This feature class contains road data derived from applying Infra data to a national forest's road GIS data. Infrastructure (Infra) is a collection of applications which house information related to an assets managed by the Forest Service (including but not limited to, Roads, Bridges, Buildings, Water Systems, Waste Water Systems, Dams, Trails, and Recreation Sites). The feature class contains records for all roads that are in each database and are correctly configured. This data would include only existing roads, ones that permit motorized use as well as those that do not. For roads that are legally open for motorized use, it identifies the authorized modes of travel and season of use. This data may not represent a forest's currently published Motor Vehicle Use Map (MVUM). This feature class is derived from the Infra table II_MVUM_ROAD_ALLOW. Access and Travel Management (ATM) data included is pulled from the Allowed Uses tab in the Infra ATM for Roads form. Since this feature class is a current snapshot of Infra data, it is different than the currently published MVUM data and thus is for internal use only, primarily for review of Infra data during development or update of MVUM. This feature class will not be published for public use.

This dataset contains information about Africa's Infrastructure National Data for 1990-2008.Data from The World Bank.Notes:The Africa Infrastructure Country Diagnostic (AICD) has data collection and analysis on the status of the main network infrastructures. The AICD database provides cross-country data on network infrastructure for nine major sectors: air transport, information and communication technologies, irrigation, ports, power, railways, roads, water and sanitation. The indicators are defined as to cover key areas for policy making: affordability, access, pricing as well as institutional, fiscal and financial aspects. The analysis encompasses public expenditure trends, future investment needs and sector performance reviews. It offers users the opportunity to view AICD results, download documents and materials, search databases and perform customized analysis.

A route feature stores the spatial locations (geography) of the road. These feature classes have an (M) value or measure on their vertices. A route system depicts all roads within or in close proximity to an administrative unit. A road is a motor vehicle travel way over 50 inches wide, unless classified and managed as a trail. This feature is only SPATIAL ROAD DATA, other data (open, closed, jurisdiction, maintenance level) is stored in INFRA. Used to link spatial roads to INFRA data, ROAD NO., BMP, EMP and Calibration. Routed roads are a single spatial line, all have data in INFRA and this data must be attached. Routed roads need to have INFRA data table attached by use of R10 Geospatial Interface (GI) tool and Visualization named Roads with Core Attributes RSW - This creates an output roads layer and adds the following fields from the INFRA database at NITC: name, lanes, service life, system, surface type, jurisdiction, objective maintenance level, operational maintenance level, route status, functional class and primary maintainer. Routed ROADS CAN HAVE OTHER DATA TABLES ATTACHED, (R10 Stream Data Point-RSW, Road Points -RSW, Bridges-RSW, MVUM Roads and Transportation Atlas. A road may be classified or unclassified. Classified roads are roads within the National Forest System lands planned and managed for motor vehicle access including State roads, county roads, private roads, permitted roads, and Forest Service roads. Unclassified roads are roads not intended to be a part of nor managed as a part of the forests transportation system, such as temporary roads, and unplanned, unengineered, unauthorized off-road vehicle tracks and abandoned travel ways. Route measurements and route directions must correspond to those stored in the INFRA Oracle table RTE_BASICS. Associated National Application: INFRA Travel Routes. IWeb Infra Roads webpage http://basenet.fs.fed.us/support/help/roads/. All routed roads are required to have data in INFRA and all roads having data in INFRA are required to be routed.

This dataset displays the approximate location of US Forest Service, Great American Outdoors Act (GAOA) projects. The data is refreshed on a nightly basis from the US Forest Service database of infrastructure projects which is stewarded by the individual National Forests and Grasslands. This dataset is a spatial data layer of points representing the approximate or general location where the project takes place. The point location is intended for use in small scale maps to indicate the general location of the projects across the country. The location data is maintained by staff on the individual National Forest or Grassland using the database of record. Because a project can be made up of many assets distributed across a land area, a single project location point will not always reflect the specific location and extent of the work in the project. The project detail data can be used to display the individual assets that make up the project. For more information about Forest Service GAOA projects visit our website: https://www.fs.usda.gov/managing-land/gaoaMetadata and DownloadsThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

Data here contain and describe an open-source structured query language (SQLite) portable database containing high resolution mass spectrometry data (MS1 and MS2) for per- and polyfluorinated alykl substances (PFAS) and associated metadata regarding their measurement techniques, quality assurance metrics, and the samples from which they were produced. These data are stored in a format adhering to the Database Infrastructure for Mass Spectrometry (DIMSpec) project. That project produces and uses databases like this one, providing a complete toolkit for non-targeted analysis. See more information about the full DIMSpec code base - as well as these data for demonstration purposes - at GitHub (https://github.com/usnistgov/dimspec) or view the full User Guide for DIMSpec (https://pages.nist.gov/dimspec/docs). Files of most interest contained here include the database file itself (dimspec_nist_pfas.sqlite) as well as an entity relationship diagram (ERD.png) and data dictionary (DIMSpec for PFAS_1.0.1.20230615_data_dictionary.json) to elucidate the database structure and assist in interpretation and use.

