The Digital Bedrock Geologic-GIS Map of Minuteman National Historical Site and Vicinity, Massachusetts is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (mima_bedrock_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (mima_bedrock_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) this file (mima_geology.gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (mima_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (mima_bedrock_geology_metadata_faq.pdf). Please read the mima_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: http://www.google.com/earth/index.html. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Boston College and U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (mima_bedrock_geology_metadata.txt or mima_bedrock_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 25.4 meters or 83.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This dataset provides resources for identifying flight lines of interest for the MODIS/ASTER Airborne Simulator (MASTER) instrument based on spatial and temporal criteria. MASTER first flew in 1998 and has ongoing deployments as a Facility Instrument in the NASA Airborne Science Program (ASP). MASTER is a joint project involving the Airborne Sensor Facility (ASF) at the Ames Research Center, the Jet Propulsion Laboratory (JPL), and the Earth Resources Observation and Science Center (EROS). The primary goal of these airborne campaigns is to demonstrate important science and applications research that is uniquely enabled by the full suite of MASTER thermal infrared bands as well as the contiguous spectroscopic measurements of the AVIRIS (also flown in similar campaigns), or combinations of measurements from both instruments. This dataset includes a table of flight lines with dates, bounding coordinates, site names, investigators involved, flight attributes, and associated campaigns for the MASTER Facility Instrument Collection. A shapefile containing flights for all years, a GeoJSON version of the shapefile, and separate KMZ files for each year allow users to visualize flight line locations using GIS software.

Data set that contains information on archaeological remains of the pre historic settlement of the Letolo valley on Savaii on Samoa. It is built in ArcMap from ESRI and is based on previously unpublished surveys made by the Peace Corps Volonteer Gregory Jackmond in 1976-78, and in a lesser degree on excavations made by Helene Martinsson Wallin and Paul Wallin. The settlement was in use from at least 1000 AD to about 1700- 1800. Since abandonment it has been covered by thick jungle. However by the time of the survey by Jackmond (1976-78) it was grazed by cattle and the remains was visible. The survey is at file at Auckland War Memorial Museum and has hitherto been unpublished. A copy of the survey has been accessed by Olof Håkansson through Martinsson Wallin and Wallin and as part of a Masters Thesis in Archeology at Uppsala University it has been digitised.

Olof Håkansson has built the data base structure in the software from ESRI, and digitised the data in 2015 to 2017. One of the aims of the Masters Thesis was to discuss hierarchies. To do this, subsets of the data have been displayed in various ways on maps. Another aim was to discuss archaeological methodology when working with spatial data, but the data in itself can be used without regard to the questions asked in the Masters Thesis. All data that was unclear has been removed in an effort to avoid errors being introduced. Even so, if there is mistakes in the data set it is to be blamed on the researcher, Olof Håkansson. A more comprehensive account of the aim, questions, purpose, method, as well the results of the research, is to be found in the Masters Thesis itself. Direkt link http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1149265&dswid=9472

Purpose:

The purpose is to examine hierarchies in prehistoric Samoa. The purpose is further to make the produced data sets available for study.

Prehistoric remains of the settlement of Letolo on the Island of Savaii in Samoa in Polynesia

The Digital Geologic-GIS Map of Yellowstone National Park and Vicinity, Wyoming, Montana, and Idaho is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (yell_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (yell_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (yell_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (yell_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (yell_geology_metadata_faq.pdf). Also included is a zip containing a Montana State University Master's thesis and supporting documents and data. The thesis focuses on addressing map boundary inconsistencies and remapping portions of the park. Data and documents supporting the thesis are 1.) a geodatabase containing field data points, 2.) a collection of documents describing field sites, 3.) spreadsheets containing geochemical analysis results, and 4.) photographs taken during field work. Please read the yell_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey, Montana Bureau of Mines and Geology and Montana State University. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (yell_geology_metadata.txt or yell_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:125,000 and United States National Map Accuracy Standards features are within (horizontally) 63.5 meters or 208.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

K.C. Shoreline Management Master Program. Related to SAO wetlands and FEMA floodpln (has boolean attributes floodpln and wetlands).

