The landscape of the conterminous United States has changed dramatically over the last 200 years, with agricultural land use, urban expansion, forestry, and other anthropogenic activities altering land cover across vast swaths of the country. While land use and land cover (LULC) models have been developed to model potential future LULC change, few efforts have focused on recreating historical landscapes. Researchers at the US Geological Survey have used a wide range of historical data sources and a spatially explicit modeling framework to model spatially explicit historical LULC change in the conterminous United States from 1992 back to 1938. Annual LULC maps were produced at 250-m resolution, with 14 LULC classes. Assessment of model results showed good agreement with trends and spatial patterns in historical data sources such as the Census of Agriculture and historical housing density data, although comparison with historical data is complicated by definitional and methodological differences. The completion of this dataset allows researchers to assess historical LULC impacts on a range of ecological processes.

This GIS Dataset is prepared strictly for illustrative and reference purposes only and should not be used, and is not intended for legal, survey, engineering or navigation purposes.No warranty is made by the Bureau of Indian Affairs (BIA) for the use of the data for purposes not intended by the BIA. This GIS Dataset may contain errors. There is no impact on the legal status of the land areas depicted herein and no impact on land ownership. No legal inference can or should be made from the information in this GIS Dataset. The GIS Dataset is to be used solely for illustrative, reference and statistical purposes and may be used for government to government Tribal consultation. Reservation boundary data is limited in authority to those areas where there has been settled Congressional definition or final judicial interpretation of the boundary. Absent settled Congressional definition or final judicial interpretation of a reservation boundary, the BIA recommends consultation with the appropriate Tribe and then the BIA to obtain interpretations of the reservation boundary.The land areas and their representations are compilations defined by the official land title records of the Bureau of Indian Affairs (BIA) which include treaties, statutes, Acts of Congress, agreements, executive orders, proclamations, deeds and other land title documents. The trust, restricted, and mixed ownership land area shown here, are suitable only for general spatial reference and do not represent the federal government’s position on the jurisdictional status of Indian country. Ownership and jurisdictional status is subject to change and must be verified with plat books, patents, and deeds in the appropriate federal and state offices.Included in this dataset are the exterior extent of off reservation trust, restricted fee tracts and mixed tracts of land including Public Domain allotments, Dependent Indian Communities, Homesteads and government administered lands and those set aside for schools and dormitories. There are also land areas where there is more than one tribe having an interest in or authority over a tract of land but this information is not specified in the AIAN-LAR dataset. The dataset includes both surface and subsurface tracts of land (tribal and individually held) “off reservation” tracts and not simply off reservation “allotments” as land has in many cases been subsequently acquired in trust.These data are public information and may be used by various organizations, agencies, units of government (i.e., Federal, state, county, and city), and other entities according to the restrictions on appropriate use. It is strongly recommended that these data be acquired directly from the BIA and not indirectly through some other source, which may have altered or integrated the data for another purpose for which they may not have been intended. Integrating land areas into another dataset and attempting to resolve boundary differences between other entities may produce inaccurate results. It is also strongly recommended that careful attention be paid to the content of the metadata file associated with these data. Users are cautioned that digital enlargement of these data to scales greater than those at which they were originally mapped can cause misinterpretation.The BIA AIAN-LAR dataset’s spatial accuracy and attribute information are continuously being updated, improved and is used as the single authoritative land area boundary data for the BIA mission. These data are available through the Bureau of Indian Affairs, Office of Trust Services, Division of Land Titles and Records, Branch of Geospatial Support.These data have been made publicly available from an authoritative source other than this Atlas and data should be obtained directly from that source for any re-use. See the original metadata from the authoritative source for more information about these data and use limitations. The authoritative source of these data can be found at the following location: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2021.html#list-tab-790442341The BIA Indian Lands dataset’s spatial accuracy and attribute information are continuously being updated, improved and is used as the single authoritative land area boundary data for the BIA mission. This data are available through the Bureau of Indian Affairs, Office of Trust Services, Division of Land Titles and Records, Branch of Geospatial Support. Please feel free to contact us at 1-877-293-9494 geospatial@bia.gov

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the Northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the Southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe’s Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe’s Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS.

