Data Dictionary (Codebook) for CLEAR Relational Database

Jurisdictional Unit, 2022-05-21. For use with WFDSS, IFTDSS, IRWIN, and InFORM.This is a feature service which provides Identify and Copy Feature capabilities. If fast-drawing at coarse zoom levels is a requirement, consider using the tile (map) service layer located at https://nifc.maps.arcgis.com/home/item.html?id=3b2c5daad00742cd9f9b676c09d03d13.OverviewThe Jurisdictional Agencies dataset is developed as a national land management geospatial layer, focused on representing wildland fire jurisdictional responsibility, for interagency wildland fire applications, including WFDSS (Wildland Fire Decision Support System), IFTDSS (Interagency Fuels Treatment Decision Support System), IRWIN (Interagency Reporting of Wildland Fire Information), and InFORM (Interagency Fire Occurrence Reporting Modules). It is intended to provide federal wildland fire jurisdictional boundaries on a national scale. The agency and unit names are an indication of the primary manager name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIdentifier=null,JurisdictionalUnitAgency=null, JurisdictionalUnitKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).These data are used to automatically populate fields on the WFDSS Incident Information page.This data layer implements the NWCG Jurisdictional Unit Polygon Geospatial Data Layer Standard.Relevant NWCG Definitions and StandardsUnit2. A generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Unit, Protecting; LandownerUnit IdentifierThis data standard specifies the standard format and rules for Unit Identifier, a code used within the wildland fire community to uniquely identify a particular government organizational unit.Landowner Kind & CategoryThis data standard provides a two-tier classification (kind and category) of landownership. Attribute Fields JurisdictionalAgencyKind Describes the type of unit Jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, and Other. A value may not be populated for all polygons.JurisdictionalAgencyCategoryDescribes the type of unit Jurisdiction using the NWCG Landowner Category data standard. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State. A value may not be populated for all polygons.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Standard Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available from the Unit ID standard, linked above.LandownerKindThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. There are three valid values: Federal, Private, or Other.LandownerCategoryThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State, Private.DataSourceThe database from which the polygon originated. Be as specific as possible, identify the geodatabase name and feature class in which the polygon originated.SecondaryDataSourceIf the Data Source is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if Data Source is "PAD-US 2.1", then for a USDA Forest Service polygon, the Secondary Data Source would be "USDA FS Automated Lands Program (ALP)". For a BLM polygon in the same dataset, Secondary Source would be "Surface Management Agency (SMA)."SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.MapMethod:Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality. MapMethod will be Mixed Method by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; OtherDateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using 24 hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature. GeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JoinMethodAdditional information on how the polygon was matched information in the NWCG Unit ID database.LocalNameLocalName for the polygon provided from PADUS or other source.LegendJurisdictionalAgencyJurisdictional Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.LegendLandownerAgencyLandowner Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.DataSourceYearYear that the source data for the polygon were acquired.Data InputThis dataset is based on an aggregation of 4 spatial data sources: Protected Areas Database US (PAD-US 2.1), data from Bureau of Indian Affairs regional offices, the BLM Alaska Fire Service/State of Alaska, and Census Block-Group Geometry. NWCG Unit ID and Agency Kind/Category data are tabular and sourced from UnitIDActive.txt, in the WFMI Unit ID application (https://wfmi.nifc.gov/unit_id/Publish.html). Areas of with unknown Landowner Kind/Category and Jurisdictional Agency Kind/Category are assigned LandownerKind and LandownerCategory values of "Private" by use of the non-water polygons from the Census Block-Group geometry.PAD-US 2.1:This dataset is based in large part on the USGS Protected Areas Database of the United States - PAD-US 2.`. PAD-US is a compilation of authoritative protected areas data between agencies and organizations that ultimately results in a comprehensive and accurate inventory of protected areas for the United States to meet a variety of needs (e.g. conservation, recreation, public health, transportation, energy siting, ecological, or watershed assessments and planning). Extensive documentation on PAD-US processes and data sources is available.How these data were aggregated:Boundaries, and their descriptors, available in spatial databases (i.e. shapefiles or geodatabase feature classes) from land management agencies are the desired and primary data sources in PAD-US. If these authoritative sources are unavailable, or the agency recommends another source, data may be incorporated by other aggregators such as non-governmental organizations. Data sources are tracked for each record in the PAD-US geodatabase (see below).BIA and Tribal Data:BIA and Tribal land management data are not available in PAD-US. As such, data were aggregated from BIA regional offices. These data date from 2012 and were substantially updated in 2022. Indian Trust Land affiliated with Tribes, Reservations, or BIA Agencies: These data are not considered the system of record and are not intended to be used as such. The Bureau of Indian Affairs (BIA), Branch of Wildland Fire Management (BWFM) is not the originator of these data. The

