As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of 1244.08; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

As of June 2024, the most popular relational database management system (RDBMS) worldwide was Oracle, with a ranking score of 1244.08. Oracle was also the most popular DBMS overall. MySQL and Microsoft SQL server rounded out the top three.

As of June 2024, the most popular commercial database management system (DBMS) in the world was Oracle, with a ranking score of 1244. MySQL was the most popular open source DBMS at that time, with a ranking score of 1061.

The statistic displays the most popular SQL databases used by software developers worldwide, as of April 2015. According to the survey, 64 percent of software developers were using MySQL, an open-source relational database management system (RDBMS).

The global embedded database management system (eDBMS) market is experiencing robust growth, driven by the increasing demand for real-time data processing in diverse sectors. The proliferation of IoT devices, coupled with the need for efficient data management in resource-constrained environments, fuels this expansion. Applications span critical sectors like healthcare (patient monitoring systems), manufacturing (industrial automation), and the automotive industry (connected car technologies). The market is segmented by operating system (Linux, macOS/iOS, Windows) and industry vertical (retail, healthcare, defense, oil and gas, manufacturing). While Linux dominates due to its open-source nature and suitability for embedded systems, Windows and macOS/iOS maintain significant presence depending on the target application. Growth is further propelled by advancements in cloud connectivity and the increasing adoption of edge computing, which requires efficient local data handling. Major players like Microsoft, IBM, Oracle, and others are actively developing and optimizing eDBMS solutions, leading to heightened competition and innovation. However, market restraints include challenges in data security and integration with legacy systems. The need for robust security measures to protect sensitive data in embedded devices is a key concern. Furthermore, integrating eDBMS with existing infrastructure in various industries requires significant investments and expertise. Despite these challenges, the long-term forecast points towards continued market expansion, especially with the increasing adoption of AI and machine learning at the edge. The market is expected to show a healthy Compound Annual Growth Rate (CAGR) throughout the forecast period (2025-2033), resulting in substantial market size expansion. We can reasonably estimate the 2025 market size at approximately $5 billion, based on typical growth rates observed in similar technology sectors, with a potential CAGR of 10% annually. This growth will be largely influenced by regional variations; North America and Europe will likely maintain substantial market shares while the Asia-Pacific region experiences significant growth due to industrialization and digital transformation.

The Airport Database and Resource Management (ADRM) market is experiencing robust growth, driven by the increasing need for efficient airport operations and enhanced passenger experience. The global market, estimated at $2 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 10% from 2025 to 2033, reaching approximately $5 billion by 2033. This expansion is fueled by several key factors. Firstly, the surge in air travel globally necessitates sophisticated systems to manage resources effectively, reducing delays and optimizing workflows. Secondly, the adoption of advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML) within ADRM solutions is improving data analysis and predictive capabilities, leading to better resource allocation and improved decision-making. Furthermore, rising security concerns are driving demand for robust database management systems that can efficiently handle vast amounts of passenger and operational data, while ensuring compliance with stringent regulations. The increasing focus on sustainability within airports also contributes to market growth, as ADRM solutions facilitate optimized energy consumption and reduced waste. Segmentation within the ADRM market reveals strong growth in both software and service components. Software solutions, offering data analytics and predictive modeling capabilities, are experiencing higher demand due to their ability to provide actionable insights. Civil airports represent a larger market segment compared to military airports, primarily due to the higher volume of passenger traffic and operational complexities. Key players like SITA, Rockwell Collins, Amadeus, Sabre, and Thales are strategically investing in research and development to enhance their product offerings, fostering competition and driving innovation within the market. Regional analysis indicates North America and Europe are currently the dominant markets, driven by technological advancements and high adoption rates. However, rapid infrastructure development in Asia-Pacific and the Middle East & Africa is expected to create lucrative growth opportunities in these regions over the forecast period. Challenges remain, including the high initial investment costs associated with implementing ADRM systems and the need for skilled personnel to operate and maintain these complex technologies.