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

The 2020 North American Land Cover 30-meter dataset was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between Natural Resources Canada, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadística y Geografía), National Commission for the Knowledge and Use of the Biodiversity (Comisión Nacional Para el Conocimiento y Uso de la Biodiversidad), and the National Forestry Commission of Mexico (Comisión Nacional Forestal). The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries. The general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each country’s specific requirements. This 30-meter dataset of North American Land Cover reflects land cover information for 2020 from Mexico and Canada, 2019 over the conterminous United States and 2021 over Alaska. Each country developed its own classification method to identify Land Cover classes and then provided an input layer to produce a continental Land Cover map across North America. Canada, Mexico, and the United States developed their own 30-meter land cover products; see specific sections on data generation below. The main inputs for image classification were 30-meter Landsat 8 Collection 2 Level 1 data in the three countries (Canada, the United States and Mexico). Image selection processes and reduction to specific spectral bands varied among the countries due to study-site-specific requirements. While Canada selected most images from the year 2020 with a few from 2019 and 2021, the Conterminous United States employed mainly images from 2019, while Alaska land cover maps are mainly based on the use of images from 2021. The land cover map for Mexico was based on land cover change detection between 2015 and 2020 Mexico Landsat 8 mosaics. In order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by CONABIO, INEGI, and CONAFOR; and for the United States by the USGS. Each country chose their own approaches, ancillary data, and land cover mapping methodologies to create national datasets. This North America dataset was produced by combining the national land cover datasets. The integration of the three national products merged four Land Cover map sections, Alaska, Canada, the conterminous United States and Mexico.

Total-Assets Time Series for Simon Property Group Inc. Simon Property Group, Inc. (NYSE:SPG) is a self-administered and self-managed real estate investment trust ("REIT"). Simon Property Group, L.P., or the Operating Partnership, is our majority-owned partnership subsidiary that owns all of our real estate properties and other assets. In this package, the terms Simon, we, our, or the Company refer to Simon Property Group, Inc., the Operating Partnership, and its subsidiaries. We own, develop and manage premier shopping, dining, entertainment and mixed-use destinations, which consist primarily of malls, Premium Outlets, The Mills, and International Properties. At December 31, 2024, we owned or had an interest in 229 properties comprising 183 million square feet in North America, Asia and Europe. We also owned an 88% interest in The Taubman Realty Group, or TRG, which owns 22 regional, super-regional, and outlet malls in the U.S. and Asia. Additionally, at December 31, 2024, we had a 22.4% ownership interest in Klepierre, a publicly traded, Paris-based real estate company, which owns shopping centers in 14 European countries.

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

OverviewThe Jurisdictional Units dataset outlines wildland fire jurisdictional boundaries for federal, state, and local government entities on a national scale and is used within multiple wildland fire systems including the Wildland Fire Decision Support System (WFDSS), the Interior Fuels and Post-Fire Reporting System (IFPRS), the Interagency Fuels Treatment Decision Support System (IFTDSS), the Interagency Fire Occurrence Reporting Modules (InFORM), the Interagency Reporting of Wildland Fire Information System (IRWIN), and the Wildland Computer-Aided Dispatch Enterprise System (WildCAD-E).In this dataset, agency and unit names are an indication of the primary manager’s 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 JurisdictionalUnitIID=null, JurisdictionalKind=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.).AttributesField NameDefinitionGeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. Not populated for Census Block Groups.JurisdictionalUnitIDWhere it could be determined, this is the NWCG 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 in the Unit ID standard.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.