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Estimating the presence of paved surface parking lots in the conterminous U.S. from land-use coefficients for 1974, 1982, 1992, 2002, and 2012

Dates

Publication Date
Start Date
1974-01-01
End Date
2012-12-31

Citation

Falcone, J.A., and Nott, M.A., 2019, Estimating the presence of paved surface parking lots in the conterminous U.S. from land-use coefficients for 1974, 1982, 1992, 2002, and 2012: U.S. Geological Survey data release, https://doi.org/10.5066/P9UTMB64.

Summary

Parking lots may be a significant source of pollution. Oil, sediments, and heavy metals may accumulate on their surface, then be flushed into rivers, streams, and lakes via rainfall. At present no dataset provides a mapping or estimation of parking lot area or locations nationwide. This product consists of a time series of five national 60-meter raster datasets which estimate the proportion of each pixel represented by parking lots, based on land-use coefficients. The rasters span the conterminous United States, for the years 1974, 1982, 1992, 2002, and 2012. The dataset was derived by calculating coefficients for 18 land-use types (Commercial, Industrial, Residential, Recreation, and so on) from the 2012 U.S. Geological Survey NAWQA [...]

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Attached Files

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metadata-parking-lots-estimate-april3-2019.xml
Original FGDC Metadata

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32.01 KB
training-and-overall-validation-mar27-2019.xlsx 161.9 KB
county-stats-1974-2012-mar27-2019.xlsx 268.28 KB
validation-indiv-cities-mar27-2019.xlsx 24.49 KB
parking_2012_tif.zip 352.78 MB
parking_2002_tif.zip 350.77 MB
parking_1992_tif.zip 349.61 MB
parking_1982_tif.zip 348.58 MB
parking_1974_tif.zip 345.5 MB

Purpose

The data are intended for GIS-based area calculations, for example calculating mean values for watersheds or regions. They are not well-suited for very small areas. Of the six validation areas tested, the accuracy for the smallest one, which had an area of 14 square kilometers (sq km), was poor. However, for the other five validation areas, which all had areas greater than 80 sq km, the accuracy was much better (see Attribute Accuracy Report section below).

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DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/P9UTMB64

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