All data associated with the Town of Young Floodplain Risk Management Study and Plan. GIS Data Outputs, Hydraulics, Hydrology, Reporting, Survey. Data and Resources Data associated with Town of Young Floodplain Risk Management Study and PlanZIP (11.5 GB) All Data and GIS data associated with the Town of Young Floodplain Risk Management Study and Plan. Explore More information Download More info Creative Commons Attribution 4.0 International Public License By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution 4.0 International Public License (“Public License”). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions. Section 1 – Definitions. Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image. 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The National Public Toilet Map shows the location of more than 17,000 public\r and private public toilet facilities across Australia. Details of toilet\r facilities can also be found along major travel routes and for shorter\r journeys as well. Useful information is provided about each toilet, such as\r location, opening hours, availability of baby change rooms, accessibility for\r people with disabilities and the details of other nearby toilets.\r \r Licence\r \r To download the National Public Toilet Map dataset, you must agree to the\r following terms and conditions:\r \r These are the terms and conditions (the Terms) upon which the Commonwealth of\r Australia represented by the Department of Finance and Deregulation (ABN 61\r 970 632 495) of Canberra A.C.T. (the Commonwealth, us, we or our as the\r context requires) makes available to you the Database referred to below. Your\r right to access the Database for the Permitted Purpose is conditional upon you\r first agreeing to the Terms. You may not access the Database if you do not\r accept the Terms.\r \r By accessing the database you will be deemed to have accepted the Terms.\r \r By accepting the Terms you warrant to us that you are of legal age and have\r capacity to form a binding contract with the Commonwealth.\r \r Before continuing you should print or save a local copy of the Terms for your\r records.\r \r 1. Definitions\r \r Commencement Date\r \r means the date you accept the Terms (or are deemed to accept the Terms).\r \r Database\r \r means the database (known as the National Public Toilet Database) owned by and\r provided on behalf of the Commonwealth including any updates provided by or on\r behalf of the Commonwealth, that records some or all of the following details\r for public toilets in Australia:\r \r (a) toilet name;\r \r (b) address;\r \r (c) latitude and longitude;\r \r (d) general toilet features;\r \r (e) location;\r \r (f) accessibility;\r \r (g) opening hours;\r \r (h) additional features (e.g. showers, baby change facilities etc);\r \r (i) notes (e.g. coin operated showers etc).\r \r Derivative Product\r \r means any product or service that you may design or build, or have designed or\r built on your behalf, that includes or otherwise incorporates the Database (or\r part of the Database).\r \r Intellectual Property Rights\r \r (or IPRs) means all intellectual property rights, including but not\r limited to all rights existing or arising in respect of the Database whether\r or not such rights are registered or capable of being registered.\r \r Permitted Purpose\r \r means the right to:\r \r (a) use, adapt, reproduce, publish and communicate to the public the Database\r in any format (including any part of the Database); and\r \r (b) design and build, or have designed and built on your behalf, any\r Derivative Products.\r \r Terms\r \r means the terms and conditions of this licence.\r \r 2. 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The impact of a chief executive officer’s (CEO’s) functional experience on firm performance has gained the attention of many scholars. However, the measurement of functional experience is rarely disclosed in the public database. Few studies have been conducted on the comprehensive functional experience of CEOs. This paper used the upper echelons theory and obtained deep-level curricula vitae (CVs) data through the named entity recognition technique. First, we mined 15 consecutive years of CEOs’ CVs from 2006 to 2020 from Chinese listed companies. Second, we extracted information throughout their careers and automatically classified their functional hierarchy. Finally, we constructed breadth (functional breadth: functional experience richness) and depth (functional depth: average tenure and the hierarchy of function) for empirical analysis. We found that a CEO’s breadth is significantly negatively related to firm performance, and the quadratic term is significantly positive. A CEO’s depth is significantly positively related to firm performance, and the quadratic term is significantly negative. The research results indicate a u-shaped relationship between a CEO’s breadth and firm performance and an inverted u-shaped relationship between their depth and firm performance. The study’s findings extend the literature on factors influencing firm performance and CEOs’ functional experience. The study expands from the horizontal macro to the vertical micro level, providing new evidence to support the recruitment and selection of high-level corporate talent.

This dataset brings together information collected since 2001 on PPPs that have been supported by USAID. For the purposes of this dataset a Public-Private Partnership is defined as a USAID-supported development project or initiative which engages the private sector (including corporations, foundations, and other non-governmental actors) as a core resource partner. Due to changes in Agency data collection systems, standards, and internal organization, the data has been collected according to different mechanisms, definitions, and timeframes from 2001-2015, and therefore cannot be considered to be comprehensive of all PPPs during this period.

Parcel boundaries exported nightly from the Assessor's Office managed parcel fabric and joined with attributes related to owner information, values and size, the water source (Public Health database) and a link to the SmartGov public portal (permitting database). Most features are within 3 feet however some features can be up to 20 feet off. Please read the full data disclaimer when using this dataset.

