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. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

The Residential Schools Locations Dataset in shapefile format contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Indian Residential School Settlement Agreement are included in this data set, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The data set was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this data set,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School. When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites. The geographic coordinate system for this dataset is WGS 1984. The data in shapefile format [IRS_locations.zip] can be viewed and mapped in a Geographic Information System software. Detailed metadata in xml format is available as part of the data in shapefile format. In addition, the field name descriptions (IRS_locfields.csv) and the detailed locations descriptions (IRS_locdescription.csv) should be used alongside the data in shapefile format.

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. Our final map product is a geographic information system (GIS) database of vegetation structure and composition across the Crater Lake National Park terrestrial landscape, including wetlands. The database includes photos we took at all relevé, validation, and accuracy assessment plots, as well as the plots that were done in the previous wetlands inventory. We conducted an accuracy assessment of the map by evaluating 698 stratified random accuracy assessment plots throughout the project area. We intersected these field data with the vegetation map, resulting in an overall thematic accuracy of 86.2 %. The accuracy of the Cliff, Scree & Rock Vegetation map unit was difficult to assess, as only 9% of this vegetation type was available for sampling due to lack of access. In addition, fires that occurred during the 2017 accuracy assessment field season affected our sample design and may have had a small influence on the accuracy. Our geodatabase contains the locations where particular associations are found at 600 relevé plots, 698 accuracy assessment plots, and 803 validation plots.

This Python script (Shape2DJI_Pilot_KML.py) will scan a directory, find all the ESRI shapefiles (.shp), reproject to EPSG 4326 (geographic coordinate system WGS84 ellipsoid), create an output directory and make a new Keyhole Markup Language (.kml) file for every line or polygon found in the files. These new *.kml files are compatible with DJI Pilot 2 on the Smart Controller (e.g., for M300 RTK). The *.kml files created directly by ArcGIS or QGIS are not currently compatible with DJI Pilot.

Context

This dataset contains shapefiles outlining 558 neighborhoods in 50 major cities in New York state, notably including Albany, Buffalo, Ithaca, New York City, Rochester, and Syracuse. This adds context to your datasets by identifying the neighborhood of any locations you have, as coordinates on their own don't carry a lot of information.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. What fields does it include? What's the time period of the data and how was it collected?

Four files are included containing data about the shapes: an SHX file, a DBF file, an SHP file, and a PRJ file. Including all of them in your input data are necessary, as they all contain pieces of the data; one file alone will not have everything that you need.

Seeing how none of these files are plaintext, it can be a little difficult to get set up with them. I highly recommend using mapshaper.org to get started- this site will show you the boundaries drawn on a plane, as well as allow you to export the files in a number of different formats (e.g. GeoJSON, CSV) if you are unable to use them in the format they are provided in. Personally, I have found it easier to work with the shapefile format though.

To get started with the shapefile in R, you can use the the rgdal and rgeos packages. To see an example of these being used, be sure to check out my kernel, "Incorporating neighborhoods into your model".

Acknowledgements

These files were provided by Zillow and are available under a Creative Commons license.

Inspiration

I'll be using these in the NYC Taxi Trip Duration competition to add context to the pickup and dropoff locations of the taxi rides and hopefully greatly improve my predictions.

Scientists collected acoustic fisheries data in mid-water depths approximately 30 to 1000 meters. Fishery acoustics data will be used to characterize broad-scale fish abundance, biomass, and utilization patterns, as well as to locate and document fish spawning aggregations.

Activity Project Area NEPA represents an area (polygon) within which one or more activities related to the National Environmental Policy Act (NEPA) are aggregated or organized. The data comes from the Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS), which is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service.These data are a central source for project area boundaries for use in national information requests and cross unit analysis and makes the project area boundaries and their basic attributes more easily available to field units. It also provides public access to the data during project planning and implementation. Please note that this dataset is not complete and forests continue to improve the quality of the data over time.Metadata and DownloadsThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService CSV Shapefile GeoJSON KML https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_ActivityProjectAreas_01/MapServer/0 https://www.fs.fed.us/emc/nepa/index.htm For complete information, please visit https://data.gov.

Description: This zipfile contains three shapefiles of linear geometries showing channel networks across the SAFE landscape.

