This dataset, termed "GAGES II", an acronym for Geospatial Attributes of Gages for Evaluating Streamflow, version II, provides geospatial data and classifications for 9,322 stream gages maintained by the U.S. Geological Survey (USGS). It is an update to the original GAGES, which was published as a Data Paper on the journal Ecology's website (Falcone and others, 2010b) in 2010. The GAGES II dataset consists of gages which have had either 20+ complete years (not necessarily continuous) of discharge record since 1950, or are currently active, as of water year 2009, and whose watersheds lie within the United States, including Alaska, Hawaii, and Puerto Rico. Reference gages were identified based on indicators that they were the least-disturbed watersheds within the framework of broad regions, based on 12 major ecoregions across the United States. Of the 9,322 total sites, 2,057 are classified as reference, and 7,265 as non-reference. Of the 2,057 reference sites, 1,633 have (through 2009) 20+ years of record since 1950. Some sites have very long flow records: a number of gages have been in continuous service since 1900 (at least), and have 110 years of complete record (1900-2009) to date.

The geospatial data include several hundred watershed characteristics compiled from national data sources, including environmental features (e.g. climate – including historical precipitation, geology, soils, topography) and anthropogenic influences (e.g. land use, road density, presence of dams, canals, or power plants). The dataset also includes comments from local USGS Water Science Centers, based on Annual Data Reports, pertinent to hydrologic modifications and influences. The data posted also include watershed boundaries in GIS format.

This overall dataset is different in nature to the USGS Hydro-Climatic Data Network (HCDN; Slack and Landwehr 1992), whose data evaluation ended with water year 1988. The HCDN identifies stream gages which at some point in their history had periods which represented natural flow, and the years in which those natural flows occurred were identified (i.e. not all HCDN sites were in reference condition even in 1988, for example, 02353500). The HCDN remains a valuable indication of historic natural streamflow data. However, the goal of this dataset was to identify watersheds which currently have near-natural flow conditions, and the 2,057 reference sites identified here were derived independently of the HCDN. A subset, however, noted in the BasinID worksheet as “HCDN-2009”, has been identified as an updated list of 743 sites for potential hydro-climatic study. The HCDN-2009 sites fulfill all of the following criteria: (a) have 20 years of complete and continuous flow record in the last 20 years (water years 1990-2009), and were thus also currently active as of 2009, (b) are identified as being in current reference condition according to the GAGES-II classification, (c) have less than 5 percent imperviousness as measured from the NLCD 2006, and (d) were not eliminated by a review from participating state Water Science Center evaluators.

The data posted here consist of the following items:- This point shapefile, with summary data for the 9,322 gages.- A zip file containing basin characteristics, variable definitions, and a more detailed report.- A zip file containing shapefiles of basin boundaries, organized by classification and aggregated ecoregion.- A zip file containing mainstem stream lines (Arc line coverages) for each gage.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Expedited Level 1A Reconstructed Unprocessed Instrument Data (AST_L1AE) global product contains reconstructed, unprocessed instrument digital data derived from the acquired telemetry streams of the telescopes: Visible and Near Infrared (VNIR), Shortwave Infrared (SWIR), and Thermal Infrared (TIR). This data product is similar to the (AST_L1A) (http://doi.org/10.5067/ASTER/AST_L1A.003) with a few notable exceptions. These include: * The AST_L1AE is available for download within 48 hours of acquisition in support of field calibration and validation efforts, in addition to emergency response for natural disasters where the quick turn-around time from acquisition to availability would prove beneficial in initial damage or impact assessments. * The registration quality of the AST_L1AE is likely to be lower than the AST_L1A, and may vary from scene to scene. * The AST_L1AE data product does not contain the VNIR 3B (aft-viewing) Band. * This dataset does not have short-term calibration for the Thermal Infrared (TIR) sensor. * The AST_L1AE data product is only available for download 30 days after acquisition. It is then removed and reprocessed into an AST_L1A product.

This research is framed in the area of biomathematics and contributes to the epidemiological surveillance entities in Colombia to clarify how breast cancer mortality rate (BCM) is spatially distributed in relation to the forest area index (FA) and circulating vehicle index (CV). In this regard, the World Health Organization has highlighted the scarce generation of knowledge that relates mortality from tumor diseases to environmental factors. Quantitative methods based on geospatial data science are used with cross-sectional information from the 2018 census; it’s found that the BCM in Colombia is not spatially randomly distributed, but follows cluster aggregation patterns. Under multivariate modeling methods, the research provides sufficient statistical evidence in terms of not rejecting the hypothesis that if a spatial unit has high FA and low CV, then it has significant advantages in terms of lower BCM.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Point Conception map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore of Point Conception map area data layers. Data layers are symbolized as shown on the associated map sheets.

