This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

These data were manually extracted by trained reviewers from published research reports.

This dataset consists of the 1km raster, percentage target class version of the Land Cover Map 2015 (LCM2015) for Great Britain. The 1km percentage product provides the percentage cover for each of 21 land cover classes for 1km x 1km pixels. This product contains one band per target habitat class (producing a 21 band image). The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. This dataset is derived from the vector version of the Land Cover Map, which contains individual parcels of land cover and is the highest available spatial resolution. LCM2015 is a land cover map of the UK which was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. LCM2015 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the CEH web site and the LCM2015 Dataset documentation) to select the product most suited to their needs. LCM2015 was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. It is one of a series of land cover maps, produced by UKCEH since 1990. They include versions in 1990, 2000, 2007, 2015, 2017, 2018 and 2019. Full details about this dataset can be found at https://doi.org/10.5285/505d1e0c-ab60-4a60-b448-68c5bbae403e

Amur honeysuckle bush (Lonicera maackii) and Morrow's honeysuckle (Lonicera morrowii) are two of the most aggressively invasive species to become established throughout areas along the Blue River in metropolitan Kansas City, Missouri. These two large, spreading shrubs (locally referred to as bush honeysuckle in the Kansas City metropolitan area) colonize the understory, crowd out native plants, and may be allelopathic, producing a chemical that restricts growth of native species. Removal efforts have been underway for more than a decade by local conservation groups such as Bridging The Gap and Heartland Conservation Alliance, who are concerned with the loss of native species diversity associated with the spread of bush honeysuckle. Bush honeysuckle produces leaves early in the spring before almost all other vegetation and retains leaves late in the fall after almost all other species have lost their leaves. Appropriately timed imagery can be used during early spring and late fall to map the extent of bush honeysuckle. Using multispectral imagery collected in February 2016 and true color aerial imagery collected in March 2016, a coverage map of bush honeysuckle in the study area was made to investigate the extent of bush honeysuckle in a study area along the middle reach of the Blue River in the Kansas City metropolitan area in Jackson County, Missouri. The coverage map was further classified into unlikely, low-, and high-density bush honeysuckle density at a 30-foot cell size. The unlikely density class correctly predicted the absence and approximate density of bush honeysuckle for 86 percent of the field-verification points, the low-density class predicted the presence and approximate density with 73-percent confidence, and the high-density class was predicted with 67-percent confidence. This data was used to support the project work described in: Ellis, J.T., 2018, Remote sensing of bush honeysuckle in the Middle Blue River Basin, Kansas City, Missouri, 2016–17: U.S. Geological Survey Scientific Investigations Map XXXX, 1 sheet., https://doi.org/xxxx.

Median household income is the middle value of the incomes earned in the prior year by households in an area. Income and earnings are inflation-adjusted for the last year of the 5-year period. The median value is used as opposed to the average so that both extremely high and extremely low prices do not distort the total amount of income earned by households in an area. Source: American Community SurveyYears Available: 2006-2010, 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, 2019-2023Please note: We do not recommend comparing overlapping years of data due to the nature of this dataset. For more information, please visit: https://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html

This dataset consists of the 1km raster, dominant aggregate class version of the Land Cover Map 2015 (LCM2015) for Great Britain. The 1km dominant coverage product is based on the 1km percentage product and reports the aggregated habitat class with the highest percentage cover for each 1km pixel. The 10 aggregate classes are groupings of 21 target classes, which are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. The aggregate classes group some of the more specialised classes into more general categories. For example, the five coastal classes in the target class are grouped into a single aggregate coastal class. This dataset is derived from the vector version of the Land Cover Map, which contains individual parcels of land cover and is the highest available spatial resolution. LCM2015 is a land cover map of the UK which was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. LCM2015 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the CEH web site and the LCM2015 Dataset documentation) to select the product most suited to their needs. LCM2015 was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. It is one of a series of land cover maps, produced by UKCEH since 1990. They include versions in 1990, 2000, 2007, 2015, 2017, 2018 and 2019. Full details about this dataset can be found at https://doi.org/10.5285/711c8dc1-0f4e-42ad-a703-8b5d19c92247

