The Minnesota DNR Toolbox and Hydro Tools provide a number of convenience geoprocessing tools used regularly by MNDNR staff. Many of these may be useful to the wider public. However, some tools may rely on data that is not available outside of the DNR. All tools require at least ArcGIS 10+.

If you create a GDRS using GDRS Manager and include this toolbox resource and MNDNR Quick Layers, the DNR toolboxes will automatically be added to the ArcToolbox window whenever Quick Layers GDRS Location is set to the GDRS location that has the toolboxes.

Toolsets included in MNDNR Tools V10:
- Analysis Tools
- Conversion Tools
- Division Tools
- General Tools
- Hydrology Tools
- LiDAR and DEM Tools
- Raster Tools
- Sampling Tools

These toolboxes are provided free of charge and are not warrantied for any specific use. We do not provide support or assistance in downloading or using these tools. We do, however, strive to produce high-quality tools and appreciate comments you have about them.

The Grid Garage Toolbox is designed to help you undertake the Geographic Information System (GIS) tasks required to process GIS data (geodata) into a standard, spatially aligned format. This format is required by most, grid or raster, spatial modelling tools such as the Multi-criteria Analysis Shell for Spatial Decision Support (MCAS-S). Grid Garage contains 36 tools designed to save you time by batch processing repetitive GIS tasks as well diagnosing problems with data and capturing a record of processing step and any errors encountered.

Grid Garage provides tools that function using a list based approach to batch processing where both inputs and outputs are specified in tables to enable selective batch processing and detailed result reporting. In many cases the tools simply extend the functionality of standard ArcGIS tools, providing some or all of the inputs required by these tools via the input table to enable batch processing on a 'per item' basis. This approach differs slightly from normal batch processing in ArcGIS, instead of manually selecting single items or a folder on which to apply a tool or model you provide a table listing target datasets. In summary the Grid Garage allows you to:

• List, describe and manage very large volumes of geodata.
• Batch process repetitive GIS tasks such as managing (renaming, describing etc.) or processing (clipping, resampling, reprojecting etc.) many geodata inputs such as time-series geodata derived from satellite imagery or climate models.
• Record any errors when batch processing and diagnose errors by interrogating the input geodata that failed.
• Develop your own models in ArcGIS ModelBuilder that allow you to automate any GIS workflow utilising one or more of the Grid Garage tools that can process an unlimited number of inputs.
• Automate the process of generating MCAS-S TIP metadata files for any number of input raster datasets.

The Grid Garage is intended for use by anyone with an understanding of GIS principles and an intermediate to advanced level of GIS skills. Using the Grid Garage tools in ArcGIS ModelBuilder requires skills in the use of the ArcGIS ModelBuilder tool.

Download Instructions: Create a new folder on your computer or network and then download and unzip the zip file from the GitHub Release page for each of the following items in the 'Data and Resources' section below. There is a folder in each zip file that contains all the files. See the Grid Garage User Guide for instructions on how to install and use the Grid Garage Toolbox with the sample data provided.

This is a link to the QGIS website where you can download open-source GIS software for viewing, analyzing and manipulating geodata like our downloadable shapefiles.

The Minnesota DNR Toolbox provides a number of convenience geoprocessing tools used regularly by MNDNR staff. Many of these may be useful to the wider public. However, some tools may rely on data that is not available outside of the DNR.

Toolsets included in MNDNR Tools:
- Analysis Tools
- Conversion Tools
- General Tools
- LiDAR and DEM Tools
- Sampling Tools

The application download includes a comprehensive help document, which you can also access separately here: ArcGISPro_MNDNR_Toolbox_Pro_User_Guide.pdf

These toolboxes are provided free of charge and are not warrantied for any specific use. We do not provide support or assistance in downloading or using these tools. We do, however, strive to produce high-quality tools and appreciate comments you have about them.

The Travel Time Tool was created by the MN DNR to use GIS analysis for calculation of hydraulic travel time from gridded surfaces and develop a downstream travel time raster for each cell in a watershed. This hydraulic travel time process, known as Time of Concentration, is a concept from the science of hydrology that measures watershed response to a precipitation event. The analysis uses watershed characteristics such as land-use, geology, channel shape, surface roughness, and topography to measure time of travel for water. Described as Travel Time, it calculates the elapsed time for a simulated drop of water to migrate from its source along a hydraulic path across different surfaces of the replicated watershed landscape, ultimately reaching the watershed outlet. The Travel Time Tool creates a raster whereas each cell is a measure of the length of time (in seconds) that it takes water to flow across it, and then accumulates the time (in hours) from the cell to the outlet of the watershed.

The Travel Time Tool creates an impedance raster from Manning's Equation that determines the velocity of water flowing across the cell as a measure of time (in feet per second). The Flow Length Tool uses the travel time Grid for the impedance factor and determines the downstream flow time from each cell to the outlet of the watershed.

The toolbox works with ArcMap 10.6.1 and newer and ArcGIS Pro.

