The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. GIS Database 2002-2005: Project Size = 1,898 acres Fort Larned National Historic Site (including the Rut Site) = 705 acres 16 Map Classes 11 Vegetated 5 Non-vegetated Minimum Mapping Unit = ½ hectare is the program standard but this was modified at FOLS to ¼ acre. Total Size = 229 Polygons Average Polygon Size = 8.3 acres Overall Thematic Accuracy = 92% To produce the digital map, a combination of 1:8,500-scale (0.75 meter pixels) color infrared digital ortho-imagery acquired on October 26, 2005 by the Kansas Applied Remote Sensing Program and 1:12,000-scale true color ortho-rectified imagery acquired in 2005 by the U.S. Department of Agriculture - Farm Service Agency’s Aerial Photography Field Office, and all of the GPS referenced ground data were used to interpret the complex patterns of vegetation and land-use. In the end, 16 map units (11 vegetated and 5 land-use) were developed and directly cross-walked or matched to corresponding plant associations and land-use classes. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcGIS© software. Draft maps were printed, field tested, reviewed and revised. One hundred and six accuracy assessment (AA) data points were collected in 2006 by KNSHI and used to determine the map’s accuracy. After final revisions, the accuracy assessment revealed an overall thematic accuracy of 92%.

Web Map Service to view health care thematic data.

This dataset is the first 1: 100,000 desert spatial database in China based on the graphic data of desert thematic maps. It mainly reflects the geographical distribution, area size, and mobility of sand dunes in China. According to the system design requirements and relevant standards, the input data is standardized and uniformly converted into a standard format for various types of data input. Build a library to run the delivery system. This project uses the TM image in 2000 as the information source, and interprets, extracts, and edits the coverage of the national land use map and TM digital image information in 2000. It uses remote sensing and geographic information system technology to 1: 100,000 Thematic mapping requirements for scale bar maps were made on the desert, sandy land and gravel Gobi in China. The 1: 100,000 desert map across the country can save users a lot of data entry and editing work when they are engaged in research on resources and the environment. Digital maps can be easily converted into layout maps The dataset properties are as follows: Divided into two folders e00 and shp: Desert map name and province comparison table in each folder 01 Ahsm Anhui 02 Bjsm Beijing 03 Fjsm Fujian 04 Gdsm Guangdong 05 Gssm Gansu 06 Gxsm Guangxi Zhuang Autonomous Region 07 Gzsm Guizhou 08 Hebsm Hebei 09 Hensm Henan 10 Hljsm Heilongjiang 11 Hndsm Hainan 12 Hubsm Hubei 13 Jlsm Jilin Province 14 Jssm Jiangsu 15 Jxsm Jiangxi 16 Lnsm Liaoning 17 Nmsm Inner Mongolia Gu Autonomous Region 18 Nxsm Ningxia Hui Autonomous Region 19 Qhsm Qinghai 20 Scsm Sichuan 21 Sdsm Shandong 22 Sxsm Shaanxi Province 23 Tjsm Tianjin 24 Twsm Taiwan Province 25 Xjsm Xinjiang Uygur Autonomous Region 26 Xzsm Tibet Autonomous Region 27 Zjsm Zhejiang 28 Shxsm Shanxi 1. Data projection: Projection: Albers False_Easting: 0.000000 False_Northing: 0.000000 Central_Meridian: 105.000000 Standard_Parallel_1: 25.000000 Standard_Parallel_2: 47.000000 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) 2. Data attribute table: area (area) perimeter ashm_ (sequence code) class (desert encoding) ashm_id (desert encoding) 3. Desert coding: mobile sandy land 2341010 Semi-mobile sandy land Semi-fixed sandy land 2341030 Gobi 2342000 Saline land 2343000 4: File format: National, sub-provincial and county-level desert map data types are vector shapefiles and E00 5: File naming: Data organization based on the National Basic Resources and Environmental Remote Sensing Dynamic Information Service System is performed on the file management layer of Windows NT. The file and directory names are compound names of English characters and numbers. Pinyin + SM composition, such as the desert map of Gansu Province is GSSM. The flag and county desert map is the pinyin + xxxx of the province name, and xxxx is the last four digits of the flag and county code. The division of provinces, districts, flags and counties is based on the administrative division data files in the national basic resources and environmental remote sensing dynamic information service operation system.