Content Diverse data about modeling of a cyclodextrin-ferrociphenol supramolecular assemblage. Starting from the best models calculated with different simple CD, 21 oxygen atoms were modified (called "mutations") giving 21 derived models). The best one was selected to do the same, creating tree of modellings/mutations. For models with one CD, 10 series were created and for models with 2 CD 4 series. The related published article (Int. J. Mol. Sci. 2023, 24, 15, 12288. (open access) https://doi.org/10.3390/ijms241512288) shows that uncommon hydrogen bonds (simple or clamp) can be formed between the iron atom of ferrocene of ferrociphenol and one or two OH of the cyclodextrin (see the graphical abstract of the article in this dataset, file GA.jpeg). To the best of our knowledge, this is the first time that it has been described by modelling in supramolecular systems involving ferrocene and a cyclodextrin. The ferrociphenol (somethimes called ferrocifen) is SuccFerr (also known as P722) and have shown very good IC50 on certain cancer cells, for example 0.035 µM on Triple Negative Breast Cancer (MDA-MB-231) and on Ovarian Cancer A2780. On Ovarian Cancer A2780-Cis (resistant to Cis-platin) the IC50 is 0.049 µM: J. Med. Chem. 2017, 60, 8358-8368 All ferrociphenols seem to act by oxidation into the cells, forming very reactive species called quinone methides: Angew. Chem. Int. Ed. 2009, 48, 9124-9126. These quinone methides can form adducts with some proteins, on their cystein or selenocystein amino acids: Angew. Chem. Int. Ed. 2016, 55, 10431-10434 (free access on Hal). SuccFerr has a particularity: Its quinone methide is stabilized by a lone pair - π interaction that seems to permit it to reach its target into the cell: Angew. Chem. Int. Ed. 2019, 58, 8421-8425 (free access on Hal) However, SuccFerr is lipophilic and has a very low solubility in water, so a formulation is needed, using: lipid nanocapsules (LNCs): Int. J. Pharm. 2022, 626, 122164 (free access on Hal), or Langmuir 2023, 39, 5, 1885-1896 (free access on Hal) Cyclodextrins (subject of this dataset): Molecules 2022, 14, 4651 (open access) This dataset is focused on modelling on SuccFerr-cyclodextrin supramolecular assemblages. The structure of SuccFerr and the 4 moieties that can be inserted into a cyclodextrin can be seen in the furnished picture "SuccFerr-RDG.jpg". The publication related to this dataset is: Pascal Pigeon, Feten Najlaoui, Michael James McGlinchey, Juan Sanz García, Gérard Jaouen, Stéphane Gibaud, Unravelling the Role of Uncommon Hydrogen Bonds in Cyclodextrin Ferrociphenol Supramolecular Complexes: A Computational Modelling and Experimental Study, Int. J. Mol. Sci. 2023, 24, 15, 12288. (open access) https://doi.org/10.3390/ijms241512288. This article belongs to the Special Issue "Cyclodextrins: Properties and Applications" that belongs to the section "Macromolecules" of International Journal of Molecular Sciences. GA.jpeg is the graphical abstract that show the uncommon hydrogen bonds between Fe and OH of CD. See README.txt file for detail. Note: In the article (Int. J. Mol. Sci.), ΔrH° was replaced by ΔE, but the meaning is the same. Database and export as CSV/TAB files : CSV/tab file containing the table calculscd of the database for 1-CD models: calculscd.tab CSV/tab file containing the table calculs2cd of the database for 2-CD models: calculs2cd.tab CSV/tab file containing the table cdxcd of the database for 36 combinations of 2-CD models for series 2-CD S4: cdxcd.tab Other version of the calculscd.tab but with tab as separators and without quotation marks enclosing the Columns, used for the C program to work properly: calculscdprog.tab Other version of the calculs2cd.tab but with tab as separators and without quotation marks enclosing the Columns, used for the C program to work properly: calculs2cdprog.tab JSON file containing all the database (tables calculscd, calculs2cd and cdxcd) in JSON format: pascal.json XML file containing all the database (tables calculscd, calculs2cd and cdxcd) in XML format: pascal.xml sql file containing all the database (tables calculscd, calculs2cd and cdxcd) in SQLformat. This is this file that should be used to import all the database into a database management software (as MySQL/ MariaDB, ...) since it also contains the constraints on keys (unicity, index, autoincrement): pascal.sql Models zip archive containing 13261 XYZ files (models in XYZ format classified by number of series). They were created by Spartan, but were corrected by the C program (removal of Lig atoms (confused with lithium on other software), addition of the missing two first lines, reduction of size (spaces replaced by tab)): XYZ.zip zip archive containing 4 XYZ files describing 4 special models calculated in DFT (DFT calculation on the best model of series 1-CD S2 in water: best 1-CD series 2 DFT water.xyz, DFT calculation on the best model of series 1-CD S8 in vacuum: best 1-CD series 8 DFT vacuum.xyz, DFT calculation...