For step-by-step instructions on how to use the tool, please view MN DNR Travel Time Guidance.pdf

ArcGIS tool and tutorial to convert the shapefiles into network format. The latest version of the tool is available at http://csun.uic.edu/codes/GISF2E.htmlUpdate: we now have added QGIS and python tools. To download them and learn more, visit http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

Parcels and property data maintained and provided by Lee County Property Appraiser are converted to points. Property attribute data joined to parcel GIS layer by Lee County Government GIS. This dataset is generally used in spatial analysis.Process description: Parcel polygons, condominium points and property data provided by the Lee County Property Appraiser are processed by Lee County's GIS Department using the following steps:Join property data to parcel polygons Join property data to condo pointsConvert parcel polygons to points using ESRI's ArcGIS tool "Feature to Point" and designate the "Source" field "P".Load Condominium points into this layer and designate the "Source" field "C". Add X/Y coordinates in Florida State Plane West, NAD 83, feet using the "Add X/Y" tool.Projected coordinate system name: NAD_1983_StatePlane_Florida_West_FIPS_0902_FeetGeographic coordinate system name: GCS_North_American_1983

 Name
 Type
 Length
 Description


 STRAP
 String
 25
 17-digit Property ID (Section, Township, Range, Area, Block, Lot)


 BLOCK
 String
 10
 5-digit portion of STRAP (positions 9-13)


 LOT
 String
 8
 Last 4-digits of STRAP


 FOLIOID
 Double
 8
 Unique Property ID


 MAINTDATE
 Date
 8
 Date LeePA staff updated record


 MAINTWHO
 String
 20
 LeePA staff who updated record


 UPDATED
 Date
 8
 Data compilation date


 HIDE_STRAP
 String
 1
 Confidential parcel ownership


 TRSPARCEL
 String
 17
 Parcel ID sorted by Township, Range & Section


 DORCODE
 String
 2
 Department of Revenue. See https://leepa.org/Docs/Codes/DOR_Code_List.pdf


 CONDOTYPE
 String
 1
 Type of condominium: C (commercial) or R (residential)


 UNITOFMEAS
 String
 2
 Type of Unit of Measure (ex: AC=acre, LT=lot, FF=frontage in feet)


 NUMUNITS
 Double
 8
 Number of Land Units (units defined in UNITOFMEAS)


 FRONTAGE
 Integer
 4
 Road Frontage in Feet


 DEPTH
 Integer
 4
 Property Depth in Feet


 GISACRES
 Double
 8
 Total Computed Acres from GIS


 TAXINGDIST
 String
 3
 Taxing District of Property


 TAXDISTDES
 String
 60
 Taxing District Description


 FIREDIST
 String
 3
 Fire District of Property


 FIREDISTDE
 String
 60
 Fire District Description


 ZONING
 String
 10
 Zoning of Property


 ZONINGAREA
 String
 3
 Governing Area for Zoning


 LANDUSECOD
 SmallInteger
 2
 Land Use Code


 LANDUSEDES
 String
 60
 Land Use Description


 LANDISON
 String
 5
 BAY,CANAL,CREEK,GULF,LAKE,RIVER & GOLF


 SITEADDR
 String
 55
 Lee County Addressing/E911


 SITENUMBER
 String
 10
 Property Location - Street Number


 SITESTREET
 String
 40
 Street Name


 SITEUNIT
 String
 5
 Unit Number


 SITECITY
 String
 20
 City


 SITEZIP
 String
 5
 Zip Code


 JUST
 Double
 8
 Market Value


 ASSESSED
 Double
 8
 Building Value + Land Value


 TAXABLE
 Double
 8
 Taxable Value


 LAND
 Double
 8
 Land Value


 BUILDING
 Double
 8
 Building Value


 LXFV
 Double
 8
 Land Extra Feature Value


 BXFV
 Double
 8
 Building Extra Feature value


 NEWBUILT
 Double
 8
 New Construction Value


 AGAMOUNT
 Double
 8
 Agriculture Exemption Value


 DISAMOUNT
 Double
 8
 Disability Exemption Value


 HISTAMOUNT
 Double
 8
 Historical Exemption Value


 HSTDAMOUNT
 Double
 8
 Homestead Exemption Value


 SNRAMOUNT
 Double
 8
 Senior Exemption Value


 WHLYAMOUNT
 Double
 8
 Wholly Exemption Value


 WIDAMOUNT
 Double
 8
 Widow Exemption Value


 WIDRAMOUNT
 Double
 8
 Widower Exemption Value


 BLDGCOUNT
 SmallInteger
 2
 Total Number of Buildings on Parcel


 MINBUILTY
 SmallInteger
 2
 Oldest Building Built


 MAXBUILTY
 SmallInteger
 2
 Newest Building Built


 TOTALAREA
 Double
 8
 Total Building Area


 HEATEDAREA
 Double
 8
 Total Heated Area


 MAXSTORIES
 Double
 8
 Tallest Building on Parcel


 BEDROOMS
 Integer
 4
 Total Number of Bedrooms


 BATHROOMS
 Double
 8
 Total Number of Bathrooms / Not For Comm


 GARAGE
 String
 1
 Garage on Property 'Y'


 CARPORT
 String
 1
 Carport on Property 'Y'


 POOL
 String
 1
 Pool on Property 'Y'


 BOATDOCK
 String
 1
 Boat Dock on Property 'Y'


 SEAWALL
 String
 1
 Sea Wall on Property 'Y'


 NBLDGCOUNT
 SmallInteger
 2
 Total Number of New Buildings on ParcelTotal Number of New Buildings on Parcel