The Clean Water Act requires three components to water quality standards that set goals for and protect each States' waters. The three components are: (1) designated uses that set goals for each water body (e.g., recreational use), (2) criteria that set the minimum conditions to support the use (e.g., bacterial concentrations below certain concentrations) and (3) an antidegradation policy that maintains high quality waters so they are not allowed to degrade to meet only the minimum standards. The designated uses and criteria set the minimum standards for Tier I. Maryland's antidegradation policy has been promulgated in three regulations within the Code of Maryland Regulations (COMAR): COMAR 26.08.02.04 sets out the policy itself, COMAR 26.08.02.04-1, which is discussed here, provides for implementation of Tier II (high quality waters) of the antidegradation policy, and COMAR 26.08.02.04-2 that describes Tier III (Outstanding National Resource Waters or ONRW), the highest quality waters. No Tier III waters have been designated at this time. Tier II antidegradation implementation has the greatest immediate effect on local government planning functions so the Maryland Department of the Environment (MDE) has prepared this set of Tier II GIS data layers to provide technical assistance to local governments working to complete the Water Resources Element of their comprehensive plans as required by HB 1141. As part of this process, MDE has created this dataset representing the official record of all Maryland Tier II (high quality) stream segments as determined by MDE, the regulatory agency responsible for identification and listing of Maryland's Tier II waters. This dataset consists of a digital geospatial representation of all identified Tier II segments which includes those stream segments promulgated in (COMAR) 26.08.02.04-1, and those additional segments proposed during the current Triennial Review of Maryland Regulations, known as the pending list. Pending segments are Tier II segments awaiting promulgation. This is a vector line file that was developed using the 24,000:1 scale National Hydrography Dataset (NHD) coverage for Maryland, and each identified Tier II stream segment has been linked to the NHD using the unique common identifier (COMID) code. MDE uses Maryland Biological Stream Survey (MBSS) data for designating streams as Tier II. Using all MBSS stations sampled within a stream reach (defined as a section of stream from confluence to confluence), an arithmetic mean of the benthic index of biotic integrity (IBI) and the fish IBI is calculated. Only if the means of both the benthic and fish IBIs are greater than or equal to 4.00 is a stream reach designated as Tier II. As such, Tier II streams represent the best streams in Maryland in terms of water quality, water chemistry, habitat, and biotic assemblages. Tier II stream segments can range in length generally terminating at confluences, impoundment outfalls, and tidal boundaries. However, in planning activities, one should consider the entire upstream watershed to a Tier II stream as any changes to this watershed can potentially have an effect on the water quality of the Tier II stream. It is worth noting that once a stream segment is designated as Tier II, this designation lasts in perpetuity regardless of changes in water quality or local landuse. The publicly maintained list of all Tier II waters and for further information regarding Maryland's High Quality Waters, Tier II, please visit http://mde.maryland.gov/programs/Water/TMDL/Integrated303dReports/Pages/Antidegradation.aspxAcknowledgement of the Maryland Department of the Environment, Science Services Administration as a data source would be appreciated in products developed from these data, and such acknowledgement as is standard for citation and legal practices for data sources is expected by users of this data. Sharing new data layers developed directly from these data would also be appreciated by Maryland Department of the Environment Science Services Administration staff. MDE shall not be held liable for improper or incorrect use of this data. These data are not legal documents and are not to be used as such.This is a MD iMAP hosted service. Find more information on https://imap.maryland.gov.Feature Service Link: https://archive.geodata.md.gov/imap/rest/services/Hydrology/MD_ArchivedWaterQuality/FeatureServer/1

An orthoimage is remotely-sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. The Landsat Mosaic orthoimagery database contains Landsat Thematic Mapper imagery for the conterminous United States. The more than 700 Landsat scenes have been resampled to a 1-arc-second (approximately 30-meter) sample interval in a geographic coordinate system using the North American Horizontal Datum of 1983. Three bands have been selected from the eight spectral bands available for each frame. These are bands 4 (near-infrared), 3 (red), and 2 (green), typically displayed as red, green, and blue, respectively. The image is a full-resolution (spectral and spatial), 24-bit color-infrared composite that simulates color infrared film as a "false color composite". NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

The MOD44B Version 6.1 Vegetation Continuous Fields (VCF) yearly product is a global representation of surface vegetation cover as gradations of three ground cover components: percent tree cover, percent non-tree cover, and percent non-vegetated (bare). VCF products provide a continuous, quantitative portrayal of land surface cover at 250 meter (m) pixel resolution, with a sub-pixel depiction of percent cover in reference to the three ground cover components. The sub-pixel mixture of ground cover estimates represents a revolutionary approach to the characterization of vegetative land cover that can be used to enhance inputs to environmental modeling and monitoring applications.

The MOD44B data product layers include percent tree cover, percent non-tree cover, percent non-vegetated, cloud cover, and quality indicators. The start date of the annual period for this product begins with day of year (DOY) 65 (March 6 except for leap year which corresponds to March 5).

