These are shapefiles and coverages that outline the areas underlain by the A, B, C, and D coal zones in the Yampa coal field. They delimit the area within which resources were calculated and reported for each zone.

This layer represents the California Department of Fish and Wildlife (CDFW) Region boundaries. CDFW has seven geographically-defined administrative regions. The terrestrial regions are delimited by county boundaries with the exception of the Region 2/Region 3 boundary which is defined as follows: Beginning at the intersection of the Stanislaus County boundary with Interstate 5, continuing north along Interstate 5 to Business 80 (Capital City Freeway) in Sacramento, then west on Business 80 to the Legal Delta boundary, then along the Legal Delta boundary north of Business 80 and Interstate 80 intersecting with Interstate 80 on the west side of the Yolo Bypass, then continuing west on Interstate 80 to the Solano County boundary, then continuing west and north along portions of the Solano, Napa, and Sonoma county boundaries ending at the intersection with the Mendocino County boundary. The Marine Region (Region 7) offshore boundary is represented by the official NOAA Three Nautical Mile Line - a maritime limt that depicts the outer extent of state jurisdiction.

A GIS database of geologic units and structural features in New Mexico, with lithology, age, data structure, and format written and arranged just like the other states.

The attitude of an easement is the spatial field (i.e., the geographical area) within which the easement applies. This space field can be defined either in 2D or in 3D in particular in the specific cases of airport clearance easements, easements to protect radio transmission centres.

The Country boundaries at level 0 dataset is part of the Global Administrative Areas (GADM) 3.6 vector dataset series which includes distinct datasets representing administrative boundaries for all countries in the world. GADM makes use of high spatial resolution images and an extensive set of attributes to map administrative areas at all levels of political sub-division. The National level 0 attributes comprise country name in English and ISO 3166-1 alpha3 coding. Please read the GADM 3.6 - Global Administrative Areas dataset series metadata for more information.

Data publication: 2020-12-01

Supplemental Information:

The dataset was originally produced for the BioGeomancer project, with collaboration from the International Rice Research Institute and of California, Berkeley, Museum of Vertebrate Zoology. The development of GADM was partly supported by the Gordon and Betty Moore foundation for the BioGeomancer project.

Citation:

© 2009-2018 GADM

Contact points:

Resource Contact: Robert Hijmans

Metadata Contact: Robert Hijmans

Data lineage:

GADM unique ID (GID) starts with the three-letter ISO 3166-1 alpha-3 country code. If there are subdivisions these are identified by a number from 1 to n, where n is the number of subdivisions at level 1. This value is concatenated with the country code, using a dot to delimit the two. For example, AFG.1, AFG.2, ..., AFG.n. If there are second-level subdivisions, numeric codes are assigned within each first-level subdivision and these are concatenated with the first level identifier, using a dot as a delimiter. For example, AFG.1.1, AFG.1.2, AFG.1.3, ..., and AFG.2.1, AFG.2.2, .... And so forth for the third, fourth, and fifth levels. Finally, there is an underscore followed by a version number appended to the code. For example, AFG.3_1 and AFG.3.2_1. The GID codes are persistent after version 3.6 (there were errors in the codes in version 3.4). If an area changes, for example, if it splits into two new areas, two new codes will be assigned, and the old code will not be used anymore. The version only changes when there is a major overhaul of the divisions in a country, for example when a whole new set of subdivisions is introduced.

Resource constraints:

The data are freely available for academic use and other non-commercial use. Redistribution, or commercial use is not allowed without prior permission. See the license for more details.

Online resources:

GADM v.3 Dataset Download: The spatial data by country

GADM Historical Data Download

Territory covered by a landscape protection and development directive (landscape directive) Perimeter delimited by municipal boundaries

Accurately delimiting species is fundamentally important for understanding species diversity and distributions and devising effective strategies to conserve biodiversity. However, species delimitation is problematic in many taxa, including ‘non-adaptive radiations’ containing morphologically cryptic lineages. Fortunately, coalescent-based species delimitation methods hold promise for objectively estimating species limits in such radiations, using multilocus genetic data. Using coalescent-based approaches, we delimit species and infer evolutionary relationships in a morphologically conserved group of Central American freshwater fishes, the Poecilia sphenops species complex. Phylogenetic analyses of multiple genetic markers (sequences of two mitochondrial DNA genes and five nuclear loci) from 10/15 species and genetic lineages recognized in the group support the P. sphenops species complex as monophyletic with respect to outgroups, with eight mitochondrial ‘major-lineages’ diverged by ≥2% pairwise genetic distances. From general mixed Yule-coalescent models, we discovered (conservatively) 10 species within our concatenated mitochondrial DNA dataset, 9 of which were strongly supported by subsequent multilocus Bayesian species delimitation and species tree analyses. Results suggested species-level diversity is underestimated or overestimated by at least ~15% in different lineages in the complex. Nonparametric statistics and coalescent simulations indicate genealogical discordance among our gene tree results has mainly derived from interspecific hybridization in the nuclear genome. However, mitochondrial DNA show little evidence for introgression, and our species delimitation results appear robust to effects of this process. Overall, our findings support the utility of combining multiple lines of genetic evidence and broad phylogeographical sampling to discover and validate species using coalescent-based methods. Our study also highlights the importance of testing for hybridization versus incomplete lineage sorting, which aids inference of not only species limits but also evolutionary processes influencing genetic diversity.