As sea levels rise, wetlands can adapt to changing conditions through vertical development (that is, soil surface elevation gains via biophysical feedbacks) and horizontal migration into upslope areas. Elevation-based models of wetland transformation from sea-level rise are often hampered from a variety of sources of uncertainty, including contemporary elevation and water levels and future water levels from sea-level rise. This data release includes geospatial data products that utilize Monte Carlo simulations to address these sources of uncertainty and highlight potential wetland transformations under various relative sea-level rise scenarios along Texas' middle and upper coast. This data release includes the current extent of coastal wetlands and decadal maps of coastal wetland transformation from 2030–2100 for three relative sea-level rise scenarios — Intermediate-low, Intermediate, and Intermediate-high — from an interagency sea-level rise report published in 2022 (Sweet and others, 2022). Datasets in this release include the following classes: 1) Upslope (that is, areas that are above the National Oceanographic and Atmospheric Administration’s (NOAA) moderate high tide flooding threshold; Sweet and others, 2022); 2) Irregularly oceanic-flooded wetlands (that is, wetlands that are flooded by oceanic water less frequently than daily [that is, below the NOAA moderate high tide flooding threshold and above the mean high water datum]); 3) Regularly oceanic-flooded wetlands (that is, wetlands that are flooded by oceanic water daily [that is, below the mean high water datum and above the mean lower low water datum] and generally fell in the upper two-thirds of this wetland zone based on elevation); 4) Converting to open water (that is, wetlands that are flooded by oceanic water daily [that is, below the mean high water datum and above the mean lower low water datum] and generally fell in the lower third of this wetland zone based on elevation; 5) Converted to open water (that is, areas where the decade of initiation for coastal wetland drowning has passed and have been in the “converting to open water” class for at least 50 years); 6) Low-lying, developed (that is, areas that fall in elevation ranges for wetland classes [that is, regularly oceanic-flooded wetlands, regularly oceanic-flooded wetlands, and converting to open water], but are located within developed areas); 7) Low-lying, leveed (that is, areas that fall in elevation ranges for wetland classes [that is, regularly oceanic-flooded wetlands, regularly oceanic-flooded wetlands, and converting to open water], but are located within levees); and 8) Low-lying, developed and leveed (that is, areas that fall in elevation ranges for wetland classes [that is, regularly oceanic-flooded wetlands, regularly oceanic-flooded wetlands, and converting to open water], but are located within levees or developed areas). Incorporating soil elevation change processes into wetland transformation models can be complex because soil elevation change processes can vary over space and time. In the past decade, there has been growing consensus regarding critical sea-level rise rate thresholds for the onset of wetland drowning (Morris and others, 2016, Horton and others, 2018, Saintilan and others, 2020, Törnqvist and others, 2020, Buffington and others, 2021, Saintilan and others, 2022, Saintilan and others, 2023). Here, our products utilize information from an analysis of when and where sea-level rise rates could cross thresholds for initiating coastal wetland drowning across the conterminous United States. The thresholds included are 4 mm/year, 7 mm/year, and 10 mm/year (see discussion in Osland and others, 2024). For this approach, we determined the relative sea-level rise rate by decade for watersheds within the study area. The decade that these rates exceeded one of these thresholds (that is, 4 mm/year, 7 mm/year, and 10 mm/year) marked the initiation of coastal wetland drowning. In other words, the 4 mm/year threshold indicates that wetland drowning would be initiated when the decadal sea-level rise rate exceeds 4 mm/year. Because wetland conversion to open water is not immediate once crossing this threshold, we left areas in that fell within “Converting to open water” class until 50 years after the threshold was surpassed. For example, if the decade the 4 mm/year threshold was crossed was 2020, then no wetlands would be moved to the “Converted to open water” class until 2070. For the 2070 map, areas in the “Converted to open water” class would include areas that were in the “Converting to open water” class in current wetland map (i.e., 50 years after the threshold was crossed). Similarly, for this threshold, areas in the “Converted to open water” class would be those that were “Converting to open water” class on and before 2030 (that is, 2030 and the current wetland map). For more information on the decades for when watershed-level drowning thresholds were passed, see the shapefile titled “Wetland_Drown_Years.shp.” Natural resource managers can utilize this information to explore potential scenarios related to the ability of current wetlands to adapt to sea-level rise via in situ vertical adjustment. For our study area, there was a high level of redundancy when the watershed-level drowning thresholds were passed, especially between maps for 4 mm/yr and 7 mm/yr. If users are interested in seeing where/when redundancy may occur, see the shapefile titled “Wetland_Drown_Years.shp.” This metadata file is for the datasets for the wetland current condition and wetland transformation by decade for sea-level rise scenario and coastal wetland drowning threshold.