The dataset presents the estimated occurrence of less common tree species (other than pine and spruce) in the form of thematic maps covering entire area of Finland. The maps series represent the following years: 1994, 2002, 2009 and 2015. The tree species maps are based on geostatistical interpolation of field measurements from national forest inventory sample plots and satellite image-based forest resource estimates. The occurrence data is presented as the average volume (m3/ha) of the tree species in forestry land. The tree species maps are available as ESRI polygon shapefiles where Finland is divided into 1 x 1 km2 square polygons for which the tree species data is estimated. Koordinaattijärjestelmä: ETRS89 / ETRS-TM35FIN (EPSG:3067)

This map provides a guide to the data confidence of DPIE's soil related thematic map products in NSW. Examples of products this map supports includes Land and Soil Capability mapping, Inherent …Show full descriptionThis map provides a guide to the data confidence of DPIE's soil related thematic map products in NSW. Examples of products this map supports includes Land and Soil Capability mapping, Inherent fertility of soils in NSW and Great Soil Group soil types in NSW. Confidence classes are determined based on the data scale, type of mapping and information collected, accuracy of the attributes and quality assurance on the product. Soil data confidence is described using a 4 class system between high and very low as outlined below.: Good (1) - All necessary soil and landscape data is available at a catchment scale (1:100,000 & 1:250,000) to undertake the assessment of LSC and other soil thematic maps. Moderate (2) - Most soil and landscape data is available at a catchment scale (1:100,000 - 1:250,000) to undertake the assessment of LSC and other soil thematic maps. Low (3) - Limited soil and landscape data is available at a reconnaissance catchment scale (1:100,000 & 1:250,000) which limits the quality of the assessment of LSC and other soil thematic maps. Very low (4) - Very limited soil and landscape data is available at a broad catchment scale (1:250,000 - 1:500,000) and the LSC and other soil thematic maps should be used as a guide only. Online Maps: This dataset can be viewed using eSPADE (NSW’s soil spatial viewer), which contains a suite of soil and landscape information including soil profile data. Many of these datasets have hot-linked soil reports. An alternative viewer is the SEED Map; an ideal way to see what other natural resources datasets (e.g. vegetation) are available for this map area. Reference: Department of Planning, Industry and Environment, 2020, Soil Data Confidence map for NSW, Version 4, NSW Department of Planning, Industry and Environment, Parramatta.

This thematic map series provides the distribution of the various environmental mitigations across hydropower plants within the US.

Soil Data Viewer is a tool built as an extension to ArcMap that allows a user to create soil-based thematic maps. The application can also be run independently of ArcMap, but output is then limited to a tabular report. The soil survey attribute database associated with the spatial soil map is a complicated database with more than 50 tables. Soil Data Viewer provides users access to soil interpretations and soil properties while shielding them from the complexity of the soil database. Each soil map unit, typically a set of polygons, may contain multiple soil components that have different use and management. Soil Data Viewer makes it easy to compute a single value for a map unit and display results, relieving the user from the burden of querying the database, processing the data and linking to the spatial map. Soil Data Viewer contains processing rules to enforce appropriate use of the data. This provides the user with a tool for quick geospatial analysis of soil data for use in resource assessment and management. Resources in this dataset:Resource Title: Soil Data Viewer. File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/home/?cid=nrcs142p2_053620 Soil Data Viewer is a tool built as an extension to ArcMap that allows a user to create soil-based thematic maps. The application can also be run independent of ArcMap, but output is then limited to a tabular report. Soil Data Viewer contains processing rules to enforce appropriate use of the data. This provides the user with a tool for quick geospatial analysis of soil data for use in resource assessment and management. Links to download and install Download Soil Data Viewer 6.2 for use with ArcGIS 10.x and Windows XP, Windows 7, Windows 8.x, or Windows 10. Earlier versions are also available.

