This data set consists of maps of various soil characteristics for all of Russia. The maps are available as ESRI Shapefiles and they are accompanied by databases of soil profiles and related characteristics. The soil classification Shapefile was generalized from the standard 1:2,500,000 soil map of Russia (Fridland, 1988). Several different soil classifications are presented as well as detailed soil characteristics. Additionally, investigators compiled two databases (.dbf files) of detailed soil characteristics from 234 measured soil profiles. These data were extracted from a larger collection entitled Land Resources of Russia. Data and documentation © 2002 copyright International Institute for Applied Systems Analysis and the Russian Academy of Sciences.

This raster depicts the percentage of lithological aluminum oxide (Al2O3) content in surface or near surface geology. We derived these rasters by calculating the average percent Al2O3 content for each map unit in combined surficial-bedrock geologic maps. We used state geologic maps (Preliminary Integrated Geologic Map Databases for the United States, Open File Reports 2004-1355, 2005-1305, 2005-1323, 2005-1324, 2005-1325, 2005-1351, and 2006-1272), which depict surficial geology instead of bedrock when the surficial layers are sufficiently deep. For the state maps that do not incorporate surficial geology (i.e., midwestern states), we overlaid surficial geologic map units with thicknesses greater than 100 feet (from Soller and Reheis [2004]) to produce combined surficial-bedrock geologic maps that were similar to other states. We characterized geology based on the 201 different lithologies that the Geologic Map Database lists as occurring in the conterminous United States. Because some of these lithologies are known to have chemical attributes that vary widely, we created an additional 78 lithologic classes based on the common modifiers used in the geologic unit descriptions to better parse chemical variability within the lithologies (e.g., calcareous and noncalcareous sandstone). Modifiers were assigned base on descriptions of geologic formations obtained through either the Lexicon of Geologic Names of the United States or literature searches. Fifteen lithologic classes were not characterized because the class was not a specific rock type (e.g., mélange, water, and landslide). These classes were characterized as no data. We translated each state’s combined surficial-bedrock geologic maps into characteristics following the methods in Olson and Hawkins (2012) by assigning an estimate of each map unit’s percent Al2O3 content to every occurrence of that map unit in the combined surficial-bedrock geologic map. This estimate was calculated as the average of literature or database values of the respective property for each lithological class contained within the map unit weighted by the prevalence of each lithological class within the map unit. The accompanying Excel workbook (Lith-MajorOxides.xls) contains a summary of all of the average geochemical characteristics for each lithology (“Lith Summary” tab) and tabs for each individual lithology that include the source of each record (e.g., originating from the Earth Chem Database or the specific literature reference), as well as the calculations used to determine the measure of central tendency (mean or median depending on the data). The final national raster was created by merging each of the individual state rasters. Users should be cognizant that some differences will exist in chemical and physical characterizations across state lines that are caused by unreconciled differences in lithologic descriptions or mapping scales used among the underlying state source maps.

This data set contains the sea floor topographic contours, sun-illuminated topographic imagery, and backscatter intensity generated from a multibeam sonar survey of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts, an area of approximately 1100 square nautical miles. The Stellwagen Bank NMS Mapping Project is designed to provide detailed maps of the Stellwagen Bank region's environments and habitats and the first complete multibeam topographic and sea floor characterization maps of a significant region of the shallow EEZ. Data were collected on four cruises over a two year period from the fall of 1994 to the fall of 1996. The surveys were conducted aboard the Candian Hydrographic Service vessel Frederick G. Creed, a SWATH (Small Waterplane Twin Hull) ship that surveys at speeds of 16 knots. The multibeam data were collected utilizing a Simrad Subsea EM 1000 Multibeam Echo Sounder (95 kHz) that is permanently installed in the hull of the Creed.

Web application that allows the user to query a variety of roadway characteristics including; Functional Classification, Scenic Roadways, National Highway System, Legislative Names, and Public Road Mileage by Maintenance Responsibility.

