TY - JOUR
T1 - Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States
AU - H. Uhl, Johannes
AU - Leyk, Stefan
AU - M. McShane, Caitlin
AU - E. Braswell, Anna
AU - S. Connor, Dylan
AU - Balk, Deborah
N1 - Funding Information:
Financial support. This research has been supported by the National Science Foundation Directorate for Engineering (grant no. 1924670) and the National Institutes of Health – National Institute of Child Health and Human Development (grant nos. R21 HD098717 01A1 and P2CHD066613).
Funding Information:
Acknowledgements. Funding for this work was provided through the Humans, Disasters and the Built Environment programme of the National Science Foundation, award number 1924670 to the University of Colorado Boulder Institute of Behavioral Science, Earth Lab, and Cooperative Institute for Research in Environmental Sciences, and through the Grand Challenge Initiative and the Innovative Seed Grant programme at the University of Colorado Boulder as well as the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health under award numbers R21 HD098717 01A1 and P2CHD066613. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We gratefully acknowledge access to the Zillow Transaction and Assessment Dataset (ZTRAX) through a data use agreement between the University of Colorado Boulder and Zillow Group, Inc. More information on accessing the data can be found at http://www.zillow.com/ztrax (last access: 25 January 2021). The results and opinions are those of the authors and do not reflect the position of Zillow Group. Support by Zillow Group, Inc., is gratefully acknowledged. Moreover, we gratefully acknowledge support by Safe Software, Inc., for providing Feature Manipulation Engine (FME) licences.
Publisher Copyright:
© 2021 Copernicus GmbH. All rights reserved.
PY - 2021/1/27
Y1 - 2021/1/27
N2 - The collection, processing, and analysis of remote sensing data since the early 1970s has rapidly improved our understanding of change on the Earth s surface. While satellite-based Earth observation has proven to be of vast scientific value, these data are typically confined to recent decades of observation and often lack important thematic detail. Here, we advance in this arena by constructing new spatially explicit settlement data for the United States that extend back to the early 19th century and are consistently enumerated at fine spatial and temporal granularity (i.e. 250m spatial and 5-year temporal resolution). We create these time series using a large, novel building-stock database to extract and map retrospective, fine-grained spatial distributions of built-up properties in the conterminous United States from 1810 to 2015. From our data extraction, we analyse and publish a series of gridded geospatial datasets that enable novel retrospective historical analysis of the built environment at an unprecedented spatial and temporal resolution. The datasets are part of the Historical Settlement Data Compilation for the United States (https://dataverse.harvard.edu/dataverse/hisdacus, last access: 25 January 2021) and are available at https://doi.org/10.7910/DVN/YSWMDR (Uhl and Leyk, 2020a), https://doi.org/10.7910/DVN/SJ213V (Uhl and Leyk, 2020b), and https://doi.org/10.7910/DVN/J6CYUJ (Uhl and Leyk, 2020c). copy; Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License.
AB - The collection, processing, and analysis of remote sensing data since the early 1970s has rapidly improved our understanding of change on the Earth s surface. While satellite-based Earth observation has proven to be of vast scientific value, these data are typically confined to recent decades of observation and often lack important thematic detail. Here, we advance in this arena by constructing new spatially explicit settlement data for the United States that extend back to the early 19th century and are consistently enumerated at fine spatial and temporal granularity (i.e. 250m spatial and 5-year temporal resolution). We create these time series using a large, novel building-stock database to extract and map retrospective, fine-grained spatial distributions of built-up properties in the conterminous United States from 1810 to 2015. From our data extraction, we analyse and publish a series of gridded geospatial datasets that enable novel retrospective historical analysis of the built environment at an unprecedented spatial and temporal resolution. The datasets are part of the Historical Settlement Data Compilation for the United States (https://dataverse.harvard.edu/dataverse/hisdacus, last access: 25 January 2021) and are available at https://doi.org/10.7910/DVN/YSWMDR (Uhl and Leyk, 2020a), https://doi.org/10.7910/DVN/SJ213V (Uhl and Leyk, 2020b), and https://doi.org/10.7910/DVN/J6CYUJ (Uhl and Leyk, 2020c). copy; Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License.
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UR - http://www.scopus.com/inward/citedby.url?scp=85100267351&partnerID=8YFLogxK
U2 - 10.5194/essd-13-119-2021
DO - 10.5194/essd-13-119-2021
M3 - Article
AN - SCOPUS:85100267351
VL - 13
SP - 119
EP - 153
JO - Earth System Science Data
JF - Earth System Science Data
SN - 1866-3508
IS - 1
ER -