The Digital Geologic-GIS Map of Knife River Indian Villages National Historic Site and Vicinity, North Dakota is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (knri_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (knri_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (knri_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (knri_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (knri_geology_metadata_faq.pdf). Please read the knri_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: University of North Dakota, Department of Anthropology and Archeology. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (knri_geology_metadata.txt or knri_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual _location as presented by this dataset. Users of this data should thus not assume the _location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Abstract:  This dataset 'approximately' represents the location of the SMP 200 foot shoreline environments of the Puyallup River and Clarks Creek within the City of Puyallup and its urban growth area.Purpose: This feature class is to be used to 'approximately' locate the 200 foot shoreline environment from the ordinary high water mark (OHWM) of the Puyallup River and Clarks Creek. The shoreline environments were created using the Clarks Creek centerline shapefile and Puyallup River polygon shapefile. Because this map was created using the afore mentioned sources (as apposed to the OHWM as required by the SMP), the shoreline evnironments shown here will extend further upland than depicted. As such these shoreline environments should be used as a reference only. Reports and field work conducted by qualified professional biologists are required to determine the true location of the OHWM/200 foot shoreline environment for any property along these waterways. NOTE: The puy_river.shp is a polygon shapefile which extends close to the shoreline but does not mark the OHWM of the river. The clarks_creek_cntr_ln.shp is a line feature class that does not come close to the shoreline of Clarks Creek. The shoreline, not the OHWM, can be anywhere from 20-30 feet on either side of the center line. For these reasons it is imperative to have a biologist establish the OHWM for Clarks Creek and the Puyallup River. Only then can the 200 foot shoreline environment be determined.

The Kansas Master Ground-water Well Inventory (MWI) is a central repository that imports and links together the State's primary ground-water well data sets- KDHE's WWC5, KDA-DWR's WIMAS, and KGS' WIZARD into a single, online source. The most "accurate" of the common source fields are used to represent the well sites, for example- GPS coordinates if available are used over other methods to locate a well. The MWI maintains the primary identification tags to allow specific well records to be linked back to the original data sources.This mapper is managed by the Kansas Geological Survey. For more information about the data, please see the Groundwater Master Well Inventory page.

Summary:

The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016.

These data are associated with an existing GitHub Repository, https://github.com/tnc-ny-science/NYC_GreenRoofMapping, and as needed and appropriate pending future work, versioned updates will be released here.

Terms of Use:

The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data.

Associated Files:

As of this release, the specific files included here are:

GreenRoofData2016_20180917.geojson is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).

GreenRoofData2016_20180917.gpkg is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.

GreenRoofData2016_20180917_Shapefile.zip is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.

GreenRoofData2016_20180917.csv is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.

Column Information for the datasets:

Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below.

fid - Unique identifier

bin - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh. Associated metadata for fields from that dataset are available at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md.

bbl - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (http://gis.nyc.gov/taxmap/map.htm) and PLUTO/MapPLUTO (https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.

gr_area - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).

bldg_area - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).

prop_gr - Proportion of the building covered by green roof according to this layer (gr_area/bldg_area).

cnstrct_yr - Year the building was constructed, pulled from the Building Footprint data.

doitt_id - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.

heightroof - Height of the roof of the associated building, pulled from the Building Footprint Data.

feat_code - Code describing the type of building, pulled from the Building Footprint Data.

groundelev - Lowest elevation at the building level, pulled from the Building Footprint Data.

qa - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'

notes - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.

classified - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)

digitized - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)

newlyadded - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)

original_source - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.

address - Address based on MapPLUTO, joined to the dataset based on bbl.

borough - Borough abbreviation pulled from MapPLUTO.

ownertype - Owner type field pulled from MapPLUTO.

zonedist1 - Zoning District 1 type pulled from MapPLUTO.

spdist1 - Special District 1 pulled from MapPLUTO.

bbl_fixed - Flag to indicate whether bbl was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)

For GreenRoofData2016_20180917.csv there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263):

xcoord - Longitude in decimal degrees.

ycoord - Latitude in decimal degrees.

Acknowledgements:

This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.