The Protected Areas Database of the United States (PAD-US) is a geodatabase, managed by USGS GAP, that illustrates and describes public land ownership, management and other conservation lands, including voluntarily provided privately protected areas. The State, Regional and LCC geodatabases contain two feature classes. The PADUS1_3_FeeEasement feature class and the national MPA feature class. Legitimate and other protected area overlaps exist in the full inventory, with Easements loaded on top of Fee. Parcel data within a protected area are dissolved in this file that powers the PAD-US Viewer. As overlaps exist, GAP creates separate analytical layers to summarize area statistics for "GAP Status Code" and "Owner Name". Contact the PAD-US Coordinator for more information. The lands included in PAD-US are assigned conservation measures that qualify their intent to manage lands for the preservation of biological diversity and to other natural, recreational and cultural uses; managed for these purposes through legal or other effective means. The geodatabase includes: 1) Geographic boundaries of public land ownership and voluntarily provided private conservation lands (e.g., Nature Conservancy Preserves); 2) The combination land owner, land manager, management designation or type, parcel name, GIS Acres and source of geographic information of each mapped land unit 3) GAP Status Code conservation measure of each parcel based on USGS National Gap Analysis Program (GAP) protection level categories which provide a measurement of management intent for long-term biodiversity conservation 4) IUCN category for a protected area's inclusion into UNEP-World Conservation Monitoring Centre's World Database for Protected Areas. IUCN protected areas are defined as, "A clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values" and are categorized following a classification scheme available through USGS GAP; 5) World Database of Protected Areas (WDPA) Site Codes linking the multiple parcels of a single protected area in PAD-US and connecting them to the Global Community. As legitimate and other overlaps exist in the combined inventory GAP creates separate analytical layers to obtain area statistics for "GAP Status Code" and "Owner Name". PAD-US version 1.3 Combined updates include: 1) State, local government and private protected area updates delivered September 2011 from PAD-US State Data Stewards: CO (Colorado State University), FL (Florida Natural Areas Inventory), ID (Idaho Fish and Game), MA (The Commonwealth's Office of Geographic Information Systems, MassGIS), MO (University of Missouri, MoRAP), MT (Montana Natural Heritage Program), NM (Natural Heritage New Mexico), OR (Oregon Natural Heritage Program), VA (Department of Conservation and Recreation, Virginia Natural Heritage Program). 2) Select local government (i.e. county, city) protected areas (3,632) across the country (to complement the current PAD-US inventory) aggregated by the Trust for Public Land (TPL) for their Conservation Almanac that tracks the conservation finance movement across the country. 3) A new Date of Establishment field that identifies the year an area was designated or otherwise protected, attributed for 86% of GAP Status Code 1 and 2 protected areas. Additional dates will be provided in future updates. 4) A national wilderness area update from wilderness.net 5) The Access field that describes public access to protected areas as defined by data stewards or categorical assignment by Primary Designation Type. . The new Access Source field documents local vs. categorical assignments. See the PAD-US Standard Manual for more information: gapanalysis.usgs.gov/padus 6) The transfer of conservation measures (i.e. GAP Status Codes, IUCN Categories) and documentation (i.e. GAP Code Source, GAP Code Date) from PAD-US version 1.2 or categorical assignments (see PAD-US Standard) when not provided by data stewards 7) Integration of non-sensitive National Conservation Easement Database (NCED) easements from August 2011, July 2012 with PAD-US version 1.2 easements. Duplicates were removed, unless 'Stacked' = Y and multiple easements exist. 8) Unique ID's transferred from NCED or requested for new easements. NCED and PAD-US are linked via Source UID in the PAD-US version 1.3 Easement feature class. 9) Official (member and eligible) MPAs from the NOAA MPA Inventory (March 2011, www.mpa.gov) translated into the PAD-US schema with conservation measures transferred from PAD-US version 1.2 or categorically assigned to new protected areas. Contact the PAD-US Coordinator for documentation of categorical GAP Status Code assignments for MPAs. 10) Identified MPA records that overlap existing protected areas in the PAD-US Fee feature class (i.e. PADUS Overlap field in MPA feature class). For example, many National Wildlife Refuges and National Parks are also MPAs and are represented in the PAD-US MPA and Fee feature classes.

Overview: The Lower Nooksack Water Budget Project involved assembling a wide range of existing data related to WRIA 1 and specifically the Lower Nooksack Subbasin, updating existing data sets and generating new data sets. This Data Management Plan provides an overview of the data sets, formats and collaboration environment that was used to develop the project. Use of a plan during development of the technical work products provided a forum for the data development and management to be conducted with transparent methods and processes. At project completion, the Data Management Plan provides an accessible archive of the data resources used and supporting information on the data storage, intended access, sharing and re-use guidelines.

One goal of the Lower Nooksack Water Budget project is to make this “usable technical information” as accessible as possible across technical, policy and general public users. The project data, analyses and documents will be made available through the WRIA 1 Watershed Management Project website http://wria1project.org. This information is intended for use by the WRIA 1 Joint Board and partners working to achieve the adopted goals and priorities of the WRIA 1 Watershed Management Plan.

Model outputs for the Lower Nooksack Water Budget are summarized by sub-watersheds (drainages) and point locations (nodes). In general, due to changes in land use over time and changes to available streamflow and climate data, the water budget for any watershed needs to be updated periodically. Further detailed information about data sources is provided in review packets developed for specific technical components including climate, streamflow and groundwater level, soils and land cover, and water use.