Analysis of ‘CDFW Regions’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/167d1e9e-774e-40bf-9b94-21a0904df75c on 26 January 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

The Administrative boundaries at the level 1 dataset are part of the Global Administrative Areas (GADM) 3.6 vector dataset series which includes distinct datasets representing administrative boundaries for all countries in the world. The Administrative level 1 distinguishes Countries, Provinces and equivalent. GADM makes use of high spatial resolution images, and an extensive set of attributes to map administrative areas at all levels of political sub-division. Information on administrative units associated attributes includes official names in Latin and non-Latin scripts, variant names, administrative type in local and English. Please read the GADM 3.6 - Global Administrative Areas dataset series metadata for more information.

Data publication: 2018-01-01

Supplemental Information:

The dataset was originally produced for the BioGeomancer project, with collaboration from the International Rice Research Institute and of California, Berkeley, Museum of Vertebrate Zoology. The development of GADM was partly supported by the Gordon and Betty Moore foundation for the BioGeomancer project.

Citation:

© 2009-2018 GADM

Contact points:

Resource Contact: Robert Hijmans

Metadata Contact: Robert Hijmans

Data lineage:

GADM unique ID (GID) starts with the three-letter ISO 3166-1 alpha-3 country code. If there are subdivisions these are identified by a number from 1 to n, where n is the number of subdivisions at level 1. This value is concatenated with the country code, using a dot to delimit the two. For example, AFG.1, AFG.2, ..., AFG.n. If there are second-level subdivisions, numeric codes are assigned within each first-level subdivision and these are concatenated with the first level identifier, using a dot as a delimiter. For example, AFG.1.1, AFG.1.2, AFG.1.3, ..., and AFG.2.1, AFG.2.2, .... And so forth for the third, fourth, and fifth levels. Finally, there is an underscore followed by a version number appended to the code. For example, AFG.3_1 and AFG.3.2_1. The GID codes are persistent after version 3.6 (there were errors in the codes in version 3.4). If an area changes, for example, if it splits into two new areas, two new codes will be assigned, and the old code will not be used anymore. The version only changes when there is a major overhaul of the divisions in a country, for example when a whole new set of subdivisions is introduced.

Resource constraints:

The data are freely available for academic use and other non-commercial use. Redistribution, or commercial use is not allowed without prior permission. See the license for more details.

Online resources:

GADM v.3 Dataset Download: The spatial data by country

GADM Historical Data Download

description: This is a shapefile and coverage that outlines the area underlain by coals in the lower Blackhawk Formation, southern Wasatch Plateau coal assessment area. The polygons delimit the area within which resources were calculated and reported.; abstract: This is a shapefile and coverage that outlines the area underlain by coals in the lower Blackhawk Formation, southern Wasatch Plateau coal assessment area. The polygons delimit the area within which resources were calculated and reported.

Quarter boundaries of NÖ delimited to municipal political boundaries.

State boundaries of the Federated States of Micronesia (FSM), delimiting the states of Yap, Chuuk, and Kosrae. These are approximated from the map image on Wikipedia at: http://en.wikipedia.org/wiki/File:Map_of_the_Federated_States_of_Micronesia_CIA.jpg

The Administrative boundaries at the level 4 dataset are part of the Global Administrative Areas (GADM) 3.6 vector dataset series which includes distinct datasets representing administrative boundaries for all countries in the world. The Administrative level 4 distinguishes sub-national administrative boundaries smaller than Communes and Municipalities and is available for 20 countries. GADM makes use of high spatial resolution images, and an extensive set of attributes to map administrative areas at all levels of political sub-division. Information on administrative units associated attributes includes official names in Latin and non-Latin scripts, variant names, administrative type in local an English. Please read the GADM 3.6 - Global Administrative Areas dataset series metadata for more information.

Data publication: 2018-01-01

Supplemental Information:

The dataset was originally produced for the BioGeomancer project, with collaboration from the International Rice Research Institute and of California, Berkeley, Museum of Vertebrate Zoology. The development of GADM was partly supported by the Gordon and Betty Moore foundation for the BioGeomancer project.

