Articles | Volume 13, issue 1
https://doi.org/10.5194/essd-13-119-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-119-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
27 Jan 2021
Data description paper |
| 27 Jan 2021
| 27 Jan 2021
Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States
Johannes H. Uhl
CORRESPONDING AUTHOR
Department of Geography, University of Colorado Boulder, Boulder, CO 80309, USA
Institute of Behavioral Science, University of Colorado Boulder, Boulder, CO 80309, USA
Stefan Leyk
Department of Geography, University of Colorado Boulder, Boulder, CO 80309, USA
Institute of Behavioral Science, University of Colorado Boulder, Boulder, CO 80309, USA
Caitlin M. McShane
Department of Geography, University of Colorado Boulder, Boulder, CO 80309, USA
Anna E. Braswell
Earth Lab, Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, CO 80303, USA
Dylan S. Connor
School of Geographical Sciences & Urban Planning, Arizona State University, Tempe, AZ 85281, USA
Deborah Balk
CUNY Institute for Demographic Research and Marxe School of Public and International Affairs, Baruch College, City University of New York, New York City, NY 10017, USA
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Cited articles
Balch, J. K., Iglesias, V., Braswell, A. E., Rossi, M. W., Joseph, M. B., Mahood, A. L., Shrum, T. R., White, C. T., Scholl, V. M., McGuire, B., and Karban, C.: Social-Environmental Extremes: Rethinking Extraordinary Events as
Outcomes of Interacting Biophysical and Social Systems, Earth's Future, 8,
e2019EF001319, https://doi.org/10.1029/2019EF001319, 2020. a
Baldauf, M., Garlappi, L., and Yannelis, C.: Does climate change affect real
estate prices? Only if you believe in it, The Review of Financial Studies,
33, 1256–1295, 2020. a
Balk, D. and Yetman, G.: The global distribution of population: evaluating the
gains in resolution refinement, Center for International Earth Science
Information Network (CIESIN), Columbia University, New York, 2004. a
Bechard, A.: Gone with the Wind: Declines in Property Values as Harmful Algal
Blooms Are Blown Towards the Shore, J. Real Estate Financ., 1–16, https://doi.org/10.1007/s11146-020-09749-6, 2020. a
Bernstein, A., Gustafson, M. T., and Lewis, R.: Disaster on the horizon: The
price effect of sea level rise, J. Financ. Econ., 134,
253–272, 2019. a
Boeing, G.: Off the Grid… and Back Again? The Recent Evolution of American
Street Network Planning and Design, J. Am. Plann.
Assoc., 87,
123–137,
https://doi.org/10.1080/01944363.2020.1819382, 2020. a
Boslett, A. and Hill, E.: Shale gas transmission and housing prices, Resour. Energy Econ., 57, 36–50, 2019. a
Buchanan, M. K., Kulp, S., Cushing, L., Morello-Frosch, R., Nedwick, T., and
Strauss, B.: Sea level rise and coastal flooding threaten affordable housing,
Environ. Res. Lett., 15, 124020, https://doi.org/10.1088/1748-9326/abb266, 2020. a
Butler, M. A.: Rural-urban continuum codes for metro and nonmetro counties,
9028, US Department of Agriculture, Economic Research Service, Agriculture
and …, Washington D.C., USA, 1990. a
CIESIN: Global Rural-Urban Mapping Project.(GRUMP): Urban/rural population
grids., 2004. a
City of Los Angeles Controller's Office: Affordable Housing: Incentives
and Oversight, available at:
https://lacontroller.org/data-stories-and-maps/affordable-housing-incentives-and-oversight/ (last access: 25 January 2021),
2017. a
Clarke, W. and Freedman, M.: The rise and effects of homeowners associations,
J. Urban Econ., 112, 1–15, 2019. a
Connor, D. S., Gutmann, M. P., Cunningham, A. R., Clement, K. K., and Leyk, S.:
How Entrenched Is the Spatial Structure of Inequality in Cities? Evidence
from the Integration of Census and Housing Data for Denver from 1940 to 2016,