Designates boundaries to establish extent of livestock distribution and management within pastures. This is a published layer created by combining GIS data managed by each National Forest and attribute data stored in the Forest Service Infra database application. This dataset is designed for reporting and analysis and is not used to enter or edit data.

This data set includes 5-minute time series runoff and precipitation data of neighborhood catchments with a variety of stormwater control measures, and definition of individual precipitation-runoff events and associated runoff metrics. Also included are geospatial data that delineates the neighborhood catchments with their land use/land cover and stormwater infrastructure. This dataset is associated with the following publication: Woznicki, S., K. Hondula, and T. Jarnagin. Effectiveness of landscape‐based green infrastructure for stormwater management in suburban catchments. Hydrological Processes. John Wiley & Sons, Ltd., Indianapolis, IN, USA, 32(15): 2346-2361, (2018).

This submission offers a link to a web mapping application hosted instance of the Global Oil & Gas Features Database (GOGI), via EDX Spatial. This offers users with the ability to visualize, interact, and create maps with data of their choice, as well as download specific attributes or fields of view from the database. This data can also be downloaded as a File Geodatabse from EDX at https://edx.netl.doe.gov/dataset/global-oil-gas-features-database. Access the technical report describing how this database was produced using the following link: https://edx.netl.doe.gov/dataset/development-of-an-open-global-oil-and-gas-infrastructure-inventory-and-geodatabase” This data was developed using a combination of big data computing, custom search and data integration algorithms, and expert driven search to collect open oil and gas data resources worldwide. This approach identified over 380 data sets and integrated more than 4.8 million features into the GOGI database. Acknowledgements: This work was funded under the Climate and Clean Air Coalition (CCAC) Oil and Gas Methane Science Studies. The studies are managed by United Nations Environment in collaboration with the Office of the Chief Scientist, Steven Hamburg of the Environmental Defense Fund. Funding was provided by the Environmental Defense Fund, OGCI Companies (Shell, BP, ENI, Petrobras, Repsol, Total, Equinor, CNPC, Saudi Aramco, Exxon, Oxy, Chevron, Pemex) and CCAC.

The Oil and Gas Infrastructure Mapping (OGIM) database is a global, spatially explicit, and granular database of oil and gas infrastructure, developed by Environmental Defense Fund (EDF) (www.edf.org) and MethaneSAT, LLC (www.methanesat.org) – a wholly owned subsidiary of EDF. The OGIM database is developed to support the quantification and source characterization of oil and gas methane emissions. The database development is based on the acquisition, analysis, curation, integration, and quality-assurance, performed at EDF and MethaneSAT, LLC, of publicly available geospatial data sources of oil and gas facilities reported by official government sources, industry, academic, and other non-government entities.

OGIM is a collection of data tables within a GeoPackage, an open-source geospatial database format. Each data table within the GeoPackage includes locations and facility attributes of oil and gas infrastructure types that are important sources of methane emissions, including oil and gas production wells, offshore production platforms, natural gas compressor stations, oil and natural gas processing facilities, liquefied natural gas facilities, crude oil refineries, and pipelines. All location data have been transformed to a common spatial reference system (WGS 1984, EPSG:4326). The GeoPackage also includes a “Data Catalog” table which lists each primary data source utilized during OGIM database development. Each source in the Data Catalog is assigned a Source Reference ID (‘SRC_ID’) and each record in the OGIM database has a 'SRC_REF_ID' attribute that can be used to join the record to its original source(s).

OGIM v2.5.1 includes approximately 6.7 million features, including 4.5 million point locations of oil and gas wells and over 1.2 million kilometers of oil and gas pipelines. This work and the OGIM database, which we anticipate updating on a regular cadence, helps fill a crucial oil and gas geospatial data need, in support of the quantification and attribution of global oil and gas methane emissions at high resolution. Please see the PDF document in the ‘Files’ section of this page for a description of all attribute columns present within the OGIM database. Full details on database development and related analytics can be found in the following Earth System Science Data (ESSD) journal paper. Please cite the paper when using any version of the database:

Omara, M., Gautam, R., O'Brien, M., Himmelberger, A., Franco, A., Meisenhelder, K., Hauser, G., Lyon, D., Chulakadabba, A., Miller, C., Franklin, J., Wofsy, S., and Hamburg, S.: Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-15-3761-2023, 2023.