Citation:

© 2009-2018 GADM

Contact points:

Resource Contact: Robert Hijmans

Metadata Contact: Robert Hijmans

Data lineage:

GADM unique ID (GID) starts with the three-letter ISO 3166-1 alpha-3 country code. If there are subdivisions these are identified by a number from 1 to n, where n is the number of subdivisions at level 1. This value is concatenated with the country code, using a dot to delimit the two. For example, AFG.1, AFG.2, ..., AFG.n. If there are second-level subdivisions, numeric codes are assigned within each first-level subdivision and these are concatenated with the first level identifier, using a dot as a delimiter. For example, AFG.1.1, AFG.1.2, AFG.1.3, ..., and AFG.2.1, AFG.2.2, .... And so forth for the third, fourth, and fifth levels. Finally, there is an underscore followed by a version number appended to the code. For example, AFG.3_1 and AFG.3.2_1. The GID codes are persistent after version 3.6 (there were errors in the codes in version 3.4). If an area changes, for example, if it splits into two new areas, two new codes will be assigned, and the old code will not be used anymore. The version only changes when there is a major overhaul of the divisions in a country, for example when a whole new set of subdivisions is introduced.

Resource constraints:

The data are freely available for academic use and other non-commercial use. Redistribution, or commercial use is not allowed without prior permission. See the license for more details.

Online resources:

GADM v.3 Dataset Download: The spatial data by country

GADM Historical Data Download

A GIS database of geologic units and structural features in California, with lithology, age, data structure, and format written and arranged just like the other states.

Web map depicting road-separated bicycle routes throughout the State of Maryland. In many instances these routes can be used by pedestrians, but not by any motorized vehicle. “Road Separated” indicates that these segments are physically separated from roads in which motorized vehicles travel. That is, these are not bike lanes, sharrows, or signed bike routes per se – these segments are separated from the main road by a physical barrier (E.g., grass, natural earth, trees, etc.) and in many instances do not follow a motorized vehicle route network at all (E.g., a wholly separated trail). This information in this web map will be used in conjunction with the One Maryland One Centerline (OMOC) initiative to provide a Level of Traffic Stress (LTS) map for the State of Maryland. The recommended LTS score for each road-separated bicycle route is listed in the pop-up menu.Bikeways included in the map are of varying surface material but are typically paved or hard-packed surface. Sidewalks, typically 6-feet-wide, hard surface pathways primarily for pedestrian use, are not included in this map. The preferred width of bikeways included is at least 10-feet wide, although bikeways may be narrower if anticipated traffic volumes are minimal. For instance, while most pathways within Columbia are 8 to 10 feet wide, some pathways are narrower when connecting to cul-du-sacs. These narrower pathways are included in the map as the anticipated traffic volume are low enough to minimize user conflict. The feature class used in the web map was compiled by the Maryland Department of Transportation (Secretary’s Office) using a variety of sources including (but not limited to) state data sources from various agencies including the Maryland Department of Transportation (MDOT), Maryland Department of Planning (MDP), and Maryland Department of Natural Resources (DNR). Data has also been augmented by counties and municipalities throughout the State of Maryland as well as several agencies, organizations, and conservancies. Certain named trails are included within this dataset and their sources have varied from state, local, and non-profits as well as through the digitizing of various aerial/satellite imagery, digital elevation models (DEM), right-of-ways, former railways, and corridors by MDOT staff. None of the alignments included in this dataset are restricted to private use.The searchable data includes attribute information containing the route’s name (if applicable), calculated segment length (using the Maryland State Plane 1983 Projection), and Level of Traffic Stress (LTS) score. The data mostly contains alignment segments with a LTS of 0, the lowest score. This score indicates that the bikeway can be comfortably used by cyclists of all levels and experience and has minimal automobile traffic interaction. There are several segments with LTS scores of 1 or 2 within this feature class as well, but these are mostly connector paths/trails between larger segments. LTS scores of 1 or 2 indicate very little automobile traffic and/or slower speeds required for the automobile traffic.This web map will be updated as needed but is scheduled to be comprehensively reviewed on an annual basis. ATTRIBUTES:Route Name (if Applicable): The name of the route is provided if the route is namedCounties within Route: The counties in Maryland through which the route passes are listedRoute's Length: The route distance is calculated and listed in miles. Note that this is the length of the entire named route - and not just the segment selected. Distance calculated using the NAD 1983 StatePlane Maryland FIPS 1900 (US Feet) Projection.LTS Score: Level of Traffic Stress. For this map (road-separated routes) the scores range from 0 (road-separated) to 2 (generally low traffic). The areas that are not 0 in this map/data represent portions of the road-separated routes that cross streets or have portions that are briefly on-road as connections.