 NMINBUILTY
 SmallInteger
 2
 Oldest New Building Built


 NMAXBUILTY
 SmallInteger
 2
 Newest New Building Built


 NTOTALAREA
 Double
 8
 Total New Building Area


 NHEATEDARE
 Double
 8
 Total New Heated Area


 NMAXSTORIE
 Double
 8
 Tallest New Building on Parcel


 NBEDROOMS
 Integer
 4
 Total Number of New Bedrooms


 NBATHROOMS
 Double
 8
 Total Number of New Bathrooms/Not For Comm


 NGARAGE
 String
 1
 New Garage on Property 'Y'


 NCARPORT
 String
 1
 New Carport on Property 'Y'


 NPOOL
 String
 1
 New Pool on Property 'Y'


 NBOATDOCK
 String
 1
 New Boat Dock on Property 'Y'


 NSEAWALL
 String
 1
 New Sea Wall on Property 'Y'


 O_NAME
 String
 30
 Owner Name


 O_OTHERS
 String
 120
 Other Owners


 O_CAREOF
 String
 30
 In Care Of Line


 O_ADDR1
 String
 30
 Owner Mailing Address Line 1


 O_ADDR2
 String
 30
 Owner Mailing Address Line 2


 O_CITY
 String
 30
 Owner Mailing City


 O_STATE
 String
 2
 Owner Mailing State


 O_ZIP
 String
 9
 Owner Mailing Zip


 O_COUNTRY
 String
 30
 Owner Mailing Country


 S_1DATE
 Date
 8
 Most Current Sale Date > $100.00


 S_1AMOUNT
 Double
 8
 Sale Amount


 S_1VI
 String
 1
 Sale Vacant or Improved


 S_1TC
 String
 2
 Sale Transaction Code


 S_1TOC
 String
 2
 Sale Transaction Override Code


 S_1OR_NUM
 String
 13
 Original Record (Lee County Clerk)


 S_2DATE
 Date
 8
 Previous Sale Date > $100.00


 S_2AMOUNT
 Double
 8
 Sale Amount


 S_2VI
 String
 1
 Sale Vacant or Improved


 S_2TC
 String
 2
 Sale Transaction Code


 S_2TOC
 String
 2
 Sale Transaction Override Code


 S_2OR_NUM
 String
 13
 Original Record (Lee County Clerk)


 S_3DATE
 Date
 8
 Next Previous Sale Date > $100.00


 S_3AMOUNT
 Double
 8
 Sale Amount


 S_3VI
 String
 1
 Sale Vacant or Improved


 S_3TC
 String
 2
 Sale Transaction Code


 S_3TOC
 String
 2
 Sale Transaction Override Code


 S_3OR_NUM
 String
 13
 Original Record (Lee County Clerk)


 S_4DATE
 Date
 8
 Next Previous Sale Date > $100.00


 S_4AMOUNT
 Double
 8
 Sale Amount


 S_4VI
 String
 1
 Sale Vacant or Improved


 S_4TC
 String
 2
 Sale Transaction Code


 S_4TOC
 String
 2
 Sale Transaction Override Code


 S_4OR_NUM
 String
 13

Succeeds and combines earlier versions of the tools - Topography Toolbox for ArcGIS 9.x - http://arcscripts.esri.com/details.asp?dbid=15996Riparian Topography Toolbox for calculating Height Above River and Height Above Nearest Drainage - http://arcscripts.esri.com/details.asp?dbid=16792PRISM Data Helper - http://arcscripts.esri.com/details.asp?dbid=15976Tools:UplandBeer’s AspectMcCune and Keon Heat Load IndexLandform ClassifcationPRISM Data HelperSlope Position ClassificationSolar Illumination IndexTopographic Convergence/Wetness IndexTopographic Position IndexRiparianDerive Stream Raster using Cost DistanceHeight Above Nearest DrainageHeight Above RiverMiscellaneousMoving Window Correlation

Deprecation notice: This tool is deprecated because this functionality is now available with out-of-the-box tools in ArcGIS Pro. The tool author will no longer be making further enhancements or fixing major bugs.Use Add GTFS to a Network Dataset to incorporate transit data into a network dataset so you can perform schedule-aware analyses using the Network Analyst tools in ArcMap.After creating your network dataset, you can use the ArcGIS Network Analyst tools, like Service Area and OD Cost Matrix, to perform transit/pedestrian accessibility analyses, make decisions about where to locate new facilities, find populations underserved by transit or particular types of facilities, or visualize the areas reachable from your business at different times of day. You can also publish services in ArcGIS Server that use your network dataset.The Add GTFS to a Network Dataset tool suite consists of a toolbox to pre-process the GTFS data to prepare it for use in the network dataset and a custom GTFS transit evaluator you must install that helps the network dataset read the GTFS schedules. A user's guide is included to help you set up your network dataset and run analyses.Instructions:Download the tool. It will be a zip file.Unzip the file and put it in a permanent location on your machine where you won't lose it. Do not save the unzipped tool folder on a network drive, the Desktop, or any other special reserved Windows folders (like C:\Program Files) because this could cause problems later.The unzipped file contains an installer, AddGTFStoaNetworkDataset_Installer.exe. Double-click this to run it. The installation should proceed quickly, and it should say "Completed" when finished.Read the User's Guide for instructions on creating and using your network dataset.System requirements:ArcMap 10.1 or higher with a Desktop Standard (ArcEditor) license. (You can still use it if you have a Desktop Basic license, but you will have to find an alternate method for one of the pre-processing tools.) ArcMap 10.6 or higher is recommended because you will be able to construct your network dataset much more easily using a template rather than having to do it manually step by step. This tool does not work in ArcGIS Pro. See the User's Guide for more information.Network Analyst extensionThe necessary permissions to install something on your computer.Data requirements:Street data for the area covered by your transit system, preferably data including pedestrian attributes. If you need help preparing high-quality street data for your network, please review this tutorial.A valid GTFS dataset. If your GTFS dataset has blank values for arrival_time and departure_time in stop_times.txt, you will not be able to run this tool. You can download and use the Interpolate Blank Stop Times tool to estimate blank arrival_time and departure_time values for your dataset if you still want to use it.Help forum