The shared archived combined in Supplementary Datasets represent the actual databases used in the investigation considered in two papers:

Meteorological conditions affecting black vulture (Coragyps atratus) soaring behavior in the southeast of Brazil: Implications for bird strike abatement (in submission)

Remote sensing applications for abating the aircraft-bird strike risks in the southeast of Brazil (Human-Wildlife Interactions Journal, in print)

The papers were based on my Master’s thesis defended in 2016 in the Institute of Biology of the University of Campinas (UNICAMP) in partial fulfilment of the requirements for the degree of Master in Ecology. Our investigation was devoted to reducing the risk of aircraft collision with Black vultures. It had two parts considered in these two papers. In the first one we studied the relationship between soaring activity of Black vultures and meteorological characteristics. In the second one we explored the dependence of soaring activity of vultures on superficial and anthropogenic characteristics. The study was implemented within surroundings of two airports in the southeast of Brazil taken as case studies. We developed the methodological approaches combining application of GIS and remote sensing technologies for data processing, which were used as the main research instrument. By dint of them we joined in the georeferenced databases (shapefiles) the data of bird's observation and three types of environmental factors: (i) meteorological characteristics collected together with the bird’s observation, (ii) superficial parameters (relief and surface temperature) obtained from the products of ASTER imagery; (iii) parameters of surface covering and anthropogenic pressure obtained from the satellite images of high resolution. Based on the analyses of the georeferenced databases, the relationship between soaring activity of vultures and environmental factors was studied; the behavioral patterns of vultures in soaring flight were revealed; the landscape types highly attractive for this species and forming the increased concentration of birds over them were detected; the maps giving a numerical estimation of hazard of bird strike events over the airport vicinities were constructed; the practical recommendations devoted to decrease the risk of collisions with vultures and other bird species were formulated.

This archive contains all materials elaborated and used for the study, including the GIS database for two papers, remote sensing data, and Microsoft Excel datasets. You can find the description of supplementary files in the Description of Supplementary Dataset.docx. The links on supplementary files and their attribution to the text of papers are considered in the Attribution to the text of papers.docx. The supplementary files are in the folders Datasets, GIS_others, GIS_Raster, GIS_Shape.

For any question please write me on this email: natalieenov@gmail.com

Natalia Novoselova

The Narcotic Treatment Program Master List contains a list of all state-licensed and certified narcotic treatment programs. The Master List contains vital information for each program listed and additional details, such as the program’s address and contact information, total capacity, hours of operation and program director and medical director.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles.

Polygon boundary edits were transferred from the paper maps used in the field to the digital shapefiles each week using ArcMap GIS software. Field edits were also transferred to a set of master paper maps that did not go into the field; these will be archived along with the datasheets. The polygons were contained in a field geodatabase structure (.mdb), enabling topography rules and relationships to be established. The geodatabase was archived each week to ensure no loss of data and to allow for reversion or retrieval if needed. Strict nomenclature was enforced for polygons, and a unique name was assigned to each polygon. The names reflected the verified physiognomic formation type by a prefix of representative letters (W = Woodland, SS = shrub savanna, etc.) followed by a number. In the final map, there are 16 vegetation alliances or associations attributed to 74 polygons (Figure 2-3). For each, there is a oneto- one correlation between the alliance or association and map units (polygons). Table 2-3 shows each vegetation community type, the number of polygons attributed with that type, and the total area.

These data, which comprise part of the Smithsonian Institution Master Sediment data file, were abstracted by the staff of the Smithsonian Institution from materials submitted for archival by various groups and individuals. Most of the data in this set were collected by the National Ocean Service (NOS, formerly the U.S. Coast and Geodetic Survey) for the purpose of charting the coastal waters and navigable waterways of the United States. Prior to 1985, the NOS data were released as part of the National Ocean Surveys Hydrographic Database. After 1985, sediment samples collected by NOS during surveys were transferred to the Smithsonian for archival and textural analysis. All of the data in this set were collected post 1985 and have been processed by the Smithsonian. These data were supplied by the National Geophysical Data Center (NGDC), but this data set contains fields that are only a subset of those fields available in the full Smithsonian data set. For example, the data have been clipped to eliminate those stations that were not from the Gulf of Maine, Georges Bank, or the shelf and slope off southeastern New England. Last update of this file was July, 2001.