The 2023 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Block Groups (BGs) are clusters of blocks within the same census tract. Each census tract contains at least one BG, and BGs are uniquely numbered within census tracts. BGs have a valid code range of 0 through 9. BGs have the same first digit of their 4-digit census block number from the same decennial census. For example, tabulation blocks numbered 3001, 3002, 3003,.., 3999 within census tract 1210.02 are also within BG 3 within that census tract. BGs coded 0 are intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. Block groups generally contain between 600 and 3,000 people. A BG usually covers a contiguous area but never crosses county or census tract boundaries. They may, however, cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. The generalized BG boundaries in this release are based on those that were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

Thematic soil maps show the distribution of a specific soil property or theme, such as topsoil organic carbon content or soil texture.

These raster and vector datasets were developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using Historical and Concurrent Multispectral Imagery” (#R/MPA 30 10-049). The study region is the South Coast Region (SCR). Kelp thematic maps from 1999-2012 were used for this product. Ocean Imaging acquired the imagery and classification data used for this analysis from its own Digital Multispectral Camera (DMSC), the Microsoft UltraCam-X aerial camera system and from kelp habitat classifications archived by the California Department of Fish and Wildlife (CDFW). Details on this systems and the data processing are below in the Lineage section of this document. The persistence analyses show the persistence of kelp beds in the SCR and MPAs as a percentage of the number of years analyzed. The coverage area is from Conception, CA south to Imperial Beach, CA. The GIS analysis product files are deliverd are in GeoTIFF (.tif) and ESRI Shapefile formats as well as Adobe Acrobat PDF files. This raster dataset contains a persistence analysis of giant kelp beds along the California South Coast Region (SCR) from from Point Conception, CA down to Imperial beach, CA. This specific raster classification includes the Lover's Cove SMCA and Casino Point SMCA. This dataset was originally uploaded to Oceanspaces (http://oceanspaces.org/) and the Knowledge Network for Biocomplexity (KNB, https://knb.ecoinformatics.org/data) in 2013 as part of the South Coast baseline monitoring program. In 2022 this dataset was moved to the California Ocean Protection Council Data Repository (https://opc.dataone.org/) by Mike Esgro (Michael.Esgro@resources.ca.gov) and Rani Gaddam (gaddam@ucsc.edu). At that time the GIS analysis products were added to the dataset. The long-term California MPA boundary and project info tables can be found as a separate dataset here: https://opc.dataone.org/view/doi:10.25494/P64S3W.

CanVec contains more than 60 topographic features classes organized into 8 themes: Transport Features, Administrative Features, Hydro Features, Land Features, Manmade Features, Elevation Features, Resource Management Features and Toponymic Features. This multiscale product originates from the best available geospatial data sources covering Canadian territory. It offers quality topographic information in vector format complying with international geomatics standards. CanVec can be used in Web Map Services (WMS) and geographic information systems (GIS) applications and used to produce thematic maps. Because of its many attributes, CanVec allows for extensive spatial analysis. Related Products: Constructions and Land Use in Canada - CanVec Series - Manmade Features Lakes, Rivers and Glaciers in Canada - CanVec Series - Hydrographic Features Administrative Boundaries in Canada - CanVec Series - Administrative Features Mines, Energy and Communication Networks in Canada - CanVec Series - Resources Management Features Wooded Areas, Saturated Soils and Landscape in Canada - CanVec Series - Land Features Transport Networks in Canada - CanVec Series - Transport Features Elevation in Canada - CanVec Series - Elevation Features Map Labels - CanVec Series - Toponymic Features

Abstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. Metadata was not provided and has been compiled by the …Show full descriptionAbstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. Metadata was not provided and has been compiled by the Bioregional Assessment Programme based on the known details at the time of acquisition. A list of hard copy maps. The map catalogue list almost 250 map titles. It contains maps that can be divided into three categories: (1) Statewide Geology, geophysical and thematic maps (2) Geology - Standard series geology maps covering a map sheet, mainly 1:250,000 and 1:100,000 scales with some at 1:500,00, 1:50,000 and 1:25,000. (3) Metallogenic - Maps depicting mineral resources, mainly 1:250,00 scale. Contact: geoscience.products@minerals.nsw.gov.au This dataset has been provided to the BA Programme for use within the programme only. Third parties should contact the NSW Department of Industry. http://www.industry.nsw.gov.au/ Dataset History This shapefile consists of published geological maps by Geological Survey of NSW for various locations located in NSW and held by NSW Department of Trade and Investment. Dataset Citation NSW Trade and Investment (2004) Geological Maps Combined for NSW. Bioregional Assessment Source Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/f507bb28-8095-43f8-901e-565699a290b5.