Important note: While the results section of the manuscript is specific to v1 of the OGIM, the methods described therein are the the same methods used to develop and update OGIM_v2.5.1. Additionally, while we describe our data sources in detail in the manuscript above, and include maps for all acquired datasets, this open-access version of the OGIM database does not include the locations of about 300 natural gas compressor stations in Russia. Future updates may include these datasets when appropriate permissions to make them publicly accessible are obtained.

OGIM_v2.5.1.gpkg. Key changes since v1.1:

• Significant updates have been made to the oil and natural gas wells layer. In most US states, v2.5.1 relies upon well location data reported by state agencies rather than the Dept. of Homeland Security’s Homeland Infrastructure Foundation-Level Data (HIFLD). Across all countries, about 50% of well records in v2.5.1 were published by their primary source 1-April-2024 or later.
  
  
• The earlier OGIM_v1.1 version included a layer of publicly sourced, spatially explicit oil and natural gas production volumes for the year 2022. This production layer is not present in v2.5.1 as we continue to develop and improve that data product.

OGIM v2.5.1 is based on public-domain datasets reported on or prior to April 2024. Each record in OGIM indicates a source date (SRC_DATE) when the original source of the data was last updated. Some records may have out-of-date information, for example, if facility status has changed since we last acquired the data. We are continuing to update the OGIM database as new public-domain datasets become available.

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Point of Contact at Environmental Defense Fund and MethaneSAT, LLC: Mark Omara (momara@edf.org) and Ritesh Gautam (rgautam@edf.org).

This dataset contains the detailed information about the individual asset linear features such as roads and trails that make up Great American Outdoors Act (GAOA) projects. This data can be used together with the project summary data to display general project locations. The data is refreshed on a nightly basis from the US Forest Service database of infrastructure projects which is stewarded by the individual National Forests and Grasslands. For more information about Forest Service GAOA projects visit our website: https://www.fs.usda.gov/managing-land/gaoaMetadata and DownloadsThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

Designates boundaries to establish extent of livestock distribution and management within the allotment. This is a published layer created by combining GIS data managed by each National Forest and attribute data stored in the Forest Service Infra database application. This dataset is designed for reporting and analysis and is not used to enter or edit data.

The Private Participation in Infrastructure (PPI) Project Database has data on over 6,400 infrastructure projects in 137 low- and middle-income countries. The database is the leading source of PPI trends in the developing world, covering projects in the energy, transport, water and sewerage, ICT backbone, and Municipal Solid Waste (MSW) sectors (MSW data includes projects since 2008) Projects include management or lease contracts, concessions, greenfield projects, and divestitures.

The Advanced Infrastructure Integrity Modeling (AIIM) Onshore Pipeline Database is an interoperable spatial resource containing critical environmental, operational, and reported stressors tied to publicly available oil and gas pipeline locations across the contiguous U.S. and Alaska. This database contains two layers: 1. Pipeline point locations (‘pipeline_points’) – More than 500,000 points (at every kilometer along pipelines, and end points) to which more than 350 stress-related variables have been appended. 2. Merged pipelines (‘merged_pipelines’) – The original, publicly available pipeline data (see table below) merged together into one feature class.

The global Data Center Infrastructure Management (DCIM) market size reached USD 2.08 Billion in 2022 and is expected to reach USD 6.01 Billion in 2032 registering a CAGR of 11.2%. Data Center Infrastructure Management market growth is primarily driven owing to rapid adoption of cloud computing along...

Big Data Infrastructure Market Outlook

The global Big Data Infrastructure market size was valued at approximately $98 billion in 2023 and is projected to grow to around $235 billion by 2032, exhibiting a compound annual growth rate (CAGR) of about 10.1% during the forecast period. This impressive growth can be attributed to the increasing demand for big data analytics across various sectors, which necessitates robust infrastructure capable of handling vast volumes of data effectively. The need for real-time data processing has also been a significant driver, as organizations seek to harness data to gain competitive advantages, improve operational efficiencies, and enhance customer experiences.