"Due to the size of this dataset, both Shapefile and Spreadsheet download options will not work as expected. The File Geodatabase is an alternative option for this data download"This is SCAG's 2019 Annual Land Use (ALU v. 2019.1) at the parcel-level, updated as of February 2021. This dataset has been modified to include additional attributes in order to feed SCAG's Housing Element Parcel Tool (HELPR), version 2.0. The dataset will be further reviewed and updated as additional information is released. Please refer to the tables below for data dictionary and SCAG’s land use classification.Field NameData TypeField DescriptionPID19Text2019 SCAG’s parcel unique IDAPN19Text2019 Assessor’s parcel numberCOUNTYTextCounty name (based on 2016 county boundary)COUNTY_IDDoubleCounty FIPS code (based on 2016 county boundary)CITYTextCity name (based on 2016 city boundary)CITY_IDDoubleCity FIPS code (based on 2016 city boundary)MULTIPARTShort IntegerMultipart feature (the number of multiple polygons; '1' = singlepart feature)STACKLong IntegerDuplicate geometry (the number of duplicate polygons; '0' = no duplicate polygons)ACRESDoubleParcel area (in acreage)GEOID20Text2020 Census Block Group GEOIDSLOPEShort IntegerSlope information1APN_DUPLong IntegerDuplicate APN (the number of multiple tax roll property records; '0' = no duplicate APN)IL_RATIODoubleRatio of improvement assessed value to land assessed valueLU19Text2019 existing land useLU19_SRCTextSource of 2019 existing land use2SCAGUID16Text2016 SCAG’s parcel unique IDAPNText2016 Assessor’s parcel numberCITY_GP_COText2016 Jurisdiction’s general plan land use designationSCAG_GP_COText2016 SCAG general plan land use codeSP_INDEXShort IntegerSpecific plan index ('0' = outside specific plan area; '1' = inside specific plan area)CITY_SP_COText2016 Jurisdiction’s specific plan land use designationSCAG_SP_COText2016 SCAG specific plan land use codeCITY_ZN_COText2016 Jurisdiction’s zoning codeSCAG_ZN_COText2016 SCAG zoning codeLU16Text2016 existing land useYEARLong IntegerDataset yearPUB_OWNShort IntegerPublic-owned land index ('1' = owned by public agency)PUB_NAMETextName of public agencyPUB_TYPETextType of public agency3BF_SQFTDoubleBuilding footprint area (in square feet)4BSF_NAMETextName of brownfield/superfund site5BSF_TYPETextType of brownfield/superfund site5FIREShort IntegerParcel intersects CalFire Very High Hazard Local Responsibility Areas or State Responsibility Areas (November 2020 version) (CalFIRE)SEARISE36Short IntegerParcel intersects with USGS Coastal Storm Modeling System (CoSMos)1 Meter Sea Level Rise inundation areas for Southern California (v3.0, Phase 2; 2018)SEARISE72Short IntegerParcel intersects with USGS Coastal Storm Modeling System (CoSMos)2 Meter Sea Level Rise inundation areas for Southern California (v3.0, Phase 2; 2018)FLOODShort IntegerParcel intersects with a FEMA 100 Year Flood Plain data from the Digital Flood Insurance Rate Map (DFIRM), obtained from Federal Emergency Management Agency (FEMA) in August 10, 2017EQUAKEShort IntegerParcel intersects with an Alquist-Priolo Earthquake Fault Zone (California Geological Survey; 2018)LIQUAFAShort IntegerParcel intersects with a Liquefaction Susceptibility Zone (California Geological Survey; 2016)LANDSLIDEShort IntegerParcel intersects with a Landslide Hazard Zone (California Geological Survey; 2016)CPADShort IntegerParcel intersects with a protected area from the California Protected Areas Database(CPAD) – www.calands.org (accessed April 2021)RIPARIANShort IntegerParcel centroid falls within Active River Areas(2010)or parcel intersects with a Wetland Area in the National Wetland Inventory(Version 2)WILDLIFEShort IntegerParcel intersects with wildlife habitat (US Fish & Wildlife ServiceCritical Habitat, Southern California Missing Linkages, Natural Lands & Habitat Corridors from Connect SoCal, CEHC Essential Connectivity Areas,Critical Coastal Habitats)CNDDBShort IntegerThe California Natural Diversity Database (CNDDB)includes the status and locations of rare plants and animals in California. Parcels that overlap locations of rare plants and animals in California from the California Natural Diversity Database (CNDDB)have a greater likelihood of encountering special status plants and animals on the property, potentially leading to further legal requirements to allow development (California Department of Fish and Wildlife). Data accessed in October 2020.HCPRAShort IntegerParcel intersects Natural Community & Habitat Conservation Plans Reserve Designs from the Western Riverside MHSCP, Coachella Valley MHSCP, and the Orange County Central Coastal NCCP/HCP, as accessed in October 2020WETLANDShort IntegerParcel intersects a wetland or deepwater habitat as defined by the US Fish & Wildlife Service National Wetlands Inventory, Version 2.UAZShort IntegerParcel centroid lies within a Caltrans Adjusted Urbanized AreasUNBUILT_SFDoubleDifference