This project is a collaboration between Esri, the United Nation's World Food Programme (WFP), Catholic Relief Services (CRS), and World Vision. Data sets and multimedia were also provided by the UN High Commissioner for Refugees (UNHCR) and the UN Office for the Coordination of Humanitarian Affairs (UNOCHA).

This submission includes raster datasets for each layer of evidence used for weights of evidence analysis as well as the deterministic play fairway analysis (PFA). Data representative of heat, permeability and groundwater comprises some of the raster datasets. Additionally, the final deterministic PFA model is provided along with a certainty model. All of these datasets are best used with an ArcGIS software package, specifically Spatial Data Modeler. This is the Phase 2 deterministic geothermal play fairway analysis (PFA) model of the Tularosa Basin and the area just east-northeast of Las Cruces New Mexico. This GIS dataset shows areas with low, medium, and high risk for geothermal exploration within the study area. It was created using composite risk segments (CRS) which included heat of the Earth and Quaternary fault related fracturing (these can be found separately). Groundwater is left out of this model due to its abundance throughout the area, as verified in by the accompanying water dataset.

Here we provide a mosaic of the Copernicus DEM 30m for Europe and the corresponding hillshade derived from the GLO-30 public instance of the Copernicus DEM. The CRS is the same as the original Copernicus DEM CRS: EPSG:4326. Note that GLO-30 Public provides limited coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters. The Copernicus DEM for Europe at 30 m in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/). Processing steps: The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in https://gdal.org/drivers/raster/vrt.html format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized: gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt The pixel values were scaled with 1000 (storing the pixels as integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using r.relief, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.

A robust and comprehensive assessment of intrinsic aquifer vulnerability at continental scale map may represent an essential initial step towards a more sustainable land-use and water management.

This repository contains the outcomes of an intrinsic aquifer vulnerability assessment of South America, performed by the DRASTIC method. The assets included in this repository are mainly raster maps (.tif, .geotif), created and georeferenced in QGIS (v3.16). Coordinate reference system (CRS) of the dataset is WGS84.

Technical specifications of all graphical outcomes are stored in a dedicated file (README.txt).

Dataset for: Bedding scale correlation on Mars in western Arabia Terra

A.M. Annex et al.

Data Product Overview

This repository contains all source data for the publication. Below is a description of each general data product type, software that can load the data, and a list of the file names along with the short description of the data product.

HiRISE Digital Elevation Models (DEMs).

HiRISE DEMs produced using the Ames Stereo Pipeline are in geotiff format ending with ‘*X_0_DEM-adj.tif’, the “X” prefix denotes the spatial resolution of the data product in meters. Geotiff files are able to be read by free GIS software like QGIS.

HiRISE map-projected imagery (DRGs).

Map-projected HiRISE images produced using the Ames Stereo Pipeline are in geotiff format ending with ‘*0_Y_DRG-cog.tif’, the “Y” prefix denotes the spatial resolution of the data product in centimeters. Geotiff files are able to be read by free GIS software like QGIS. The DRG files are formatted as COG-geotiffs for enhanced compression and ease of use.

3D Topography files (.ply).

Traingular Mesh versions of the HiRISE/CTX topography data used for 3D figures in “.ply” format. Meshes are greatly geometrically simplified from source files. Topography files can be loaded in a variety of open source tools like ParaView and Meshlab. Textures can be applied using embedded texture coordinates.

3D Geological Model outputs (.vtk)

VTK 3D file format files of model output over the spatial domain of each study site. VTK files can be loaded by ParaView open source software. The “block” files contain the model evaluation over a regular grid over the model extent. The “surfaces” files contain just the bedding surfaces as interpolated from the “block” files using the marching cubes algorithm.

Geological Model geologic maps (geologic_map.tif).

Geologic maps from geological models are standard geotiffs readable by conventional GIS software. The maximum value for each geologic map is the “no-data” value for the map. Geologic maps are calculated at a lower resolution than the topography data for storage efficiency.

Beds Geopackage File (.gpkg).