This dataset provides the spatial distribution of vegetation types, soil carbon, and physiographic features in the Imnavait Creek area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology. Data are also provided on the research grids for georeferencing. The map data are from a variety of sources and encompass the period 1970-06-01 to 2015-08-31.

This data set provides the spatial distributions of vegetation types, soil carbon, and physiographic features in the Toolik Lake area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology.

This data set provides a collection of vegetation, landscape, geobotanical, elevation, hydrology, and geologic maps for the Kuparuk River Basin, North Slope, Alaska. The maps cover either (1) the entire Kuparuk River Basin, from the headwaters on the north side of the Brooks Range to the Beaufort Sea coast, or (2) the selected Upper Kuparuk River Region including the Toolik Lake and Imnavait Creek research areas. The maps were produced from imagery and existing geobotanical maps covering the period 1976-08-04 to 2008-12-31.

These data are part of a larger USGS project to develop an updated geospatial database of mines, mineral deposits and mineral regions in the United States. Mine and prospect-related symbols, such as those used to represent prospect pits, mines, adits, dumps, tailings, etc., hereafter referred to as “mine” symbols or features, are currently being digitized on a state-by-state basis from the 7.5-minute (1:24, 000-scale) and the 15-minute (1:48, 000 and 1:62,500-scale) archive of the USGS Historical Topographic Maps Collection, or acquired from available databases (California and Nevada, 1:24,000-scale only). Compilation of these features is the first phase in capturing accurate locations and general information about features related to mineral resource exploration and extraction across the U.S. To date, the compilation of 400,000-plus point and polygon mine symbols from approximately 51,000 maps of 17 western states (AZ, CA, CO, ID, KS, MT, ND, NE, NM, NV, OK, OR, SD, UT, WA, WY and western TX) has been completed. The process renders not only a more complete picture of exploration and mining in the western U.S., but an approximate time line of when these activities occurred. The data may be used for land use planning, assessing abandoned mine lands and mine-related environmental impacts, assessing the value of mineral resources from Federal, State and private lands, and mapping mineralized areas and systems for input into the land management process. The data are presented as three groups of layers based on the scale of the source maps. No reconciliation between the data groups was done.

The database represents point locations and associated stand assessment data collected within known aspen stands in the Inyo National Forest, Inyo County, California. The Inyo National Forest assessed aspen as a part of the Terrestrial Ecological Unit Inventory (TEUI). This data were gathered during the summers of 2005. The purpose of the TEUIs is to identify ecological map units and ecological types and interpretations for ecosystem management and planning. The aspen are within the Potential Natural Vegetation (PNV) layer, which is a base layer of the TEUIs. The associated Polygon layer delineates stands based on dominant vegetation types from aerial imagery and field verification. Associated with this point layer is a polygon layer (INYO_NF_POLY) containing aspen stands delineated in conjunction with the aspen assessment information. Data Compilation: The Aspen Delineation Project (ADP) is a collaborative effort of the U.S. Forest Service's Pacific Southwest Region, the California Department of Fish and Games Resource Assessment Program, and the California Office of Bureau of Land Management. Principal Investigator for ADP is David Burton; visit: www.aspensite.org for more information regarding the ADP. The Department of Fish and Games, Resource Assessment Program compiled this information from the collaborating agencies and other researchers, and formatted the data into a common database for the purpose of facilitating access to data related to the conservation of Quaking Aspen in California. This information portal falls within the ADP goals to help agencies and land managers identify, map, treat, and monitor aspen habitats. This dataset is a portion of a master database compiled during a year long effort in 2005 to pull together current GIS layers and maps depicting Aspen communities in California.

High resolution orthorectified images combine the image characteristics of an aerial photograph with the geometric qualities of a map. An orthoimage is a uniform-scale image where corrections have been made for feature displacement such as building tilt and for scale variations caused by terrain relief, sensor geometry, and camera tilt. A mathematical equation based on ground control points, sensor calibration information, and a digital elevation model is applied to each pixel to rectify the image to obtain the geometric qualities of a map.