between parcel area and building footprint area expressed in square feet.6GRCRY_1MIShort IntegerThe number of grocery stores within a 1-mile drive7HEALTH_1MIShort IntegerThe number of healthcare facilities within a 1-mile drive7OPENSP_1MIShort IntegerQuantity of open space (roughly corresponding to city blocks’ worth) within a 1-mile drive7TCAC_2021TextThe opportunity level based on the 2021 CA HCD/TCAC opportunity scores.HQTA45Short IntegerField takes a value of 1 if parcel centroid lies within a 2045 High-Quality Transit Area (HQTA)JOB_CTRShort IntegerField takes a value of 1 if parcel centroid lies within a job centerNMAShort IntegerField takes a value of 1 if parcel centroid lies within a neighborhood mobility area.ABS_CONSTRShort IntegerField takes a value of 1 if parcel centroid lies within an absolute constraint area. See the Sustainable Communities Strategy Technical Reportfor details.VAR_CONSTRShort IntegerField takes a value of 1 if parcel centroid lies within a variable constraint area. See the Sustainable Communities Strategy Technical Reportfor details.EJAShort IntegerField takes a value of 1 if parcel centroid lies within an Environmental Justice Area. See the Environmental Justice Technical Reportfor details.SB535Short IntegerField takes a value of 1 if parcel centroid lies within an SB535 Disadvantaged Community area. See the Environmental Justice Technical Reportfor details.COCShort IntegerField takes a value of 1 if parcel centroid lies within a Community of Concern See the Environmental Justice Technical Reportfor details.STATEShort IntegerThis field is a rudimentary estimate of which parcels have adequate physical space to accommodate a typical detached Accessory Dwelling Unit (ADU)8.SBShort IntegerIndex of ADU eligibility according to the setback reduction policy scenario (from 4 to 2 feet) (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SMShort IntegerIndex of ADU eligibility according to the small ADU policy scenario (from 800 to 600 square feet ADU) (1 = ADU eligible parcel, Null = Not ADU eligible parcel)PKShort IntegerIndex of ADU eligibility according to parking space exemption (200 square feet) policy scenario (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_SMShort IntegerIndex of ADU eligibility according to both the setback reduction and small ADU policy scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_PKShort IntegerIndex of ADU eligibility according to both the setback reduction and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SM_PKShort IntegerIndex of ADU eligibility according to both the small ADU policy and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_SM_PKShort IntegerIndex of ADU eligibility according to the setback reduction, small ADU, and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)1. Slope: '0' - 0~4 percent; '5' - 5~9 percent; '10' - 10~14 percent; '15' = 15~19 percent; '20' - 20~24 percent; '25' = 25 percent or greater.2. Source of 2019 existing land use: SCAG_REF- SCAG's regional geospatial datasets;ASSESSOR- Assessor's 2019 tax roll records; CPAD- California Protected Areas Database (version 2020a; accessed in September 2020); CSCD- California School Campus Database (version 2018; accessed in September 2020); FMMP- Farmland Mapping and Monitoring Program's Important Farmland GIS data (accessed in September 2020); MIRTA- U.S. Department of Defense's Military Installations, Ranges, and Training Areas GIS data (accessed in September 2020)3. Type of public agency includes federal, state, county, city, special district, school district, college/university, military.4. Based on 2019 building footprint data obtained from BuildingFootprintUSA (except that 2014 building footprint data was used for Imperial County). Please note that 2019 building footprint data does not cover the entire SCAG region (overlapped with 83% of parcels in the SCAG Region).5. Includes brownfield/superfund site whose address information are matched by SCAG rooftop address locator. Brownfield data was obtained from EPA's Assessment, Cleanup and Redevelopment Exchange System (ACRES) database, Cleanups in my community (CIMC), DTSC brownfield Memorandum of Agreement (MOA). Superfund site data was obtained from EPA's Superfund Enterprise Management System (SEMS) database.6. Parcels with a zero value for building footprint area are marked as NULL to indicate this field is not reliable.7. These values are intended as a rudimentary indicator of accessibility developed by SCAG using 2016 InfoUSA business establishment data and 2017 California Protected Areas data. See documentation for details.8. A detailed study conducted by Cal Poly Pomona (CPP) and available hereconducted an extensive review of state and local requirements and development trends for ADUs in the SCAG region and developed a baseline set of assumptions for estimating how many of a jurisdiction’s parcels

