This data repository hosts datasets that are used for students to practice spatial operations introduced in R-as-GIS lectures and workshops.

Earth Data Analysis Center (EDAC) at The University of New Mexico (UNM) develops, manages, and enhances the New Mexico Resource Geographic Information System (RGIS) Program and Clearinghouse. Nationally, NM RGIS is among the largest of state-based programs for digital geospatial data and information and continues to add to its data offerings, services, and technology.

The RGIS Program mission is to develop and expand geographic information and use of GIS technology, creating a comprehensive GIS resource for state and local governments, educational institutions, nonprofit organizations, and private businesses; to promote geospatial information and GIS technology as primary analytical tools for decision makers and researchers; and to provide a central Clearinghouse to avoid duplication and improve information transfer efficiency.

As a repository for digital geospatial data acquired from local and national public agencies and data created expressly for RGIS, the clearinghouse serves as a major hub in New Mexico’s network for inter-agency and intergovernmental coordination, data sharing, information transfer, and electronic communication. Data sets available for download include political and administrative boundaries, place names and locations, census data (current and historical), 30 years of digital orthophotography, 80 years of historic aerial photography, satellite imagery, elevation data, transportation data, wildfire boundaries and natural resource data.

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (guis_geomorphology_metadata.txt or guis_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

This shapefile contains the boundary of the New Mexico Junior College special voting district, from the State of New Mexico, as a polygon feature. It was collected/created by the Earth Data Analysis Center, The University of New mexico (EDAC), for the New Mexico Office of the Secretary of State (NMSOS). It is part of an effort to create a GIS data repository of all voting districts in the State of New Mexico, accessible for download at http://rgis.unm.edu/rgis6/ , under RGIS > SOS Voting Districts. This data is current as of Spring 2019, and will be updated as newer data becomes available from an authoritative source. For information about how special voting district boundaries were processed, see "Lineage"/"Processing Steps".

This Jupyter Notebook created by Laurence lin and Young-Don Choi to simulate the Paine Run subwatershed (12.7 km2) of Shenandoah National Park. This notebook shows how to create RHESssys input using grass GIS from GIS data, simulate RHESsys Model and visualize the output of RHESsys model.

This shapefile contains the boundary of the Eastern New Mexico University - Roswell special voting district, from the State of New Mexico, as a polygon feature. It was collected/created by the Earth Data Analysis Center, The University of New mexico (EDAC), for the New Mexico Office of the Secretary of State (NMSOS). It is part of an effort to create a GIS data repository of all voting districts in the State of New Mexico, accessible for download at http://rgis.unm.edu/rgis6/ , under RGIS > SOS Voting Districts. This data is current as of Spring 2019, and will be updated as newer data becomes available from an authoritative source. For information about how special voting district boundaries were processed, see "Lineage"/"Processing Steps".

rgis_sde_test2

All data associated with the Town of Young Floodplain Risk Management Study and Plan.\r \r GIS Data Outputs, Hydraulics, Hydrology, Reporting, Survey.

This shapefile contains the boundary of the San Juan Community College District special voting district, from the State of New Mexico, as a polygon feature. It was collected/created by the Earth Data Analysis Center, The University of New mexico (EDAC), for the New Mexico Office of the Secretary of State (NMSOS). It is part of an effort to create a GIS data repository of all voting districts in the State of New Mexico, accessible for download at http://rgis.unm.edu/rgis6/ , under RGIS > SOS Voting Districts. This data is current as of Fall 2023, and will be updated as newer data becomes available from an authoritative source. For information about how special voting district boundaries were processed, see "Lineage"/"Processing Steps".

The SR43-44 dataset is a topographic dataset, detailing features within Princess Elizabeth Land, more specifically along the Ingrid Christensen Coast.\r \r The area includes Prydz Bay and the Amery Ice Shelf.\r \r The database contains all natural features. Attributes are held for line, point and polygon features.\r \r The dataset conforms to the Australian Antarctic Spatial model.\r \r The dataset was originally produced as a base to supply data for the second edition hard copy map series.\r \r It was updated in 2001/02 with the integration of data from the AAT Coastline 2001 dataset.

R. Padskočienės GIS duomenų sudarymo įmonė financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

SR41-42 Northern Prince Charles Mountains - 1:1 Million Topographic GIS Dataset is part of the International Map of the World 1:1000000 map series.\r \r The dataset contains all natural features. Attributes are held for line point and polygon features.\r \r The dataset was originally produced as a base to supply data for the second edition hard copy map series.\r \r This dataset was updated in 2001/02 with the integration of data from the AAT Coastline 2001 dataset.

This GIS dataset includes:\r \r 1 Point data representing southern elephant seal haulout sites in the Vestfold Hills.\r \r 2 Polygon data representing weddell seal pupping areas in the Vestfold Hills.\r \r The data is based on field work and research by John van den Hoff, AAD biologist.\r \r The data has been formatted according to the SCAR Feature Catalogue.\r \r Data that are part of this dataset have Dataset_id = 289 in the SCAR Feature Catalogue format. Dataset_id is an attribute in the attribute table.\r \r Data quality information for any feature can be searched for at a Related URL by entering the Qinfo number of the feature at the 'Search datasets and quality' tab. Qinfo is an attribute in the attribute table.