One of the primary growth factors driving the Big Data Infrastructure market is the exponential increase in data generation from digital sources. With the proliferation of connected devices, social media, and e-commerce, the volume of data generated daily is staggering. Organizations are realizing the value of this data in gaining insights and making informed decisions. Consequently, there is a growing demand for infrastructure solutions that can store, process, and analyze this data effectively. Additionally, developments in cloud computing have made big data technology more accessible and affordable, further fueling market growth. The ability to scale resources on-demand without significant upfront capital investment is particularly appealing to businesses.

Another critical factor contributing to the growth of the Big Data Infrastructure market is the advent of advanced technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT). These technologies require sophisticated data management solutions capable of handling complex and large-scale data sets. As industries across the spectrum from healthcare to manufacturing integrate these technologies into their operations, the demand for capable infrastructure is scaling correspondingly. Moreover, regulatory requirements around data management and security are prompting organizations to invest in reliable infrastructure solutions to ensure compliance and safeguard sensitive information.

The role of data analytics in shaping business strategies and operations has never been more pertinent, driving organizations to invest in Big Data Infrastructure. Businesses are keenly focusing on customer-centric approaches, understanding market trends, and innovating based on data-driven insights. The ability to predict trends, consumer behavior, and potential challenges offers a significant strategic advantage, further pushing the demand for robust data infrastructure. Additionally, strategic partnerships between technology providers and enterprises are fostering an ecosystem conducive to big data initiatives.

From a regional perspective, North America currently holds the largest share in the Big Data Infrastructure market, driven by the early adoption of advanced technologies and the presence of major technology companies. The region's strong digital economy and a high degree of IT infrastructure sophistication are further bolstering its market position. Europe is expected to follow suit, with significant investments in data infrastructure to meet regulatory standards and drive digital transformation. The Asia Pacific region, however, is anticipated to witness the highest growth rate, attributed to rapid digitalization, the proliferation of IoT devices, and increasing awareness of the benefits of big data analytics among businesses. Other regions like Latin America and the Middle East & Africa are also poised for growth, albeit at a relatively moderate pace, as they continue to embrace digital technologies.

Component Analysis

In the realm of Big Data Infrastructure, the component segment is categorized into hardware, software, and services. The hardware segment consists of the physical pieces needed to store and process big data, such as servers, storage devices, and networking equipment. This segment is crucial because the efficiency of data processing depends significantly on the capabilities of these physical components. With the rise in data volumes, there’s an increased demand for scalable and high-performance hardware solutions. Organizations are investing heavily in upgrading their existing hardware to ensure they can handle the data influx effectively. Furthermore, the development of advanced processors and storage systems is enabling faster data processing and retrieval, which is critical for real-time analytics.

The software segment of Big Data Infrastructure encompasses analytics soft

Designates boundaries to establish extent of distribution and management of Wild Horse and Burro territories. This is a published layer created by combining GIS data managed by each National Forest and attribute data stored in the Forest Service Infra database application. This dataset is designed for reporting and analysis and is not used to enter or edit data.

"The Mekong Infrastructure Tracker database builds on existing data to present a comprehensive source of information on energy, transportation, and water infrastructure in the Mekong countries. The database relies primarily on open sources of data, including government agencies, news and media, non-governmental organizations, companies involved in infrastructure development, as well as multinational institutions and other research institutions which share data sets. Most are publicly available on the Internet." Suggested citation: Stimson Mekong Infrastructure Tracker, supported by USAID and The Asia Foundation, [your date of access], https://www.stimson.org/2020/mekong-infrastructure-tracker-tool/

Abandoned railroads and infrastructure from the anthracite coal mining industry are significant features in abandoned mine lands and are an important part of history; however, these features are often lost and masked by the passage of time and the regrowth of forests. The application of modern light detection and ranging (lidar) topographic analysis, combined with ground-truthing "boots on the ground" mapping, enable recovery of the location of these historical features. Waste rock piles and abandoned mine lands from historical mining locally appear as distinct features on the landscape depicted on the percent slope map. Abandoned, and in many places demolished, infrastructure such as breakers, turntables, rail beds, water tanks, tram piers, and bridge abutments, to name a few, were confirmed in the field and located with a global positioning system (GPS) receiver. This map captures the locations of many of the abandoned features from the coal mining industry near Forest City, Pen ...

Explore Market Research Intellect's Big Data Infrastructure Market Report, valued at USD 32.7 billion in 2024, with a projected market growth to USD 69.0 billion by 2033, and a CAGR of 9.0% from 2026 to 2033.

The data set comprises the infrastructure of transport including roads (in KM), number of motor vehicles per 1000 population and railways in countries around the world.