EF_NPL is a subset of facilities from FRS_PROGRAM_FACILITY and associated best-available geospatial coordinates. Facility Registry Service (FRS) data are refreshed daily. The layer shows only NPL (National Priority List) points from the SEMS (Superfund Enterprise Management System) database. The NPL subset is updated weekly.

The White House Subcommittee on Biodiversity and Ecosystem Dynamics has identified systematics as a research priority that is fundamental to ecosystem management and biodiversity conservation. This primary need identified by the Subcommittee requires improvements in the organization of, and access to, standardized nomenclature. ITIS (originally referred to as the Interagency Taxonomic Information System) was designed to fulfill these requirements. In the future, the ITIS will provide taxonomic data and a directory of taxonomic expertise that will support the system. The ITIS is the result of a partnership of federal agencies formed to satisfy their mutual needs for scientifically credible taxonomic information. Since its inception, ITIS has gained valuable new partners and undergone a name change; ITIS now stands for the Integrated Taxonomic Information System. The goal is to create an easily accessible database with reliable information on species names and their hierarchical classification. The database will be reviewed periodically to ensure high quality with valid classifications, revisions, and additions of newly described species. The ITIS includes documented taxonomic information of flora and fauna from both aquatic and terrestrial habitats. The original ITIS partners include: Department of Commerce National Oceanic and Atmospheric Administration (NOAA) Department of Interior (DOI) Geological Survey (USGS) Environmental Protection Agency (EPA) Department of Agriculture (USDA) Agriculture Research Service (ARS) Natural Resources Conservation Service (NRCS) Smithsonian Institution National Museum of Natural History (NMNH) These agencies signed a Memorandum of Understanding and have formed a Steering Committee that directs two technical work groups - the Database Work Group (DWG) and the Taxonomy Work Group (TWG). The DWG is responsible for the database design and overseeing development of the system to meet the requirements of the ITIS partners. The TWG is responsible for the quality and integrity of the database information. In addition to the database, the working groups have created "Taxonomic Workbench" software designed for easy entry and manipulation of taxonomic data. Primary objectives of the TWG include the review of data prior to incorporation into the ITIS and the establishment of a process for periodic peer review to ensure data quality. The TWG has evaluated the taxonomic information priorities of the agencies and is locating data sources for the highest priority groups. Efforts to gather data are helping to identify gaps in taxonomic coverage in both scientific expertise and available information. The TWG hopes to promote collaboration among, and provide a point of focus for, taxonomists, scientific institutions, and taxonomic information users. For each scientific name, ITIS will include the authority (author and date), taxonomic rank, associated synonyms and vernacular names where available, a unique taxonomic serial number, data source information (publications, experts, etc.) and data quality indicators. Expert reviews and changes to taxonomic information in the database will be tracked. Geographic coverage will be worldwide with initial emphasis on North American taxa. The TWG is coordinating its efforts with several national and international biodiversity programs. ITIS will be a significant contribution to the scientific infrastructure that is fundamental to the description, conservation, and management of the nation's biodiversity. Use of the ITIS and the taxonomic serial numbers will facilitate sharing of biological information among researchers and cooperating agencies by providing a common framework for taxonomic data. Agencies that typically cannot afford to maintain taxonomic data will have access to high quality taxonomic information through ITIS. This project allows the coordination of efforts among federal agencies, thereby increasing productivity and saving resources. Status reports on ITIS system development may be found in the What's New section. You can also contact Gerald Guala, Ph.D., Director, Integrated Taxonomic Information System (ITIS) at U.S. Geological Survey, 12201 Sunrise Valley Drive, MS 302, Reston, VA 20192 or via email at itiswebmaster@itis.gov .