Ann. Am. Assoc. Geogr., 110, 1022–1039, 2020. a
D'Lima, W. and Schultz, P.: Residential Real Estate Investments and Investor
Characteristics, J. Real Estate Financ., 1–40, https://doi.org/10.1007/s11146-020-09771-8,
2020. a
Dobson, J. E., Bright, E. A., Coleman, P. R., Durfee, R. C., and Worley, B. A.:
LandScan: a global population database for estimating populations at risk,
Photogramm. Eng. Rem. S., 66, 849–857, 2000. a
ESRI: ArcPy, available at: https://pro.arcgis.com/en/pro-app/arcpy/ (last access: 25 January 2021), 2019. a
Financial Times: Mapping how railroads built America, available at:
https://www.ft.com/video/28ef4d98-ac46-4b68-84da-3ef5cc70e730, last access: 15 July 2020. a
Freire, S., Doxsey-Whitfield, E., MacManus, K., Mills, J., and Pesaresi, M.: Development of new open and free multi-temporal global population grids at 250 m resolution,
Proceedings of the 19th AGILE International Conference on Geographic Information Science, AGILE 2016, Helsinki, Finland, 14–17 June 2016. a
GDAL/OGR contributors: GDAL/OGR Geospatial Data Abstraction software
Library, Open Source Geospatial Foundation, available at: https://gdal.org (last access: 25 January 2021),
2020. a
Gindelsky, M., Moulton, J., and Wentland, S. A.: Valuing housing services in
the era of big data: A user cost approach leveraging Zillow microdata, in:
Big Data for 21st Century Economic Statistics, University of Chicago Press, Chicago, USA,
2019. a
Hedefalk, F., Svensson, P., and Harrie, L.: Spatiotemporal historical datasets
at micro-level for geocoded individuals in five Swedish parishes, 1813–1914,
Sci. Data, 4, 1–13, 2017. a
Homer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., Larson, C., Herold, N., McKerrow, A., VanDriel, J. N., and Wickham, J.: Completion of the 2001
national land cover database for the counterminous United States,
Photogramm. Eng. Rem. S., 73, 337–341, 2007. a
Huber, P. J.: Robust regression: asymptotics, conjectures and Monte
Carlo, Ann. Stat., 1, 799–821, 1973. a
Iglesias, V. and Travis, W.: Escalating risk and pronounced inter-regional differences in exposure to natural hazards due to development patterns in the United States, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21287, https://doi.org/10.5194/egusphere-egu2020-21287, 2020 a
Jordahl, K., den Bossche, J. V., Wasserman, J., McBride, J., Fleischmann, M.,
Gerard, J., Tratner, J., Perry, M., Farmer, C., Hjelle, G. A., Gillies, S.,
Cochran, M., Bartos, M., Culbertson, L., Eubank, N., Bilogur, A., and
Albert, M.: geopandas/geopandas: v0.7.0, Zenodo, https://doi.org/10.5281/zenodo.3669853, 2020. a
Kaim, D., Kozak, J., Kolecka, N., Ziółkowska, E., Ostafin, K.,
Ostapowicz, K., Gimmi, U., Munteanu, C., and Radeloff, V. C.: Broad scale
forest cover reconstruction from historical topographic maps, Appl.
Geogr., 67, 39–48, 2016. a
Kim, M., Norwood, B., O'Connor, S., and Shen, L.: I am Jane. Do I pay more
in the housing market?, Econ. Bull., 39, 1612–1620, 2019. a
Kitchin, R.: The data revolution: Big data, open data, data infrastructures and
their consequences, Sage, London, UK, 2014. a
Klein Goldewijk, K., Beusen, A., Van Drecht, G., and De Vos, M.: The HYDE 3.1
spatially explicit database of human-induced global land-use change over the
past 12,000 years, Global Ecol. Biogeogr., 20, 73–86, 2011. a
Kugler, T. A., Grace, K., Wrathall, D. J., de Sherbinin, A., Van Riper, D., Aubrecht, C., Comer, D., Adamo, S. B., Cervone, G., Engstrom, R., and Hultquist, C.:
People and Pixels 20 years later: the current data landscape and research
trends blending population and environmental data, Popul.
Environ., 41, 209–234, 2019. a
Leyk, S. and Uhl, J. H.: Historical settlement composite layer for the U.S.