Currently available measurements and summary statistics available in the foot package.

This dataset consists of the 1km raster, dominant target class version of the Land Cover Map 2015 (LCM2015) for Northern Ireland. The 1km dominant coverage product is based on the 1km percentage product and reports the habitat class with the highest percentage cover for each 1km pixel. The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. This dataset is derived from the vector version of the Land Cover Map, which contains individual parcels of land cover and is the highest available spatial resolution. LCM2015 is a land cover map of the UK which was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. LCM2015 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the CEH web site and the LCM2015 Dataset documentation) to select the product most suited to their needs. LCM2015 was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. It is one of a series of land cover maps, produced by UKCEH since 1990. They include versions in 1990, 2000, 2007, 2015, 2017, 2018 and 2019. Full details about this dataset can be found at https://doi.org/10.5285/4278d500-a165-452d-ae5f-b503323df9cb

This dataset consists of the vector version of the Land Cover Map 2015 (LCM2015) for Great Britain. The vector data set is the core LCM data set from which the full range of other LCM2015 products is derived. It provides a number of attributes including land cover at the target class level (given as an integer value and also as text), the number of pixels within the polygon classified as each land cover type and a probability value provided by the classification algorithm (for full details see the LCM2015 Dataset Documentation). The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. LCM2015 is a land cover map of the UK which was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. LCM2015 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the CEH web site and the LCM2015 Dataset documentation) to select the product most suited to their needs. LCM2015 was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. It is one of a series of land cover maps, produced by UKCEH since 1990. They include versions in 1990, 2000, 2007, 2015, 2017, 2018 and 2019.