Improvements/Changes from Previous Versions

• The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
• A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).

This accession contains a GIS database of Aids to Navigation in the Gulf of Mexico and coastal waters of Alabama, Florida, Louisiana, Mississippi and Texas. These data were compiled on 1999-10-21.

The term "Aids to Navigation" (ATONS or AIDS) refers to a device outside of a vessel used to assist mariners in determining their position or safe course, or to warn them of obstructions. AIDS to navigation include lighthouses, lights, buoy, sound signals, landmarks, racons, radio beacons, LORAN, and omega. These include AIDS which are installed and maintained by the Coast Guard as well as privately installed and maintained aids (permit required). This does not include unofficial AIDS (illegal) such as stakes, PVC pipes, and such placed without permission.

Each USCG District Headquarters is responsible for updating their database on an "as needed" basis. When existing AIDS are destroyed or relocated and new AIDS are installed the database is updated. Each AID is assigned an official "light listing number". The light list is a document listing the current status of ATONS and it is published and distributed on a regular basis. Interim changes to the light list are published in local Notices to Mariners which are the official means which navigators are supposed to keep their charts current. In addition, the USCG broadcasts Notices to Mariners on the marine band radio as soon as changes of the status of individual AIDS are reported.

The light list number and local Notices to Mariners reports are suggested ways to keep the database current on a regular or even "real time" basis. However, annual (or more frequent) updates of the entire dataset may be obtained from each USCG District Headquarters.

Geographic Information System (GIS) software is required to display the data in this NCEI accession.

Great Smoky Mountains National Park 1949 Park Topographic Map

How should this data set be cited?

United States Geologic Survey, 1949 , Topographic Map. Great Smoky Mountains National Park. Tennessee and North Carolina. Online Links:

http://science.nature.nps.gov/nrdata

What geographic area does the data set cover?

West_Bounding_Coordinate: -84.007769 East_Bounding_Coordinate: -83.037582 North_Bounding_Coordinate: 35.790660 South_Bounding_Coordinate: 35.418718

What does it look like?

Does the data set describe conditions during a particular time period?

Calendar_Date: 09-Mar-2015Currentness_Reference: publication date

What is the general form of this data set?

Geospatial_Data_Presentation_Form: raster digital data

How does the data set represent geographic features?

How are geographic features stored in the data set? This is a Raster data set.

What coordinate system is used to represent geographic features? Horizontal positions are specified in geographic coordinates, that is, latitude and longitude. Latitudes are given to the nearest 0.000000. Longitudes are given to the nearest 0.000000. Latitude and longitude values are specified in Decimal degrees.

The horizontal datum used is North American Datum of 1983. The ellipsoid used is Geodetic Reference System 80. The semi-major axis of the ellipsoid used is 6378137.000000. The flattening of the ellipsoid used is 1/298.257222.

Vertical_Coordinate_System_Definition:

Altitude_System_Definition:

Altitude_Datum_Name: North American Vertical Datum of 1988 Altitude_Resolution: 0.000025 Altitude_Distance_Units: feet Altitude_Encoding_Method:

Explicit elevation coordinate included with horizontal coordinates

How does the data set describe geographic features?

Entity_and_Attribute_Overview:

Where possible entity attribute population is completed automatically by the GIS/SQL database software. Enclosed herein are Attribute Domains and lists of legal values (LOV) where attributes are populated by "Picklists".

Who produced the data set?

Who are the originators of the data set? (may include formal authors, digital compilers, and editors)

United States Geologic Survey

Who also contributed to the data set?

To whom should users address questions about the data?

National Park Service Attn: Thomas Colson GIS Specialist 107 Park Headquarters Road Gatlinburg, Tennessee 37738 United States

(865)436-1701 (voice) GRSM_Resource_Management@nps.gov

Hours_of_Service: 0800-1730

Why was the data set created?

For the display, query, and analysis of spatial and tabular data.

How was the data set created?

From what previous works were the data drawn?

How were the data generated, processed, and modified?

Date: 28-Mar-2015 (process 1 of 1)

These data contain location values from numerous resource research, inventory, and monitoring projects spanning over many decades. The National Park Service is unable to determin the process steps used to depict many locations, citing lack of reliable data. When known, map source is given as a range of values.

What similar or related data should the user be aware of?

How reliable are the data; what problems remain in the data set?

How well have the observations been checked? Attribute accuracy is tested by manual comparison of the source with hard copy plots and/or symbolized display of the map data on an interactive computer graphic system. Selected attributes that cannot be visually verified on plots or on screen are interactively queried and verified on screen. In addition, the attributes are tested against a master set of valid attributes. All attribute data conform to the attribute codes in the signed classification and correlation document and amendment(s).

How accurate are the geographic locations? These data contain location values from numerous resource research, inventory, and monitoring projects spanning over many decades. The National Park Service is unable to asses the positional accuracy of many locations, citing lack of reliable data. When known, estimated horizontal precision is given as a range of possible values.

Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.

This hardcopy map was scanned and georectified using current USGS 1:24k-scale topographic maps. This map is for reference purpose only, and there are likey several gross horizontal errors committed during rectification.

How accurate are the heights or depths? Statements of vertical positional accuracy for elevation of these points are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of points printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map. This statement is generally true for the most common sources of these data. Other sources and methods may have been used to create or update these data. In some cases, additional information may be found in the feature-level metadata report.

Where are the gaps in the data? What is missing? Data completeness for these data reflect content of the source data. Features may have been eliminated or generalized on the source data due to scale and legibility constraints. For information on collection and inclusion criteria, see U.S. Geological Survey, 1994, Standards for 1:24,000-Scale Digital Line Graphs and Quadrangle Maps: National Mapping Program Technical Instructions and U.S. Geological Survey, 1994, Standards for Digital Line Graphs: National Mapping Program Technical Instructions.

How consistent are the relationships among the observations, including topology? No duplicate features exist nor duplicate points in a data string. Point data are represented by two sets of coordinate pairs, each with the same coordinate values, contained in the "Shape" Column, and "X_COORD, Y_COORD" Columns.

Database engine scripts automatically populate many of the possible "List of Values" for those columns that derive their attrtibute from other source data (see Entity Attribute Section of this document for details), thereby enforcing Attribute Accuracy. Database engine scripts also prevent the entry of duplication location coordinates, ensure the consistency and format of binary data representing geographic coordinates, and spatial and attribute index integrity.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?

Who distributes the data set? (Distributor 1 of 1)

Thomas Colson National Park Service GIS Specialist 107 Park Headquarters Rd. Gatlinburg, Tennessee 37738 United States

(865)436-1701 (voice) GRSM_Resource_Management@nps.gov

Hours_of_Service: 0800-1730 EST

What's the catalog number I need to order this data set? Downloadable Data

What legal disclaimers am I supposed to read?

The National Park Service shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics (i.e. GIF or JPG format files) are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time. The data are not better than the original sources from which they were derived. It is the responsibility of the data user to use the data appropriately and consistent within the limitations of geospatial data in general and these data in particular. The related graphics are intended to aid the data user in acquiring relevant data; it is not appropriate to use the related graphics as data. The National Park Service gives no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data. It is strongly recommended that these data are directly acquired from an NPS server and not indirectly through other sources which may have changed the data in some way. Although these data have been processed successfully on computer systems at the National Park Service, no warranty expressed or implied is made regarding the utility of the data on other

Esri ArcGIS Online (AGOL) Feature Layer for accessing the Maryland 2100 Nuisance Tidal Inundation - Flood Depth Grid data.Maryland 2100 Nuisance Tidal Inundation - Flood Depth Grid data consists of geographic areas throughout the State of Maryland that are projected to be impacted by a nuisance tidal flooding scenario in year 2100.The notion of nuisance tidal flooding, also known as sunny-day flooding, as a current threat to transportation infrastructure has become better understood. In most coastal locations, these abnormally high tides have traditionally occurred a few times per year, degrading road support facilities and causing traffic congestion. However, NOAA has documented that nuisance flooding has become significantly more common in the Chesapeake Bay watershed. In time, nuisance flooding is expected to become even more frequent and more hazardous as the area experiences sea-level change. Currently, there is no known map of areas prone to nuisance flooding.Maryland 2100 Nuisance Tidal Inundation - Flood Depth Grid data represents projected stillwater depths (ft) during a 'nuisance' tidal flooding scenario. This data is an attempt to aid in identifying potential vulnerabilities along Maryland's roadway infrastructure. The data supports MDOT State Highway Administration leadership and planners as they endeavor to mitigate or prevent the impacts of sea level change resulting from land surface subsidence and rising sea levels. The product uses sea-level projections to forecasts areas of inundation for a given scenario. University of Maryland Center for Environmental Science (UMCES) provides the sea level change (SLC) estimate. SLC is localized using qualifying National Oceanic and Atmospheric Administration (NOAA) tidal reference stations.The data may be used and redistributed for free but is not intended for legal use, since it likely contains inaccuracies. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use. The information contained in these data is dynamic and will change over time. The data are not better than the original sources from which they were derived, and both scale and accuracy may vary across the data set. These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. These data are neither legal documents nor land surveys, and must not be used as such. Maryland Department of Transportation State Highway Administration (MDOT SHA) and the Eastern Shore Regional GIS Cooperative (ESRGC) should be cited as the data source in any products derived from these data. Any Users wishing to modify the data should describe the types of modifications they have performed. The User should not misrepresent the data, nor imply that changes made were approved or endorsed by MDOT SHA and/or ESRGC. Neither MDOT SHA, ESRGC, nor the State of Maryland, nor any of their employees or contractors, makes any warranty, express or implied, including warranties of merchantability and fitness for a particular purpose, or assumes any legal liability for the accuracy, completeness, or usefulness, of this information.Last Updated: June 2020For complete project overview and metadata information:Website: https://www.esrgc.org/project/sha-to8For more information about the data, contact Eastern Shore Regional GIS Cooperative (ESRGC):Email: esrgc@salisbury.eduFor more information, contact MDOT SHA Geospatial Technologies:Email: GIS@mdot.maryland.gov