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 except for Census Blocks Group and for PAD-US polygons that did not have an associated name.LocalNameLocal name for the polygon provided from agency authoritative data, PAD-US, or other source.JurisdictionalKindDescribes the type of unit jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, Other, and Private. A value is not populated for Census Block Groups.JurisdictionalCategoryDescribes the type of unit jurisdiction using the NWCG Landowner Category data standard. Valid values include: BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, State, OtherLoc (other local, not in the standard), Private, and ANCSA. A value is not populated for Census Block Groups.LandownerKindThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. Legal values align with the NWCG Landowner Kind data standard. A value is populated for all polygons.LandownerCategoryThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. Legal values align with the NWCG Landowner Category data standard. A value is populated for all polygons.LandownerDepartmentFederal department information that aligns with a unit’s landownerCategory information. Legal values include: Department of Agriculture, Department of Interior, Department of Defense, and Department of Energy. A value is not populated for all polygons.DataSourceThe database from which the polygon originated. An effort is made to be as specific as possible (i.e. identify the geodatabase name and feature class in which the polygon originated).SecondaryDataSourceIf the DataSource field is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if DataSource is "PAD-US 4.0", then for a TNC polygon, the SecondaryDataSource would be " TNC_PADUS2_0_SA2015_Public_gdb ".SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.DataSourceYearYear that the source data for the polygon were acquired.MapMethodControlled vocabulary to define how the geospatial feature was derived. MapMethod will be Mixed Methods 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; Other.DateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using the 24-hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature.JoinMethodAdditional information on how the polygon was matched to information in the NWCG Unit ID database.LegendJurisdictionalCategoryJurisdictionalCategory values grouped for more intuitive use in a map legend or summary table. Census Block Groups are classified as “No Unit”.LegendLandownerCategoryLandownerCategory values grouped for more intuitive use in a map legend or summary table.Other Relevant NWCG Definition StandardsUnitA 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: Protecting Unit; LandownerData SourcesThis dataset is an aggregation of multiple spatial data sources: • Authoritative land ownership records from BIA, BLM, NPS, USFS, USFWS, and the Alaska Fire Service/State of Alaska• The Protected Areas Database US (PAD-US 4.0)• Census Block-Group Geometry BIA and Tribal Data:BIA and Tribal land management data were aggregated from BIA regional offices. These data date from 2012 and were reviewed/updated in 2024. 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 spatial data coverage is a consolidation of the best available records/data received from each of the 12 BIA Regional Offices. The data are no better than the original sources from which they were derived. Care was taken when consolidating these files. However, BWFM cannot accept any responsibility for errors, omissions, or positional accuracy in the original digital data. The information contained in these data is dynamic and is continually changing. Updates to these data will be made whenever such data are received from a Regional Office. The BWFM gives no guarantee, expressed, written, or implied, regarding the accuracy, reliability, or completeness of these data.Alaska:The state of Alaska and Alaska Fire Service (BLM) co-manage a process to aggregate authoritative land ownership, management, and jurisdictional boundary data, based on Master Title Plats. Data ProcessingTo compile this dataset, the authoritative land ownership records and the PAD-US data mentioned above were crosswalked into the Jurisdictional Unit Polygon schema and aggregated through a series of python scripts and FME models. Once aggregated, steps were taken to reduce overlaps within the data. All overlap areas larger than 300 acres were manually examined and removed with the assistance of fire management SMEs. Once overlaps were removed, Census Block Group geometry were crosswalked to the Jurisdictional Unit Polygon schema and appended in areas in which no jurisdictional boundaries were recorded within the authoritative land ownership records and the PAD-US data. Census Block Group geometries represent areas of unknown Landowner Kind/Category and Jurisdictional Kind/Category and were assigned LandownerKind and LandownerCategory values of "Private".Update FrequencyThe Authoritative land ownership records and PAD-US data used to compile this dataset are dynamic and are continually changing. Major updates to this dataset will be made once a year, and minor updates will be incorporated throughout the year as needed. New to the Latest Release (1/15/25)Now pulling from agency authoritative sources for BLM, NPS, USFS, and USFWS (instead of getting this data from PADUS).