The Home Owners' Loan Corporation (HOLC) was created in the New Deal Era and trained many home appraisers in the 1930s. The HOLC created a neighborhood ranking system infamously known today as redlining. Local real estate developers and appraisers in over 200 cities assigned grades to residential neighborhoods. These maps and neighborhood ratings set the rules for decades of real estate practices. The grades ranged from A to D. A was traditionally colored in green, B was traditionally colored in blue, C was traditionally colored in yellow, and D was traditionally colored in red. A (Best): Always upper- or upper-middle-class White neighborhoods that HOLC defined as posing minimal risk for banks and other mortgage lenders, as they were "ethnically homogeneous" and had room to be further developed.B (Still Desirable): Generally nearly or completely White, U.S. -born neighborhoods that HOLC defined as "still desirable" and sound investments for mortgage lenders.C (Declining): Areas where the residents were often working-class and/or first or second generation immigrants from Europe. These areas often lacked utilities and were characterized by older building stock.D (Hazardous): Areas here often received this grade because they were "infiltrated" with "undesirable populations" such as Jewish, Asian, Mexican, and Black families. These areas were more likely to be close to industrial areas and to have older housing.Banks received federal backing to lend money for mortgages based on these grades. Many banks simply refused to lend to areas with the lowest grade, making it impossible for people in many areas to become homeowners. While this type of neighborhood classification is no longer legal thanks to the Fair Housing Act of 1968 (which was passed in large part due to the activism and work of the NAACP and other groups), the effects of disinvestment due to redlining are still observable today. For example, the health and wealth of neighborhoods in Chicago today can be traced back to redlining (Chicago Tribune). In addition to formerly redlined neighborhoods having fewer resources such as quality schools, access to fresh foods, and health care facilities, new research from the Science Museum of Virginia finds a link between urban heat islands and redlining (Hoffman, et al., 2020). This layer comes out of that work, specifically from University of Richmond's Digital Scholarship Lab. More information on sources and digitization process can be found on the Data and Download and About pages. NOTE: This map has been updated as of 1/16/24 to use a newer version of the data layer which contains more cities than it previously did. As mentioned above, over 200 cities were redlined and therefore this is not a complete dataset of every city that experienced redlining by the HOLC in the 1930s. Map opens in Sacramento, CA. Use bookmarks or the search bar to get to other cities.Cities included in this mapAlabama: Birmingham, Mobile, MontgomeryArizona: PhoenixArkansas: Arkadelphia, Batesville, Camden, Conway, El Dorado, Fort Smith, Little Rock, Russellville, TexarkanaCalifornia: Fresno, Los Angeles, Oakland, Sacramento, San Diego, San Francisco, San Jose, StocktonColorado: Boulder, Colorado Springs, Denver, Fort Collins, Fort Morgan, Grand Junction, Greeley, Longmont, PuebloConnecticut: Bridgeport and Fairfield; Hartford; New Britain; New Haven; Stamford, Darien, and New Canaan; WaterburyFlorida: Crestview, Daytona Beach, DeFuniak Springs, DeLand, Jacksonville, Miami, New Smyrna, Orlando, Pensacola, St. Petersburg, TampaGeorgia: Atlanta, Augusta, Columbus, Macon, SavannahIowa: Boone, Cedar Rapids, Council Bluffs, Davenport, Des Moines, Dubuque, Sioux City, WaterlooIllinois: Aurora, Chicago, Decatur, East St. Louis, Joliet, Peoria, Rockford, SpringfieldIndiana: Evansville, Fort Wayne, Indianapolis, Lake County Gary, Muncie, South Bend, Terre HauteKansas: Atchison, Greater Kansas City, Junction City, Topeka, WichitaKentucky: Covington, Lexington, LouisvilleLouisiana: New Orleans, ShreveportMaine: Augusta, Boothbay, Portland, Sanford, WatervilleMaryland: BaltimoreMassachusetts: Arlington, Belmont, Boston, Braintree, Brockton, Brookline, Cambridge, Chelsea, Dedham, Everett, Fall River, Fitchburg, Haverhill, Holyoke Chicopee, Lawrence, Lexington, Lowell, Lynn, Malden, Medford, Melrose, Milton, Needham, New Bedford, Newton, Pittsfield, Quincy, Revere, Salem, Saugus, Somerville, Springfield, Waltham, Watertown, Winchester, Winthrop, WorcesterMichigan: Battle Creek, Bay City, Detroit, Flint, Grand Rapids, Jackson, Kalamazoo, Lansing, Muskegon, Pontiac, Saginaw, ToledoMinnesota: Austin, Duluth, Mankato, Minneapolis, Rochester, Staples, St. Cloud, St. PaulMississippi: JacksonMissouri: Cape Girardeau, Carthage, Greater Kansas City, Joplin, Springfield, St. Joseph, St. LouisNorth Carolina: Asheville, Charlotte, Durham, Elizabeth City, Fayetteville, Goldsboro, Greensboro, Hendersonville, High Point, New Bern, Rocky Mount, Statesville, Winston-SalemNorth Dakota: Fargo, Grand Forks, Minot, WillistonNebraska: Lincoln, OmahaNew Hampshire: ManchesterNew Jersey: Atlantic City, Bergen County, Camden, Essex County, Monmouth, Passaic County, Perth Amboy, Trenton, Union CountyNew York: Albany, Binghamton/Johnson City, Bronx, Brooklyn, Buffalo, Elmira, Jamestown, Lower Westchester County, Manhattan, Niagara Falls, Poughkeepsie, Queens, Rochester, Schenectady, Staten Island, Syracuse, Troy, UticaOhio: Akron, Canton, Cleveland, Columbus, Dayton, Hamilton, Lima, Lorain, Portsmouth, Springfield, Toledo, Warren, YoungstownOklahoma: Ada, Alva, Enid, Miami Ottawa County, Muskogee, Norman, Oklahoma City, South McAlester, TulsaOregon: PortlandPennsylvania: Allentown, Altoona, Bethlehem, Chester, Erie, Harrisburg, Johnstown, Lancaster, McKeesport, New Castle, Philadelphia, Pittsburgh, Wilkes-Barre, YorkRhode Island: Pawtucket & Central Falls, Providence, WoonsocketSouth Carolina: Aiken, Charleston, Columbia, Greater Anderson, Greater Greensville, Orangeburg, Rock Hill, Spartanburg, SumterSouth Dakota: Aberdeen, Huron, Milbank, Mitchell, Rapid City, Sioux Falls, Vermillion, WatertownTennessee: Chattanooga, Elizabethton, Erwin, Greenville, Johnson City, Knoxville, Memphis, NashvilleTexas: Amarillo, Austin, Beaumont, Dallas, El Paso, Forth Worth, Galveston, Houston, Port Arthur, San Antonio, Waco, Wichita FallsUtah: Ogden, Salt Lake CityVirginia: Bristol, Danville, Harrisonburg, Lynchburg, Newport News, Norfolk, Petersburg, Phoebus, Richmond, Roanoke, StauntonVermont: Bennington, Brattleboro, Burlington, Montpelier, Newport City, Poultney, Rutland, Springfield, St. Albans, St. Johnsbury, WindsorWashington: Seattle, Spokane, TacomaWisconsin: Kenosha, Madison, Milwaukee County, Oshkosh, RacineWest Virginia: Charleston, Huntington, WheelingAn example of a map produced by the HOLC of Philadelphia:

