermore, the rise of mobile GIS applications, which allow field data collectio

The ZIP file consist of GIS files and an Access database with information about the excavations, findings and other metadata about the archaeological survey.

The Spatiotemporal Big Data Store Tutorial introduces you the the capabilities of the spatiotemporal big data store in ArcGIS Data Store, available with ArcGIS Enterprise. Observation data can be moving objects, changing attributes of stationary sensors, or both. The spatiotemporal big data store enables archival of high volume observation data, sustains high velocity write throughput, and can run across multiple machines (nodes). Adding additional machines adds capacity, enabling you to store more data, implement longer retention policies of your data, and support higher data write throughput.

After completing this tutorial you will:

Understand the concepts and best practices for working with the spatiotemporal big data store available with ArcGIS Data Store. Have configured the appropriate security settings and certificates on a enterprise server, real-time server, and a data server which are necessary for working with the spatiotemporal big data store. Have learned how to process and archive large amounts of observational data in the spatiotemporal big data store. Have learned how to visualize the observational data that is stored in the spatiotemporal big data store.

Releases

Each release contains a tutorial compatible with the version of GeoEvent Server listed. The release of the component you deploy does not have to match your version of ArcGIS GeoEvent Server, so long as the release of the component is compatible with the version of GeoEvent Server you are using. For example, if the release contains a tutorial for version 10.6; this tutorial is compatible with ArcGIS GeoEvent Server 10.6 and later. Each release contains a Release History document with a compatibility table that illustrates which versions of ArcGIS GeoEvent Server the component is compatible with.

NOTE: The release strategy for ArcGIS GeoEvent Server components delivered in the ArcGIS GeoEvent Server Gallery has been updated. Going forward, a new release will only be created when

  a component has an issue,
  is being enhanced with new capabilities,
  or is not compatible with newer versions of ArcGIS GeoEvent Server.

This strategy makes upgrades of these custom
components easier since you will not have to
upgrade them for every version of ArcGIS GeoEvent Server
unless there is a new release of
the component. The documentation for the
latest release has been
updated and includes instructions for updating
your configuration to align with this strategy.

Latest

Release 4 - February 2, 2017 - Compatible with ArcGIS GeoEvent Server 10.5 and later.

Previous

Release 3 - July 7, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Release 2 - May 17, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Release 1 - March 18, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Xtract.io’s Pharmacy & Drug Store Location Data provides a complete geospatial view of pharmaceutical retail across the United States and Canada. This dataset includes handcrafted polygons and geocoded coordinates for each pharmacy location, making it a powerful resource for healthcare planners, market researchers, and retail strategists.

Organizations can leverage this dataset to:

• Conduct healthcare accessibility mapping and identify underserved areas.

• Evaluate market penetration and retail coverage across regions.

• Analyze the competitive landscape in pharmaceutical retail.

• Support site selection and expansion strategies.

How We Build Pharmacy Polygons

• Manually crafted polygons created using GIS tools like QGIS and ArcGIS, with aerial and street-level imagery.

• Integration of venue layouts and elevation plans from official sources for enhanced accuracy.

• Rigorous multi-stage quality checks ensure accuracy, completeness, and relevance.

What Else We Offer

• Custom polygon creation for any retail chain, healthcare facility, or point of interest.

• Enhanced metadata including entry/exit points, parking areas, and surrounding context.

• Flexible formats: WKT, GeoJSON, Shapefile, and GDB for smooth system integration.

• Regular updates tailored to client needs (30, 60, 90 days).

• Unlock the Power of Healthcare Geospatial Data

With detailed pharmacy polygon data and POI datasets, businesses can:

• Map healthcare service coverage and accessibility.

• Identify growth opportunities in underserved communities.

• Decode consumer behavior in the pharmaceutical retail space.

• Strengthen location-driven strategies with spatial intelligence.

Why Choose LocationsXYZ?

LocationsXYZ is trusted by enterprises worldwide to deliver 95% accurate, handcrafted POI and polygon data. With our pharma dataset, you gain actionable insights to support healthcare planning, retail expansion, and competitive benchmarking.