Overview: Actual Natural Vegetation (ANV): probability of occurrence for the Common hazel in its realized environment for the period 2000 - 2022 Traceability (lineage): This is an original dataset produced with a machine learning framework which used a combination of point datasets and raster datasets as inputs. Point dataset is a harmonized collection of tree occurrence data, comprising observations from National Forest Inventories (EU-Forest), GBIF and LUCAS. The complete dataset is available on Zenodo. Raster datasets used as input are: harmonized and gapfilled time series of seasonal aggregates of the Landsat GLAD ARD dataset (bands and spectral indices); monthly time series air and surface temperature and precipitation from a reprocessed version of the Copernicus ERA5 dataset; long term averages of bioclimatic variables from CHELSA, tree species distribution maps from the European Atlas of Forest Tree Species; elevation, slope and other elevation-derived metrics; long term monthly averages snow probability and long term monthly averages of cloud fraction from MODIS. For a more comprehensive list refer to Bonannella et al. (2022) (in review, preprint available at: https://doi.org/10.21203/rs.3.rs-1252972/v1). Scientific methodology: Probability and uncertainty maps were the output of a spatiotemporal ensemble machine learning framework based on stacked regularization. Three base models (random forest, gradient boosted trees and generalized linear models) were first trained on the input dataset and their predictions were used to train an additional model (logistic regression) which provided the final predictions. More details on the whole workflow are available in the listed publication. Usability: Probability maps can be used to detect potential forest degradation and compositional change across the time period analyzed. Some possible applications for these topics are explained in the listed publication. Uncertainty quantification: Uncertainty is quantified by taking the standard deviation of the probabilities predicted by the three components of the spatiotemporal ensemble model. Data validation approaches: Distribution maps were validated using a spatial 5-fold cross validation following the workflow detailed in the listed publication. Completeness: The raster files perfectly cover the entire Geo-harmonizer region as defined by the landmask raster dataset available here. Consistency: Areas which are outside of the calibration area of the point dataset (Iceland, Norway) usually have high uncertainty values. This is not only a problem of extrapolation but also of poor representation in the feature space available to the model of the conditions that are present in this countries. Positional accuracy: The rasters have a spatial resolution of 30m. Temporal accuracy: The maps cover the period 2000 - 2020, each map covers a certain number of years according to the following scheme: (1) 2000--2002, (2) 2002--2006, (3) 2006--2010, (4) 2010--2014, (5) 2014--2018 and (6) 2018--2020 Thematic accuracy: Both probability and uncertainty maps contain values from 0 to 100: in the case of probability maps, they indicate the probability of occurrence of a single individual of the target species, while uncertainty maps indicate the standard deviation of the ensemble model.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The polygons in this dataset are a digital representation of the distribution or extent of geological units within the area. Polygons have a range of attributes including unit name, age, lithological description and an abbreviated symbol for use in labelling the polygons. These have been extracted from the Rock Units Table held …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The polygons in this dataset are a digital representation of the distribution or extent of geological units within the area. Polygons have a range of attributes including unit name, age, lithological description and an abbreviated symbol for use in labelling the polygons. These have been extracted from the Rock Units Table held in the Department of Natural Resources, Mines and Energy Merlin Database. Purpose To display the geology polygons which define the extent of rock units. Dataset History Supplemental_Information: Data captured at 1:40 000 scale. The data set is sourced from the Department's Geoscience and Resources Database (GRDB), a component of the Mineral and Energy Resources Location and Information Network (MERLIN) corporate database.