RIVERS_UTM.shp: From Igor Lysenko's SAFE pack, this is a river network with two areas, covering Maliau and the area around the SAFE experimental region. The network density is much higher in Maliau than SAFE and the channel network around SAFE is not complete. This is projected in UTM50N WGS84. LFEriver_SL.shp: This is provided (by Sarah Luke?) through Clare Wilkinson's GIS files and adds a critical missing stream for the Logged Forest Edge (LFE) catchment. This is projected in UTM50N WGS84. all_rivers.shp: This is provided through Clare Wilkinson's GIS files and provides streams for a single region covering Danum and the SAFE experimental region but not sampled watersheds in Oil Palm plantations to the south of SAFE. The original file is missing projection information (no .prj file) but other files in the same dataset are projected in RSO Timbalai 1948 and using this projection fits with the context of other data. The version uploaded onto Zenodo has been reprojected into UTM50N WGS84.Although the files are not consistent, they do contain channels and channel data that are referenced in some studies. The provenance of these files are unknown, although the following suggests that they may be traced using GPS or from imagery:

Incomplete coverage within the regions they cover. Treatment of larger rivers, changing from a single line feature showing the stream centreline (?) to double lines showing the river banks. Variation in network density: the western edge of all_rivers.shp shows a vertical band about 4 km wide of higher stream density than the rest of the region; RIVERS_UTM.shp shows marked differences in stream density between SAFE and Maliau.

Project: This dataset was collected as part of the following SAFE research project: SAFE CORE DATA XML metadata: GEMINI compliant metadata for this dataset is available here Files: This dataset consists of 2 files: SAFE_Alternative_Stream_network_metadata.xlsx, Preexisting_SAFE_river_files.zip SAFE_Alternative_Stream_network_metadata.xlsx This file only contains metadata for the files below Preexisting_SAFE_river_files.zip Description: Contains three shapefiles of channel networks. This file contains 3 data tables:

Feature properties (described in worksheet LFEriver_SL) Description: Field descriptions for shapefile properties Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Id: Identity of river (Field type: id)

Feature properties (described in worksheet RIVERS_UTM) Description: Field descriptions for shapefile properties Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

NAME: Local name of segment (Field type: comments)

Feature properties (described in worksheet all_river_UTM50N_WGS84) Description: Field descriptions for shapefile properties Number of fields: 10 Number of data rows: Unavailable (table metadata description only). Fields:

FNODE_: Unknown (Field type: numeric) TNODE_: Unknown (Field type: numeric) LPOLY_: Unknown (Field type: numeric) RPOLY_: Unknown (Field type: numeric) LENGTH_MET: Length of channel segment in metres (Field type: numeric) RIV_YSC_: Unknown (Field type: numeric) RIV_YSC_ID: Unknown (Field type: numeric) CODE: Unknown (Field type: numeric) NAME: Local name of segment (Field type: comments) AREA: Local area of segment (Field type: comments)

Date range: 2010-10-01 to 2019-10-01 Latitudinal extent: 4.0223 to 5.9761 Longitudinal extent: 116.0242 to 117.9758

This dataset describes the areas of research activities for the 38 National Environmental Research Program Tropical Ecosystem (NERP TE) Hub projects. It represents the areas that have field work being conducted, where measurements have been taken and where modelling or analysis is being done.

It consists of three master shapefiles (points, lines and polygons) that capture the regions that are being studied by all these projects. These master shapefiles are also made available split by project.

The data contained in this dataset was compiled from June 2013 submissions from each of the NERP TE projects asking them to describe their areas being researched. They submitted this information in a range of formats including spreadsheet tables for site information (points) and bounding boxes, KMLs for study areas, transects and animal tracks (lines and polygons), raster files and for some simply a description of the region. All of these formats were converted, digitised or drawn into three shapefiles: one for points, one for polygons and one for lines. Each feature was given a description of the type of research or measurement that was being under taken at that location and a link to the associated project pages. Features were also classified to roughly group the type of research work being done at each location.

This data was originally intended to be part of the creation of detailed spatial data for the creation of metadata records describing each of the projects. It was also used for creating maps for each project. These maps are available for download and are available as WMS layers from the e-Atlas.

