Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States

Johannes H. Uhl, Stefan Leyk, Caitlin M. McShane, Anna E. Braswell, Dylan S. Connor, Deborah Balk

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)119-153
Number of pages35
JournalEarth System Science Data
Volume13
Issue number1
DOIs
StatePublished - Jan 27 2021

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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