This is the DEM data for the watershed boundary for French Broad River near Newport. This resource includes one TIFF image presenting the projected DEM data of watershed boundary for French Broad River Near Newport and one txt file containing the Python code used to download and process the original DEM data. The input data is http://www.hydroshare.org/resource/860155baa06d46f1a0c3e0bf48e9b348 , provided by Dr. Venkatesh Merwade. The code has some minor problems with the last line of Cell 7. Currently it can download, merge and project the DEM data and save them as TIFF file. We're still working on clipping the TIFF image so it can be complete. To run the code, read the instructions in readme.txt.

This link provides information and additional metadata related to the USGS National Seismic Hazard Maps. A direct shapefile download is available at https://www.sciencebase.gov/catalog/item/5db9be62e4b06957974eb5caBackground on Hazard Explorer Tool:The Hazard Explorer Tool is a web mapping application available in FEMA's Preparedness Toolkit that allows exercise planners to identify hazards that exist in their community, where their population is most vulnerable, and where their critical infrastructure/key resources are at risk.The Hazard Explorer Tool was developed under the National Exercise Program, which serves as the principal mechanism for examining the preparedness and readiness of the United States across the entire homeland security and management exercise. Communities design, coordinate, conduct, and evaluate exercises across the US as a part of their preparedness efforts.The Map Journal serves as a tool to help you identify and evaluate potential exercise scenario locations, hazard exposure, and other risk-related factors to support exercise planning. In this tool, you will identify:Which hazards exist near your location;Where your population is most vulnerable; andWhat infrastructure and resources would be most impacted in your selected scenario location.The final output of this tool is a basic PDF map of your selected scenario location, as well as links to data sources that you can share with your GIS staff to conduct more in-depth analysis for use in planning and conducting your exercise.For more information on the Hazard Explorer Tool, please visit: https://preptoolkit.fema.gov/web/hazard-explorer/hazard-explorer-tool

Download Report Card Data File GeodatabaseThese data contain feature classes that are used in the Virginia GIS Data Report Card Tools for NG9-1-1 Preparation.Seeherefor the user guide and tools.Additionalresources and recommendations on GIS related topics are available on theVGIN 9-1-1 & GISpage.

DNRGPS is an update to the popular DNRGarmin application. DNRGPS and its predecessor were built to transfer data between Garmin handheld GPS receivers and GIS software.

DNRGPS was released as Open Source software with the intention that the GPS user community will become stewards of the application, initiating future modifications and enhancements.

DNRGPS does not require installation. Simply run the application .exe

See the DNRGPS application documentation for more details.

Compatible with: Windows (XP, 7, 8, 10, and 11), ArcGIS shapefiles and file geodatabases, Google Earth, most hand-held Garmin GPSs, and other NMEA output GPSs

Limited Compatibility: Interactions with ArcMap layer files and ArcMap graphics are no longer supported. Instead use shapefile or geodatabase.

Prerequisite: .NET 4 Framework

DNR Data and Software License Agreement

Subscribe to the DNRGPS announcement list to be notified of upgrades or updates.

National Monuments Service - Archaeological Survey of Ireland. Published by Department of Housing, Local Government and Heritage. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).This Archaeological Survey of Ireland dataset is published from the database of the National Monuments Service Sites and Monuments Record (SMR). This dataset also can be viewed and interrogated through the online Historic Environment Viewer: https://heritagedata.maps.arcgis.com/apps/webappviewer/index.html?id=0c9eb9575b544081b0d296436d8f60f8

A Sites and Monuments Record (SMR) was issued for all counties in the State between 1984 and 1992. The SMR is a manual containing a numbered list of certain and possible monuments accompanied by 6-inch Ordnance Survey maps (at a reduced scale). The SMR formed the basis for issuing the Record of Monuments and Places (RMP) - the statutory list of recorded monuments established under Section 12 of the National Monuments (Amendment) Act 1994. The RMP was issued for each county between 1995 and 1998 in a similar format to the existing SMR. The RMP differs from the earlier lists in that, as defined in the Act, only monuments with known locations or places where there are believed to be monuments are included.

The large Archaeological Survey of Ireland archive and supporting database are managed by the National Monuments Service and the records are continually updated and supplemented as additional monuments are discovered. On the Historic Environment viewer an area around each monument has been shaded, the scale of which varies with the class of monument. This area does not define the extent of the monument, nor does it define a buffer area beyond which ground disturbance should not take place – it merely identifies an area of land within which it is expected that the monument will be located. It is not a constraint area for screening – such must be set by the relevant authority who requires screening for their own purposes. This data has been released for download as Open Data under the DPER Open Data Strategy and is licensed for re-use under the Creative Commons Attribution 4.0 International licence. http://creativecommons.org/licenses/by/4.0

Please note that the centre point of each record is not indicative of the geographic extent of the monument. The existing point centroids were digitised relative to the OSI 6-inch mapping and the move from this older IG-referenced series to the larger-scale ITM mapping will necessitate revisions. The accuracy of the derived ITM co-ordinates is limited to the OS 6-inch scale and errors may ensue should the user apply the co-ordinates to larger scale maps. Records that do not refer to 'monuments' are designated 'Redundant record' and are retained in the archive as they may relate to features that were once considered to be monuments but which on investigation proved otherwise. Redundant records may also refer to duplicate records or errors in the data structure of the Archaeological Survey of Ireland.