The polygon features from the various projects overlap significantly and thus many boundaries are hidden with default styling. The order of the features within the shapefile has been chosen so that at least some part of each polygon is visible when all polygons are shown.

This dataset is not a complete representation of the work being done by the NERP TE projects as it was collected only 2 years into a 3.5 year program. In addition to this some project have chosen to protect the exact location of sensitive sites and so have only provided coarse spatial information about the project.

This dataset will be updated periodically as more NERP TE data becomes available.

National Wetland Inventory (NWI) data for Minnesota provide information on the location, extent, and type of Minnesota wetlands. Natural resource managers use NWI data to improve the management, protection, and restoration of wetlands. Wetlands provide many ecological benefits including habitat for fish and wildlife, reducing floods, recharging, improving water quality, and supporting recreation.

These data were updated through a decade-long, multi-agency collaborative effort under leadership of the Minnesota Department of Natural Resources (MNDNR). Major funding was provided by the Environmental and Natural Resources Trust Fund.

This is the first statewide update of the NWI for Minnesota since the original inventory in the mid-1980s. The work was completed in phases by dividing the state into five project areas. Those project areas have all been edgematched into a final seamless statewide dataset.

Ducks Unlimited (Ann Arbor, MI) and St. Mary’s University Geospatial Services (Winona, MN) conducted the wetland mapping and classification under contract to the MNDNR. The Remote Sensing and Geospatial Analysis Laboratory at the University of Minnesota provided support for methods development and field validation. The DNR Resource Assessment Office provided additional support for data processing, field checking, and quality control review.

The updated NWI data delineate and classify wetlands according to the system developed by Cowardin et al. (1979), which is consistent with the original NWI. The updated data also contain a simplified plant community classification (SPCC) and a simplified hydrogeomorphic (HGM) classification. Quality assurance of the data included visual inspection, automated checks for attribute validity and topologic consistency, as well as a formal accuracy assessment based on an independent field verified data set. Further details on the methods employed can be found in the technical procedures document for this project located on the project website (http://www.dnr.state.mn.us/eco/wetlands/nwi_proj.html ).

DOWNLOAD NOTE: NWI data are only provided in either ESRI File Geodatabase or OGC GeoPackage formats. A Shapefile is not available because the size of the NWI dataset exceeds the limit for that format. If you are unable to use the File Geodatabase or GeoPackage, you can view data through Wetland Finder, an interactive mapping application on the DNR’s website (https://arcgis.dnr.state.mn.us/ewr/wetlandfinder ).

SYMBOLOGY NOTE: The ESRI File Geodatabase download includes four layer files that symbolize the data using four different wetland classification systems. The symbology layer files for the Cowardin class and the simplified HGM class are grouped into a smaller number of classes than the full elaborated classifications. Detail is available in the Minnesota Wetland Inventory User Guide and Summary Statistics report (https://files.dnr.state.mn.us/eco/wetlands/nwi-user-guide.pdf ). The layer files for these data have been set up to restrict drawing of the data when zoomed out beyond 1:250,000 scale. This is, in part, to prevent problems with slow performance with this large dataset.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

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.

We used ERDAS Imagine ® Professional 9.2, ENVI ® 4.5, and ArcGIS ® 9.3 with Arc Workstation to develop the vegetation spatial database. Existing GIS datasets that we used to provide mapping information include a NPS park boundary shapefile for VICK (including a 100 meter buffer boundary around the Louisiana Circle, South Fort, and Navy Circle satellite units), a land cover shapefile created by the NWRC (Rangoonwala et al. 2007), and the National Elevation Dataset (NED) (used as the source of the 10-meter elevation model and derived streams, slope, and hillshade). To make the entire spatial data set consistent with NPSVI policies to map only to park boundaries, we clipped the vegetation in and around the previously buffered areas around the Louisiana Circle, South Fort, and Navy Circle satellite unit NPS boundaries. We also added to the spatial database vegetation polygons for the previously omitted Grant’s Canal satellite unit by heads-up digitizing this area from a National Agricultural Information Program (NAIP) image.