This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source: NASA FIRMS - Active Fire Data - for WorldScale/Resolution: 1kmUpdate Frequency: 1/2 Hour (every 30 minutes) using the Aggregated Live Feed MethodologyArea Covered: WorldWhat can I do with this layer?The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Additional InformationMODIS stands for MODerate resolution Imaging Spectroradiometer. The MODIS instrument is on board NASA’s Earth Observing System (EOS) Terra (EOS AM) and Aqua (EOS PM) satellites. The orbit of the Terra satellite goes from north to south across the equator in the morning and Aqua passes south to north over the equator in the afternoon resulting in global coverage every 1 to 2 days. The EOS satellites have a ±55 degree scanning pattern and orbit at 705 km with a 2,330 km swath width.It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.Attribute InformationLatitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.Acquisition Date: Derived Date/Time field combining Date and Time attributes.Satellite: Whether the detection was picked up by the Terra or Aqua satellite.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.RevisionsJune 22, 2022: Added 'HOURS_OLD' field to enhance Filtering data. Added 'Last 7 days' Layer to extend data to match time range of VIIRS offering. Added Field level descriptions.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

FOR non-AGOL ACCOUNT HOLDERS, DOWNLOAD THIS GEOSPATIAL DATA HERE: https://gis-fws.opendata.arcgis.com/search?tags=lmvjvPolygon shapefile of the various state game agency managed lands (typically known as WMAs or Wildlife Management Areas) of the West Gulf Coastal Plain & Ouachitas ecological region, as compiled by the Lower Mississippi Valley Joint Venture partnership. Data was pulled from PAD US database aggregated by USGS and selected by location for inclusion in the region and includes data from Mississippi, Arkansas, Oklahoma and Texas. Units are considered areas that are actively managed for game and wildlife species.The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://communities.geoplatform.gov/ngda-cadastre/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1). Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/ ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the "State Name" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency. 5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field. State updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-history for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground. These lands are commonly known as Wildlife Management Areas (WMAs) but that nomenclature varies by state.

Great Smoky Mountains National Park 1931 Park East Topographic Map

How should this data set be cited?

United States Geologic Survey, 1931, Topographic Map. Great Smoky Mountains National Park. Tennessee and North Carolina. (East Half). Online Links:

http://science.nature.nps.gov/nrdata

What geographic area does the data set cover?

West_Bounding_Coordinate: -84.007769 East_Bounding_Coordinate: -83.037582 North_Bounding_Coordinate: 35.790660 South_Bounding_Coordinate: 35.418718

What does it look like?

Does the data set describe conditions during a particular time period?

Calendar_Date: 09-Mar-2015Currentness_Reference: publication date

What is the general form of this data set?

Geospatial_Data_Presentation_Form: raster digital data

How does the data set represent geographic features?

How are geographic features stored in the data set? This is a Raster data set.

What coordinate system is used to represent geographic features? Horizontal positions are specified in geographic coordinates, that is, latitude and longitude. Latitudes are given to the nearest 0.000000. Longitudes are given to the nearest 0.000000. Latitude and longitude values are specified in Decimal degrees.

The horizontal datum used is North American Datum of 1983. The ellipsoid used is Geodetic Reference System 80. The semi-major axis of the ellipsoid used is 6378137.000000. The flattening of the ellipsoid used is 1/298.257222.

Vertical_Coordinate_System_Definition:

Altitude_System_Definition:

Altitude_Datum_Name: North American Vertical Datum of 1988 Altitude_Resolution: 0.000025 Altitude_Distance_Units: feet Altitude_Encoding_Method:

Explicit elevation coordinate included with horizontal coordinates

How does the data set describe geographic features?

Entity_and_Attribute_Overview:

Where possible entity attribute population is completed automatically by the GIS/SQL database software. Enclosed herein are Attribute Domains and lists of legal values (LOV) where attributes are populated by "Picklists".

Who produced the data set?

Who are the originators of the data set? (may include formal authors, digital compilers, and editors)

United States Geologic Survey

Who also contributed to the data set?

To whom should users address questions about the data?

National Park Service Attn: Thomas Colson GIS Specialist 107 Park Headquarters Road Gatlinburg, Tennessee 37738 United States

(865)436-1701 (voice) GRSM_Resource_Management@nps.gov

Hours_of_Service: 0800-1730

Why was the data set created?

For the display, query, and analysis of spatial and tabular data.