This GIS dataset includes:\r \r 1 Point data representing southern elephant seal haulout sites as shown in Figure 1 of the paper J.van den Hoff, R.Davies and H.Burton, 'Origins, age composition and change in numbers of moulting southern elephant seals (Mirounga leonina L.) in the Windmill Islands, Vincennes Bay, east Antarctica: 1988-2001', Wildlife Research vol 30 no 3 2003.\r \r 2 Polygon data representing some of the above sites. This data is based on field work by John van den Hoff, Australian Antarctic Division biologist, in 2000/01.\r \r 3 Polygon data representing two Weddell seal pupping sites in the Windmill Islands. This data is based on John van den Hoff's knowledge from field work and research.\r \r The data has been formatted according to the SCAR Feature Catalogue.\r \r Data that are part of this dataset have Dataset_id = 179 in the SCAR Feature Catalogue format. Dataset_id is an attribute in the attribute table.\r \r Data quality information for any feature can be searched for at a Related URL by entering the Qinfo number of the feature at the 'Search datasets and quality' tab. Qinfo is an attribute in the attribute table.

Published as a tile layer, the 2020 Forest Habitat layer for Rhode Island was created to help in forest management for wildlife. Raster data is available for download. View More for options.

This is a statewide digital 1 m resolution dataset of land cover for the State of Rhode Island based on NOAA CCAP 2016 land cover enhanced with additional forest classes including upland and wetland evergreen and mixed forests. The 2020 Forest Habitat Map for Rhode Island was created to help in forest management for wildlife.

To assist communities in identifying racially/ethnically-concentrated areas of poverty (R/ECAPs), HUD has developed a census tract-based definition of R/ECAPs. The definition involves a racial/ethnic concentration threshold and a poverty test. The racial/ethnic concentration threshold is straightforward: R/ECAPs must have a non-white population of 50 percent or more. Regarding the poverty threshold, Wilson (1980) defines neighborhoods of extreme poverty as census tracts with 40 percent or more of individuals living at or below the poverty line. Because overall poverty levels are substantially lower in many parts of the country, HUD supplements this with an alternate criterion. Thus, a neighborhood can be a R/ECAP if it has a poverty rate that exceeds 40% or is three or more times the average tract poverty rate for the metropolitan/micropolitan area, whichever threshold is lower. Census tracts with this extreme poverty that satisfy the racial/ethnic concentration threshold are deemed R/ECAPs. This translates into the following equation: Where i represents census tracts, () is the metropolitan/micropolitan (CBSA) mean tract poverty rate, is the ith tract poverty rate, () is the non-Hispanic white population in tract i, and Pop is the population in tract i.While this definition of R/ECAP works well for tracts in CBSAs, place outside of these geographies are unlikely to have racial or ethnic concentrations as high as 50 percent. In these areas, the racial/ethnic concentration threshold is set at 20 percent.

Data Source: American Community Survey (ACS), 2009-2013; Decennial Census (2010); Brown Longitudinal Tract Database (LTDB) based on decennial census data, 1990, 2000 & 2010.

Related AFFH-T Local Government, PHA Tables/Maps: Table 4, 7; Maps 1-17. Related AFFH-T State Tables/Maps: Table 4, 7; Maps 1-15, 18.

References:Wilson, William J. (1980). The Declining Significance of Race: Blacks and Changing American Institutions. Chicago: University of Chicago Press.

To learn more about R/ECAPs visit:https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 11/2017

New Mexico Debris Flow study areas and analysis results.

This shapefile contains the boundary of the City of Rio Rancho Tax Increment Development Districts special voting districts, from the State of New Mexico, as a polygon feature. It was collected/created by the Earth Data Analysis Center, The University of New mexico (EDAC), for the New Mexico Office of the Secretary of State (NMSOS). It is part of an effort to create a GIS data repository of all voting districts in the State of New Mexico, accessible for download at http://rgis.unm.edu/rgis6/ , under RGIS > SOS Voting Districts. This data is current as of Fall 2023, and will be updated as newer data becomes available from an authoritative source. For information about how special voting district boundaries were processed, see "Lineage"/"Processing Steps".

This layer represents boundaries for New Mexico tax district "OUT" categories and incorporated/municipal "IN" categories as identified on the "Certificate of Tax Rates" published for each of the State's thirty-three counties by the Department of Finance and Administration's Budget and Finance Bureau. Initial municipal boundaries acquired from RGIS and based on layers developed by the Earth Data Analysis Center (EDAC) at UNM. TRD revisions have been made by acquiring updated boundaries from data stewards at local jurisdictions. Data is a vector polygon digital data structure taken from the Census Bureau's TIGER/Line Files, 1994, for New Mexico. Known issues: This data layer may contain unintended inaccuracies and omissions. It is meant to serve as a baseline representation from which to make additions and improvements.