The Millennium Coral Reef Mapping Project provides thematic maps of coral reefs worldwide at geomorphological scale. Maps were created by photo-interpretation of Landsat 7 and Landsat 8 satellite images. Maps are provided as standard Shapefiles usable in GIS software. The geomorphological classification scheme is hierarchical and includes 5 levels. The GIS products include for each polygon a number of attributes. The 5 level geomorphological attributes are provided (numerical codes or text). The Level 1 corresponds to the differentiation between oceanic and continental reefs. Then from Levels 2 to 5, the higher the level, the more detailed the thematic classification is. Other binary attributes specify for each polygon if it belongs to terrestrial area (LAND attribute), and sedimentary or hard-bottom reef areas (REEF attribute). Examples and more details on the attributes are provided in the references cited. The products distributed here were created by IRD, in their last version. Shapefiles for 245 atolls of the Pacific Ocean as mapped by the Global coral reef mapping project at geomorphological scale using LANDSAT satellite data (L7 and L8). The data set provides one zip file per country or region of interest. Global coral reef mapping project at geomorphological scale using LANDSAT satellite data (L7 and L8). Funded by National Aeronautics and Space Administration, NASA grants NAG5-10908 (University of South Florida, PIs: Franck Muller-Karger and Serge Andréfouët) and CARBON-0000-0257 (NASA, PI: Julie Robinson) from 2001 to 2007. Funded by IRD since 2003 (in kind, PI: Serge Andréfouët).

This shapefile denotes the location of underwater photos and/or video that were collected by NOAA scientists using a Spectrum Phantom S2 ROV (remotely operated vehicle). Photos and/or video were collected between 03/18/2010 and 04/05/2010 along 33 transects south of St. John and St. Thomas in the U.S. Virgin Islands. These photos and videos will be manually classified into different habitat classes, and integrated with the abiotic data collected by the acoustic sonar (sound navigation and ranging) systems to develop a benthic habitat map for the U.S. Caribbean. Habitat maps describe the location of habitat features (in relation to the shoreline), their physical composition and the types of organisms that colonize them. Fundamentally, habitat maps provide critical information about the extent, health and composition of marine resources, which is vital for communicating information about the distribution and abundance of species to resource managers, scientists and the p ublic. Habitat maps also support an increasing number of landscape ecology studies, as well as the process of marine spatial planning, including the design and evaluation of marine protected areas (MPAs).

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. We converted the photointerpreted data into a GIS-usable format employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM) projection, Zone 16, using North American Datum of 1983 (NAD83). To produce a polygon vector layer for use in ArcGIS, we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format using ArcGIS (Version 9.2, © 2006 Environmental Systems Research Institute, Redlands, California). In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map attribute codes (both map class codes and physiognomic modifier codes) to the polygons, and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer of INDU and immediate environs. At this stage, the map layer has only map attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map class names, physiognomic definitions, link to NVC association and alliance codes), we produced a feature class table along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature class layers produced from this project, including vegetation sample plots, accuracy assessment sites, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

This phase of Project 3DGBR involved manual digitising of geomorphic map boundaries for the key seafloor features identified in the gbr100 grid, particularly for the inter-reefal area on the GBR shelf and in the Coral Sea Conservation Zone (CSCZ). See map for CSCZ boundary at: https://www.environment.gov.au/topics/marine/marine-reserves/coral-sea/conservation-zone

Methods:

GIS spatial analysis of the gbr100 grid was conducted in order to derive a number of useful background datasets for assisting in the digitising process, such as slope, aspect, hillshading, and dense contour lines.

The digitising initially focused on the deep-water (>100 m) environment to develop geomorphic maps for the continental slope, Queensland and Townsville Troughs lying within the Great Barrier Reef World Heritage Area (GBRWHA), and for the Queensland Plateau, Coral Sea Basin, Tasman Basin, and Lord Howe Rise area lying within the adjoining Coral Sea Conservation Zone (CSCZ). The project lastly focuses on the shallow-water (<100 m) environment to develop geomorphic maps for the GBR shelf to complement the shallow reef feature maps provided by GBRMPA. These shallow-water geomorphic features will be added to the project as they come available.

Format:

This dataset consists of 21 shapefiles and a GeoTiff raster file containing hillshading. Each of the shapefiles is described below.