(GRDB), a component of the Mineral and Energy Resources Location and Information Network (MERLIN) corporate database. NOTE: GEOLDATA was in most cases compiled based on Datum AGD66. The map tile coverages so compiled have now been projected to geographics based on Datum GDA94. Consequently the boundaries for these map tiles will not conform to the Latitude and Longitude graticule based on Datum GDA94. Entity_and_Attribute_Information: Detailed_Description: Entity_Type: Entity_Type_Label: 9341_r Entity_Type_Definition: Polygons have a range of attributes including unit name, age, lithological description and an abbreviated symbol for use in labelling the polygons. Entity_Type_Definition_Source: The Rock Units Table held in the Department of Natural Resources, Mines and Energy Merlin Database. Attribute: Attribute_Label: FID Attribute_Definition: Internal feature number. Attribute_Definition_Source: ESRI Attribute_Domain_Values: Unrepresentable_Domain: Sequential unique whole numbers that are automatically generated. Beginning_Date_of_Attribute_Values: March 2004 Attribute: Attribute_Label: Shape Attribute_Definition: Feature geometry. Attribute_Definition_Source: ESRI Attribute_Domain_Values: Unrepresentable_Domain: Coordinates defining the features. Attribute: Attribute_Label: KEY Attribute_Definition: Unique polygon identifier and relate item for poygon attributes Attribute: Attribute_Label: ROCK_U_NAM Attribute_Definition: The Map Unit Name of the polygon. In the case of named units it comprises of the standard binomial name. Unnamed subdivisions of named units include the binomial name with a letter symbol as a suffix. Unnamed units are represented by a letter symbol, usually in combination with a map sheet number. Attribute: Attribute_Label: AGE Attribute_Definition: Geological age of unit Attribute: Attribute_Label: LITH_SUMMA Attribute_Definition: Provides a brief description of the map units as they have been described in the course of the project work, or as has appeared on relevant hard copy map legends. Attribute: Attribute_Label: ROCK_U_TYP Attribute_Definition: Provides a means of separating map units, eg for constructing a map reference. This item will contain one of the following: STRAT- Stratigraphic unit, including sedimentary, volcanic and metamorphic rock units. INTRU- Intrusive rock units; COMPST- Compound unit where the polygon includes two or more rock units, either stratigraphic, intrusive or both; COMPST- Compound unit, as above where the dominant or topmost unit is of the STRAT type; COMPIN- Compound unit, as above, where the dominant unit is of the INTRU type; WATER- Water bodies- Large dams, lakes, waterholes. Attribute: Attribute_Label: SEQUENCE_N Attribute_Definition: A numeric field to allow sorting of the rock units in approximate stratigraphic order as they would appear on a map legend. Attribute: Attribute_Label: DOMINANT_R Attribute_Definition: A simplified lithological description to allow generation of thematic maps based on broad rock types. Attribute: Attribute_Label: MAP_SYMBOL Attribute_Definition: Provides an abbreviated label for polygons. Mostly based on the letter symbols as they appear on published maps or the original hard copy compilation sheets. These are not unique across the State, but should be unique within a single map tile, and usually adjacent tiles. Attribute: Attribute_Label: NAME_100K Attribute_Definition: Name of 1:100 000 map sheet coincident with the data extent. Overview_Description: Entity_and_Attribute_Overview: Polygon Attribute information includes Polygon Key, Rock Unit Name, Age, Lithology, Rock Unit Type, Map Symbol and 1:100 000 sheet name. Dataset Citation "Queensland Department of Natural Resources, Mines and Energy" (2014) Qld 100k mapsheets - Warwick. Bioregional Assessment Source Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/3e2fa307-1f06-4873-96d3-5c3e5638894a.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