Field Name Changes

This data was collected by the U.S. Bureau of Land Management (BLM) in New Mexico at both the New Mexico State Office and at the various field offices. This dataset is meant to depict the surface owner or manager of the land parcels. In the vast majority of land parcels, they will be one and the same. However, there are instances where the owner and manager of the land surface are not the same. When this occurs, the manager of the land is usually indicated. BLM's Master Title Plats are the official land records of the federal government and serve as the primary data source for depiction of all federal lands. Information from State of New Mexico is the primary source for the depiction of all state lands. Auxilliary source are referenced, as well, for the depiction of all lands. Collection of this dataset began in the 1980's using the BLM's ADS software to digitize information at the 1:24,000 scale. In the mid to late 1990's the data was converted from ADS to ArcInfo software and merged into tiles of one degree of longitude by one half degree of latitude. These tiles were regularly updated. The tiles were merged into a statewide coverage. The source geodatabase for this shapefile was created by loading the merged ArcInfo coverage into a personal geodatabase. The geodatabase data were snapped to a more accurate GCDB derived land network, where available. In areas where GCDB was not available the data were snapped to digitized PLSS. In 2006, the personal geodatabase was loaded into an enterprise geodatabase (SDE). This shapefile has been created by exporting the feature class from SDE.

All organisations hold information about the core of their business. Forestry England holds information on trees and forests. We use this information to help us run our business and make decisions. The role of the Forest Inventory (the Sub-compartment Database (SCDB) and the stock maps) is to be our authoritative data source, giving us information for recording, monitoring, analysis and reporting. Through this it supports decision-making on the whole of the FE estate. Information from the Inventory is used by FE, wider government, industry and the public for economic, environmental and social forest-related decision-making. Furthermore, it supports forest-related national policy development and government initiatives, and helps us meet our national and international forest-related reporting responsibilities. Information on our current forest resource, and the future expansion and availability of wood products from our forests, is vital for planners both in and outside FE. It is used when looking at the development of processing industries, regional infrastructure, the effect upon communities of our actions, and to prepare and monitor government policies. The Inventory (SCDB and stock maps), with ‘Future Forest Structure’ and the ‘rollback’ functionality of Forester, will help provide a definitive measure of trends in extent, structure, composition, health, status, use, and management of all FE land holdings. We require this to meet national and international commitments, to report on the sustainable management of forests as well as to help us through the process of business and Forest Design Planning. As well as helping with the above, the SCDB helps us address detailed requests from industry, government, non-government organisations and the public for information on our estate. FE's growing national and international responsibilities and the requirements for monitoring and reporting on a range of forest statistics have highlighted the technical challenges we face in providing consistent, national level data. A well kept and managed SCDB and GIS (Geographical Information System - Forester) will provide the best solution for this and assist countries in evidence-based policy making. Looking ahead at international reporting commitments; one example of an area where requirements look set to increase will be reporting on our work to combat climate change and how our estate contributes to carbon sequestration. We have put in place processes to ensure that at least the basics of our inventory are covered: The inventory of forests; The land-uses; The land we own ( Deeds); The roads we manage. We depend on others to allow us to manage the forests and to provide us with funds and in doing so we need to be seen to be responsible and accountable for our actions. A foundation of achieving this is good record keeping. A subcompartment should be recognisable on the ground. It will be similar enough in land use, species or habitat composition, yield class, age, condition, thinning history etc. to be treated as a single unit. They will generally be contiguous in nature and will not be split by roads, rivers, open space etc. Distinct boundaries are required, and these will often change as crops are felled, thinned, replanted and resurveyed. In some parts of the country foresters used historical and topographical features to delineate subcompartment boundaries, such as hedges, walls and escarpments. In other areas no account of the history and topography of the site was taken, with field boundaries, hedges, walls, streams etc. being subsumed into the sub-compartment. Also, these features may or may not appear on the OS backdrop, again this was dependent on the staff involved and what they felt was relevant to the map. The main point is that, as managers we may find such obvious features in the middle of a subcompartment when nothing is indicated on the stock map, while the same thing would be indicated elsewhere. Attributes; FOREST Cost centre Nos. COMPTMENT Compartment Nos. SUBCOMPT Sub-compartment letter BLOCK Block nos. CULTCODE Cultivation Code CULTIVATN Cultivation PRIHABCODE Primary Habitat Code PRIHABITAT Primary Habitat PRILANDUSE Land Use of primary component PRISPECIES Primary component tree species PRI_PLYEAR prim. component year planted PRIPCTAREA Prim. component %Area of sub-compartment SECHABCODE Secondary Habitat Code SECHABITAT Secondary Habitat SECLANDUSE Land Use of secondary component SECSPECIES Secondary component tree species SEC_PLYEAR Secondary component year planted SECPCTAREA Secondary component %Area of sub-compartment TERLANDUSE Land Use of tertiary component TERSPECIES Tertiary component tree species TER_PLYEAR Tertiary component year planted TERPCTAREA Tertiary component %Area of sub-compartment TERHABITAT Tertiary Habitat TERHABCODE Tertiary Habitat Code. Any maps produced using this data should contain the following Forestry Commission acknowledgement: “Contains, or is based on, information supplied by the Forestry Commission. © Crown copyright and database right 2025 Ordnance Survey AC0000814847”. Attribution statement: © Forestry Commission copyright and/or database right 2025. All rights reserved. Contains OS data © Crown copyright and database right 2025.

--- DATASET OVERVIEW --- This dataset captures detailed information about each vacation rental property listing, providing insights that help users understand property distribution, characteristics, management styles, and guest preferences across different regions. With extensive global coverage and regular weekly updates, this dataset offers in-depth snapshots of vacation rental supply traits at scale.

The data is sourced directly from major OTA platforms using advanced data collection methodologies that ensure high accuracy and reliability. Each property listing is tracked over time, enabling users to observe changes in supply, amenity offerings, and host practices.

--- KEY DATA ELEMENTS --- Our dataset includes the following core performance metrics for each property: - Property Identifiers: Unique identifiers for each property with OTA-specific IDs - Geographic Information: Location data including neighborhood, city, region, and country - Listing Characteristics: Property type, bedroom count, bathroom count, in-service dates. - Amenity Inventory: Comprehensive list of available amenities, including essential facilities, luxury features, and safety equipment. - Host Information: Host details, host types, superhost status, and portfolio size - Guest Reviews: Review counts, average ratings, detailed category ratings (cleanliness, communication, etc.), and review timestamps - Property Rules: House rules, minimum stay requirements, cancellation policies, and check-in/check-out procedures

--- USE CASES --- Market Research and Competitive Analysis: VR professionals and market analysts can use this dataset to conduct detailed analyses of vacation rental supply across different markets. The data enables identification of property distribution patterns, amenity trends, pricing strategies, and host behaviors. This information provides critical insights for understanding market dynamics, competitive positioning, and emerging trends in the short-term rental sector.

Property Management Optimization: Property managers can leverage this dataset to benchmark their properties against competitors in the same geographic area. By analyzing listing characteristics, amenity offerings and guest reviews of similar properties, managers can identify optimization opportunities for their own portfolio. The dataset helps identify competitive advantages, potential service gaps, and management optimization strategies to improve property performance.