Group Layer 1. Boundaries: gbrwha_outer.shp This Great Barrier Reef World Heritage Area (GBRWHA) layer was initially provided by GBRMPA using a GDA94 datum. The shapefile was reprojected to the WGS84 datum, and then the western coastline boundaries deleted to derive a line shapefile showing only the outer boundary of the GBRWHA where it extends away from the mainland.

qld_gbrwha_cscz.shp This line shapefile combines both the GBRWHA and Coral Sea Conservation Zone (CSCZ) areas, with a western boundary limit at the Queensland mainland coastline. This area was used to clip all geomorphic features created in this project.

Group Layer 2. GBRMPA features: gbr_dryreef.shp The GBR shelf dryreefs shapefile was initially provided by GBRMPA for this project using a GD94 datum. The shapefile was reprojected to the WGS84 datum and not modified in any other way. It is provided here only for completeness but and products using this shapefile should also acknowledge GBRMPA (see under licensing).

gbr_features.shp The GBR shelf features were initially provided by GBRMPA for this project using a GDA94 datum. The shapefile was reprojected to the WGS84 datum, and then the Ashmore Reef polygon deleted due to a grossly incorrect position. The shapefile comprises Cay, Island, Mainland, Reef, Rock and Sand features. Users may contact GBRMPA to obtain details for the creation of these features. Any products using this shapefile should also acknowledge GBRMPA (see under licensing).

Group Layer 3. Finer-scale features: coralsea_cay.shp Cay is a sand island elevated above Australian Height Datum (AHD), and located on offshore coral reefs and seamounts. Cays were mapped initially using a shapefile provided by Geoscience Australia for this project, and then their boundaries checked or remapped using Landsat imagery as background source data to help delineate the white sand areas against the surrounding ocean.

coralsea_dryreef.shp Dryreef is rock/coral lying at or near the sea surface that may constitute a hazard to surface navigation. Dryreefs were mapped initially using a shapefile provided by Geoscience Australia for this project, which identified those reef areas lying above approximately Lowest Astronomic Tide (LAT). Landsat imagery was used as background source data to check or remap their boundaries.

coralsea_reef.shp Reef is rock/coral lying at or near the sea surface that may constitute a hazard to surface navigation. For this project, the boundaries of reef areas were mapped to show the outer-most extent of each coral reef that could be observed in Landsat imagery, thus identifying the greatest area of each reef observed in the Coral Sea. This methodology is consistent with the methodology used to map the outer-most extents of reefs on the GBR shelf conducted by GBRMPA.

coralsea_ridge.shp Ridge is a long, narrow elevation with steep sides. In this project, ridges were mapped as widely-scattered and uncommon, finer-scale features identified in the gbr100 grid. These elongate ridges are distinct from the smaller knolls or hills which have a more rounded shape. They are usually found on the plateaus of the Lord Howe Rise.

coralsea_bank.shp Bank is an elevation over which the depth of water is relatively shallow but normally sufficient for safe surface navigation. In this project, banks were mapped as the base or pedestal boundaries of the coral reefs found in the Coral Sea. For example, the coral atolls and reefs on the Queensland Plateau are considered banks and their bases digitised where they emerge from the surrounding flat seafloor.

coralsea_knoll.shp Knoll is a relatively small isolated elevation of a rounded shape. This shapefile also includes Abyssal hill, a low (100 – 500 m) elevation on the deep seafloor. For this project, knolls and abyssal hills were mapped using background datasets that showed relatively steep changes in elevation contours and variations in slope gradients. Knolls are numerous throughout the Coral Sea area and are greatly underestimated.

coralsea_canyon.shp Canyon is a relatively narrow, deep depression with steep sides, the bottom of which generally has a continuous slope, developed characteristically on continental slopes. Canyons were mapped by closely following the narrow sides of canyon axes, digitising from the foot of the canyon where they merge with the surrounding basin floor, and up to the canyon head and into any connecting side gullies. This project identified numerous canyons on any slope gradient >1° and are also greatly underestimated across the area.

coralsea_seamount.shp Seamount is a large isolated elevation >1000 m in relief above the seafloor, characteristically of conical form. This shapefile also includes Guyot, a seamount having a comparatively smooth flat top. Seamounts and guyots were mapped mostly within the Tasmantid Seamount Chain with elevations >1000 m. This project identified several large knolls and hills close to 1000 m in height within this chain that may also be seamounts but currently lack detailed bathymetry data.