Climate change poses a greater threat for more exposed and vulnerable countries, communities and social groups. People whose livelihood depends on the agriculture and food sector, especially in low- and middle-income countries (LMICs), face significant risk. In contexts with gendered roles in agri-food systems or where structural constraints to gender equality underlie unequal access to resources and services and constrain women’s agency, local climate hazards and stressors, such as droughts, floods, or shortened crop-growing seasons, tend to negatively affect women more than men and women’s adaptive capacities tend to be more restrained than men’s. Transformation toward just and sustainable agri-food systems in the face of climate change will not only depend on reducing but also on averting aggravated gender inequality in agri-food systems. In this paper, we developed and applied an accessible and versatile methodology to identify and map localities where climate change poses high risk especially for women in agri-food systems because of gendered exposure and vulnerability. We label these localities climate-agriculture-gender inequality hotspots. Applying our methodology to LMICs reveals that the countries at highest risk are majorly situated in Africa and Asia. Applying our methodology for agricultural activity-specific hotspot subnational areas to four focus countries, Mali, Zambia, Pakistan and Bangladesh, for instance, identifies a cluster of districts in Dhaka and Mymensingh divisions in Bangladesh as a hotspot for rice. The relevance and urgency of identifying localities where climate change hits agri-food systems hardest and is likely to negatively affect population groups or sectors that are particularly vulnerable is increasingly acknowledged in the literature and, in the spirit of leaving no one behind, in climate and development policy arenas. Hotspot maps can guide the allocation of scarce resources to most-at-risk populations. The climate-agriculture-gender inequality hotspot maps show where women involved in agri-food systems are at high climate risk while signaling that reducing this risk requires addressing the structural barriers to gender equality.

Map and evaluate the effectiveness of food loss and waste reduction interventions in low- and middle-income countries, determine the reduction pathways related to waste prevention, re-use or recycling, and identify social, economic, environmental and nutritional co-benefits as they relate to the intervention. This will be done by: (i) systematically reviewing scientific and grey literature sources and (ii) conducting meta-analyses by food group where appropriate.

Amur honeysuckle bush (Lonicera maackii) and Morrow's honeysuckle (Lonicera morrowii) are two of the most aggressively invasive species to become established throughout areas along the Blue River in metropolitan Kansas City, Missouri. These two large, spreading shrubs (locally referred to as bush honeysuckle in the Kansas City metropolitan area) colonize the understory, crowd out native plants, and may be allelopathic, producing a chemical that restricts growth of native species. Removal efforts have been underway for more than a decade by local conservation groups such as Bridging The Gap and Heartland Conservation Alliance, who are concerned with the loss of native species diversity associated with the spread of bush honeysuckle. Bush honeysuckle produces leaves early in the spring before almost all other vegetation and retains leaves late in the fall after almost all other species have lost their leaves. Appropriately timed imagery can be used during early spring and late fall to map the extent of bush honeysuckle. Using multispectral imagery collected in February 2016 and true color aerial imagery collected in March 2016, a coverage map of bush honeysuckle in the study area was made to investigate the extent of bush honeysuckle in a study area along the middle reach of the Blue River in the Kansas City metropolitan area in Jackson County, Missouri. The coverage map was further classified into unlikely, low-, and high-density bush honeysuckle density at a 30-foot cell size. The unlikely density class correctly predicted the absence and approximate density of bush honeysuckle for 86 percent of the field-verification points, the low-density class predicted the presence and approximate density with 73-percent confidence, and the high-density class was predicted with 67-percent confidence. This data was used to support the project work described in: Ellis, J.T., 2018, Remote sensing of bush honeysuckle in the Middle Blue River Basin, Kansas City, Missouri, 2016–17: U.S. Geological Survey Scientific Investigations Map XXXX, 1 sheet., https://doi.org/xxxx.