Investment Decision Support: Real estate investors focused on the vacation rental sector can utilize this dataset to identify investment opportunities in specific markets. The property-level data provides insights into high-performing property types, optimal locations, and amenity configurations that drive guest satisfaction and revenue. This information enables data-driven investment decisions based on actual market performance rather than anecdotal evidence.

Academic and Policy Research: Researchers studying the impact of short-term rentals on housing markets, urban development, and tourism trends can use this dataset to conduct quantitative analyses. The comprehensive data supports research on property distribution patterns and the relationship between short-term rentals and housing affordability in different markets.

Travel Industry Analysis: Travel industry analysts can leverage this dataset to understand accommodation trends, property traits, and supply and demand across different destinations. This information provides context for broader tourism analysis and helps identify connections between vacation rental supply and destination popularity.

--- ADDITIONAL DATASET INFORMATION --- Delivery Details: • Delivery Frequency: weekly | monthly | quarterly | annually • Delivery Method: scheduled file loads • File Formats: csv | parquet • Large File Format: partitioned parquet • Delivery Channels: Google Cloud | Amazon S3 | Azure Blob • Data Refreshes: weekly

Dataset Options: • Coverage: Global (most countries) • Historic Data: N/A • Future Looking Data: N/A • Point-in-Time: N/A • Aggregation and Filtering Options: • Area/Market • Time Scales (weekly, monthly) • Listing Source • Property Characteristics (property types, bedroom counts, amenities, etc.) • Management Practices (professionally managed, by owner)

Contact us to learn about all options.

--- DATA QUALITY AND PROCESSING --- Our data collection and processing methodology ensures high-quality data with comprehensive coverage of the vacation rental market. Regular quality assurance processes verify data accuracy, completeness, and consistency.

The dataset undergoes continuous enhancement through advanced data enrichment techniques, including property categorization, geographic normalization, and time series alignment. This processing ensures that users receive clean, structured data ready for immediate analysis without extensive preprocess...

DescriptionThis is a vector tile layer built from the same data as the Jurisdictional Units Public feature service located here: https://nifc.maps.arcgis.com/home/item.html?id=4107b5d1debf4305ba00e929b7e5971a. This service can be used alone as a fast-drawing background layer, or used in combination with the feature service when Identify and Copy Feature capabilities are needed. At fine zoom levels, the feature service will be needed.OverviewThe Jurisdictional Units dataset outlines wildland fire jurisdictional boundaries for federal, state, and local government entities on a national scale and is used within multiple wildland fire systems including the Wildland Fire Decision Support System (WFDSS), the Interior Fuels and Post-Fire Reporting System (IFPRS), the Interagency Fuels Treatment Decision Support System (IFTDSS), the Interagency Fire Occurrence Reporting Modules (InFORM), the Interagency Reporting of Wildland Fire Information System (IRWIN), and the Wildland Computer-Aided Dispatch Enterprise System (WildCAD-E).In this dataset, agency and unit names are an indication of the primary manager’s 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 JurisdictionalUnitIID=null, JurisdictionalKind=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.).AttributesField NameDefinitionGeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. Not populated for Census Block Groups.JurisdictionalUnitIDWhere it could be determined, this is the NWCG 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 in the Unit ID standard.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.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 except for Census Blocks Group and for PAD-US polygons that did not have an associated name.LocalNameLocal name for the polygon provided from agency authoritative data, PAD-US, or other source.JurisdictionalKindDescribes the type of unit jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, Other, and Private. A value is not populated for Census Block Groups.JurisdictionalCategoryDescribes the type of unit jurisdiction using the NWCG Landowner Category data standard. Valid values include: BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, State, OtherLoc (other local, not in the standard), Private, and ANCSA. A value is not populated for Census Block Groups.LandownerKindThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. Legal values align with the NWCG Landowner Kind data standard. A value is populated for all polygons.LandownerCategoryThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. Legal values align with the NWCG Landowner Category data standard. A value is populated for all polygons.LandownerDepartmentFederal department information that aligns with a unit’s landownerCategory information. Legal values include: Department of Agriculture, Department of Interior, Department of Defense, and Department of Energy. A value is not populated for all polygons.DataSourceThe database from which the polygon originated. An effort is made to be as specific as possible (i.e. identify the geodatabase name and feature class in which the polygon originated).SecondaryDataSourceIf the DataSource field is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if DataSource is "PAD-US 4.0", then for a TNC polygon, the SecondaryDataSource would be " TNC_PADUS2_0_SA2015_Public_gdb ".SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.DataSourceYearYear that the source data for the polygon were acquired.MapMethodControlled vocabulary to define how the geospatial feature was derived. MapMethod will be Mixed Methods 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; Other.DateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using the 24-hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature.JoinMethodAdditional information on how the polygon was matched to information in the NWCG Unit ID database.LegendJurisdictionalCategoryJurisdictionalCategory values grouped for more intuitive use in a map legend or summary table. Census Block Groups are classified as “No Unit”.LegendLandownerCategoryLandownerCategory values grouped for more intuitive use in a map legend or summary table.Other Relevant NWCG Definition StandardsUnitA 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: Protecting Unit; LandownerData SourcesThis dataset is an aggregation of multiple spatial data sources: • Authoritative land ownership records from BIA, BLM, NPS, USFS, USFWS, and the Alaska Fire Service/State of Alaska• The Protected Areas Database US (PAD-US 4.0)• Census Block-Group Geometry BIA and Tribal Data:BIA and Tribal land management data were aggregated from BIA regional offices. These data date from 2012 and were reviewed/updated in 2024. 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 spatial data coverage is a consolidation of the best available records/data received from each of the 12 BIA Regional Offices. The data are no better than the original sources from which they were derived. Care was taken when consolidating these files. However, BWFM cannot accept any responsibility for errors, omissions, or positional accuracy in the original digital data. The information contained in these data is dynamic and is continually changing. Updates to these data will be made whenever such data are received from a Regional Office. The BWFM gives no guarantee, expressed, written, or implied, regarding the accuracy, reliability, or completeness of these data.Alaska:The state of Alaska and Alaska Fire Service (BLM) co-manage a process to aggregate authoritative land ownership, management, and jurisdictional boundary data, based on Master Title Plats. Data ProcessingTo compile this dataset, the authoritative land ownership records and the PAD-US data mentioned above were crosswalked into the Jurisdictional Unit Polygon schema and aggregated through a series of python scripts and FME models. Once aggregated, steps were taken to reduce overlaps within the data. All overlap areas larger than 300 acres were manually examined and removed with the assistance of fire management SMEs. Once overlaps were removed, Census Block Group geometry were crosswalked to the Jurisdictional Unit Polygon schema and appended in areas in which no jurisdictional boundaries were recorded within the authoritative land ownership records and the PAD-US data. Census Block Group geometries represent areas of unknown Landowner Kind/Category and Jurisdictional Kind/Category and were assigned LandownerKind and LandownerCategory values of "Private".Update