Geopackage vector data file containing all mapped layers and associated metadata including dip corrected bed thickness as well as WKB encoded 3D linestrings representing the sampled topography data to which the bedding orientations were fit. Geopackage files can be read using GIS software like QGIS and ArcGIS as well as the OGR/GDAL suite. A full description of each column in the file is provided below.

Column	Type	Description
uuid	String	unique identifier
stratum_order	Real	0-indexed bed order
section	Real	section number
layer_id	Real	bed number/index
layer_id_bk	Real	unused backup bed number/index
source_raster	String	dem file path used
raster	String	dem file name
gsd	Real	ground sampling distant for dem
wkn	String	well known name for dem
rtype	String	raster type
minx	Real	minimum x position of trace in dem crs
miny	Real	minimum y position of trace in dem crs
maxx	Real	maximum x position of trace in dem crs
maxy	Real	maximum y position of trace in dem crs
method	String	internal interpolation method
sl	Real	slope in degrees
az	Real	azimuth in degrees
error	Real	maximum error ellipse angle
stdr	Real	standard deviation of the residuals
semr	Real	standard error of the residuals
X	Real	mean x position in CRS
Y	Real	mean y position in CRS
Z	Real	mean z position in CRS
b1	Real	plane coefficient 1
b2	Real	plane coefficient 2
b3	Real	plane coefficient 3
b1_se	Real	standard error plane coefficient 1
b2_se	Real	standard error plane coefficient 2
b3_se	Real	standard error plane coefficient 3
b1_ci_low	Real	plane coefficient 1 95% confidence interval low
b1_ci_high	Real	plane coefficient 1 95% confidence interval high
b2_ci_low	Real	plane coefficient 2 95% confidence interval low
b2_ci_high	Real	plane coefficient 2 95% confidence interval high
b3_ci_low	Real	plane coefficient 3 95% confidence interval low
b3_ci_high	Real	plane coefficient 3 95% confidence interval high
pca_ev_1	Real	pca explained variance ratio pc 1
pca_ev_2	Real	pca explained variance ratio pc 2
pca_ev_3	Real	pca explained variance ratio pc 3
condition_number	Real	condition number for regression
n	Integer64	number of data points used in regression
rls	Integer(Boolean)	unused flag
demeaned_regressions	Integer(Boolean)	centering indicator
meansl	Real	mean section slope
meanaz	Real	mean section azimuth
angular_error	Real	angular error for section
mB_1	Real	mean plane coefficient 1 for section
mB_2	Real	mean plane coefficient 2 for section
mB_3	Real	mean plane coefficient 3 for section
R	Real	mean plane normal orientation vector magnitude
num_valid	Integer64	number of valid planes in section
meanc	Real	mean stratigraphic position
medianc	Real	median stratigraphic position
stdc	Real	standard deviation of stratigraphic index
stec	Real	standard error of stratigraphic index
was_monotonic_increasing_layer_id	Integer(Boolean)	monotonic layer_id after projection to stratigraphic index
was_monotonic_increasing_meanc	Integer(Boolean)	monotonic meanc after projection to stratigraphic index
was_monotonic_increasing_z	Integer(Boolean)	monotonic z increasing after projection to stratigraphic index
meanc_l3sigma_std	Real	lower 3-sigma meanc standard deviation
meanc_u3sigma_std	Real	upper 3-sigma meanc standard deviation
meanc_l2sigma_sem	Real	lower 3-sigma meanc standard error
meanc_u2sigma_sem	Real	upper 3-sigma meanc standard error
thickness	Real	difference in meanc
thickness_fromz	Real	difference in Z value
dip_cor	Real	dip correction
dc_thick	Real	thickness after dip correction
dc_thick_fromz	Real	z thickness after dip correction
dc_thick_dev	Integer(Boolean)	dc_thick <= total mean dc_thick
dc_thick_fromz_dev	Integer(Boolean)	dc_thick <= total mean dc_thick_fromz
thickness_fromz_dev	Integer(Boolean)	dc_thick <= total mean thickness_fromz
dc_thick_dev_bg	Integer(Boolean)	dc_thick <= section mean dc_thick
dc_thick_fromz_dev_bg	Integer(Boolean)	dc_thick <= section mean