This dataset consists of a 1km resolution raster version of the Land Cover Map 2000 for Great Britain. Each 1km pixel represents the dominant target class (or 'sub class') across the 1km area. The target classes broadly represent Broad Habitats (see below). The dataset is part of a series of data products produced by the Centre for Ecology & Hydrology known as LCM2000. LCM2000 is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB). Like the earlier 1990 products, LCM2000 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors and also incorporates information derived from other ancillary datasets. LCM2000 was classified using a nomenclature corresponding to the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible. The series of LCM2000 products includes vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. Full details about this dataset can be found at https://doi.org/10.5285/abff8409-0995-48d2-9303-468e1a9fe3df

🏳️‍🌈 국제기구

Comparison of footprint pattern classes and the 2011 census rural-urban classification for output areas in England and Wales.

Biogeoclimatic Ecosystem Classification (BEC) has been applied extensively in characterizing forested ecosystems in British Columbia. With a lack of qualified vectorization method used for BEC data transformation, the main goal of this research is to polygonize discontinuous BEC raster classes into vector map with better overall effectiveness and efficiency especially regarding the linear areas. The original data input for analysis is a machine-learning BEC zone raster map of Deception Study Area located in middle BC near Telkwa, with a resolution of 5m*5m. A comprehensive comparison between vectorization algorithms in GIS applications was conducted, including different filtering, simplifying and smoothing algorithms. Since we have the original predicted BEC raster map as the performance measurement, accuracy was directly measured as the percentage of correctly classified pixels when rasterizing the polygons. The evaluation criteria include visual effect, number of polygons, linear patches accuracy processing time. We found an appropriate vectorization routine to polygonize the classification raster maps. The polygonal map using Scenario D has overall satisfactory effectiveness and efficiency with a 46% linear patch accuracy and 62,014 polygons. The method also provides good approximations of the areas with moderate processing time. This is partly because we allow vertices to be located anywhere and not just exactly on the boundary of the original raster zones. We can promote this polygonization method in future predicted ecosystem mapping (PEM) product with similar linear and discontinuous areas. Priority of several key BEC zone classification with importance level regarding to the ecosystem condition related to endangered species can be further explored and added to the algorithms to better polygonize those areas in future studies.