A digital orthoimage may be created from several photographs mosaicked to form the final image. The source imagery may be black-and-white, natural color, or color infrared with a pixel resolution of 1-meter or finer. With orthoimagery, the resolution refers to the distance on the ground represented by each pixel.

Background and Data Limitations The Massachusetts 1830 map series represents a unique data source that depicts land cover and cultural features during the historical period of widespread land clearing for agricultural. To our knowledge, Massachusetts is the only state in the US where detailed land cover information was comprehensively mapped at such an early date. As a result, these maps provide unusual insight into land cover and cultural patterns in 19th century New England. However, as with any historical data, the limitations and appropriate uses of these data must be recognized: (1) These maps were originally developed by many different surveyors across the state, with varying levels of effort and accuracy. (2) It is apparent that original mapping did not follow consistent surveying or drafting protocols; for instance, no consistent minimum mapping unit was identified or used by different surveyors; as a result, whereas some maps depict only large forest blocks, others also depict small wooded areas, suggesting that numerous smaller woodlands may have gone unmapped in many towns. Surveyors also were apparently not consistent in what they mapped as ‘woodlands’: comparison with independently collected tax valuation data from the same time period indicates substantial lack of consistency among towns in the relative amounts of ‘woodlands’, ‘unimproved’ lands, and ‘unimproveable’ lands that were mapped as ‘woodlands’ on the 1830 maps. In some instances, the lack of consistent mapping protocols resulted in substantially different patterns of forest cover being depicted on maps from adjoining towns that may in fact have had relatively similar forest patterns or in woodlands that ‘end’ at a town boundary. (3) The degree to which these maps represent approximations of ‘primary’ woodlands (i.e., areas that were never cleared for agriculture during the historical period, but were generally logged for wood products) varies considerably from town to town, depending on whether agricultural land clearing peaked prior to, during, or substantially after 1830. (4) Despite our efforts to accurately geo-reference and digitize these maps, a variety of additional sources of error were introduced in converting the mapped information to electronic data files (see detailed methods below). Thus, we urge considerable caution in interpreting these maps. Despite these limitations, the 1830 maps present an incredible wealth of information about land cover patterns and cultural features during the early 19th century, a period that continues to exert strong influence on the natural and cultural landscapes of the region.

    Acknowledgements
    Financial support for this project was provided by the BioMap Project of the Massachusetts Natural Heritage and Endangered Species Program, the National Science Foundation, and the Andrew Mellon Foundation. This project is a contribution of the Harvard Forest Long Term Ecological Research Program.

This CSV file shows total population counts by sex, age, and race groupsdata from the2020 CensusDemographic andHousing Characteristics. Thisisshown by Nation, Consolidated City, Census Designated Place, Incorporated Placeboundaries. Eachgeographylayercontainsa common set of Census countsbased on available attributes from the U.S. Census Bureau. There are alsoadditionalcalculated attributes related to this topic, which can be mapped or used within analysis.  Vintageof boundaries and attributes:2020Demographic andHousing CharacteristicsTable(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this file.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDatethe Data was Downloaded: May 25, 2023Geography Levels included: Nation, Consolidated City, Census Designated Place, Incorporated PlaceNational Figures: included in Nation layerThe United States Census BureauDemographic andHousing Characteristics:2020 Census Results2020 Census Data QualityGeography &2020 CensusTechnical DocumentationData Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & UpdatesData Processing Notes:These 2020 Census boundaries come from the US Census TIGER geodatabases.These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. ForCensustractsand block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square metersor larger (mid tolarge sizedwater bodies) are erased from the tractand block groupboundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased tomore accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters). The layercontainsall US states, Washington D.C., and Puerto Rico.Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can beidentifiedby the "_calc_" stub in the field name).Field alias names were created based on the Table Shells file available from the Data Table Guide for theDemographic Profile and Demographic andHousing Characteristics.Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected usingdifferential privacy techniquesby the U.S. Census Bureau.