Purpose: This project involves assembling a wide range of existing data related to the WRIA 1 and specifically the Lower Nooksack Subbasin, updating existing data sets and generating new data sets. Data will be used as input to various hydrologic, climatic and geomorphic components of the Topnet-Water Management (WM) model, but will also be available to support other modeling efforts in WRIA 1. Much of the data used as input to the Topnet model is publicly available and maintained by others, (i.e., USGS DEMs and streamflow data, SSURGO soils data, University of Washington gridded meteorological data). Pre-processing is performed to convert these existing data into a format that can be used as input to the Topnet model. Post-processing of Topnet model ASCII-text file outputs is subsequently combined with spatial data to generate GIS data that can be used to create maps and illustrations of the spatial distribution of water information. Other products generated during this project will include documentation of methods, input by WRIA 1 Joint Board Staff Team during review and comment periods, communication tools developed for public engagement and public comment on the project.

In order to maintain an organized system of developing and distributing data, Lower Nooksack Water Budget project collaborators should be familiar with standards for data management described in this document, and the following issues related to generating and distributing data: 1. Standards for metadata and data formats 2. Plans for short-term storage and data management (i.e., file formats, local storage and back up procedures and security) 3. Legal and ethical issues (i.e., intellectual property, confidentiality of study participants) 4. Access policies and provisions (i.e., how the data will be made available to others, any restrictions needed) 5. Provisions for long-term archiving and preservation (i.e., establishment of a new data archive or utilization of an existing archive) 6. Assigned data management responsibilities (i.e., persons responsible for ensuring data Management, monitoring compliance with the Data Management Plan)

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

Lovers Jump Creek Floodplain Risk Management Study and Plan Report PDFs - all volumes Lovers Jump Creek Final Floodplain Risk Management Study and Plan Final FRMSP Data and Resources Final FRMSP reportpdf (174.8 MB) Final FRMSP report Explore More information Download More info Creative Commons Attribution 4.0 International Public License By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution 4.0 International Public License (“Public License”). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions. Section 1 – Definitions. Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image. Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public License. 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Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License. No downstream restrictions. You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material. No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A):info:. Other rights. 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If You Share the Licensed Material (including in modified form), You must: retain the following if it is supplied by the Licensor with the Licensed Material: identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated); a copyright notice; a notice that refers to this Public License; a notice that refers to the disclaimer of warranties; a URI or hyperlink to the Licensed Material to the extent reasonably practicable; indicate if You modified the Licensed Material and retain an indication of any previous modifications; and indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License. You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information. If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable. If You Share Adapted Material You produce, the Adapter\'s License You apply must not prevent recipients of the Adapted Material from complying with this Public License. Section 4 – Sui Generis Database Rights. 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The Protected Areas Database of the United States (PAD-US) is a geodatabase, managed by U. S. Geological Survey Gap Analysis Program, that illustrates and describes public land ownership, management and other conservation lands, including voluntarily provided privately protected areas. The geodatabase contains four feature classes such as, Marine Protected Areas (MPA) and Easements that each contains uniquely associated attributes. These two feature classes are combined with the PAD-US Fee feature class to provide a full inventory of protected areas in a common schema (i.e. Combined file). Legitimate and other protected area overlaps exist in the full inventory, with Easements loaded on top of Fee and MPAs under both. Parcel data within a protected area are dissolved in this file that powers the PAD-US Viewer. As overlaps exist, GAP creates separate analytical layers to summarize area statistics for "GAP Status Code" and "Owner Name". Contact the PAD-US Coordinator for more information. The lands included in PAD-US are assigned conservation measures that qualify their intent to manage lands for the preservation of biological diversity and to other natural, recreational and cultural uses; managed for these purposes through legal or other effective means. The geodatabase includes: 1) Geographic boundaries of public land ownership and voluntarily provided private conservation lands (e.g., Nature Conservancy Preserves); 2) The combination land owner, land manager, management designation or type, parcel name, GIS Acres and source of geographic information of each mapped land unit 3) GAP Status Code conservation measure of each parcel based on USGS National Gap Analysis Program (GAP) protection level categories which provide a measurement of management intent for long-term biodiversity conservation 4) IUCN category for a protected area's inclusion into UNEP-World Conservation Monitoring Centre's World Database for Protected Areas. IUCN protected areas are defined as, "A clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values" and are categorized following a classification scheme available through USGS GAP; 5) World Database of Protected Areas (WDPA) Site Codes linking the multiple parcels of a single protected area in PAD-US and connecting them to the Global Community. The geodatabase contains a Marine Protected Area (MPA) feature class and Easements feature class, each with uniquely associated attribute. These two feature classes are combined with the PAD-US fee feature class with standard PAD-US attributes to provide a full inventory of protected areas in a common schema. As legitimate and other overlaps exist in the combined inventory GAP creates separate analytical layers to obtain area statistics for "GAP Status Code" and "Owner Name". PAD-US version 1.3 Combined updates include: 1) State, local government and private protected area updates delivered September 2011 from PAD-US State Data Stewards: CO (Colorado State University), FL (Florida Natural Areas Inventory), ID (Idaho Fish and Game), MA (The Commonwealth's Office of Geographic Information Systems, MassGIS), MO (University of Missouri, MoRAP), MT (Montana Natural Heritage Program), NM (Natural Heritage New Mexico), OR (Oregon Natural Heritage Program), VA (Department of Conservation and Recreation, Virginia Natural Heritage Program). 2) Select local government (i.e. county, city) protected areas (3,632) across the country (to complement the current PAD-US inventory) aggregated by the Trust for Public Land (TPL) for their Conservation Almanac that tracks the conservation finance movement across the country. 3) A new Date of Establishment field that identifies the year an area was designated or otherwise protected, attributed for 86% of GAP Status Code 1 and 2 protected areas. Additional dates will be provided in future updates. 4) A national wilderness area update from wilderness.net 5) The Access field that describes public access to protected areas as defined by data stewards or categorical assignment by Primary Designation Type. . The new Access Source field documents local vs. categorical assignments. See the PAD-US Standard Manual for more information: gapanalysis.usgs.gov/padus 6) The transfer of conservation measures (i.e. GAP Status Codes, IUCN Categories) and documentation (i.e. GAP Code Source, GAP Code Date) from PAD-US version 1.2 or categorical assignments (see PAD-US Standard) when not provided by data stewards 7) Integration of non-sensitive National Conservation Easement Database (NCED) easements from August 2011, July 2012 with PAD-US version 1.2 easements. Duplicates were removed, unless 'Stacked' = Y and multiple easements exist. 8) Unique ID's transferred from NCED or requested for new easements. NCED and PAD-US are linked via Source UID in the PAD-US version 1.3 Easement feature class. 9) Official (member and eligible) MPAs from the NOAA MPA Inventory (March 2011, http://marineprotectedareas.noaa.gov/) translated into the PAD-US schema with conservation measures transferred from PAD-US version 1.2 or categorically assigned to new protected areas. Contact the PAD-US Coordinator for documentation of categorical GAP Status Code assignments for MPAs. 10) Identified MPA records that overlap existing protected areas in the PAD-US Fee feature class (i.e. PADUS Overlap field in MPA feature class). For example, many National Wildlife Refuges and National Parks are also MPAs and are represented in the PAD-US MPA and Fee feature classes.