This dataset is derived from the article: Huang, M., Wang, Z.C., Pan, X.H., Gong, B.H., Tu, M.Z., & Liu, Z.F. (2022). Delimiting China's urban growth boundaries under localized shared socioeconomic pathways and various urban expansion modes. Earth's Future, 10, e2021EF002572. The dataset shows the urban expansion and urban growth boundaries of China in 2021-2100 under different socioeconomic scenarios and diverse urban expansion modes. To produce this dataset, the patch-based LUSD-urban model was used to simulate the urban expansion with 11 modes under the localized shared socioeconomic pathways, and the morphology approach was used to delimit urban growth boundaries according to the maximum extent of urban expansion. Using this dataset, the authors quantified the impacts of future urban expansion on ecosystem services under different scenarios and diverse modes, as well as the pressure of urban shrinkage, which is helpful to the Chinese government to demarcate urban development boundaries.

The following files are included:
[Item 1 'stations_ghi.txt'] : complete station metadata, all 1677 stations, pipe(|) delimited
[Item 2 'hydromet_annual.txt'] : hydrometeorological time series for stations in Groups 1, 2 and 3, annual, pipe(|) delimited
[Item 3 'hydromet_monthly.txt'] : hydrometeorological time series for stations in Groups 1, 2 and 3, monthly, pipe(|) delimited
[Item 4 'basins_ghi'] : one shapefile of ghi composite basins
[Item 5 folder 'by_station'] : shapefiles of delineated catchment boundaries for stations in Groups 1, 2 and 3
[Item 6 folder 'pdfs'] : PDF files of station summary, annual time series charts and monthly time series charts for stations in Groups 1, 2 and 3 (one PDF per composite basin

Worldwide distribution patterns of living animals are structured in multiple zoogeographical regions, characterized by faunas with homogeneous composition that are separated by sharp boundaries. Tectonic movements, abrupt climatic transitions, and orographic barriers have shaped extant boundaries between these zoogeographical regions. These data allow to assess whether and how the drivers of global zoogeographical boundaries vary between vertebrate clades taxa with different life-history traits.Description of files:We provide nine tables reporting data on the global zoogeographical boundaries of mammal, bird and amphibian class. For each class, we provide one table with all the boundaries, one table with the deep boundaries only (i.e. between realms) and with the shallow boundaries only (i.e. between subregions).Data are in Mollweide projection, with cell size of 200 km, and can be imported in any GIS software (delimited text).The 9 tables include the following column:Boundary: 1: the cell is on the boundary; 0: the cell is not on the boundaryx: Mollweide x coordinatesy: Mollweide y coordinatesp_bio1.cv.k: heterogeneity of absolute annual mean temperaturep_bio4.cv: heterogeneity of temperature seasonalityp_bio12.cv: heterogeneity of annual precipitationp_bio15.cv: heterogeneity of precipitation seasonalityp_alt_d: averaged altitude difference (mean of absolute vales) between each cell and the eight neighbouring ones (i.e. orographic barriers)points_stab: tectonic separation, calculated as the variation of geographical distance between each cell and the neighbouring ones during the last 65 million yearsp_vel: mean velocity of temperature change during the late quaternary for each cell (Sandel et al., 2011)Climatic layers were log-transformed to reduce skewness and improve normality, and then scaled and normalized (mean = 0 and variance = 1)Furthermore, we provide the R code to run the analyses using hierarchical hierarchical generalised linear mixed models (HGLM) with spatially autocorrelated error. The running example is the analysis on all the geographical boundaries for mammals; the analyses of all the vertebrates can be obtained just by changing the name of the tableThe analyses of this dataset have been published in the following paper:Ficetola, G. F., F. Mazel, M. Falaschi, S. Marta, and W. Thuiller. 2021. Determinants of zoogeographical boundaries differ between vertebrate groups. Global Ecology and Biogeography 30:1796-1809. https://doi.org/10.1111/geb.13345

Reference parcels are continuous agricultural parcels delimited by the boundaries of stable, nature-identifiable objects or by the boundaries of real estate (minimum area of the reference parcel is 0.30 ha)

Part of the Nova Scotia Topographic Database, the delimiter lines are obtained from various sources which could include Nova Scotia Department of Natural Resources, Property Records Database, or Parks Canada. These areas are generally administrative Boundaries. Delimiter Line feature codes and their descriptions are provided with the download in a NSTDB feature code table. Data download also available via GeoNova: https://nsgi.novascotia.ca/WSF_DDS/DDS.svc/DownloadFile?tkey=fhrTtdnDvfytwLz6&id=12 Map service view also available via GeoNova: https://nsgiwa.novascotia.ca/arcgis/rest/services/BASE/BASE_NSTDB_10K_Delimiter_Boundaries_UT83/MapServer?f=jsapi