Geodatabase of ultramafic soils of the Americas
Data files
Oct 11, 2023 version files 1.29 MB
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geodatabase_Americas.zip
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README.md
Apr 17, 2024 version files 1.30 MB
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geodatabase_Americas.zip
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README.md
Abstract
This is a compiled geospatial dataset in ESRI polygon shapefile format of ultramafic soils of the Americas showing the location of ultramafic soils in Canada, the United States of America, Mexico, Guatemala, Cuba, Dominican Republic, Puerto Rico, Costa Rica, Colombia, Argentina, Chile, Venezuela, Ecuador, Brazil, Suriname, French Guiana, and Bolivia. The R code used to compile the dataset as well as an image of the compiled dataset are also included.
README
README: Geodatabase of ultramafic soils of the Americas
Author: Catherine Hulshof, Virginia Commonwealth University, cmhulshof@vcu.edu
Abstract: This is a compiled geospatial dataset in ESRI polygon shapefile format of ultramafic soils of many countries in the Americas showing the location of ultramafic soils in Canada, the United States of America, Guatemala, Cuba, Dominican Republic, Puerto Rico, Costa Rica, Colombia, Argentina, Chile, Venezuela, Ecuador, Brazil, Suriname, French Guiana, and Bolivia. The data are derived from nine geospatial datasets. Original datasets were subset to include only ultramafic areas, datasets were assigned a common projection (WGS84), attribute tables were reconciled to a common set of fields, and the datasets were combined.
Contents: The data are in ESRI shapefile format and thus have four components with extensions .shp, .shx, .prj, and .dbf. The .shp file contains the feature geometries, the .prj file contains the geographic coordinate system information (WGS 1984), and the .dbf file contains the attribute table. A .jpg file demonstrates the data extent across North, Central, and South America. The R code used to compile the dataset as well as an image of the compiled dataset are also included.
Data Fields:
GeogUnit: [text] Typically, the country where the ultramafic polygon is located; Puerto Rico is also used as a GeogUnit.
Formation: [text] Code corresponding to geologic formation. As recorded in source dataset. NA values signify no data recorded in source dataset.
Lithology: [text] Description of rock characteristics. As recorded in source dataset. NA values signify no data recorded in source dataset.
Lat: [numeric] Latitude of polygon centroid; decimal degrees.
Lon: [numeric] Longitude of polygon centroid; decimal degrees.
Source: [integer] Numeric code corresponding to data sources. See 'Sources' below for detail.
Area_ha: [numeric] Area of polygon; hectares.
Perim_m: [numeric] Perimeter of polygon; meters.
Sources:
[1] Bonis, S., Bohnenberger, O.H., Dengo, G. (1970) Instituto Geográfico Nacional (Guatemala). Mapa geológico de la República de Guatemala. and Upie-Maga, & Maga-Bid. (2001). Mapa Fisiográfico-Geomorfológico de la República de Guatemala, a escala 1:250,000 - Memoria Técnica-. Ministerio de Agricultura, Ganadería y Alimentación y Programa de Emergencia por Desastres Naturales, Ciudad de Guatemala, Guatemala.
[2] Bawiec, W.J. (1998) Geology, geochemistry, geophysics, mineral occurrences, and mineral resource assessment for the commonwealth of Puerto Rico. U.S. Geological Survey, Reston, VA, USA. https://doi.org/10.3133/ofr9838, https://mrdata.usgs.gov/geology/pr/
[3] Schruben, Paul G. (1997) Geology and resource assessment of Costa Rica at 1:500,000 scale; a digital representation of maps of the U.S. Geological Survey's 1987 Folio I-1865. Digital Data Series DDS-19-R. U.S. Geological Survey, Reston VA, USA. https://doi.org/10.3133/ds19, https://mrdata.usgs.gov/dds-19/.
[4] Mollat, H., Wagner, B. M., Cepek, P., & Weiss, W. (2004). Mapa Geológico de la República Dominicana 1:250.000. Texto Explicativo. Stuttgart: E. Schweizerbart’sche Verlagsbuchhandiung.
[5] Gómez, J., Schobbenhaus, C. & Montes, N.E., compilers. (2019) Geological Map of South America 2019. Scale 1:5 000 000. Commission for the Geological Map of the World (CGMW), Colombian Geological Survey, and Geological Survey of Brazil. Paris. https://doi.org/10.32685/10.143.2019.929
[6] Garrity, C.P. and Hackley, P.C. (2006) Geologic_units 1.0. Digital Data. Data Series 199, U.S. Geological Survey, Reston, VA, USA. http://pubs.usgs.gov/ds/2006/199.
[7] Garea, E., Martín, G., Estrada, R. & Gerhartz, J.L. 2003. Mapa Geológico de Cuba. Escala 1: 100 000. Centro de Programas y Proyectos Priorizados. Ministerio de Ciencia, Tecnología y Medio Ambiente. La Habana, Cuba.
[8] The National Soil Database [NSDB]. Available online at http://sis.agr.gc.ca/cansis/nsdb/index.html
[9] U.S. General Soil Map (STATSGO2) CONUS. Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Available online at http://websoilsurvey.nrcs.usda.gov.
[10] Cartografía Geológica de la República Mexicana escala 1:250,000. Continúo Nacional de Geología de la República Mexicana escala 1:250,000. Servicio Geológico Mexicano. 2017. Available online at https://datos.gob.mx/busca/dataset/cartografia-geologica-de-la-republica-mexicana-escala-1-250000
Usage Notes: Both source [5] and source [6] contain data on the location of ultramafic soils in Venezuela. They are partly, but not completely overlapping and there are different spatial resolutions; Source [5] has coarser spatial resolution and does not contain several small polygons present in [6].
Funding Information: This material is based upon work supported by the National Science Foundation under Grant No. NSF MSB-ECA #1833358 and NSF CAREER #2042453 to CMH.
Acknowledgements: We are grateful to Jon Walters for help building the geospatial database and documentation. We would also like to thank José Ramón Martínez Batlle for help accessing the geological map of the Dominican Republic.
The neotropical portion of this dataset accompanies the article: https://doi.org/10.1007/s12229-022-09278-2
Methods
The data are derived from ten geospatial datasets. Original datasets were subset to include only ultramafic areas, datasets were assigned a common projection (WGS84), attribute tables were reconciled to a common set of fields, and the datasets were combined.