This dataset is provided for re-use in a number of ways and the technical options are outlined below. For a live and current view of the data, please use the web services or the data extract tool in the Historic Environment Viewer. The National Monuments Service also provide an Open Data snapshot of its national dataset in CSV as a bulk data download. Users should consult the National Monument Service website https://www.archaeology.ie/ for further information and guidance on the National Monument Act(s) and the legal significance of this dataset.

Open Data Bulk Data Downloads (version date: 23/08/2023)

The Sites and Monuments Record (SMR) is provided as a national download in Comma Separated Value (CSV) format. This format can be easily integrated into a number of software clients for re-use and analysis. The Longitude and Latitude coordinates are also provided to aid its re-use in web mapping systems, however, the ITM easting/northings coordinates should be quoted for official purposes. ERSI Shapefiles of the SMR points and SMRZone polygons are also available The SMRZones represent an area around each monument, the scale of which varies with the class of monument. This area does not define the extent of the monument, nor does it define a buffer area beyond which ground disturbance should not take place – it merely identifies an area of land within which it is expected that the monument will be located. It is not a constraint area for screening – such must be set by the relevant authority who requires screening for their own purposes.

GIS Web Service APIs (live views):

For users with access to GIS software please note that the Archaeological Survey of Ireland data is also available spatial data web services. By accessing and consuming the web service users are deemed to have accepted the Terms and Conditions. The web services are available at the URL endpoints advertised below:

SMR; https://services-eu1.arcgis.com/HyjXgkV6KGMSF3jt/arcgis/rest/services/SMROpenData/FeatureServer

SMRZone; https://services-eu1.arcgis.com/HyjXgkV6KGMSF3jt/arcgis/rest/services/SMRZoneOpenData/FeatureServer

Historic Environment Viewer - Query Tool

The "Query" tool can alternatively be used to selectively filter and download the data represented in the Historic Environment Viewer. The instructions for using this tool in the Historic Environment Viewer are detailed in the associated Help file: https://www.archaeology.ie/sites/default/files/media/pdf/HEV_UserGuide_v01.pdf...

Summary

Geojson files used to visualize geospatial layers relevant to identifying and assessing trucking fleet decarbonization opportunities with the MIT Climate & Sustainability Consortium's Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE) tool.

Relevant Links

Link to the online version of the tool (requires creation of a free user account).

Link to GitHub repo with source code to produce this dataset and deploy the Geo-TIDE tool locally.

Funding

This dataset was produced with support from the MIT Climate & Sustainability Consortium.

Original Data Sources

These geojson files draw from and synthesize a number of different datasets and tools. The original data sources and tools are described below:

Filename(s) Description of Original Data Source(s) Link(s) to Download Original Data License and Attribution for Original Data Source(s)

faf5_freight_flows/*.geojson

trucking_energy_demand.geojson

highway_assignment_links_*.geojson

infrastructure_pooling_thought_experiment/*.geojson

Regional and highway-level freight flow data obtained from the Freight Analysis Framework Version 5. Shapefiles for FAF5 region boundaries and highway links are obtained from the National Transportation Atlas Database. Emissions attributes are evaluated by incorporating data from the 2002 Vehicle Inventory and Use Survey and the GREET lifecycle emissions tool maintained by Argonne National Lab.

Shapefile for FAF5 Regions

Shapefile for FAF5 Highway Network Links

FAF5 2022 Origin-Destination Freight Flow database

FAF5 2022 Highway Assignment Results

Attribution for Shapefiles: United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Available at: https://geodata.bts.gov/search?collection=Dataset.

License for Shapefiles: This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.

Attribution for Origin-Destination Freight Flow database: National Transportation Research Center in the Oak Ridge National Laboratory with funding from the Bureau of Transportation Statistics and the Federal Highway Administration. Freight Analysis Framework Version 5: Origin-Destination Data. Available from: https://faf.ornl.gov/faf5/Default.aspx. Obtained on Aug 5, 2024. In the public domain.

Attribution for the 2022 Vehicle Inventory and Use Survey Data: United States Department of Transportation Bureau of Transportation Statistics. Vehicle Inventory and Use Survey (VIUS) 2002 [supporting datasets]. 2024. https://doi.org/10.21949/1506070

Attribution for the GREET tool (original publication): Argonne National Laboratory Energy Systems Division Center for Transportation Research. GREET Life-cycle Model. 2014. Available from this link.

Attribution for the GREET tool (2022 updates): Wang, Michael, et al. Summary of Expansions and Updates in GREET® 2022. United States. https://doi.org/10.2172/1891644

grid_emission_intensity/*.geojson

Emission intensity data is obtained from the eGRID database maintained by the United States Environmental Protection Agency.

eGRID subregion boundaries are obtained as a shapefile from the eGRID Mapping Files database.

eGRID database

Shapefile with eGRID subregion boundaries

Attribution for eGRID data: United States Environmental Protection Agency: eGRID with 2022 data. Available from https://www.epa.gov/egrid/download-data. In the public domain.