Over the past decade, Oracle Corporation’s annual revenue has grown from around 22 billion U.S. dollars to almost 53 billion, with fiscal year 2024 marking one of the company’s highest revenue figures to date. The company’s cloud services and license support segment is its biggest earner, accounting for more than half of its overall revenues. Oracle Corporation Oracle was founded by Larry Ellison in 1977, as a tech company primarily focused on relational databases. Today Oracle ranks among the largest companies in the world in terms of market value, and serves as the world’s most popular database management system provider. Oracle’s database products have remained popular throughout the years, and the company has more recently widened its focus to include cloud computing resources as well. Cloud computing Like Oracle, many of the world’s largest technology companies have begun to dedicate significant portions of their resources towards the development of cloud computing platforms and services. Cloud computing allows customers to make use of storage and computing resources without the need for physical server equipment. The public cloud computing market brings in hundreds of billions of dollars’ worth of revenue each year, and being a relatively new technology, shows no signs of slowing down. The fiscal year end of the company is May, 31st.

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Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

The sample excludes the northern part of the country because of inaccessibility and nomadic populations. The excluded area represents 10% of the total adult population.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in Mali was 1,000 individuals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

The sample excludes the Norther region because of security risks. The excluded area represents approximately 10% of the total adult population.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in the majority of economies was 1,000 individuals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above. The sample includes only Omani nationals and Arab expatriates. The excluded population represents approximately 10% of the total adult population. The sample overrepresents adults with more than a primary education.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in the majority of economies was 1,000 individuals.

Mode of data collection

Landline telephone

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

The sample excludes the Northeast states and remote islands. The excluded area represents approximately 10% of the total adult population.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in India was 3,518 individuals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in Azerbaijan was 1,000 individuals. The sample excludes Nagorno-Karabakh and territories because of security risks. The excluded area represents approximately 10% of the total adult population.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