The Cardiac ARIA project, with its extensive use of Geographic Information Systems (GIS), ranks each of Australia’s 20,387 urban, rural and remote population centres by accessibility to essential services or resources for the management of a cardiac event. Cardiac ARIA is based upon the accessibility concepts used to develop ARIA in that it calculates distance along the road network from all 20,387 population locations across Australia to the geolocated cardiac services and resources identified by the expert panel. Similar to the ARIA index, Cardiac ARIA can then be aggregated to any other area unit such as a local government area, statistical local area, postcode, census collection district or any other user defined catchments. Cardiac ARIA required a significant amount of detailed data, and sourcing these data for this project was a major undertaking (Seen in Table 2 of related publication). These data included context data such as the road network for Australia and population locations, to detailed data on the location of ambulance stations, hospitals, cardiac units etc. For a detailed list of all data collected and their sources for this project see Appendix 2 of related publication. The data collection consisted of two stages: Phase 1 - Expert panel consensus process.
To provide a single master list of services and resources, based on a composite of the practice guidelines for a “cardiac event”. Context of the study Implementation of the guidelines was to be set within all community settings with careful consideration given to the needs of populations (e.g. greater than 60 minutes from a CBD) in the urban fringes and rural and remote areas, and current literature. Once the master list was agreed upon, datasets with location information were sourced and applied to GIS software. The Cardiac ARIA index was developed by locating CVD services and resources within a population centre and then calculating the distance/time needed to travel by road to each CVD service (see the methods section for more detail). Distance by air (rotary and fixed wing aircraft) was also calculated, though not used in this version of the Cardiac ARIA index. Each population centre was assigned an index for CVD accessibility. Phase 2 - Data acquisition and GIS modelling. Phase 2 of the project relates directly to Objective 2 of the project: to derive a classification for each of Australia’s 20,387 urban, rural and remote population locations based on its access to cardiac services. The GIS methodology used to calculate both the acute and aftercare Cardiac ARIA uses raster based cost-distance mapping to generate cost distance layers for each input layer and the raster calculator to combine the layers into the final ARIA indices using ESRI’s Arc Map, version 9.3.1. The GIS software utilised for this project was ESRI ArcMap Version 9.3.1, with all of the data conforming to Australian Lamberts Conformal Conic projection, Geocentric Datum of Australia 1994 (ESRI Arcview 2011; ESRI Software 2011).

Displays citywide address points using TRANSPO.MAFDAP_PV. Differs from TRANSPO.DAP in that it contains address data. Attributes include house number and modifier, directional, street name, and street type. Does not display when zoomed out beyond 1:10,000. Labels are based on the attribute MAF_HSENUMMOD and do not display when zoomed out beyond 1:3,000. ATTRIBUTE INFORMATION: MAILUSECODE ? Identifies suitability of MAF address and associated MAFUNIT record(s) for use as a mailing address. This field serves as an indicator whether the address is being utilized in the City?s Utility Billing System. If so, it is more likely (but still not guaranteed) to be a valid mailing address. DCLUSTAT - Description of address establishment and validation status related to DCLU business process. Valid values: ?INITIAL VALUE? ? SPU-added records are assigned this value upon creation. ?DRAFT? ? only DPD-added records are assigned this value upon creation. ?FIELD VERIFIED? ? only DPD can assign this value. Indicates that DPD at some point conducted a site visit. This value is not reliably assigned and is not necessarily an indicator of a correct address. ?CANCELED? ? only DPD can assign this value. The address was never utilized. ?RETIRED? ? DPD or SPU can assign this value. The address may have been utilized for some period of time but was then replaced by a different address for the location or retired from use completely. DCLUSTATDT - Date of creation or modification of record. SOURCENAME - Descriptive character string identifying agency, department or divisional record source or usage. Valid values: ?DPD_MAF? ? Added or modified by DPD ?CGDB_MAFEDITS? ? Added or modified by SPU ?INIT_MAF? ? The initial record value, likely harvested from King County Assessor data when the MAF/DAP was first implemented.

For more information about this layer please see the GIS Data Catalog.SMP Environment Designations

This map shows locations that provide ADN (associate degree nursing), AE-MSN (alternate entry master of science in nursing), Diploma, BSN (bachelor of science in nursing), DE-MSN (direct entry master of science in nursing), and LVN (licensed vocation nursing) certifications. The data includes information on pass rates from 2020 through 2024.This map was created with data from Texas Center for Nursing Workforce Studies and last updated in May 2025.