Attribution for shapefile: United States Environmental Protection Agency: eGRID Mapping Files. Available from https://www.epa.gov/egrid/egrid-mapping-files. In the public domain.

US_elec.geojson

US_hy.geojson

US_lng.geojson

US_cng.geojson

US_lpg.geojson

Locations of direct current fast chargers and refueling stations for alternative fuels along U.S. highways. Obtained directly from the Station Data for Alternative Fuel Corridors in the Alternative Fuels Data Center maintained by the United States Department of Energy Office of Energy Efficiency and Renewable Energy.

US_elec.geojson

US_hy.geojson

US_lng.geojson

US_cng.geojson

US_lpg.geojson

Attribution: U.S. Department of Energy, Energy Efficiency and Renewable Energy. Alternative Fueling Station Corridors. 2024. Available from: https://afdc.energy.gov/corridors. In the public domain.

These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.

daily_grid_emission_profiles/*.geojson

Hourly emission intensity data obtained from ElectricityMaps.

Original data can be downloaded as csv files from the ElectricityMaps United States of America database

Shapefile with region boundaries used by ElectricityMaps

License: Open Database License (ODbL). Details here: https://www.electricitymaps.com/data-portal

Attribution for csv files: Electricity Maps (2024). United States of America 2022-23 Hourly Carbon Intensity Data (Version January 17, 2024). Electricity Maps Data Portal. https://www.electricitymaps.com/data-portal.

Attribution for shapefile with region boundaries: ElectricityMaps contributors (2024). electricitymaps-contrib (Version v1.155.0) [Computer software]. https://github.com/electricitymaps/electricitymaps-contrib.

gen_cap_2022_state_merged.geojson

trucking_energy_demand.geojson

Grid electricity generation and net summer power capacity data is obtained from the state-level electricity database maintained by the United States Energy Information Administration.

U.S. state boundaries obtained from this United States Department of the Interior U.S. Geological Survey ScienceBase-Catalog.

Annual electricity generation by state

Net summer capacity by state

Shapefile with U.S. state boundaries

Attribution for electricity generation and capacity data: U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data/state/. In the public domain.

electricity_rates_by_state_merged.geojson

Commercial electricity prices are obtained from the Electricity database maintained by the United States Energy Information Administration.

Electricity rate by state

Attribution: U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data.php. In the public domain.

demand_charges_merged.geojson

demand_charges_by_state.geojson

Maximum historical demand charges for each state and zip code are derived from a dataset compiled by the National Renewable Energy Laboratory in this this Data Catalog.

Historical demand charge dataset

The original dataset is compiled by the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), and the Alliance for Sustainable Energy, LLC ('Alliance').

Attribution: McLaren, Joyce, Pieter Gagnon, Daniel Zimny-Schmitt, Michael DeMinco, and Eric Wilson. 2017. 'Maximum demand charge rates for commercial and industrial electricity tariffs in the United States.' NREL Data Catalog. Golden, CO: National Renewable Energy Laboratory. Last updated: July 24, 2024. DOI: 10.7799/1392982.

eastcoast.geojson

midwest.geojson

la_i710.geojson

h2la.geojson

bayarea.geojson

saltlake.geojson

northeast.geojson

Highway corridors and regions targeted for heavy duty vehicle infrastructure projects are derived from a public announcement on February 15, 2023 by the United States Department of Energy.

The shapefile with Bay area boundaries is obtained from this Berkeley Library dataset.

The shapefile with Utah county boundaries is obtained from this dataset from the Utah Geospatial Resource Center.

Shapefile for Bay Area country boundaries

Shapefile for counties in Utah

Attribution for public announcement: United States Department of Energy. Biden-Harris Administration Announces Funding for Zero-Emission Medium- and Heavy-Duty Vehicle Corridors, Expansion of EV Charging in Underserved Communities (2023). Available from https://www.energy.gov/articles/biden-harris-administration-announces-funding-zero-emission-medium-and-heavy-duty-vehicle.

Attribution for Bay area boundaries: San Francisco (Calif.). Department Of Telecommunications and Information Services. Bay Area Counties. 2006. In the public domain.

Attribution for Utah boundaries: Utah Geospatial Resource Center & Lieutenant Governor's Office. Utah County Boundaries (2023). Available from https://gis.utah.gov/products/sgid/boundaries/county/.

License for Utah boundaries: Creative Commons 4.0 International License.

incentives_and_regulations/*.geojson

State-level incentives and regulations targeting heavy duty vehicles are collected from the State Laws and Incentives database maintained by the United States Department of Energy's Alternative Fuels Data Center.

Data was collected manually from the State Laws and Incentives database.

Attribution: U.S. Department of Energy, Energy Efficiency and Renewable Energy, Alternative Fuels Data Center. State Laws and Incentives. Accessed on Aug 5, 2024 from: https://afdc.energy.gov/laws/state. In the public domain.

These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.

costs_and_emissions/*.geojson

diesel_price_by_state.geojson

trucking_energy_demand.geojson

Lifecycle costs and emissions of electric and diesel trucking are evaluated by adapting the model developed by Moreno Sader et al., and calibrated to the Run on Less dataset for the Tesla Semi collected from the 2023 PepsiCo Semi pilot by the North American Council for Freight Efficiency.