The gSSURGO dataset provides detailed soil survey mapping in raster format with ready-to-map attributes organized in statewide tiles for desktop GIS. gSSURGO is derived from the official Soil Survey Geographic (SSURGO) Database. SSURGO generally has the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes and are derived from properties and characteristics stored in the National Soil Information System (NASIS).

The gSSURGO data were prepared by merging the traditional vector-based SSURGO digital map data and tabular data into statewide extents, adding a statewide gridded map layer derived from the vector layer, and adding a new value-added look up table (valu) containing ready-to-map attributes. The gridded map layer is in an ArcGIS file geodatabase in raster format, thus it has the capacity to store significantly more data and greater spatial extents than the traditional SSURGO product. The raster map data have a 10-meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link the raster cells and polygons to attribute tables.

For more information, see the gSSURGO webpage: https://www.nrcs.usda.gov/resources/data-and-reports/description-of-gridded-soil-survey-geographic-gssurgo-database

GapMaps GIS Data by Azira 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 GIS 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?

Mobile Location data provides a range of benefits that make it a valuable GIS Data source for 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 Mobile location 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.

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.

GeoTIFF raster data with worldwide wind speed and wind power density potential. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). This link provides access to the following layers: (1) Wind speed (WS): at 3 heights (50m, 100m, and 200m) , stored as separate bands in the raster file (2) Power Density (PD): at 3 heights (50m, 100m, and 200m) , stored as separate bands in the raster file. (3) Elevation (ELEV): at ground level (4) Air Density (RHO): at ground level (5) Ruggedness Index (RIX): at ground level All layers have 250m resolution.

Publishing your data and maps from desktop GIS to ArcGIS Online is essential to unlock modern GIS capabilities like collaboration or sharing your projects using interactive data-driven applications. The key to unlock this connected GIS is ArcGIS Identity.With an ArcGIS Identity you are unlocking a connected GIS. You can share your maps or selected map layers as a web layer. Web layers are stored in your organization's ArcGIS Online as one of the 7 different layer types of hosted layers. Depending on the layer type, the hosted layer will be shared with different capabilities.

This digital dataset was created as part of a U.S. Geological Survey study, done in cooperation with the Monterey County Water Resource Agency, to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Salinas Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Salinas Valley Geological Framework (SVGF), that define the elevation, thickness, extent, and lithology-based texture variations of nine hydrogeologic units in Salinas Valley, CA. The digital dataset includes a geospatial database that contains two main elements as GIS feature datasets: (1) input data to the 3D framework and textural models, within a feature dataset called “ModelInput”; and (2) interpolated elevation, thicknesses, and textural variability of the hydrogeologic units stored as arrays of polygonal cells, within a feature dataset called “ModelGrids”. The model input data in this data release include stratigraphic and lithologic information from water, monitoring, and oil and gas wells, as well as data from selected published cross sections, point data derived from geologic maps and geophysical data, and data sampled from parts of previous framework models. Input surface and subsurface data have been reduced to points that define the elevation of the top of each hydrogeologic units at x,y locations; these point data, stored in a GIS feature class named “ModelInputData”, serve as digital input to the framework models. The location of wells used a sources of subsurface stratigraphic and lithologic information are stored within the GIS feature class “ModelInputData”, but are also provided as separate point feature classes in the geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also released as a set of tables, each of which may be joined or related to well location through a unique well identifier present in each table. Tables are in Excel and ascii comma-separated value (CSV) format and include separate but related tables for well location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole, downhole lithologic information reported at 10-foot intervals, and information on how lithologic descriptors were classed as sediment texture. Two types of geologic frameworks were constructed and released within a GIS feature dataset called “ModelGrids”: a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the nine hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on interpolation of classed downhole lithologic data. Each framework is stored as an array of polygonal cells: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a single polygon feature class SVGF_3DHFM, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each hydrogeologic unit. Textural information for each hydrogeologic unit are stored in a second array of polygonal cells called SVGF_TextureModel. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units that describes the nine hydrogeologic units modeled in this study. A data dictionary defines the structure of the dataset, defines all fields in all spatial data attributer tables and all columns in all nonspatial tables, and duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles. Downhole data from boreholes are released as a set of tables related by a unique well identifier, tables are in Excel and ascii comma-separated value (CSV) format.