The MDOT SHA OOTS Sign Structures dataset is an actively maintained database of sign structures in Maryland. The Maryland Department of Transportation, State Highway Administration, Office of Traffic Safety "OOTS" built and maintains the database with help from the MDOT SHA Office of Information Technology. Sign Structures in this open data database are only those set to an active* status. Structures information is further described by detailed attributes; the attributes definitions are listed below in the Data Dictionary section.Points of ContactData Owner: Faramarz "Faz" Sadeghi-Bajgiran for OOTS fbajgiran@mdot.maryland.govData Steward: Elliott Plack for OIT EISD: eplack.consultant@mdot.maryland.gov Technical Support: MDOT SHA OIT Enterprise Information Services - GIS Team: GIS@mdot.maryland.govData DictionarySign structure data are described by a number of attributes, including general ones, inspection details, and more.Str. ID: the Structure ID, a six-digit identifier. The first two digits represent the county. The last four digits are sequential.Status: the structure's status. Most structures are marked active. Structures with a "CFR" status are noted as "Call For Removal" and a slated to be removed or replaced in the noted time period.eDR Number: e-Design Number:Str. Type: the structure type, based on the design. Structures can either be cantilevered (CN), overhead (OH), or a combination (CM).Owned by: the owner. Most are owned by MDOT SHA but certain special ones are owned by another state agency, a federal agency, e.g., NPS, or the tri-state compact, the Woodrow Wilson Bridge Commission.Maintained by: the agency responsible for maintaining/inspecting the structure. Usually, the same as owner but some structures have unique maintenance agreements.Acceptance Year: the year the structure was accepted into the state inventory and a good estimate of the age.End-of-Service: a future year in which the structure should be removed based on criteria.Removal Year: the year the structure was removed, in the case of inactive structures.Inspection ParametersStructures are repeatedly inspected as they are critical assets for navigation and would adversely affect the public if they were to fall. The inspections are closely monitored by OOTS. Inspections are categorized by In-Depth, Routine, or NDT (Non-Destructive Testing). Each inspection regime has the same attributes:Frequency: frequency in which inspections are due.Status: status of the current inspection cycle.CRS: Component Rating Score. A comprehensive, weighted rating score the considers each component of the sign.RAS: Risk Assessment Score: A criticality score based on criteria like traffic volume, speed limit, age, type, and proximity to critical infrastructure.CRS Date: The most recent CRS date.Due Date: The due date of the next inspection of the type.MOT: Maintenance of Traffic (MOT) procedures recommended for the particular structure.Other AttributesDMS ID: if there is a Dynamic Message Sign on the structure, the ID will be listed.# of AB's / Pole: Number of Anchor Bolts per Pole. The number varies by structure type. A reference to the numbering regime is here.AB Dia.: Anchor Bolt diameter in US inches.Span: the span is the length of the structure, in US feet. Span is the distance between posts or from the post to tip for cantilever structures.Clearance: the clearance underneath the structure in US feet.Cross Section Type: the cross-section type of the pole, typically round but there are other less common types.Contractor: the contractor that installed the structure, if known.Contract No: the contract under which the structure was installed.Manufacturer: the manufacture of the structure.Shop Drawings Exist: whether or not the shop drawings exist within MDOT SHA.B-2-P Connection: Base-to-Plate connection typeB-2-P Gusseted: Whether or not the Base-to-Plate is gusseted.TEDD Comments: comments made by the Traffic Engineering Design Division in OOTS. Contact OOTS with any questions.Creator/Editor: There are several automatic fields that track the username that create and last updated the asset, and at what time.Inactive Structures* Inactive structures, e.g., those that have been removed, are also captured in this database but that information is not available here. Contact OOTS if interested in inactive structures.