Group Layer 4. Broader-scale features: gbr_shelf.shp Shelf is a zone adjacent to a continent (or around an island) extending from the low water line to a depth at which there is usually a marked increase of slope towards oceanic depths. The eastern boundary of the Queensland continental shelf was mapped by closely following the change in gradient along the shelf edge. The shelf break in the north was at approximately 80 m and became deeper at about 110 m towards the south. The western boundary was clipped at the Queensland mainland coastline.

coralsea_slope.shp Slope lies seaward from the shelf edge to the upper edge of a continental rise or the point where there is a general reduction in slope. The continental slope was mapped lying adjacent to the shelf and extending into the adjacent deep basins and troughs. The shelf feature was used to erase the western boundary of the slope and the various basins and troughs erased the eastern slope border. The slope has extensive canyons incising its surface.

coralsea_terrace.shp Terrace is a relatively flat horizontal or gently inclined surface, sometimes long and narrow, which is bounded by a steeper ascending slope on one side and by a steeper descending slope on the opposite side. In this project, one broad-scale terrace feature was mapped lying on the slope between the Swains Reefs and Capricorn-Bunker Group of reefs, and near the Capricorn Trough.

coralsea_plateau.shp Plateau is a flat or nearly flat area of considerable extent, dropping off abruptly on one or more sides. Extensive areas of plateaus were mapped across the Coral Sea with the largest being the Queensland Plateau. Lord Howe Rise consists of a series of plateaus separated by broad-scale valleys linking adjacent basins and troughs. Plateau boundaries were mapped around their bases where the gradient first becomes steeper. The exceptions are the Marion and Saumarez Plateaus on the Queensland continental slope, where the boundaries were mapped as the slope gradient becomes flat or nearly flat.

coralsea_valley.shp Valley is a relatively shallow, wide depression, the bottom of which usually has a continuous gradient. This term is generally not used for features that have canyon-like characteristics for a significant portion of their extent. The shapefile includes Hole, a local depression, often steep sided, of the seafloor. Valleys and holes were mapped as long shallow depressions that often separated the numerous plateaus. These features link the basins and troughs that surround these plateaus, and in some cases can be incised with finer-scale canyons.

coralsea_trough.shp Trough is a long depression of the seafloor characteristically flat bottomed and steep sided and normally shallower than a trench. In this project, two trough features were mapped that are essentially long basins. The larger feature is a combined Queensland and Townsville Trough lying between the continental slope and the Queensland Plateau. The smaller feature is the Bligh Trough separating the northern slope and Eastern Plateau. Both trough features feed into the Osprey Embayment and huge Bligh Canyon.

coralsea_rise.shp Rise is a gentle slope rising from the oceanic depths towards the foot of a continental slope. For this project, an elongate rise is mapped between the Queensland Plateau and the adjacent Coral Sea Basin. The Queensland Plateau is remnant continental crust from the Gondwana breakup and so its seaward edge provides a geomorphic extension of the Australian margin, albeit at a

