4.7 Article Data Paper

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

期刊

EARTH SYSTEM SCIENCE DATA
卷 13, 期 1, 页码 119-153

出版社

COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/essd-13-119-2021

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资金

  1. National Science Foundation Directorate for Engineering [1924670]
  2. National Institutes of Health - National Institute of Child Health and Human Development [R21 HD098717 01A1, P2CHD066613]

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The construction of new spatially explicit settlement data for the United States dating back to the early 19th century provides researchers with a novel tool for historical analysis of the built environment. These datasets, available for download, offer unprecedented spatial and temporal resolution for studying changes in built properties over time.
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).

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