This wye pipes feature class represents current wastewater information connecting the sewer service to either side of the street in the City of Los Angeles. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most rigorous geographic information of the sanitary sewer system using a geometric network model, to ensure that its sewers reflect current ground conditions. The sanitary sewer system, pump plants, wyes, maintenance holes, and other structures represent the sewer infrastructure in the City of Los Angeles. Wye and sewer information is available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works.Associated information about the wastewater Wye is entered into attributes. Principal attributes include:WYE_SUBTYPE: wye subtype is the principal field that describes various types of points as either Chimney, Chimney Riser, Offset Chimney Riser, Siphon, Special Case, Spur, Tap, Tee, Unclassified, Vertical Tee, Vertical Tee Riser, Wye, Wye Drawn as a Tap.For a complete list of attribute values, please refer to (TBA Wastewater data dictionary).Wastewater Wye pipes lines layer was created in geographical information systems (GIS) software to display the location of wastewater wye pipes. The wyes lines layer is a feature class in the LACityWastewaterData.gdb Geodatabase dataset. The layer consists of spatial data as a line feature class and attribute data for the features. The lines are entered manually based on wastewater sewer maps and BOE standard plans, and information about the lines is entered into attributes. The wye pipes lines features are sewer pipe connections for buildings. The features in the Wastewater connector wye points layer is a related structure connected with the wye pipe line. The WYE_ID field value is the unique ID. The WYE_ID field relates to the Sewer Permit tables. The annotation wye features are displayed on maps alongside features from the Wastewater Sewer Wye pipes lines layer. The wastewater wye pipes lines are inherited from a sewer spatial database originally created by the City's Wastewater program. The database was known as SIMMS, Sewer Inventory and Maintenance Management System. Wye pipe information should only be added to the Wastewater wye pipes layer if documentation exists, such as a wastewater map approved by the City Engineer. Sewers plans and specifications proposed under private development are reviewed and approved by by Bureau of Engineering. The Department of Public Works, Bureau of Engineering's, Brown Book (current as of 2010) outlines standard specifications for public works construction. For more information on sewer materials and structures, look at the Bureau of Engineering Manual, Part F, Sewer Design, F 400 Sewer Materials and Structures section, and a copy can be viewed at http://eng.lacity.org/techdocs/sewer-ma/f400.pdf.List of Fields:SPECIAL_STRUCT: This attribute is the basin number.TOP_: When a chimney is present, this is the depth at the top of the chimney.BOTTOM: When a chimney is present, this is the depth at the bottom of the chimney.PL_HUNDS: This value is the hundreds portion of the stationing at the property line.SHAPE: Feature geometry.USER_ID: The name of the user carrying out the edits of the wye pipes data.TYPE: This is the old wye status and is no longer referenced.REMARKS: This attribute contains additional comments regarding the wye line segment, such as a line through in all caps when lined out on wye maps.WYE_NO: This value is the number of the line segment for the wye structure located along the pipe segment. This is a 2 digit value. The number starts at 1 for the first wye connected to a pipe. The numbers increase sequentially with each wye being unique.WYE_ID: The value is a combination of PIPE_ID and WYE_NO fields, forming a unique number. This 19 digit value is a key attribute of the wye lines data layer. This field relates to the Permit tables.C_TENS: This value is the tens portion of the stationing at the curb line.C_HUNDS: This value is the hundreds portion of the stationing at the curb line.WYE_SUBTYPE: This value is the type of sewer connection. Values: • 2 - Tap. • 8 - Siphon. • 13 - Wye Drawn as a Tap. • 9 - Special Case. • 6 - Chimney riser. • 4 - Chimney. • 5 - Vertical Tee Riser. • 7 - Vertical tee. • 10 - Spur. • 11 - Unclassified. • 12 - Offset Chimney Riser. • 1 - Wye. • 3 - Tee.SIDE: The side of the pipe looking up stream to which structure attaches. Values: • U - Unknown. • L - Left. • R - Right. • C - Centered.ASSETID: User-defined unique feature number that is automatically generated.PL_DEPTH: This value is the depth of the service connection at the property line.DEPTH: This value is the depth of the Wye from the surface in feet.STAT_HUND: This value is the hundreds portion of the stationing.ENG_DIST: LA City Engineering District. The boundaries are displayed in the Engineering Districts index map. Values: • H - Harbor Engineering District. • C - Central Engineering District. • V - Valley Engineering District. • W - West LA Engineering District.PIPE_ID: The value is a combination of the values in the UP_STRUCT, DN_STRUCT, and PIPE_LABEL fields. This is the 17 digit identifier of each pipe segment and is a key attribute of the pipe line data layer. This field named PIPE_ID relates to the field in the Annotation Pipe and to the field named PIPE_ID in the Pipe line feature class data layers.OBJECTID: Internal feature number.ENABLED: Internal feature number.REHAB: This attribute indicates if the wye pipe has been rehabilitated.C_DEPTH: This value is the depth of the service connection at the curb line.STAT_TENS: This value is the tens portion of the stationing.BASIN: This attribute is the basin number.LAST_UPDATE: Date of last update of the point feature.STATUS: This value is the active or inactive status of the wye pipes. Values: • Capped - Capped. • INACTIVE - Inactive.PL_TENS: This value is the tens portion of the stationing at the property line.CRTN_DT: Creation date of the point feature.SERVICEID: User-defined unique feature number that is automatically generated.SHAPE_Length: Length of feature in internal units.