This dataset contains files created, digitized, or georeferenced by Chris DeRolph for mapping the pre-urban renewal community within the boundaries of the Riverfront-Willow St. and Mountain View urban renewal projects in Knoxville TN. Detailed occupant information for properties within boundaries of these two urban renewal projects was extracted from the 1953 Knoxville City Directory. The year 1953 was chosen as a representative snapshot of the Black community before urban renewal projects were implemented. The first urban renewal project to be approved was the Riverfront-Willow Street project, which was approved in 1954 according to the University of Richmond Renewing Inequality project titled ‘Family Displacements through Urban Renewal, 1950-1966’ (link below in the 'Other shapefiles' section). For ArcGIS Online users, the shapefile and tiff layers are available in AGOL and can be found by clicking the ellipsis next to the layer name and selecting 'Show item details' for the layers in this webmap https://knoxatlas.maps.arcgis.com/apps/webappviewer/index.html?id=43a66c3cfcde4f5f8e7ab13af9bbcebecityDirectory1953 is a folder that contains:JPG images of 1953 City Directory for street segments within the urban renewal project boundaries; images collected at the McClung Historical CollectionTXT files of extracted text from each image that was used to join occupant information from directory to GIS address datashp is a folder that contains the following shapefiles:Residential:Black_owned_residential_1953.shp: residential entries in the 1953 City Directory identified as Black and property ownersBlack_rented_residential_1953.shp: residential entries in the 1953 City Directory identified as Black and non-owners of the propertyNon_Black_owned_residential_1953.shp: residential entries in the 1953 City Directory identified as property owners that were not listed as BlackNon_Black_rented_residential_1953.shp: residential entries in the 1953 City Directory not listed as Black or property ownersResidential shapefile attributes:cityDrctryString: full text string from 1953 City Directory entryfileName: name of TXT file that contains the information for the street segmentsOccupant: the name of the occupant listed in the City Directory, enclosed in square brackets []Number: the address number listed in the 1953 City DirectoryBlackOccpt: flag for whether the occupant was identified in the City Directory as Black, designated by the (c) or (e) character string in the cityDrctryString fieldOwnerOccpd: flag for whether the occupant was identified in the City Directory as the property owner, designated by the @ character in the cityDrctryString fieldUnit: unit if listed (e.g. Apt 1, 2d fl, b'ment, etc)streetName: street name in ~1953Lat: latitude coordinate in decimal degrees for the property locationLon: longitude coordinate in decimal degrees for the property locationrace_own: combines the BlackOccpt and OwnerOccpd fieldsmapLabel: combines the Number and Occupant fields for map labeling purposeslastName: occupant's last namelabelShort: combines the Number and lastName fields for map labeling purposesNon-residential:Black_nonResidential_1953.shp: non-residential entries in the 1953 City Directory listed as Black-occupiedNonBlack_nonResidential_1953.shp: non-residential entries in the 1953 City Directory not listed as Black-occupiedNon-residential shapefile attributes:cityDrctryString: full text string from 1953 City Directory entryfileName: name of TXT file that contains the information for the street segmentsOccupant: the name of the occupant listed in the City Directory, enclosed in square brackets []Number: the address number listed in the 1953 City DirectoryBlackOccpt: flag for whether the occupant was identified in the City Directory as Black, designated by the (c) or (e) character string in the cityDrctryString fieldOwnerOccpd: flag for whether the occupant was identified in the City Directory as the property owner, designated by the @ character in the cityDrctryString fieldUnit: unit if listed (e.g. Apt 1, 2d fl, b'ment, etc)streetName: street name in ~1953Lat: latitude coordinate in decimal degrees for the property locationLon: longitude coordinate in decimal degrees for the property locationNAICS6: 2022 North American Industry Classification System (NAICS) six-digit business code, designated by Chris DeRolph rapidly and without careful considerationNAICS6title: NAICS6 title/short descriptionNAICS3: 2022 North American Industry Classification System (NAICS) three-digit business code, designated by Chris DeRolph rapidly and without careful considerationNAICS3title: NAICS3 title/short descriptionflag: flags whether the occupant is part of the public sector or an NGO; a flag of '0' indicates the occupant is assumed to be a privately-owned businessrace_own: combines the BlackOccpt and OwnerOccpd fieldsmapLabel: combines the Number and Occupant fields for map labeling purposesOther shapefiles:razedArea_1972.shp: approximate area that appears to have been razed during urban renewal based on visual overlay of usgsImage_grayscale_1956.tif and usgsImage_colorinfrared_1972.tif; digitized by Chris DeRolphroadNetwork_preUrbanRenewal.shp: road network present in urban renewal area before razing occurred; removed attribute indicates whether road was removed or remains today; historically removed roads were digitized by Chris DeRolph; remaining roads sourced from TDOT GIS roads dataTheBottom.shp: the approximate extent of the razed neighborhood known as The Bottom; digitized by Chris DeRolphUrbanRenewalProjects.shp: boundaries of the East Knoxville urban renewal projects, as mapped by the University of Richmond's Digital Scholarship Lab https://dsl.richmond.edu/panorama/renewal/#view=0/0/1&viz=cartogram&city=knoxvilleTN&loc=15/35.9700/-83.9080tiff is a folder that contains the following images:streetMap_1952.tif: relevant section of 1952 map 'Knoxville Tennessee and Surrounding Area'; copyright by J.U.G. Rich and East Tenn Auto Club; drawn by R.G. Austin; full map accessed at McClung Historical Collection, 601 S Gay St, Knoxville, TN 37902; used as reference for street names in roadNetwork_preUrbanRenewal.shp; georeferenced by Chris DeRolphnewsSentinelRdMap_1958.tif: urban renewal area map from 1958 Knox News Sentinel article; used as reference for street names in roadNetwork_preUrbanRenewal.shp; georeferenced by Chris DeRolphusgsImage_grayscale_1956.tif: May 18, 1956 black-and-white USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/ARA550590030582/usgsImage_colorinfrared_1972.tif: April 18, 1972 color infrared USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/AR6197002600096/usgsImage_grayscale_1976.tif: November 8, 1976 black-and-white USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/AR1VDUT00390010/