How was the data set created?

From what previous works were the data drawn?

How were the data generated, processed, and modified?

Date: 28-Mar-2015 (process 1 of 1)

These data contain location values from numerous resource research, inventory, and monitoring projects spanning over many decades. The National Park Service is unable to determin the process steps used to depict many locations, citing lack of reliable data. When known, map source is given as a range of values.

What similar or related data should the user be aware of?

How reliable are the data; what problems remain in the data set?

How well have the observations been checked? Attribute accuracy is tested by manual comparison of the source with hard copy plots and/or symbolized display of the map data on an interactive computer graphic system. Selected attributes that cannot be visually verified on plots or on screen are interactively queried and verified on screen. In addition, the attributes are tested against a master set of valid attributes. All attribute data conform to the attribute codes in the signed classification and correlation document and amendment(s).

How accurate are the geographic locations? These data contain location values from numerous resource research, inventory, and monitoring projects spanning over many decades. The National Park Service is unable to asses the positional accuracy of many locations, citing lack of reliable data. When known, estimated horizontal precision is given as a range of possible values.

Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.

This hardcopy map was scanned and georectified using current USGS 1:24k-scale topographic maps. This map is for reference purpose only, and there are likey several gross horizontal errors committed during rectification.

How accurate are the heights or depths? Statements of vertical positional accuracy for elevation of these points are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of points printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map. This statement is generally true for the most common sources of these data. Other sources and methods may have been used to create or update these data. In some cases, additional information may be found in the feature-level metadata report.

Where are the gaps in the data? What is missing? Data completeness for these data reflect content of the source data. Features may have been eliminated or generalized on the source data due to scale and legibility constraints. For information on collection and inclusion criteria, see U.S. Geological Survey, 1994, Standards for 1:24,000-Scale Digital Line Graphs and Quadrangle Maps: National Mapping Program Technical Instructions and U.S. Geological Survey, 1994, Standards for Digital Line Graphs: National Mapping Program Technical Instructions.

How consistent are the relationships among the observations, including topology? No duplicate features exist nor duplicate points in a data string. Point data are represented by two sets of coordinate pairs, each with the same coordinate values, contained in the "Shape" Column, and "X_COORD, Y_COORD" Columns.

Database engine scripts automatically populate many of the possible "List of Values" for those columns that derive their attrtibute from other source data (see Entity Attribute Section of this document for details), thereby enforcing Attribute Accuracy. Database engine scripts also prevent the entry of duplication location coordinates, ensure the consistency and format of binary data representing geographic coordinates, and spatial and attribute index integrity.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?

Who distributes the data set? (Distributor 1 of 1)

Thomas Colson National Park Service GIS Specialist 107 Park Headquarters Rd. Gatlinburg, Tennessee 37738 United States

(865)436-1701 (voice) GRSM_Resource_Management@nps.gov

Hours_of_Service: 0800-1730 EST

What's the catalog number I need to order this data set? Downloadable Data

What legal disclaimers am I supposed to read?

The National Park Service shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics (i.e. GIF or JPG format files) are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time. The data are not better than the original sources from which they were derived. It is the responsibility of the data user to use the data appropriately and consistent within the limitations of geospatial data in general and these data in particular. The related graphics are intended to aid the data user in acquiring relevant data; it is not appropriate to use the related graphics as data. The National Park Service gives no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data. It is strongly recommended that these data are directly acquired from an NPS server and not indirectly through other sources which may have changed the data in some way. Although these data have been processed successfully on computer systems at the National Park Service, no warranty expressed or implied is made regarding the utility of the data

PerCapita_CO2_Footprint_InDioceses_FULLBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.MethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

We developed the first publicly available spatially explicit estimates of the human alterations along the global floodplains during the recent 27 years (1992-2019) at 250-m resolution. To maximize the reuse of our datasets and advance the open science of human floodplain alteration, we developed three web-based programming tools: (1) Floodplain Mapping Tool, (2) Land Use Change Tool, and (3) Human Alteration Tool supported with tutorials and step-by-step audiovisual instructions. Our data reveal a significant loss of natural floodplains worldwide with 460,000 km2 of new agricultural and 140,000 km2 of new developed areas between 1992 and 2019. This dataset offers critical new insights into how floodplains are being destroyed, which will help decision-makers to reinforce strategies to conserve and restore floodplain functions and habitat. This dataset is not publicly accessible because: EPA scientists provided context and commentary but did not do any of the analyses or handle any of the data. It can be accessed through the following means: The entire data record can be downloaded as a single zip file from this web link: http://www.hydroshare.org/resource/cdb5fd97e0644a14b22e58d05299f69b. The global floodplain alteration dataset is derived entirely through ArcGIS 10.5 and ENVI 5.1 geospatial analysis platforms. To assist in reuse and application of the dataset, we developed additional Python codes aggregated as three web-based tools: Floodplain Mapping Tool: https://colab.research.google.com/drive/1xQlARZXKPexmDInYV-EMoJ-HZxmFL-eW?usp=sharing. Land Use Change Tool: https://colab.research.google.com/drive/1vmIaUCkL66CoTv4rNRIWpJXYXp4TlAKd?usp=sharing. Human Alteration Tool: https://colab.research.google.com/drive/1r2zNJNpd3aWSuDV2Kc792qSEjvDbFtBy?usp=share_link See Usage Notes section in the journal article for details. Format: The global floodplain alteration dataset is available through the HydroShare open geospatial data platform. Our data record also includes all corresponding input data, intermediate calculations, and supporting information. This dataset is associated with the following publication: Rajib, A., Q. Zheng, C. Lane, H. Golden, J. Christensen, I. Isibor, and K. Johnson. Human alterations of the global floodplains 1992–2019. Scientific Data. Springer Nature, New York, NY, USA, 10: 499, (2023).