Abstract This dataset was derived by the Bioregional Assessment Programme. The parent dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains cartographic mask polygon shapefiles for maps created in COO 230. These polygons are used for clear annotation and to mask-out unwanted features in report maps. ## Dataset History Polygon mask features were created using the 'Features Outline Masks (Cartography)' tool (ArcMap) on annotation layers in maps for product COO 2.3. For this dataset, masks were created from the annotations created from the following layer (dataset): 1. PopulatedPlaces Feature Class from the "GEODATA TOPO 250K Series 3" dataset (GUID: a0650f18-518a-4b99-a553-44f82f28bb5f). Masks polygons were also created for clear visualisation of graticules and state annotation graphics, as well as other cartographic labels and graphics in the same maps. ## Dataset Citation Bioregional Assessment Programme (2016) Cartographic masks for map products COO 230. Bioregional Assessment Derived Dataset. Viewed 27 November 2017, http://data.bioregionalassessments.gov.au/dataset/a969c477-a943-4ad2-8964-b521ccdc3d19. ## Dataset Ancestors * Derived From GEODATA TOPO 250K Series 3

Named Landforms of the World version 2 (NLWv2) contains four sub-layers representing geomorphological landforms, provinces, divisions, and their respective cartographic boundaries. The latter supports map making, while the first three represent basic units, such as landforms, which comprise provinces, and provinces comprise divisions. NLW is a substantial update to World Named Landforms in both compilation method and the attributes that describe each landform. For more details, please refer to our paper, Named Landforms of the World: A Geomorphological and Physiographic Compilation, in Annals of the American Association of Geographers. July 2, 2025: We have made Named Landforms of the World v3 (NLWv3) available. Please explore this group containing all of the layers and data. NLWv2 will remain available. Landforms are commonly defined as natural features on the surface of the Earth. The National Geographic Society specifies terrain as the basis for landforms and lists four major types: mountains, hills, plateaus, and plains. Here, however, we define landforms in a richer way that includes properties relating to underlying geologic structure, erosional and depositional character, and tectonic setting and processes. These characteristics were asserted by Dr. Richard E. Murphy in 1968 in his map, titled Landforms of the World. We blended Murphy"s definition for landforms with the work E.M. Bridges, who in his 1990 book, World Geomorphology, provided a globally consistent description of geomorphological divisions, provinces, and sections to give names to the landform regions of the world. AttributeDescriptionBridges Full NameFull name from E.M. Bridges" 1990 "World Geomorphology" Division and if present province and section - intended for labeling print maps of small extents. Bridges DivisionGeomorphological Division as described in E.M. Bridges" 1990 "World Geomorphology" - All Landforms have a division assigned, i.e., no nulls. Bridges ProvinceGeomorphological Province as described in E.M. Bridges" 1990 "World Geomorphology" - Not all divisions are subdivided into provinces. Bridges SectionGeomorphological Section as described in E.M. Bridges" 1990 "World Geomorphology" - Not all provinces are subdivided into sections.StructureLandform Structure as described in Richard E. Murphy"s 1968 "Landforms of the World" map. Coded Value Domain. Values include: - Alpine Systems: Area of mountains formed by orogenic (collisions of tectonic plates) processes in the past 350 to 500 million years. - Caledonian/Hercynian Shield Remnants: Area of mountains formed by orogenic (collisions of tectonic plates) processes 350 to 500 million years ago. - Gondwana or Laurasian Shields: Area underlaid by mostly crystalline rock formations fromed one billion or more years ago and unbroken by tectonic processes. - Rifted Shield Areas: fractures or spreading along or adjacent to tectonic plate edges. - Isolated Volcanic Areas: volcanic activity occurring outside of Alpine Systems and Rifted Shields. - Sedimentary: Areas of deposition occurring within the past 2.5 million years Moist or DryLandform Erosional/Depositional variable as described in Richard E. Murphy"s 1968 "Landforms of the World" map. Coded Value Domain. Values include: - Moist: where annual aridity index is 1.0 or higher, which implies precipitation is absorbed or lost via runoff. - Dry: where annual aridity index is less than 1.0, which implies more precipitation evaporates before it can be absorbed or lost via runoff. TopographicLandform Topographic type variable as described in Richard E. Murphy"s 1968 "Landforms of the World" map. Karagulle et. al. 2017 - based on rich morphometric characteristics. Coded Value Domain. Values include: - Plains: Areas with less than 90-meters of relief and slopes under 20%. - Hills: Areas with 90- to 300-meters of local relief. - Mountains: Areas with over 300-meters of relief - High Tablelands: Areas with over 300-meters of relief and 50% of highest elevation areas are of gentle slope. - Depressions or Basins: Areas of land surrounded land of higher elevation. Glaciation TypeLandform Erosional/Depositional variable as described in Richard E. Murphy"s 1968 "Landforms of the World" map. Values include: - Wisconsin/Wurm Glacial Extent: Areas of most recent glaciation which formed 115,000 years ago and ended 11,000 years ago. - Pre-Wisconsin/Wurm Glacial Extent: Areas subjected only to glaciation prior to 140,000 years ago. ContinentAssigned by Author during data compilation. Bridges Short NameThe name of the smallest of Division, Province, or Section containing this landform feature. Murphy Landform CodeCombination of Richard E. Murphy"s 1968 "Landforms of the World" variables expressed as a 3- or 4- letter notation. Used to label medium scale maps. Area_GeoGeodesic area in km2. Primary PlateName of tectonic plate that either completely underlays this landform feature or underlays the largest portion of the landform"s area.Secondary PlateWhen a landform is underlaid by two or more tectonic plates, this is the plate that underlays the second largest area.3rd PlateWhen a landform is underlaid by three or more tectonic plates, this is the plate that underlays the third largest area.4th PlateWhen a landform is underlaid by four or more tectonic plates, this is the plate that underlays the fourth largest area.5th PlateWhen a landform is underlaid by five tectonic plates, this is the plate that underlays the fifth largest area.NotesContains standard text to convey additional tectonic process characteristics. Tectonic ProcessAssigns values of orogenic, rift zone, or above subducting plate. These data are also available as an ArcGIS Pro Map Package: Named_Landforms_of_the_World_v2.0.mpkx.These data supersede the earlier v1.0: World Named Landforms. Change Log:DateDescription of ChangeJuly 20, 2022Corrected spelling of Guiana from incorrect representation, "Guyana", used by Bridges.July 27, 2022Corrected Structure coded value domain value, changing "Caledonian/Hercynian Shield" to "Caledonian , Hercynian, or Appalachian Remnants". Cite as: Frye, C., Sayre R., Pippi, M., Karagulle, Murphy, A., D. Soller, D.R., Gilbert, M., and Richards, J., 2022. Named Landforms of the World. DOI: 10.13140/RG.2.2.33178.93129. Accessed on:

This raster depicts the percentage of lithological silicon dioxide (SiO2) content in surface or near surface geology. We derived these rasters by calculating the average percent SiO2 content for each map unit in combined surficial-bedrock geologic maps. We used state geologic maps (Preliminary Integrated Geologic Map Databases for the United States, Open File Reports 2004-1355, 2005-1305, 2005-1323, 2005-1324, 2005-1325, 2005-1351, and 2006-1272), which depict surficial geology instead of bedrock when the surficial layers are sufficiently deep. For the state maps that do not incorporate surficial geology (i.e., midwestern states), we overlaid surficial geologic map units with thicknesses greater than 100 feet (from Soller and Reheis [2004]) to produce combined surficial-bedrock geologic maps that were similar to other states. We characterized geology based on the 201 different lithologies that the Geologic Map Database lists as occurring in the conterminous United States. Because some of these lithologies are known to have chemical attributes that vary widely, we created an additional 78 lithologic classes based on the common modifiers used in the geologic unit descriptions to better parse chemical variability within the lithologies (e.g., calcareous and noncalcareous sandstone). Modifiers were assigned base on descriptions of geologic formations obtained through either the Lexicon of Geologic Names of the United States or literature searches. Fifteen lithologic classes were not characterized because the class was not a specific rock type (e.g., mélange, water, and landslide). These classes were characterized as no data. We translated each state’s combined surficial-bedrock geologic maps into characteristics following the methods in Olson and Hawkins (2012) by assigning an estimate of each map unit’s percent SiO2 content to every occurrence of that map unit in the combined surficial-bedrock geologic map. This estimate was calculated as the average of literature or database values of the respective property for each lithological class contained within the map unit weighted by the prevalence of each lithological class within the map unit. The accompanying Excel workbook (Lith-MajorOxides.xls) contains a summary of all of the average geochemical characteristics for each lithology (“Lith Summary” tab) and tabs for each individual lithology that include the source of each record (e.g., originating from the Earth Chem Database or the specific literature reference), as well as the calculations used to determine the measure of central tendency (mean or median depending on the data). The final national raster was created by merging each of the individual state rasters. Users should be cognizant that some differences will exist in chemical and physical characterizations across state lines that are caused by unreconciled differences in lithologic descriptions or mapping scales used among the underlying state source maps.