Updated Continually

This Layer is a compilation of all GPS tracks recorded by GPSs used in the field by the Belize River Archaeological Settlment Survey Team from the years 2014 to 2017. The data was cleaned and clipped at the MesoAmerican Research Center GIS Lab at UCSB.

This spatial data contains Surface Management Agency (SMA, also sometimes called Land Status) information for Idaho from the Idaho Bureau of Land Management (BLM). For federal government lands, this data displays the managing agency of the surface of the land, which does not mean the agency "owns" the land. SMA is sometimes referred to as "ownership", although this term is inaccurate when describing public lands. This Surface Management Agency data should not be used to depict boundaries (for example National Forest, National Park, National Wildlife Refuge, or Indian Reservation boundaries among others). Attribute information for the federal and private lands are from the BLM Master Title Plats (MTPs), the BLM case files, the BLM Legacy Rehost 2000 (LR2000) database, and corresponding federal Orders and official documents. Please note that because these official sources are strictly used, OTHER NON-BLM FEDERAL AGENCY LANDS MAY NOT BE ATTRIBUTED CORRECTLY unless the proper documents have been filed with the BLM and the land actions have been noted on the MTPs and in LR2000. Starting in the spring of 2011 a field called AGNCY_NAME is present in the data. The AGNCY_NAME field is intended to indicate the managing agency for polygons coded as OTHER in the MGMT_AGNCY field. The AGNCY_NAME field will not be used for the 100K Map Series published by the BLM for use by the public as all agencies in this field are not included in H-1553 Publication Standards Manual Handbook and, therefore, have no BLM Cartographic Standard. Except for polygons coded as OTHER in the MGMT_AGNCY field, all managing agency information in the AGNCY_NAME field should be the same as that of the MGMT_AGNCY field. The only intended difference between the AGNCY_NAME field and the MGMT_AGNCY field is where the MGMT_AGNCY is OTHER. In this case, the AGNCY_NAME will contain an abbreviation for an agency that is not represented in the H-1553 Publication Standards Manual Handbook. Examples of the agencies there are BIA (Bureau of Indian Affairs), USGS (United States Geological Survey), and FAA (Federal Aviation Administration). Attribute information for the State lands is received primarily through cooperation with the Idaho Department of Lands. This information might not reflect all State agency lands completely. A detailed analysis of State owned lands has not been done since June 2011; therefore, recent changes in ownership of State lands may not be reflected. Inclusion of State land information into this dataset is supplemental and should not be viewed as the authoritative source of State lands; please contact State agencies for questions about State lands. This data does not depict land management arrangements between government agencies such as Memorandums of Understanding or other similar agreements. When this data was originally generated in the early 2000's, the primary source of the geometry was the BLM Geographic Coordinate Database (GCDB), if it was available. In areas where GCDB was/is unavailable, the spatial features are taken from a variety of sources including the BLM Idaho Resource Base Data collection, BLM Idaho Master Title Plat AutoCad files, US Geological Survey Digital Line Graphs (DLGs), and US Forest Service Cartographic Feature Files (CFFs), among others (see Process Steps). It should be stressed that the geometry of a feature may not be GCDB-based in the first place, the geometry may shift away from GCDB due to a variety of reasons (topology procedures, automated software processes such as projections, etc.), and the GCDB-based features are not necessarily currently being edited to match improved GCDB. Therefore this data should NOT be considered actual GCDB data. For the latest Idaho GCDB spatial data, please contact the BLM Idaho State Office Cadastral Department at 208-373-4000. The BLM in Idaho creates and maintains this spatial data. This dataset is derived by dissolving based on the "MGMT_AGNCY" field from the master SMA GIS dataset (which is edited often) kept by the BLM Idaho State Office. Please get a fresh copy of this data a couple times a year as the SMA data is continually changing. Official actions that affect the managing agency happen often and changes to correct errors are always being made. Nevada SMA data was acquired from the BLM Nevada web site and clipped to the area that is managed by Idaho BLM Boise District. The data steward approved this dataset in September 2018