In

Per California Water Code Section 10609.80 (a), DWR has released an update to the indicators analyzed for the rural communities water shortage vulnerability analysis and a new interactive tool to explore the data. This page remains to archive the original dataset, but for more current information, please see the following pages: - https://water.ca.gov/Programs/Water-Use-And-Efficiency/SB-552/SB-552-Tool - https://data.cnra.ca.gov/dataset/water-shortage-vulnerability-technical-methods - https://data.cnra.ca.gov/dataset/i07-water-shortage-vulnerability-sections - https://data.cnra.ca.gov/dataset/i07-water-shortage-social-vulnerability-blockgroup This dataset is made publicly available pursuant to California Water Code Section 10609.42 which directs the California Department of Water Resources to identify small water suppliers and rural communities that may be at risk of drought and water shortage vulnerability and propose to the Governor and Legislature recommendations and information in support of improving the drought preparedness of small water suppliers and rural communities. As of March 2021, two datasets are offered here for download. The background information, results synthesis, methods and all reports submitted to the legislature are available here: https://water.ca.gov/Programs/Water-Use-And-Efficiency/2018-Water-Conservation-Legislation/County-Drought-Planning Two online interactive dashboards are available here to explore the datasets and findings. https://dwr.maps.arcgis.com/apps/MapSeries/index.html?appid=3353b370f7844f468ca16b8316fa3c7b The following datasets are offered here for download and for those who want to explore the data in tabular format. (1) Small Water Suppliers: In total, 2,419 small water suppliers were examined for their relative risk of drought and water shortage. Of these, 2,244 are community water systems. The remaining 175 systems analyzed are small non-community non-transient water systems that serve schools for which there is available spatial information. This dataset contains the final risk score and individual risk factors for each supplier examined. Spatial boundaries of water suppliers' service areas were used to calculate the extent and severity of each suppliers' exposure to projected climate changes (temperature, wildfire, and sea level rise) and to current environmental conditions and events. The boundaries used to represent service areas are available for download from the California Drinking Water System Area Boundaries, located on the California State Geoportal, which is available online for download at https://gispublic.waterboards.ca.gov/portal/home/item.html?id=fbba842bf134497c9d611ad506ec48cc (2) Rural Communities: In total 4,987 communities, represented by US Census Block Groups, were analyzed for their relative risk of drought and water shortage. Communities with a record of one or more domestic well installed within the past 50 years are included in the analysis. Each community examined received a numeric risk score, which is derived from a set of indicators developed from a stakeholder process. Indicators used to estimate risk represented three key components: (1) the exposure of suppliers and communities to hazardous conditions and events, (2) the physical and social vulnerability of communities to the exposure, and (3) recent history of shortage and drought impacts. The unit of analysis for the rural communities, also referred to as "self-supplied communities" is U.S. Census Block Groups (ACS 2012-2016 Tiger Shapefile). The Census Block Groups do not necessarily represent socially-defined communities, but they do cover areas where population resides. Using this spatial unit for this analysis allows us to access demographic information that is otherwise not available in small geographic units.

Use the Attachment Viewer template to provide an app for users to explore a layer's features and review attachments with the option to update attribute data. Present your images, videos, and PDF files collected using ArcGIS Field Maps or ArcGIS Survey123 workflows. Choose an attachment-focused layout to display individual images beside your map or a map-focused layout to highlight your map next to a gallery of images. Examples: Review photos collected during emergency response damage inspections. Display the results of field data collection and support downloading images for inclusion in a report. Present a map of land parcel along with associated documents stored as attachments. Data requirements The Attachment Viewer template requires a feature layer with attachments. It includes the capability to view attachments of a hosted feature service or an ArcGIS Server feature service (10.8 or later). Currently, the app can display JPEG, JPG, PNG, GIF, MP4, QuickTime (.mov), and PDF files in the viewer window. All other attachment types are displayed as a link. Key app capabilities App layout - Choose between an attachment-focused layout, which displays one attachment at a time in the main panel of the app with the map on the side, or a map-focused layout, which displays the map in the main panel of the app with a gallery of attachments. Feature selection - Allows users to select features in the map and view associated attachments. Review data - Enable tools to review and update existing records. Zoom, pan, download images - Allow users to interact with and download attachments. Language switcher - Provide translations for custom text and create a multilingual app. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

Introduction

Geographical scale, in terms of spatial extent, provide a basis for other branches of science. This dataset contains newly proposed geographical and geological GIS boundaries for the Pan-Tibetan Highlands (new proposed name for the High Mountain Asia), based on geological and geomorphological features. This region comprises the Tibetan Plateau and three adjacent mountain regions: the Himalaya, Hengduan Mountains and Mountains of Central Asia, and boundaries are also given for each subregion individually. The dataset will benefit quantitative spatial analysis by providing a well-defined geographical scale for other branches of research, aiding cross-disciplinary comparisons and synthesis, as well as reproducibility of research results.

The dataset comprises three subsets, and we provide three data formats (.shp, .geojson and .kmz) for each of them. Shapefile format (.shp) was generated in ArcGIS Pro, and the other two were converted from shapefile, the conversion steps refer to 'Data processing' section below. The following is a description of the three subsets:

(1) The GIS boundaries we newly defined of the Pan-Tibetan Highlands and its four constituent sub-regions, i.e. the Tibetan Plateau, Himalaya, Hengduan Mountains and the Mountains of Central Asia. All files are placed in the "Pan-Tibetan Highlands (Liu et al._2022)" folder.