THIS DATASET WAS LAST UPDATED AT 7:11 AM EASTERN ON DEC. 1

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

https://www.youtube.com/watch?v=A8syQeFtBKc

Context

The Stanford Mass Shootings in America (MSA) is a dataset released under Creative Commons Attribution 4.0 international license by the Stanford Geospatial Center. While not an exhaustive collection of mass shootings, it is a high-quality dataset ranging from 1966 to 2016 with well-defined methodology, definitions and source URLs for user validation.

This dataset can be used to validate other datasets, such as us-mass-shootings-last-50-years, which contains more recent data, or conduct other analysis, as more information is provided.

Content

This dataset contains data by the MSA project both from it's website and from it's Github account. The difference between the two sources is only on the data format (i.e. .csv versus .geojson for the data, or .csv versus .pdf for the dictionary).

• mass_shooting_events_stanford_msa_release_06142016
  • Contains a nonexaustive list of US Mass Shootings from 1966 to 2016 in both .csv and .geojson formats.
• dictionary_stanford_msa_release_06142016
  • Contains the data dictionary in .csv and .pdf formats. Note the .pdf format provides an easier way to visualize sub-fields.

Note the data was reproduced here without any modifications other than file renaming for clarity, the content is the same as in the source.

The following sections are reproduced from the dataset creators website. For more details, please see the source.

Project background

The Stanford Mass Shootings of America (MSA) data project began in 2012, in reaction to the mass shooting in Sandy Hook, CT. In our initial attempts to map this phenomena it was determined that no comprehensive collection of these incidents existed online. The Stanford Geospatial Center set out to create, as best we could, a single point repository for as many mass shooting events as could be collected via online media. The result was the Stanford MSA.

What the Stanford MSA is

The Stanford MSA is a data aggregation effort. It is a curated set of spatial and temporal data about mass shootings in America, taken from online media sources. It is an attempt to facilitate research on gun violence in the US by making raw data more accessible.

What the Stanford MSA is not

The Stanford MSA is not a comprehensive, longitudinal research project. The data collected in the MSA are not investigated past the assessment for inclusion in the database. The MSA is not an attempt to answer specific questions about gun violence or gun laws.

The Stanford Geospatial Center does not provide analysis or commentary on the contents of this database or any derivatives produced with it.

Data collection methodology

The information collected for the Stanford MSA is limited to online resources. An initial intensive investigation was completed looking back over existing online reports to fill in the historic record going back to 1966. Contemporary records come in as new events occur and are cross referenced against a number of online reporting sources. In general a minimum of three corroborating sources are required to add the full record into the MSA (as many as 6 or 7 sources may have been consulted in many cases). All sources for each event are listed in the database.