This map layer is a thematic raster image of MTBS burn severity classes for all inventoried fires occurring in CONUS during calendar year 2019 that do not meet standard MTBS size criteria. These data are published to augment the data that are available from the MTBS program. This product was produced using the methods of the Monitoring Trends in Burn Severity Program (MTBS), however these fires do not meet the size criteria for a standard MTBS assessment. The MTBS Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. MTBS typically maps fires using an initial assessment (immediately after the fire) or an extended assessment (peak of green the season after the fire) for low-biomass and high-biomass fires respectively. Refer to MTBS.gov for more information on MTBS methods and criteria. Standard MTBS mappings must meet the size criteria of at least 500 acres for the eastern states and territories and 1,000 acres for the western states and territories to be eligible for mapping. Undersized MTBS fires are those fires that do not meet the standard MTBS size criteria but are otherwise mapped using standard MTBS methodologies.

MO_degrado_geomorf_poly_3 — represents the elements of geomorphological degradation — area type — elements of the map of alterations and degradation of the territory on a scale of 1:25 000 The maps PTPAAV (Territorial Environmental Country Plan of Vasta Area) are a series of thematic maps drawn up since 1989 and finished and approved at the end of November 1991, are divided into territorial areas for a total of 8 areas identified on the regional territory. The work was carried out by several groups of technicians, a coordination group which established by circulars the standards to be used for the drafting of plans ranging from the thickness of the graph tip to the type of retino and the nuances to be used, and 8 design groups one for each area, which have created the maps trying to standardise spatial information as much as possible. The paperwork of this work was delivered to us in 2008 by the Environmental Heritage Office of the Molise Region. The latter already had scans of some thematic cards related to some areas, the missing ones and in the case of scans not found suitable for georeference, have been scanned. The mapping basis used by the working groups for the creation of PTPAAV maps was the IGM on a scale of 1:25,000.

The 2019 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The cartographic boundary files include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The boundaries of most incorporated places in this file are as of January 1, 2019, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries of all CDPs were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2010 Census.

The 2015 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

Soil is a key natural resource that provides the foundation of basic ecosystem services. Soil determines the types of farms and forests that can grow on a landscape. Soil filters water. Soil helps regulate the Earth's climate by storing large amounts of carbon. Activities that degrade soils reduce the value of the ecosystem services that soil provides. For example, since 1850 35% of human caused green house gas emissions are linked to land use change. The Soil Science Society of America is a good source of of additional information.Dataset SummaryThis layer provides access to a 30 arc-second (roughly 1 km) cell-sized raster with attributes describing the basic properties of soil derived from the Harmonized World Soil Database v 1.2. The values in this layer are for the dominant soil in each mapping unit (sequence field = 1).Attributes in this layer include:Soil Phase 1 and Soil Phase 2 - Phases identify characteristics of soils important for land use or management. Soils may have up to 2 phases with phase 1 being more important than phase 2.Other Properties - provides additional information important for agriculture.Additionally, 3 class description fields were added by Esri based on the document Harmonized World Soil Database Version 1.2 for use in web map pop-ups:Soil Phase 1 DescriptionSoil Phase 2 DescriptionOther Properties DescriptionThe layer is symbolized with the Soil Unit Name field.The document Harmonized World Soil Database Version 1.2 provides more detail on the soil properties attributes contained in this layer.Other attributes contained in this layer include:Soil Mapping Unit Name - the name of the spatially dominant major soil groupSoil Mapping Unit Symbol - a two letter code for labeling the spatially dominant major soil group in thematic mapsData Source - the HWSD is an aggregation of datasets. The data sources are the European Soil Database (ESDB), the 1:1 million soil map of China (CHINA), the Soil and Terrain Database Program (SOTWIS), and the Digital Soil Map of the World (DSMW).Percentage of Mapping Unit covered by dominant componentMore information on the Harmonized World Soil Database is available here.Other layers created from the Harmonized World Soil Database are available on ArcGIS Online:World Soils Harmonized World Soil Database - Bulk DensityWorld Soils Harmonized World Soil Database – ChemistryWorld Soils Harmonized World Soil Database - Exchange CapacityWorld Soils Harmonized World Soil Database – HydricWorld Soils Harmonized World Soil Database – TextureThe authors of this data set request that projects using these data include the following citation:FAO/IIASA/ISRIC/ISSCAS/JRC, 2012. Harmonized World Soil Database (version 1.2). FAO, Rome, Italy and IIASA, Laxenburg, Austria.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. The source data for this layer are available here.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:Living Atlas Discussion GroupSoil Data Discussion GroupThe Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.