1810–2015, https://doi.org/10.7910/DVN/PKJ90M, 2018c. a, b, c, d
Leyk, S. and Uhl, J. H.: County-level uncertainty statistics accompanying the
historical settlement layers for the U.S. 1810–2015,
https://doi.org/10.7910/DVN/CXD9BW, 2018d. a, b, c, d
Leyk, S., Gaughan, A. E., Adamo, S. B., de Sherbinin, A., Balk, D., Freire, S., Rose, A., Stevens, F. R., Blankespoor, B., Frye, C., Comenetz, J., Sorichetta, A., MacManus, K., Pistolesi, L., Levy, M., Tatem, A. J., and Pesaresi, M.: The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use, Earth Syst. Sci. Data, 11, 1385–1409, https://doi.org/10.5194/essd-11-1385-2019, 2019. a, b
Leyk, S., Uhl, J. H., Connor, D. S., Braswell, A. E., Mietkiewicz, N., Balch,
J. K., and Gutmann, M.: Two centuries of settlement and urban development in
the United States, Sci. Adv., 6, eaba2937, https://doi.org/10.1126/sciadv.aba2937, 2020. a, b, c, d
Lieskovský, J., Kaim, D., Balázs, P., Boltižiar, M., Chmiel, M., Grabska, E., Király, G., Konkoly-Gyuró, É., Kozak, J., Antalová, K., and Kuchma, T.: Historical land use dataset of the Carpathian
region (1819–1980), J. Maps, 14, 644–651, 2018. a
Marconcini, M., Gorelick, N., Metz-Marconcini, A., and Esch, T.: Accurately
monitoring urbanization at global scale – the world settlement footprint, IOP
Conference Series: Earth and Environmental Science, 509, 012036, https://doi.org/10.1088/1755-1315/509/1/012036, 2020. a
McShane, C., Uhl, J. H., and Leyk, S.: Gridded annual land use data for the
conterminous United States 1945–2016, in preparation, 2021. a
Mietkiewicz, N., Balch, J. K., Schoennagel, T., Leyk, S., St Denis, L. A., and
Bradley, B. A.: In the Line of Fire: Consequences of Human-Ignited Wildfires
to Homes in the US (1992–2015), Fire, 3, 1–20, 2020. a
Millhouser, P.: Evaluating Landscape Connectivity and Habitat Fragmentation
Effects on Elk in the Roaring Fork and Eagle Valleys, Master's Thesis,
Pennsylvania State University, University Park, Pennsylvania, 2019. a
NYC Housing Authority: Map of NYCHA Developments, available at:
https://data.cityofnewyork.us/Housing-Development/Map-of-NYCHA-Developments/i9rv-hdr5 (last access: 25 January 2021),
2020. a
Oliphant, T. E.: A guide to NumPy, vol. 1, Trelgol Publishing, USA, 2006. a
Onda, K., Branham, J., BenDor, T. K., Kaza, N., and Salvesen, D.: Does removal
of federal subsidies discourage urban development? An evaluation of the US
Coastal Barrier Resources Act, PloS one, 15, e0233888, https://doi.org/10.1371/journal.pone.0233888, 2020. a
OpenStreetMap contributors: Planet dump retrieved from
https://planet.osm.org, available at: https://www.openstreetmap.org (last access: 1 December 2020), 2020. a
Ostafin, K., Kaim, D., Siwek, T., and Miklar, A.: Historical dataset of
administrative units with social-economic attributes for Austrian Silesia
1837–1910, Sci. Data, 7, 1–14, 2020. a
Peng, L. and Zhang, L.: House Prices and Systematic Risk: Evidence from
Microdata, Real Estate Econom., 1–24, https://doi.org/10.1111/1540-6229.12277, 2019. a
Pesaresi, M., Huadong, G., Blaes, X., Ehrlich, D., Ferri, S., Gueguen, L., Halkia, M., Kauffmann, M., Kemper, T., Lu, L., and Marin-Herrera, M. A.: A global human
settlement layer from optical HR/VHR RS data: concept and first results, IEEE J. Sel. Top. Appl.,
6, 2102–2131, 2013. a
Reba, M., Reitsma, F., and Seto, K. C.: Spatializing 6,000 years of global
urbanization from 3700 BC to AD 2000, Sci. Data, 3, 1–16, 2016. a
Safe Software Inc.: Feature Manipulation Engine, available at:
https://www.safe.com/fme/fme-desktop/, last access: 1 July 2020. a
Shen, X., Liu, P., Qiu, Y. L., Patwardhan, A., and Vaishnav, P.: Estimation of
change in house sales prices in the United States after heat pump adoption,
Nature Energy, 6, 30–37, 2021. a
Smith, J. H., Wickham, J. D., Stehman, S. V., and Yang, L.: Impacts of patch
size and land-cover heterogeneity on thematic image classification accuracy,
Photogramm. Eng. Rem. S., 68, 65–70, 2002. a
Sohl, T., Reker, R., Bouchard, M., Sayler, K., Dornbierer, J., Wika, S.,
Quenzer, R., and Friesz, A.: Modeled historical land use and land cover for
the conterminous United States, J. Land Use Sci., 11, 476–499,
2016. a
SQLite: SQLite SQL database engine, available at: https://www.sqlite.org, last access: 1 July 2020. a
Stern, M. and Lester, T. W.: Does Local Ownership of Vacant Land Reduce Crime?
An Assessment of Chicago's Large Lots Program, J. Am.