Probate, pre-probate, and divorce real estate data offers valuable insights and opportunities for real estate professionals to identify and pursue potential leads. These datasets provide information about properties involved in probate, pre-probate, and divorce cases, enabling professionals to target motivated sellers and navigate specialized market niches. In this brief, we will explore the concept of probate, pre-probate, and divorce data, and discuss their applications across various industries.

What is Probate, Pre-Probate, and Divorce Data?

Probate Data refers to the legal process of settling the estate of a deceased person. Probate data includes information about properties owned by individuals who have passed away and are being transferred to their heirs or beneficiaries through a court-supervised process. This dataset contains details about properties that may be sold to distribute the deceased person’s assets or resolve any outstanding debts.

Pre-Probate Data includes properties owned by individuals who are alive but have designated their assets to be transferred to their heirs upon their passing. This dataset allows real estate professionals to identify potential sellers who may be interested in selling their properties before going through the probate process.

Divorce Data pertains to properties involved in divorce proceedings. When couples go through a divorce, the division of assets often includes the sale or transfer of properties. This dataset provides information on properties that may become available for sale due to a divorce settlement, providing real estate professionals with opportunities to target motivated sellers.

Gain an in-depth view of probate, pre-probate and divorce characteristics for more than 155 million properties across the country (or at the state- and country-level), including: - Property Address - Owner First & Last Name - Mailing Address - Legal Description - Property Value - Property Use - Parcel ID - Year Built - Date Of Death (Probate & Pre-Probate) - Defendant Information (Divorce) - Plaintiff Information (Divorce) - Defendant Attorney Information (Divorce) - Plaintiff Attorney Information (Divorce)