This dataset captures in digital form the results of previously published U.S. Geological Survey (USGS) Water Mission Area studies related to water resource assessment of Cenozoic strata and unconsolidated deposits within the Mississippi Embayment and the Gulf Coastal Plain of the south-central United States. The data are from reports published from the late 1980s to the mid-1990s by the Gulf Coast Regional Aquifer-System Analysis (RASA) studies and in 2008 by the Mississippi Embayment Regional Aquifer Study (MERAS). These studies, and the data presented here, describe the geologic and hydrogeologic units of the Mississippi embayment, Texas coastal uplands, and the coastal lowlands aquifer systems, south-central United States. This dataset supercedes a previously released dataset on USGS ScienceBase (https://doi.org/10.5066/P9JOHHO6) that was found to contain errors. Following initial release of data, several types of errors were recognized in the well downhole stratigraphic data. Most of these errors were the result of unrecognized improper results in the optical character recognition conversion from the original source report. All downhole data have been thoroughly checked and corrected, data tables were revised, and new point feature classes were created for well location and WellHydrogeologicUnit. GIS data related to the geologic map and subsurface contours were correct in original release and are retained here in original form; only the well data have been revised from the initial data release. The Mississippi embayment, Texas coastal uplands, and coastal lowlands aquifer systems underlie about 487,000 km2 in parts of Alabama, Arkansas, Florida, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee, and Texas from the Rio Grande on the west to the western part of Florida on the east. The previously published investigations divided the Cenozoic strata and unconsolidated deposits within the Mississippi Embayment and the Gulf Coastal Plain into 11 major geologic units, typically mapped at the group level, with several additional units at the formational level, which were aggregated into six hydrogeologic units within the Mississippi embayment and Texas coastal uplands and into five hydrogeologic units within the Coastal Lowlands aquifer system. These units include the Mississippi River Valley alluvial aquifer, Vicksburg-Jackson confining unit (contained within the Jackson Group), the upper Claiborne aquifer (contained within the Claiborne Group), the middle Claiborne confining unit (contained within the Claiborne Group), the middle Claiborne aquifer (contained within the Claiborne Group), the lower Claiborne confining unit (contained within the Claiborne Group), the lower Claiborne aquifer (contained within the Claiborne Group), the middle Wilcox aquifer (contained within the Wilcox Group), the lower Wilcox aquifer (contained within the Wilcox Group), and the Midway confining unit (contained within the Midway Group). This dataset includes structure contour and thickness data digitized from plates in two reports, borehole data compiled from two reports, and a geologic map digitized from a report plate. Structure contour and thickness maps of hydrogeologic units in the Mississippi Embayment and Texas coastal uplands had been previously digitized by a USGS study from georeferenced images of altitude and thickness contours in USGS Professional Paper 1416-B (Hosman and Weiss, 1991). These data, which were stored on the USGS Water Mission Area’s NSDI node, were downloaded, reformatted, and attributed for present dataset. Structure contour maps of geologic units in the Mississippi Embayment and Texas coastal uplands were digitized and attributed from georeferenced images of altitude and thickness contours in USGS Professional Paper 1416-G (Hosman, 1996) for this data release. Borehole data in this data release include data compiled for USGS Gulf Coast RASA studies in which a scanned version of a USGS report (Wilson and Hosman, 1987) was converted through optical character recognition and then manipulated to form a data table, and from borehole data compiled for the subsequent MERAS study (Hart and Clark, 2008) where an Excel workbook was downloaded and manipulated for use in a GIS and as part of this dataset. The digital geologic map was digitized from Plate 4 of USGS Professional Paper 1416-G (Hosman, 1996) and then attributed according to the USGS National Cooperative Geologic Mapping Program’s GeMS digital geologic map schema. The digital dataset a digital geologic map with contacts and faults and geologic map polygons distributed as separate feature classes within a geographic information system geodatabase. The geologic map database is a digital representation of the geologic compilation of the Guld Coast region originally published as Plate 4 of USGS Professional Paper 1416-G (Hosman, 1996). The dataset includes a second geographic information system geodatabase that contain digital structure contour and thickness data as polyline feature classes for all of the hydrogeologic units contoured in USGS Professional Paper 1416-B (Hosman and Weiss, 1991) and all of the geologic units contoured in USGS Professional Paper 1416-G (Hosman, 1996). The geodatabase also contains separate point feature classes that portray borehole location and the depth to hydrogeologic units penetrated downhole for all boreholes compiled for the USGS RASA sturdies by Wilson and Hosman (1987) and for the subsequent USGS MERAS study (Hart and Clark, 2008). Borehole data are provided in Microsoft Excel spreadsheet that includes separate TABs for well location and tabulation of the depths to top and base of hydrogeologic units intercepted downhole, in a format suitable for import into a relational database. Each of the geographic information system geodatabases include non-spatial tables that describe the sources of geologic or hydrogeologic information, a glossary of terms, and a description of units. Also included is a Data Dictionary that duplicates the Entity and Attribute information contained in the metadata file. To maximize usability, spatial data are also distributed as shapefiles and tabular data are distributed as ascii text files in comma separated values (CSV) format. The landing page to for this data release contains a url to an external web resource where the downhole well data and a single contoured surface from the data release are rendered in 3D and can be interactively viewed by the user.

This dataset consists of the 1km raster, percentage aggregate class version of the Land Cover Map 2015 (LCM2015) for Northern Ireland. The 1km percentage product provides the percentage cover for each of 10 aggregated land cover classes for 1km x 1km pixels. This product contains one band per aggregated habitat class (producing a 10 band image). The 10 aggregate classes are groupings of 21 target classes, which are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. The aggregate classes group some of the more specialised classes into more general categories. For example, the five coastal classes in the target class are grouped into a single aggregate coastal class. This dataset is derived from the vector version of the Land Cover Map, which contains individual parcels of land cover and is the highest available spatial resolution. LCM2015 is a land cover map of the UK which was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. LCM2015 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the CEH web site and the LCM2015 Dataset documentation) to select the product most suited to their needs. LCM2015 was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. It is one of a series of land cover maps, produced by UKCEH since 1990. They include versions in 1990, 2000, 2007, 2015, 2017, 2018 and 2019. Full details about this dataset can be found at https://doi.org/10.5285/362feaea-0ccf-4a45-b11f-980c6b89a858