This feature layer is intended to be used in conjunction with the OSP Activity 420 for FEMA's CRS imagery layer. This layer represents the relevant management attributes of areas that are likely eligible for Open Space Preservation (OSP) - Activity 422a credit through the Federal Emergency Management Agency’s (FEMA) Community Rating System (CRS). It is intended to standardize screening-level OSP data for the U.S. and enable planners and floodplain managers across the nation to participate in the CRS program at a level that was not possible in the past due to data limitations. Ultimately, more communities participating in CRS will 1) help FEMA meet their mission to help communities prepare for, protect against, and recover from flood hazards, 2) help The Nature Conservancy meet their mission to make communities more resilient to flooding by conserving open space and restoring natural floodplain functions, and 3) make flood insurance more affordable for people both inside and outside of the regulatory floodplain. A tutorial on how to use this service can be found at Assess open space to lower flood insurance cost.Under the National Flood Insurance Program (NFIP), CRS is a voluntary program that provides flood insurance discounts to communities that take action to reduce their flood risk. The OSP activity is one of the largest point contributors and can greatly improve a CRS community’s overall score, which incentivizes nature-based solutions to reduce flood risk while also making flood insurance more affordable. The data in this image service are a modified subset of the USGS's Protected Areas Database of the United States (PAD-US). In accordance with the 2017 CRS Manual requirements Esri removed all Federally or Tribally owned or managed lands larger than 10 acres. The National Hydrography Database (NHD) was then intersected with the remaining PADUS areas to extract all bodies of water larger than 10 acres and major rivers. The resulting vector dataset was then converted to 30 m raster and snapped to the National Land Cover Database (NCLD) Impervious Surface Estimation dataset. The final imagery layer represents the IDs of each of those PAD-US polygons.

Change in Level of Groundwater in central Arizona-Phoenix, 1985-2000. This file shows spatial changes in groundwater levels for two separate time periods: 1985-1989 and 1996-2000.

This is a spatial data object with a Coordinate Reference System (CRS) of EPSG:3479 NAD83(NSRS2007) / Arizona Central (ft); https://www.spatialreference.org/ref/epsg/3479/).

The coordinate reference system (CRS) associated with these data when they were constructed initially was misrepresented in early versions (<= knb-lter-cap.101.8) of this dataset. The CAP LTER has attempted to assign a CRS based on reasonable values but the accuracy of the identified CRS cannot be certain.

Multivariate analysis of the effect of geographical factors on CRS.

The road centerline file for Redding, CA has been maintained as a major component of the Shasta County Integrated Public Safety System and has been updated to meet the needs of the public safety agencies for dispatch. Road Centerlines data is segmented for each intersecting feature along a route, which means multiple linear features may represent a single route. The specific road functional classifications assigned to the roadways resulted from efforts of the California Department of Transportation (Cal Trans) CRS maps. The CRS maps can be viewed at this location http://www.dot.ca.gov/hq/tsip/hseb/crs_maps/ .

DescriptionThe 15 TPRs include the 5 MPOs and 10 Rural TPRs - Pikes Peak, Denver Metro, North Front Range, Pueblo Area, Grand Valley, Eastern, Southeast, San Luis Valley, Gunnison Valley, Southwest, Intermountain, Northwest, Upper Front Range, Central Front Range and South Central.The State of Colorado has been divided into 15 Transportation Planning Regions as set forth in CRS 43-1-1102 (8)(a), 43-1-1103 (5) (C.R.S.) and the Rules and Regulations for The Statewide Transportation Planning Process and Transportation Planning Regions (The Rules). Five of these are the Metropolitan Planning Areas. The remaining 10 rural Transportation Planning Regions are grouped in geographic contiguous areas with transportation commonalities comprised of Counties and all Municipalities within the counties of these given boundaries.Adopted TPR boundaries as of 12/31/2015. This includes TPR boundaries affected by PPACG MPO and GV MPO boundary changes in 2015.

    Last Update
    2015


    Update FrequencyAs needed


    Data Owner
    Division of Transportation Development


    Data Contact
    GIS Support Unit


    Collection Method



    Projection
    NAD83 / UTM zone 13N


    Coverage Area
    Statewide


    Temporal



    Disclaimer/Limitations
    There are no restrictions and legal prerequisites for using the data set. The State of Colorado assumes no liability relating to the completeness, correctness, or fitness for use of this data.

Snapper Creek