Due to the time involved in vetting the details of any new incident, there is often a 2 to 4 week lag between a mass shooting event and its inclusion in the public release database.

It is important to note the records in the Stanford MSA span a time from well before the advent of online media reporting, through its infancy, to the modern era of web based news and information resources. Researchers using this database need to be aware of the reporting bias these changes in technology present. A spike in incidents for recent years is likely due to increased online reporting and not necessarily indicative of the rate of mass shootings alone. Researchers should look at this database as a curated collection of quality checked data regarding mass shootings, and not an exhaustive research data set itself. Independent verification and analysis will be required to use this data in examining trends in mass shootings over time.

Definition of Mass Shooting

The definition of mass shooting used for the Stanford database is 3 or more shooting victims (not necessarily fatalities), not including the shooter. The shooting must not be identifiably gang, drug, or organized crime related.

Acknowledgements

The Stanford Mass Shootings in America (MSA) is a dataset released under [Creative Commons Attribution 4.0 int...

This work reports pure component parameters for the PCP-SAFT equation of state for 1842 substances using a total of approximately 551 172 experimental data points for vapor pressure and liquid density. We utilize data from commercial and public databases in combination with an automated workflow to assign chemical identifiers to all substances, remove duplicate data sets, and filter unsuited data. The use of raw experimental data, as opposed to pseudoexperimental data from empirical correlations, requires means to identify and remove outliers, especially for vapor pressure data. We apply robust regression using a Huber loss function. For identifying and removing outliers, the empirical Wagner equation for vapor pressure is adjusted to experimental data, because the Wagner equation is mathematically rather flexible and is thus not subject to a systematic model bias. For adjusting model parameters of the PCP-SAFT model, nonpolar, dipolar and associating substances are distinguished. The resulting substance-specific parameters of the PCP-SAFT equation of state yield in a mean absolute relative deviation of the of 2.73% for vapor pressure and 0.52% for liquid densities (2.56% and 0.47% for nonpolar substances, 2.67% and 0.61% for dipolar substances, and 3.24% and 0.54% for associating substances) when evaluated against outlier-removed data. All parameters are provided as JSON and CSV files.

The IEA Wind TCP Task 54 deals with "Cold Climate Wind Power". Worldwide, most of the major challenges faced by wind turbines in cold climate are related to the icing of rotor blades. In fact, rotor icing can lead to safety risks, production losses and turbine lifetime reduction.

Turbines can be equipped with Ice Protection Systems (IPS), such as rotor blade heating systems, in order to mitigate icing impacts. The efficiency of such systems is strongly dependent on the meteorological conditions that determine ice accretion and ice ablation, i.e. on the "icing conditions". Currently, none of the state-of-the-art blade heating systems is able to prevent icing in all conditions. They all have a limited "performance envelope".

The definition, interpretation and application of this performance envelope is key to many stages during development and operation of any wind turbine/farm incorporating blade heating systems at a cold climate site. From site assessment through operational control to forecasting electricity production, the performance envelope influences equipment choice, control strategies and the residual profit from the sale of the generated electricity and the purchase of balancing capacity. Task 54 thus strives to advance the knowledge base in these aspects and to provide tools for improving the aforementioned processes.
The first step is choosing a suitable blade heating system and afterwards operating it with optimal settings for the site at hand. This implies knowledge about the typical characteristics of local icing events.

The major challenge in this respect is the large variability and diversity of icing events, in terms of ice type (from glaze to rime), icing intensity (mass and thickness of the ice) and temporal evolution (duration, growth rates, melting phases). Icing conditions can vary greatly between different regions of the world, but also between different events at the same site, or even in the course of a single icing event.

In this context, the Task 54 icing event database aims at giving concrete insights about the large diversity of icing events by gathering respective data from different parts of the world. The temporal evolution of single events shall be accessible via time series data. The goal is to focus on the icing events themselves rather than on turbine behaviour during icing.

Today, the vast majority of concrete information on wind turbine icing events is kept confidential for various reasons. Task 54 believes that a concrete and detailed public presentation of icing events will raise awareness among a wide range of stakeholders of the importance and impact of icing on the operation of wind turbines as well as the development of new projects in cold climate areas. Hence, the icing event database will be publicly accessible free of charge.

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

Asset database for the Hunter subregion on 24 February 2016 (V2.5) supersedes the previous version of the HUN Asset database V2.4 (Asset database for the Hunter subregion on 20 November 2015, GUID: 0bbcd7f6-2d09-418c-9549-8cbd9520ce18). It contains the Asset database (HUN_asset_database_20160224.mdb), a Geodatabase version for GIS mapping purposes (HUN_asset_database_20160224_GISOnly.gdb), the draft Water Dependent Asset Register spreadsheet (BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20160224.xlsx), a data dictionary (HUN_asset_database_doc_20160224.doc), and a folder (NRM_DOC) containing documentation associated with the Water Asset Information Tool (WAIT) process as outlined below. This version should be used for Materiality Test (M2) test.