This data set is part of an ongoing project to consolidate interagency fire point data. The incorporation of all available historical data is in progress.The InFORM (Interagency Fire Occurrence Reporting Modules) FODR (Fire Occurrence Data Records) are the official record of fire events. Built on top of IRWIN (Integrated Reporting of Wildland Fire Information), the FODR starts with an IRWIN record and then captures the final incident information upon certification of the record by the appropriate local authority. This service contains all wildland fire incidents from the InFORM FODR incident service that meet the following criteria:Categorized as a Wildfire (WF) or Prescribed Fire (RX) recordIs Valid and not "quarantined" due to potential conflicts with other recordsNo "fall-off" rules are applied to this service.Service is a real time display of data.Warning: Please refrain from repeatedly querying the service using a relative date range. This includes using the “(not) in the last” operators in a Web Map filter and any reference to CURRENT_TIMESTAMP. This type of query puts undue load on the service and may render it temporarily unavailable.Attributes:ABCDMiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.ADSPermissionStateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.CalculatedAcresA measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire. More specifically, the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands. The minimum size must be 0.1.ContainmentDateTimeThe date and time a wildfire was declared contained. ControlDateTimeThe date and time a wildfire was declared under control.CreatedBySystemArcGIS Server Username of system that created the IRWIN Incident record.CreatedOnDateTimeDate/time that the Incident record was created.IncidentSizeReported for a fire. The minimum size is 0.1.DiscoveryAcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.DispatchCenterIDA unique identifier for a dispatch center responsible for supporting the incident.EstimatedCostToDateThe total estimated cost of the incident to date.FinalAcresReported final acreage of incident.FinalFireReportApprovedByTitleThe title of the person that approved the final fire report for the incident.FinalFireReportApprovedByUnitNWCG Unit ID associated with the individual who approved the final report for the incident.FinalFireReportApprovedDateThe date that the final fire report was approved for the incident.FireBehaviorGeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. FireCodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. FireDepartmentIDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.FireDiscoveryDateTimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.FireMgmtComplexityThe highest management level utilized to manage a wildland fire event. FireOutDateTimeThe date and time when a fire is declared out. FSJobCodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.FSOverrideCodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.IncidentNameThe name assigned to an incident.IncidentShortDescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. IncidentTypeCategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.IncidentTypeKindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.InitialLatitudeThe latitude location of the initial reported point of origin specified in decimal degrees.InitialLongitudeThe longitude location of the initial reported point of origin specified in decimal degrees.InitialResponseDateTimeThe date/time of the initial response to the incident. More specifically when the IC arrives and performs initial size up. IsFireCauseInvestigatedIndicates if an investigation is underway or was completed to determine the cause of a fire.IsFSAssistedIndicates if the Forest Service provided assistance on an incident outside their jurisdiction.IsReimbursableIndicates the cost of an incident may be another agency’s responsibility.IsTrespassIndicates if the incident is a trespass claim or if a bill will be pursued.LocalIncidentIdentifierA number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year.ModifiedBySystemArcGIS Server username of system that last modified the IRWIN Incident record.ModifiedOnDateTimeDate/time that the Incident record was last modified.PercentContainedIndicates the percent of incident area that is no longer active. Reference definition in fire line handbook when developing standard.POOCityThe closest city to the incident point of origin.POOCountyThe County Name identifying the county or equivalent entity at point of origin designated at the time of collection.POODispatchCenterIDA unique identifier for the dispatch center that intersects with the incident point of origin. POOFipsThe code which uniquely identifies counties and county equivalents. The first two digits are the FIPS State code and the last three are the county code within the state.POOJurisdictionalAgencyThe agency having land and resource management responsibility for a incident as provided by federal, state or local law. POOJurisdictionalUnitNWCG Unit Identifier to identify the unit with jurisdiction for the land where the point of origin of a fire falls. POOJurisdictionalUnitParentUnitThe unit ID for the parent entity, such as a BLM State Office or USFS Regional Office, that resides over the Jurisdictional Unit.POOLandownerCategoryMore specific classification of land ownership within land owner kinds identifying the deeded owner at the point of origin at the time of the incident.POOLandownerKindBroad classification of land ownership identifying the deeded owner at the point of origin at the time of the incident.POOProtectingAgencyIndicates the agency that has protection responsibility at the point of origin.POOProtectingUnitNWCG Unit responsible for providing direct incident management and services to a an incident pursuant to its jurisdictional responsibility or as specified by law, contract or agreement. Definition Extension: - Protection can be re-assigned by agreement. - The nature and extent of the incident determines protection (for example Wildfire vs. All Hazard.)POOStateThe State alpha code identifying the state or equivalent entity at point of origin.PredominantFuelGroupThe fuel majority fuel model type that best represents fire behavior in the incident area, grouped into one of seven categories.PredominantFuelModelDescribes the type of fuels found within the majority of the incident area. UniqueFireIdentifierUnique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = POO protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters) FORIDUnique identifier assigned to each incident record in the FODR database.