This data set contains interpreted polygons describing different sedimentary energy environments of the Long Island Sound mapping project Phase II. This data set is the result of manual interpretation of detailed bathymetry data and resulting seafloor morphology, backscatter data, sediment core analysis results, and interpretation of sub-bottom data. It distinguishes high, low, and moderate energy environments, which can be caused by current and wave action. The outline of polygons was based on manual interpretation mostly following morphological and backscatter boundaries. Interpretation was cross-checked with sediment grab and core information. Polygon outlines are based on morphology and backscatter data that have 1 m pixel resolution, but interpretation could be several pixels (~ +/-10 m) in each direction, since the exact boundary is not always clear. Small pockets of different environments might not have been distinguished. The data is presented here as an ESRI shapefile in UTM-18 N projection. Funding was provided by the Long Island Sound Mapping Fund administered cooperatively by the EPA Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection (DEEP).

This is a zipped GIS compatible shapefile of gravity data points used in the Milford, Utah FORGE project as of March 21st, 2016. The shapefile is native to ArcGIS, but can be used with many GIS software packages. Additionally, there is a .dbf (dBase) file that contains the dataset which can be read with Microsoft Excel. The Data was downloaded from the PACES (Pan American Center for Earth and Environmental Studies) hosted by University of Texas El Paso. A readme file is included in the archive with abbreviation explanations and units.

This data set is derived from the original 2005 B4 lidar dataset collected over the southern San Andreas and San Jacinto fault zones in southern California, USA. These data have provided a fundamental resource for study of active faulting in southern California since they were released in 2005. However, these data were not classified in a manner that allowed for easy differentiation between bare ground surfaces and the objects and vegetation above that surface. This reprocessed (classified) dataset allows researchers easy and direct access to a "bare-earth" digital elevation data set as gridded half-meter resolution rasters (elevation and shaded relief), "full-feature" digital elevation models as gridded one-meter resolution rasters (elevation and shaded relief) and as classified (according to ASPRS standards) point clouds in binary .laz format, and a spatial index in shapefile and Google Earth KML format. The reprocessing of the 2005 B4 dataset was performed by Dr. Stephen B DeLong, USGS Earthquake Hazards Program, as a service to the community. The data available here were originally published on the USGS ScienceBase website as Classified point cloud and gridded elevation data from the 2005 B4 Lidar Project, southern California, USA.

Original B4 project description: The B4 Lidar Project collected lidar point cloud data of the southern San Andreas and San Jacinto Faults in southern California. Data acquisition and processing were performed by the National Center for Airborne Laser Mapping (NCALM) in partnership with the USGS and Ohio State University through funding from the EAR Geophysics program at the National Science Foundation (NSF). Optech International contributed the ALTM3100 laser scanner system. UNAVCO and SCIGN assisted in GPS ground control and continuous high rate GPS data acquisition. A group of volunteers from USGS, UCSD, UCLA, Caltech and private industry, as well as gracious landowners along the fault zones, also made the project possible. If you utilize the B4 data for talks, posters or publications, we ask that you acknowledge the B4 project. The B4 logo can be downloaded here. More information about the B4 Project.

Publications associated with this dataset can be found at NCALM's Data Tracking Center