The Asset database is registered to the BA repository as an ESRI personal goedatabase (.mdb - doubling as a MS Access database) that can store, query, and manage non-spatial data while the spatial data is in a separate file geodatabase joined by AID/ElementID.

Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. A report on the WAIT process for the Hunter is included in the zip file as part of this dataset.

Elements are initially included in the preliminary assets database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet the second Materiality Test (M2). Assets meeting both Materiality Tests comprise the water dependent asset list. Descriptions of the assets identified in the Hunter subregion are found in the "AssetList" table of the database.

Assets are the spatial features used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of "HUN_asset_database_doc_20160224.doc ", located in this filet.

The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset.

Detailed information describing the database structure and content can be found in the document "HUN_asset_database_doc_20160224.doc" located in this file.

Some of the source data used in the compilation of this dataset is restricted.

The public version of this asset database can be accessed via the following dataset: Asset database for the Hunter subregion on 24 February 2016 Public 20170112 v02 (https://data.gov.au/dataset/9d16592c-543b-42d9-a1f4-0f6d70b9ffe7)

Dataset History

OBJECTID VersionID Notes Date_

1 1 Initial database. 29/08/2014

3 1.1 Update the classification for seven identical assets from Gloucester subregion 16/09/2014

4 1.2 Added in NSW GDEs from Hunter - Central Rivers GDE mapping from NSW DPI (50 635 polygons). 28/01/2015

5 1.3 New AIDs assiged to NSW GDE assets (Existing AID + 20000) to avoid duplication of AIDs assigned in other databases. 12/02/2015

6 1.4 "(1) Add 20 additional datasets required by HUN assessment project team after HUN community workshop

       (2) Turn off previous GW point assets (AIDs from 7717-7810 inclusive) 

       (3) Turn off new GW point asset (AID: 0)

       (4) Assets (AIDs: 8023-8026) are duplicated to 4 assets (AID: 4747,4745,4744,4743 respectively) in NAM subregion . Their AID, Asset Name, Group, SubGroup, Depth, Source, ListDate and Geometry are using   

         values from that NAM assets.

      (5) Asset (AID 8595) is duplicated to 1 asset ( AID 57) in GLO subregion . Its AID, Asset Name, Group, SubGroup, Depth, Source, ListDate and Geometry are using values from that GLO assets.

      (6) 39 assets (AID from 2969 to 5040) are from NAM Asset database and their attributes were updated to use the latest attributes from NAM asset database 

     (7)The databases, especially spatial  database, were changed such as duplicated attributes fields in spatial data were removed and only ID field is kept. The user needs to join the Table Assetlist or Elementlist to 

        the spatial data"  16/06/2015

7 2 "(1) Updated 131 new GW point assets with previous AID and some of them may include different element number due to the change of 77 FTypes requested by Hunter assessment project team

      (2) Added 104 EPBC assets, which were assessed and excluded by ERIN

      (3) Merged 30 Darling Hardyhead assets to one (asset AID 60140) and deleted another 29 

      (4) Turned off 5 assets from community workshop (60358 - 60362) as they are duplicated to 5 assets from 104 EPBC excluded assets

     (5) Updated M2 test results

     (6) Asset Names (AID: 4743 and 4747) were changed as requested by Hunter assessment project team (4 lower cases to 4 upper case only). Those two assets are from Namoi asset database and their asset names 

       may not match with original names in Namoi asset database.

     (7)One NSW WSP asset (AID: 60814) was added in as requested by Hunter assessment project team. The process method (without considering 1:M relation) for this asset is not robust and is different to other NSW 

      WSP assets. It should NOT use for other subregions. 

     (8) Queries of Find_All_Used_Assets and Find_All_WD_Assets in the asset database can be used to extract all used assts and all water dependant assts" 20/07/2015

8 2.1 "(1) There are following six assets (in Hun subregion), which is same as 6 assets in GIP subregion. Their AID, Asset Name, Group, SubGroup, Depth, Source and ListDate are using values from GIP assets. You will

         not see AIDs from AID_from_HUN in whole HUN asset datable and spreadsheet anymore and you only can see AIDs from AID_from_GIP ( Actually (a) AID 11636 is GIP got from MBC (B) only AID, Asset Name 

         and ListDate are different and changed)

      (2) For BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20150827.xlsx, (a) Extracted long ( >255 characters) WD rationale for 19 assets (AIDs:  

         8682,9065,9073,9087,9088,9100,9102,9103,60000,60001,60792,60793,60801,60713,60739,60751,60764,60774,60812 ) in tab "Water-dependent asset register" and 37 assets (AIDs: 

         5040,8651,8677,8682,8650,8686,8687,8718,8762,9094,9065,9067,9073,9077,9081,9086,9087,9088,9100,9102,9103,60000,60001,60739,60742,60751,60713,60764,60771,

         60774,60792,60793,60798,60801,60809,60811,60812) in tab "Asset list" in 1.30 Excel file (b) recreated draft BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20150827.xlsx 

      (3) Modified queries (Find_All_Asset_List and Find_Waterdependent_asset_register) for (2)(a)"  27/08/2015