Plan. Assoc., 87, 73–84, https://doi.org/10.1080/01944363.2020.1792334, 2020. a
Tarafdar, S., Rimjha, M., Hinze, N., Hotle, S., and Trani, A. A.: Urban air
Mobility Regional Landing Site Feasibility and Fare Model Analysis in the
Greater Northern California Region, in: 2019 Integrated Communications,
Navigation and Surveillance Conference (ICNS), pp. 1–11, IEEE, 9–11 April 2019,
Herndon, VA, USA, 2019. a
Tatem, A. J.: WorldPop, open data for spatial demography, Sci. Data, 4,
1–4, 2017. a
Uhl, J. H. and Leyk, S.: Uncertainty surfaces accompanying the BUPR, BUPL, and
BUA gridded surface series, https://doi.org/10.7910/DVN/T8H5KF, 2020d. a, b, c
Uhl, J. H. and Leyk, S.: Towards a novel backdating strategy for creating
built-up land time series data using contemporary spatial constraints, Remote
Sens. Environ., 238, 111197, https://doi.org/10.1016/j.rse.2019.05.016, 2020e. a
Uhl, J. H., Leyk, S., Florczyk, A. J., Pesaresi, M., and Balk, D.: Assessing
spatiotemporal agreement between multi-temporal built-up land layers and
integrated cadastral and building data, in: International Conference on
GIScience Short Paper Proceedings, vol. 1, September 2016,
Montreal, Canada, 2016. a, b
Uhl, J. H., Zoraghein, H., Leyk, S., Balk, D., Corbane, C., Syrris, V., and
Florczyk, A. J.: Exposing the urban continuum: Implications and
cross-comparison from an interdisciplinary perspective, International Journal
of Digital Earth, 13,
22–44,
2020. a
Uhl, J. H., Connor, D. S., Leyk, S., and Braswell, A. E.: A century of decoupling size and structure of urban spaces in the United States, Commun. Earth Environ.,
1–14,
https://doi.org/10.1038/s43247-020-00082-7, 2021. a, b, c
US Census Bureau: Table 4: Population from 1790 to 1990, available at:
https://www.census.gov/population/censusdata/table-4.pdf (last access: 15 July 2020), 1993. a
US Census Bureau: American Community Survey: 2011–2015,
https://www.census.gov/newsroom/press-releases/2016/cb16-210.html (last access: 25 January 2021),
2016. a
US Census Bureau, Department of Commerce: TIGER/Line Shapefile, 2016, Series
Information for the Current Place State-based Shapefile, available at:
https://catalog.data.gov/dataset/tiger-line-shapefile-2016-series-information-for-the-current-place-state-based-shapefile (last access: 1 December 2020),
2016. a
USDA Economic Research Service: Rural-Urban Continuum Codes 2013,
Documentation, available at:
https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/documentation/ (last access: 1 December 2020),
2019. a
U.S. Department of Housing and Urban Development: Assisted housing: national
and local, county-level summaries 2019, available at:
https://www.huduser.gov/portal/datasets/assthsg.html (last access: 1 December 2020), 2019. a
USGS National Geospatial Technical Operations Center: USGS National
Structures Dataset – USGS National Map Downloadable Data Collection, available at:
https://catalog.data.gov/dataset/usgs-national-structures-dataset-nsd-downloadable-data-collection-national-geospatial-data-ass (last access: 1 December 2020),
2016. a
Wentland, S. A., Ancona, Z. H., Bagstad, K. J., Boyd, J., Hass, J. L.,
Gindelsky, M., and Moulton, J. G.: Accounting for land in the United States:
Integrating physical land cover, land use, and monetary valuation, Ecosystem
Services, 46, 101178, https://doi.org/10.1016/j.ecoser.2020.101178, 2020.
a
Wickham, J. D., Stehman, S. V., Gass, L., Dewitz, J., Fry, J. A., and Wade,
T. G.: Accuracy assessment of NLCD 2006 land cover and impervious surface,
Remote Sens. Environ., 130, 294–304, 2013. a
Zoraghein, H. and Leyk, S.: Data-enriched interpolation for temporally
consistent population compositions, GIScience Remote Sens., 56,
430–461, 2019. a
Short summary
Fine-grained geospatial data on the spatial distribution of human settlements are scarce prior to the era of remote-sensing-based Earth observation. In this paper, we present datasets derived from a large, novel building stock database, enabling the spatially explicit analysis of 200 years of land development in the United States at an unprecedented spatial and temporal resolution. These datasets greatly facilitate long-term studies of socio-environmental systems in the conterminous USA.
Fine-grained geospatial data on the spatial distribution of human settlements are scarce prior...
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