The Rangeland Analysis Platform ( rangelands.app) is a free online application that provides simple and fast access to geospatial vegetation data for U.S. rangelands. The tool was developed to provide landowners, resource managers, conservationists, and scientists access to data that can inform land management planning, decision making, and the evaluation of outcomes. The Rangeland Analysis Platform (RAP) uses innovative cloud computing technology to provide maps and analysis opportunities straight to your desktop, delivered securely and instantaneously. The maps and data provided by RAP are intended to be used alongside local knowledge and site-specific data to inform management actions that improve rangelands and wildlife habitat. Biomass The Rangeland Analysis Platform’s vegetation biomass product provides annual and 16-day aboveground biomass from 1986 to present of: annual forbs and grasses, perennial forbs and grasses, and herbaceous (combination of annual and perennial forbs and grasses). Estimates represent accumulated new biomass throughout the year or 16-day period and do not include biomass accumulation in previous years. Aboveground biomass was calculated by separating net primary production (paritioned by functional group) to aboveground and converting carbon to biomass (Jones et al. 2021, Robinson et al. 2019). Estimates are provided in United States customary units (lbs/acre) to facilitate use. Although these data were produced across a broad region, they are primarily intended for rangeland ecosystems. Biomass estimates may not be suitable in other ecosystems, e.g., forests., and are not to be used in agricultural lands, i.e., croplands. Cover The Rangeland Analysis Platform’s vegetation cover product provides annual percent cover estimates from 1986 to present of: annual forbs and grasses, perennial forbs and grasses, shrubs, trees, and bare ground. The estimates were produced by combining 75,000 field plots collected by BLM, NPS, and NRCS with the historical Landsat satellite record. Utilizing the power of cloud computing, cover estimates are predicted across the United States at 30m resolution, an area slightly larger than a baseball diamond. Partitioned NPP The Rangeland Analysis Platform provides net primary productivity (NPP) estimates from 1986 to present. Estimates are partitioned into the following functional groups: annual forb and grass, perennial forb and grass, shrub, and tree. NPP is the net increase (i.e., photosynthesis minus respiration) in total plant carbon, including above and below ground. NPP data download Partitioned NPP is available as GeoTIFFs from http://rangeland.ntsg.umt.edu/data/rap/rap-vegetation-npp/ and in Google Earth Engine (ImageCollection ‘projects/rap-data-365417/assets/npp-partitioned-v3’).