Introduction of the euro in the new member states. Topics: contact with and use of euro banknotes or coins; use of euro banknotes or coins in the own country or abroad; knowledge test on the euro: equal design of euro banknotes and coins in every country, number of countries that already introduced the euro, possibility of the own country to choose whether to introduce the euro or not, year of introduction of the euro in the own country; self-rated knowledge on the euro; preferred time of information about the introduction of the euro in the own country; trust in information about the introduction provided by: national government or authorities, tax administrations, national central bank, European institutions, commercial banks, journalists, trade unions or professional organizations, consumer associations; preferred places of information about the euro and the changeover; most important issues to be covered by information campaigns; significance of selected information campaign actions; satisfaction with the replacement of the national currency by the euro; assessment of the impact of the introduction on the own country and on personal life; approval of the introduction of the euro by own friends; preferred time for introducing the euro in the own country; assessment of the impact of the introduction of the euro in the countries already using the euro as positive; expected impact of the introduction on the prices in the own country; assessment of the euro compared to US dollar and Japanese Yen; expected impact of the introduction: easier price comparisons with other countries, easier shopping in other countries, save money by eliminating fees of currency exchange in other countries, more convenient travel in other countries, protection of the own country from the effects of international crises; benefits from the adoption of the euro on the own country: lower interest rates, sounder public finances, reinforcement of the place of Europe in the world, improvement of growth and employment, low inflation rates; approval of the following statements regarding the adoption of the euro: will cause personal inconvenience, concern about abusive price setting during the changeover, loss of control over national economic policy, loss of national identity, strengthening of the feeling of being European. Demography: sex; age; age at end of education; professional position; type of community. Additionally coded was: interviewer ID; language of the interview; country; date of interview; time of the beginning of the interview; duration of the interview; type of phone line; region; weighting factor. Euro-Einführung im Urteil der neuen EU-Mitgliedsländer. Themen: Kontakt mit sowie Gebrauch von Euro-Münzen und Banknoten; Ort des Gebrauchs (Inland/Ausland); Kenntnistest über die Gestalt der Münzen und Banknoten; Kenntnis der Anzahl der EU-Länder mit Euro-Währung; Wahlfreiheit des Landes über die Einführung des Euro; Kenntnis des Einführungsjahrs im eigenen Land; Selbsteinschätzung der Informiertheit über den Euro; gewünschter Zeitpunkt der Information über die Euro-Einführung; Institutionenvertrauen bei Informationen über den Euro; gewünschter Ort der Informationsversorgung über den Euro (z.B. Medien, Banken oder Supermärkten); präferierte Inhalte für eine Informationskampagne: Vorgehen bei der Einführung, Währungswert, Gestalt des Euro, Vorgehen bei der korrekten Umrechnung der einheimischen Währung in Euro, Auswirkungen auf die persönliche Lohnauszahlung oder das Bankkonto sowie wirtschaftliche und politische Auswirkungen; Informationsbedarf über duale Preisauszeichnung in Läden und in Rechnungen, Broschüren, Fernseh-, Zeitungs- und Radiowerbung; Zufriedenheit mit der Einführung einer neuen Währung; Einschätzung der Konsequenzen durch die Euro-Einführung für den Befragten persönlich sowie für das eigene Land; Einschätzung der generellen Meinung zum Euro im eigenen Land; gewünschter Einführungszeitpunkt; Einschätzung der Konsequenzen für die Länder, die den Euro bereits eingeführt haben; erwartete Auswirkungen der Euro-Einführung im eigenen Land: Preisanstieg oder Preisstabilität; Vergleichbarkeit des Euro (als internationale Leitwährung) mit dem US-Dollar oder dem japanischen Yen; erwartete Erleichterungen durch den Euro: einfacher Preisvergleich mit anderen Euro-Ländern, Einkäufe in anderen Euro-Ländern, Einsparungen von Umtauschkosten, Reiseerleichterungen, Schutz des Landes vor internationalen Krisen; Vorteile durch den Euro: niedrigere Zinsraten für Kredite, ausgeglichene öffentliche Finanzen, Stärkung des Standorts Europa, Stärkung von Wachstum und Beschäftigung, Sicherung der Preisstabilität, stärkere Identifizierung mit Europa; Nachteile durch die Euro-Einführung: persönliche Unannehmlichkeiten, Betrug bei der Preisumrechnung, nationaler Kontrollverlust über die Wirtschaftspolitik; Vorteil: idenitätsstiftend. Demographie: Geschlecht; Alter; Alter bei Beendigung der Ausbildung; berufliche Stellung; Urbanisierungsgrad. Zusätzlich verkodet wurde: Interviewer-ID; Interviewsprache; Land; Interviewdatum; Interviewdauer (Interviewbeginn und Interviewende); Interviewmodus (Mobiltelefon oder Festnetz); Region; Gewichtungsfaktor.

Sen4AgriNet is a Sentinel-2 based time series multi country benchmark dataset, tailored for agricultural monitoring applications with Machine and Deep Learning. It is annotated from farmer declarations collected via the Land Parcel Identification System (LPIS) for harmonizing country wide labels. These declarations have only recently been made available as open data, allowing for the first time the labelling of satellite imagery from ground truth data. We proceed to propose and standardise a new crop type taxonomy across Europe that address Common Agriculture Policy (CAP) needs, based on the Food and Agriculture Organization (FAO) Indicative Crop Classification scheme. Sen4AgriNet is the only multi-country, multi-year dataset that includes all spectral information. It is constructed to cover the period 2016-2020 for Catalonia and France, while it can be extended to include additional countries.