9 2.2 "(1) Updated M2 results from the internal review for 386 Sociocultural assets

      (2)Updated the class to Ecological/Vegetation/Habitat (potential species distribution) for assets/elements from sources of WAIT_ALA_ERIN, NSW_TSEC, NSW_DPI_Fisheries_DarlingHardyhead"  8/09/2015

10 2.3 "(1) Updated M2 results from the internal review

           \* Changed "Assessment team do not say No" to "All economic assets are by definition water dependent"

          \* Changed "Assessment team say No" : to "These are water dependent, but excluded by the project team based on intersection with the PAE is negligible"

          \* Changed "Rivertyles" to "RiverStyles""  22/09/2015

11 2.4 "(1) Updated M2 test results for 86 assets from the external review

      (2) Updated asset names for two assets (AID: 8642 and 8643) required from the external review

      (3) Created Draft Water Dependent Asset Register file using the template V5"  20/11/2015

12 2.5 "Total number of registered water assets was increased by 1 (= +2-1) due to:

              Two assets changed M2 test from "No" to "Yes" , but one asset assets changed M2 test from "Yes" to "No" 

             from the review done by Ecologist group." 24/02/2016

Dataset Citation

Bioregional Assessment Programme (2015) Asset database for the Hunter subregion on 24 February 2016. Bioregional Assessment Derived Dataset. Viewed 09 October 2018, http://data.bioregionalassessments.gov.au/dataset/a39290ac-3925-4abc-9ecb-b91e911f008f.

Dataset Ancestors

• Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

• Derived From Travelling Stock Route Conservation Values

• Derived From NSW Wetlands

• Derived From Climate Change Corridors Coastal North East NSW

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• **Derived

Project website: http://dhbasecamp.humanities.ucla.edu/afamfilm/about-the-project/

We are a group of undergraduate and graduate students in the Digital Humanities program at the University of California, Los Angeles. This main goal of this project was to collaboratively create a database on early African-American silent race films by drawing together information in a wide range of primary and secondary sources. For the purpose of this project, we determined that we would only include silent films created before 1930 for African-American audiences. This definition was the main factor that informed our decisions to include or exclude pieces of data. (You can read more about how we arrived at our definition here: http://dhbasecamp.humanities.ucla.edu/afamfilm/whatis/definition/.)

The database we have created contains information on films, actors, production companies, and other aspects of early silent-era African American race films. The database is intended to allow the public to learn about this period in film history that is too rarely discussed.

This is the official Live Well San Diego database with the Top 10 Indicators and Expanded Indicators. Baseline data begin in 2012 where data are available and continue through current day. Data are collected on an annual basis.

For definitions and sourcing, please use the Live Well San Diego Data Dictionary: https://data.sandiegocounty.gov/Live-Well-San-Diego/Live-Well-San-Diego-Data-Dictionary/37vr-nftn/about_data

Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services Division, Community Health Statistics Unit.

State and Local Public Health Departments in the United States Governmental public health departments are responsible for creating and maintaining conditions that keep people healthy. A local health department may be locally governed, part of a region or district, be an office or an administrative unit of the state health department, or a hybrid of these. Furthermore, each community has a unique "public health system" comprising individuals and public and private entities that are engaged in activities that affect the public's health. (Excerpted from the Operational Definition of a functional local health department, National Association of County and City Health Officials, November 2005) Please reference http://www.naccho.org/topics/infrastructure/accreditation/upload/OperationalDefinitionBrochure-2.pdf for more information. Facilities involved in direct patient care are intended to be excluded from this dataset; however, some of the entities represented in this dataset serve as both administrative and clinical locations. This dataset only includes the headquarters of Public Health Departments, not their satellite offices. Some health departments encompass multiple counties; therefore, not every county will be represented by an individual record. Also, some areas will appear to have over representation depending on the structure of the health departments in that particular region. Town health officers are included in Vermont and boards of health are included in Massachusetts. Both of these types of entities are elected or appointed to a term of office during which they make and enforce policies and regulations related to the protection of public health. Visiting nurses are represented in this dataset if they are contracted through the local government to fulfill the duties and responsibilities of the local health organization. Since many town health officers in Vermont work out of their personal homes, TechniGraphics represented these entities at the town hall. This is denoted in the [DIRECTIONS] field. Effort was made by TechniGraphics to verify whether or not each health department tracks statistics on communicable diseases. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. "#" and "*" characters were automatically removed from standard HSIP fields populated by TechniGraphics. Double spaces were replaced by single spaces in these same fields. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based on this field, the oldest record dates from 11/18/2009 and the newest record dates from 01/08/2010.

South Creek Floodplain Risk Management Study and South Creek Floodplain Risk Management Plan reports Data and Resources South Creek Floodplain Risk Management Study and Plan Reportspdf (112.1 MB) South Creek Floodplain Risk Management Study - Report Explore More information Download More info Creative Commons Attribution 4.0 International Public License By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution 4.0 International Public License (“Public License”). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions. Section 1 – Definitions. 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