This TNC Lands spatial dataset represents the lands and waters in which The Nature Conservancy (TNC) currently has, or historically had, an interest, legal or otherwise in Kansas. The system of record for TNC Lands is the Legal Records Management (LRM) system, which is TNC’s database for all TNC land transactions.TNC properties should not be considered open to the public unless specifically designated as being so. TNC may change the access status at any time at its sole discretion. It's recommended to visit preserve-specific websites or contact the organization operating the preserve before any planned visit for the latest conditions, notices, and closures. TNC prohibits redistribution or display of the data in maps or online in any way that misleadingly implies such lands are universally open to the public.The types of current land interests represented in the TNC Lands data include: Fields and Attributes included in the public dataset:Field NameField DefinitionAttributesAttribute Definitions Public NameThe name of the tract that The Nature Conservancy (TNC) Business Unit (BU) uses for public audiences.Public name of tract if applicableN/A TNC Primary InterestThe primary interest held by The Nature Conservancy (TNC) on the tractFee OwnershipProperties where TNC currently holds fee-title or exclusive rights and control over real estate. Fee Ownership can include TNC Nature Preserves, managed areas, and properties that are held for future transfer. Conservation EasementProperties on which TNC holds a conservation easement, which is a legally binding agreement restricting the use of real property for conservation purposes (e.g., no development). The easement may additionally provide the holder (TNC) with affirmative rights, such as the rights to monitor species or to manage the land. It may run forever or for an expressed term of years. Deed RestrictionProperties where TNC holds a deed restriction, which is a provision placed in a deed restricting or limiting the use of the property in some manner (e.g., if a property goes up for sale, TNC gets the first option). TransferProperties where TNC historically had a legal interest (fee or easement), then subsequently transferred the interest to a conservation partner. AssistProperties where TNC assisted another agency/entity in protecting. Management Lease or AgreementAn agreement between two parties whereby one party allows the other to use their property for a certain period of time in exchange for a periodic fee. Grazing Lease or PermitA grazing lease or permit held by The Nature Conservancy Right of WayAn access easement or agreement held by The Nature Conservancy. OtherAnother real estate interest or legal agreement held by The Nature Conservancy Fee OwnerThe name of the organization serving as fee owner of the tract, or "Private Land Owner" if the owner is a private party. If The Nature Conservancy (TNC) primary interest is a "Transfer" or "Assist", then this is the fee owner at the time of the transaction.Fee Owner NameN/A Fee Org TypeThe type of organization(s) that hold(s) fee ownership. Chosen from a list of accepted values.Organization Types for Fee OwnershipFED:Federal, TRIB:American Indian Lands, STAT:State,DIST:Regional Agency Special District, LOC:Local Government, NGO:Non-Governmental Organization, PVT:Private, JNT:Joint, UNK:Unknown, TERR:Territorial, DESG:Designation Other Interest HolderThe name of the organization(s) that hold(s) a different interest in the tract, besides fee ownership or TNC Primary Interest. This may include TNC if the Other Interest is held or co-held by TNC. Multiple interest holders should be separated by a semicolon (;).Other Interest Holder NameN/A Other Interest Org TypeThe type of organization(s) that hold(s) a different interest in the tract, besides fee ownership. This may include TNC if the Other Interest is held or co-held by TNC. Chosen from a list of accepted values.Organization Types for interest holders:FED:Federal, TRIB:American Indian Lands, STAT:State,DIST:Regional Agency Special District, LOC:Local Government, NGO:Non-Governmental Organization, PVT:Private, JNT:Joint, UNK:Unknown, TERR:Territorial, DESG:Designation Other Interest TypeThe other interest type held on the tract. Chosen from a list of accepted values.​Access Right of Way; Conservation Easement; Co-held Conservation Easement; Deed Restriction; Co-held Deed Restriction; Fee Ownership; Co-held Fee Ownership; Grazing Lease or Permit; Life Estate; Management Lease or Agreement; Timber Lease or Agreement; OtherN/A Preserve NameThe name of The Nature Conservancy (TNC) preserve that the tract is a part of, this may be the same name as the as the "Public Name" for the tract.Preserve Name if applicableN/APublic AccessThe level of public access allowed on the tract.Open AccessAccess is encouraged on the tract, trails are maintained, signage is abundant, and parking is available. The tract may include regular hours of availability.Open with Limited AccessThere are no special requirements for public access to the tract, the tract may include regular hours of availability with limited amenities.Restricted AccessThe tract requires a special permit from the owner for access, a registration permit on public land, or has highly variable times or conditions to use.Closed AccessNo public access is allowed on the tract.UnknownAccess information for the tract is not currently available.Gap CategoryThe Gap Analysis Project (GAP) code for the tract. Gap Analysis is the science of determining how well we are protecting common plants and animals. Developing the data and tools to support that science is the mission of the Gap Analysis Project (GAP) at the US Geological Survey. See their website for more information, linked in the field name.1 - Permanent Protection for BiodiversityPermanent Protection for Biodiversity2 - Permanent Protection to Maintain a Primarily Natural StatePermanent Protection to Maintain a Primarily Natural State3 - Permanently Secured for Multiple Uses and in natural coverPermanently Secured for Multiple Uses and in natural cover39 - Permanently Secured and in agriculture or maintained grass coverPermanently Secured and in agriculture or maintained grass cover4 - UnsecuredUnsecured (temporary easements lands and/or municipal lands that are already developed (schools, golf course, soccer fields, ball fields)9 - UnknownUnknownProtected AcresThe planar area of the tract polygon in acres, calculated by the TNC Lands geographic information system (GIS).Total geodesic area of polygon in acresProjection: WGS 1984 Web Mercator Auxiliary SphereOriginal Protection DateThe original protection date for the tract, from the Land Resource Management (LRM) system record.Original protection dateN/AStateThe state within the United States of America or the Canadian province where the tract is located.Chosen from a list of state names.N/ACountryThe name of the country where the tract is located.Chosen from a list of countries.N/ADivisionThe name of the TNC North America Region Division where the tract is located. Chosen from a list of TNC North America DivisionsN/A

Catholic_CO2_Footprint_Beta_FullSees_Top10Burhans, Molly A., Cheney, David M., Gerlt, R.. . “Catholic_CO2_Footprint_Beta_FullSees_Top10”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.DEVELOPED AS A POPUP LAYERMethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/