Access National Hydrography ProductsThe National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.Statements of attribute accuracy are based on accuracy statements made for U.S. Geological Survey Digital Line Graph (DLG) data, which is estimated to be 98.5 percent. One or more of the following methods were used to test attribute accuracy: manual comparison of the source with hardcopy plots; symbolized display of the DLG on an interactive computer graphic system; selected attributes that could not be visually verified on plots or on screen were interactively queried and verified on screen. In addition, software validated feature types and characteristics against a master set of types and characteristics, checked that combinations of types and characteristics were valid, and that types and characteristics were valid for the delineation of the feature. Feature types, characteristics, and other attributes conform to the Standards for National Hydrography Dataset (USGS, 1999) as of the date they were loaded into the database. All names were validated against a current extract from the Geographic Names Information System (GNIS). The entry and identifier for the names match those in the GNIS. The association of each name to reaches has been interactively checked, however, operator error could in some cases apply a name to a wrong reach.Points, nodes, lines, and areas conform to topological rules. Lines intersect only at nodes, and all nodes anchor the ends of lines. Lines do not overshoot or undershoot other lines where they are supposed to meet. There are no duplicate lines. Lines bound areas and lines identify the areas to the left and right of the lines. Gaps and overlaps among areas do not exist. All areas close.The completeness of the data reflects the content of the sources, which most often are the published USGS topographic quadrangle and/or the USDA Forest Service Primary Base Series (PBS) map. The USGS topographic quadrangle is usually supplemented by Digital Orthophoto Quadrangles (DOQs). Features found on the ground may have been eliminated or generalized on the source map because of scale and legibility constraints. In general, streams longer than one mile (approximately 1.6 kilometers) were collected. Most streams that flow from a lake were collected regardless of their length. Only definite channels were collected so not all swamp/marsh features have stream/rivers delineated through them. Lake/ponds having an area greater than 6 acres were collected. Note, however, that these general rules were applied unevenly among maps during compilation. Reach codes are defined on all features of type stream/river, canal/ditch, artificial path, coastline, and connector. Waterbody reach codes are defined on all lake/pond and most reservoir features. Names were applied from the GNIS database. Detailed capture conditions are provided for every feature type in the Standards for National Hydrography Dataset available online through https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/NHD%201999%20Draft%20Standards%20-%20Capture%20conditions.PDF.Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.Statements of vertical positional accuracy for elevation of water surfaces are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of water surface printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map.