The Agriculture, Forestry and Other Land Uses (AFOLU) policy database consists of 724 PaMs reported by the member countries of the European Union, Switzerland, Norway, and the United Kingdom within their Fifth Biennial report (in the Common Tabular Format) (UNFCCC, 2024), and those outlined in the National Energy and Climate Plans (NECPs), which have been integrated into the EEA database of policies and measures (EEA, 2024), with policies updated to 2023. It includes PaMs for the Agriculture and Forestry categories that are planned, adopted, implemented, or expiring after 2022.

The compilation includes PaMs that have a direct impact on emission reduction, as well as those of a fiscal, regulatory, or research nature that contribute indirectly to the achievement of the objectives or lay the groundwork for their future implementation. Notably, 40% of the PaMs have a direct link to some regulatory instrument of the European Union. The selected sources integrate the main PaMs for each country and should have consistency between them.

The developed database was compiled and organized into a single database, avoiding duplications and maintaining the different policies reported by each country, keeping all the relevant information that could potentially be used to model the impact of the PaMs A detailed description of each of the sources used is provided, including their processing, followed by an explanation of how the information was integrated to consolidate the database of AFOLU sector mitigation measures for Europe.

Change-In-Other-Working-Capital Time Series for Simon Property Group Inc. Simon Property Group, Inc. (NYSE:SPG) is a self-administered and self-managed real estate investment trust ("REIT"). Simon Property Group, L.P., or the Operating Partnership, is our majority-owned partnership subsidiary that owns all of our real estate properties and other assets. In this package, the terms Simon, we, our, or the Company refer to Simon Property Group, Inc., the Operating Partnership, and its subsidiaries. We own, develop and manage premier shopping, dining, entertainment and mixed-use destinations, which consist primarily of malls, Premium Outlets, The Mills, and International Properties. At December 31, 2024, we owned or had an interest in 229 properties comprising 183 million square feet in North America, Asia and Europe. We also owned an 88% interest in The Taubman Realty Group, or TRG, which owns 22 regional, super-regional, and outlet malls in the U.S. and Asia. Additionally, at December 31, 2024, we had a 22.4% ownership interest in Klepierre, a publicly traded, Paris-based real estate company, which owns shopping centers in 14 European countries.

This raster includes Ecological System or Land Use Classes from the National GAP Land Cover Data (v2) that represent Chihuahuan Desert Scrub in the Chihuahuan Desert REA Analysis Extent. See Process Steps.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the Northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the Southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe#8217;s Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe#8217;s Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS.

These data area an extraction from the National Conservation Easement Database (NCED) by the boundaries of the Colorado Plateau Ecoregion study area.

The National Conservation Easement Database (NCED) is the first national database of conservation easement information, compiling records from land trusts and public agencies throughout the United States. This public-private partnership brings together easement information from national conservation groups, local and regional land trusts, and state and federal agencies. NCED is limited to the continental U.S., Alaska, and Hawaii. It does not include conservation easement data for U.S. territories at this time. The NCED dataset portrays the nations conservation easements with a standardized spatial geometry and numerous valuable attributes on land ownership, management designations, and conservation status (using national GAP coding systems). The database represents the full range of conservation designations for conservation easements in the United States. Our database does not distinguish a protection threshold above which biodiversity is considered secure. Instead, a complete suite of conservation easement attributes are provided for each polygon with the purpose of giving users the information they need to define the most relevant conservation thresholds for their own objectives and requirements. Collaborating with the nations leading data providers, the goal is to provide regular updates.

Ecosystems benefit people in many ways, but these contributions do not appear in traditional national or corporate accounts so are often left out of policy- and decision-making. Ecosystem accounts, as formalized by the System of Environmental-Economic Accounting Experimental Ecosystem Accounts (SEEA EEA), track the extent and condition of ecosystem assets and the flows of ecosystem services they provide to people and the economy. While ecosystem accounts have been compiled in a number of countries, there have been few attempts to develop them for the United States. We explore the potential for ecosystem accounting in the United States by compiling ecosystem condition and ecosystem services supply and use accounts for a ten-state region in the Southeast. The pilot accounts include information related to air quality, water quality, biodiversity, carbon storage, recreation, and pollination for selected years from 2001 to 2015. Results from our pilot accounts illustrate how ecosystem accounts information can contribute to policy and decision-making. Using an example for Atlanta, we also show how ecosystem accounts can be considered alongside other SEEA accounts, such as land and water accounts, to give a more complete picture of a local area’s environmental-economic status. The process by which we determined where to place metrics within the accounting framework, which was strongly informed by the National Ecosystem Services Classification System (NESCS), can provide practical guidance for future ecosystem accounts in the U.S. and other countries, and for expanding the scope of U.S. ecosystem accounts. Finally, we identify knowledge and data gaps that limit the inclusion of certain ecosystem services in the accounts and suggest future research and data collection that can close these gaps and improve future ecosystem accounts in the U.S.