(2) We also provide GIS boundaries that were applied by other studies (cited in Fig. 3 of our work) in the folder "Tibetan Plateau and adjacent mountains (Others’ definitions)". If these data is used, please cite the relevent paper accrodingly. In addition, it is worthy to note that the GIS boundaries of Hengduan Mountains (Li et al. 1987a) and Mountains of Central Asia (Foggin et al. 2021) were newly generated in our study using Georeferencing toolbox in ArcGIS Pro.

(3) Geological assemblages and characters of the Pan-Tibetan Highlands, including Cratons and micro-continental blocks (Fig. S1), plus sutures, faults and thrusts (Fig. 4), are placed in the "Pan-Tibetan Highlands (geological files)" folder.

Note: High Mountain Asia: The name ‘High Mountain Asia’ is the only direct synonym of Pan-Tibetan Highlands, but this term is both grammatically awkward and somewhat misleading, and hence the term ‘Pan-Tibetan Highlands’ is here proposed to replace it. Third Pole: The first use of the term ‘Third Pole’ was in reference to the Himalaya by Kurz & Montandon (1933), but the usage was subsequently broadened to the Tibetan Plateau or the whole of the Pan-Tibetan Highlands. The mainstream scientific literature refer the ‘Third Pole’ to the region encompassing the Tibetan Plateau, Himalaya, Hengduan Mountains, Karakoram, Hindu Kush and Pamir. This definition was surpported by geological strcture (Main Pamir Thrust) in the western part, and generally overlaps with the ‘Tibetan Plateau’ sensu lato defined by some previous studies, but is more specific.

More discussion and reference about names please refer to the paper. The figures (Figs. 3, 4, S1) mentioned above were attached in the end of this document.

Data processing

We provide three data formats. Conversion of shapefile data to kmz format was done in ArcGIS Pro. We used the Layer to KML tool in Conversion Toolbox to convert the shapefile to kmz format. Conversion of shapefile data to geojson format was done in R. We read the data using the shapefile function of the raster package, and wrote it as a geojson file using the geojson_write function in the geojsonio package.

Version

Version 2022.1.

Acknowledgements

This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB31010000), the National Natural Science Foundation of China (41971071), the Key Research Program of Frontier Sciences, CAS (ZDBS-LY-7001). We are grateful to our coauthors insightful discussion and comments. We also want to thank professors Jed Kaplan, Yin An, Dai Erfu, Zhang Guoqing, Peter Cawood, Tobias Bolch and Marc Foggin for suggestions and providing GIS files.

Citation

Liu, J., Milne, R. I., Zhu, G. F., Spicer, R. A., Wambulwa, M. C., Wu, Z. Y., Li, D. Z. (2022). Name and scale matters: Clarifying the geography of Tibetan Plateau and adjacent mountain regions. Global and Planetary Change, In revision

Jie Liu & Guangfu Zhu. (2022). Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions (Version 2022.1). https://doi.org/10.5281/zenodo.6432940

Contacts

Dr. Jie LIU: E-mail: liujie@mail.kib.ac.cn;

Mr. Guangfu ZHU: zhuguangfu@mail.kib.ac.cn

Institution: Kunming Institute of Botany, Chinese Academy of Sciences

Address: 132# Lanhei Road, Heilongtan, Kunming 650201, Yunnan, China

Copyright

This dataset is available under the Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

This service depicts National Park Service tract and boundary data that was created by the Land Resources Division. NPS Director's Order #25 states: "Land status maps will be prepared to identify the ownership of the lands within the authorized boundaries of the park unit. These maps, showing ownership and acreage, are the 'official record' of the acreage of Federal and non-federal lands within the park boundaries. While these maps are the official record of the lands and acreage within the unit's authorized boundaries, they are not of survey quality and not intended to be used for survey purposes." As such this data is intended for use as a tool for GIS analysis. It is in no way intended for engineering or legal purposes. The data accuracy is checked against best available sources which may be dated and vary by location. NPS assumes no liability for use of this data. The boundary polygons represent the current legislated boundary of a given NPS unit. NPS does not necessarily have full fee ownership or hold another interest (easement, right of way, etc...) in all parcels contained within this boundary. Equivalently NPS may own or have an interest in parcels outside the legislated boundary of a given unit. In order to obtain complete information about current NPS interests both inside and outside a unit’s legislated boundary tract level polygons are also created by NPS Land Resources Division and should be used in conjunction with this boundary data. To download this data directly from the NPS go to https://irma.nps.gov Property ownership data is compiled from deeds, plats, surveys, and other source data. These are not engineering quality drawings and should be used for administrative purposes only. The National Park Service (NPS) shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time. The data are not better than the original sources from which they were derived. It is the responsibility of the data user to use the data appropriately and consistent within the limitations of geospatial data in general and these data in particular. The related graphics are intended to aid the data user in acquiring relevant data; it is not appropriate to use the related graphics as data. The National Park Service gives no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data. It is strongly recommended that these data are directly acquired from an NPS server and not indirectly through other sources which may have changed the data in some way. Although these data have been processed successfully on a computer system at the National Park Service, no warranty expressed or implied is made regarding the utility of the data on another system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data and aggregate use with other data.