This dataset represents a compilation of data from various government agencies throughout the City of New York. The underlying geography is derived from the Tax Lot Polygon feature class that is part of the Department of Finance's Digital Tax Map (DTM). The tax lots have been clipped to the shoreline, as defined by NYCMap planimetric features. The attribute information is from the Department of City Planning's PLUTO data. The attribute data pertains to tax lot and building characteristics and geographic, political and administrative information for each tax lot in New York City.The Tax Lot Polygon feature class and PLUTO are derived from different sources. As a result, some PLUTO records do not have a corresponding tax lot in the Tax Lot polygon feature class at the time of release. These records are included in a separate non-geographic PLUTO Only table. There are a number of reasons why there can be a tax lot in PLUTO that does not match the DTM; the most common reason is that the various source files are maintained by different departments and divisions with varying update cycles and criteria for adding and removing records. The attribute definitions for the PLUTO Only table are the same as those for MapPLUTO. DCP Mapping Lots includes some features that are not on the tax maps. They have been added by DCP for cartographic purposes. They include street center 'malls', traffic islands and some built streets through parks. These features have very few associated attributes.To report problems, please open a GitHub issue or email DCPOpendata@planning.nyc.gov.DATES OF INPUT DATASETS:Department of City Planning - E-Designations: 2/5/2021Department of City Planning - Zoning Map Index: 7/31/2019Department of City Planning - NYC City Owned and Leased Properties: 11/15/2020Department of City Planning - NYC GIS Zoning Features: 2/5/2021Department of City Planning - Polictical and Administrative Districts: 11/17/2020Department of City Planning - Geosupport version 20D: 11/17/2020Department of Finance - Digital Tax Map: 1/30/2021Department of Finance - Mass Appraisal System (CAMA): 2/10/2021Department of Finance - Property Tax System (PTS): 2/6/2021Landmarks Preservation Commission - Historic Districts: 2/4/2021Landmarks Preservation Commission - Individual Landmarks: 2/4/2021Department of Information Telecommunications & Technology - Building Footprints: 2/10/2021Department of Parks and Recreation - GreenThumb Garden Info: 1/4/2021

This data set provides maps produced from model output data from the National Aeronautics and Space Administration-Carnegie Ames Stanford Approach (NASA-CASA) model and other modeling approaches. The maps include estimated annual Net Primary Production (ANPP), leaf (live) biomass carbon, wood (live) biomass carbon, fine root (live) biomass carbon, metabolic leaf litter (dead) carbon, structural leaf litter (dead) carbon, woody detritus (dead) carbon, and slow soil carbon, gridded at half-degree spatial resolution for the years 1982-1998, and 2001 (NPP data) for Brazil. Maps are provided at one-degree resolution for monthly soil emissions and soil uptake of N2O, NO, CO, and CH4. In addition, there are maps in 8-km resolution for soil texture, soil carbon, soil pH, soil maximum plant available water (paw), and net primary productivity (NPP).There are three files with this data set in tar.gz format. The files are in half-degree, one-degree, and 8-km resolution. When expanded, the half degree and one degree files contain 83 map files in GeoTIFF (.tif) format. The third file (8-km resolution) contains the soil and productivity maps. When expanded, this file contains 22 files in GeoTIFF (.tif) format.

This data set provides local LAI maps for the selected measured sites in Canada. These derived maps may also be useful for validating other LAI maps over these same sites given that the areas are protected from disturbance. The maps should be used for the given period of validity. The LAI data are suitable for use in modeling the carbon, water, energy, energy and trace gas exchange between the land surface and the atmosphere at regional scales. The data set may also be useful for monitoring changes in the land surface.The Leaf Area Index (LAI) maps are at 30-m resolution for the selected sites. LAI is defined here as half the total (all-sided) live foliage area per unit horizontal projected ground surface area. Overstory LAI corresponds to all tree foliage except for treeless areas where it corresponds to total foliage. The algorithms were developed from ground measurements and Landsat TM and ETM+ images (Fernandes et. al., 2003). A mask was developed using the Landsat ETM+/TM5 image and available land cover map to identify only those areas with land cover belonging to the sample land cover classes and with Landsat ETM+/TM5 spectral reflectance values that fell within the convex hull of the spectral reflectance values over the plots. LAI was mapped within the masked region using the Landsat ETM+/TM5 image and the developed transfer function. The final LAI map was scaled by a factor of 20 (offset 0). The LAI maps are in Tagged Image File Format (TIFF).