@article{leykHISDACUSHistoricalSettlement2018,
  title = {{{HISDAC}}-{{US}}, Historical Settlement Data Compilation for the Conterminous {{United States}} over 200 Years},
  author = {Leyk, Stefan and Uhl, Johannes H.},
  date = {2018-09},
  journaltitle = {Scientific Data},
  volume = {5},
  pages = {180175+},
  issn = {2052-4463},
  doi = {10.1038/sdata.2018.175},
  url = {https://doi.org/10.1038/sdata.2018.175},
  abstract = {Human settlement plays a key role in understanding social processes such as urbanization and interactions between human and environmental systems but not much is known about the landscape evolution before the era of operational remote sensing technology. In this study, housing and property databases are used to create new gridded settlement layers describing human settlement processes at fine spatial and temporal resolution in the conterminous United States between 1810 and 2015. The main products are a raster composite layer representing the year of first settlement, and a raster time series of built-up intensity representing the sum of building areas in a pixel. Several accompanying uncertainty surfaces are provided to ensure the user is informed about inherent spatial, temporal and thematic uncertainty in the data. A validation study using high quality reference data confirms high levels of accuracy of the resulting data products. These settlement data will be of great interest in disciplines in which the long-term evolution of human settlement represents crucial information to explore novel research questions.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14633453,~to-add-doi-URL,data-integration,historical-perspective,human-settlement,integrated-modelling,integration-techniques,open-data,time-series,uncertainty,united-states}
}

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In this study, housing and property databases are used to create new gridded settlement layers describing human settlement processes at fine spatial and temporal resolution in the conterminous United States between 1810 and 2015. The main products are a raster composite layer representing the year of first settlement, and a raster time series of built-up intensity representing the sum of building areas in a pixel. Several accompanying uncertainty surfaces are provided to ensure the user is informed about inherent spatial, temporal and thematic uncertainty in the data. A validation study using high quality reference data confirms high levels of accuracy of the resulting data products. These settlement data will be of great interest in disciplines in which the long-term evolution of human settlement represents crucial information to explore novel research questions.","keywords":"*imported-from-citeulike-INRMM,~INRMM-MiD:c-14633453,~to-add-doi-URL,data-integration,historical-perspective,human-settlement,integrated-modelling,integration-techniques,open-data,time-series,uncertainty,united-states","bibtex":"@article{leykHISDACUSHistoricalSettlement2018,\n title = {{{HISDAC}}-{{US}}, Historical Settlement Data Compilation for the Conterminous {{United States}} over 200 Years},\n author = {Leyk, Stefan and Uhl, Johannes H.},\n date = {2018-09},\n journaltitle = {Scientific Data},\n volume = {5},\n pages = {180175+},\n issn = {2052-4463},\n doi = {10.1038/sdata.2018.175},\n url = {https://doi.org/10.1038/sdata.2018.175},\n abstract = {Human settlement plays a key role in understanding social processes such as urbanization and interactions between human and environmental systems but not much is known about the landscape evolution before the era of operational remote sensing technology. In this study, housing and property databases are used to create new gridded settlement layers describing human settlement processes at fine spatial and temporal resolution in the conterminous United States between 1810 and 2015. The main products are a raster composite layer representing the year of first settlement, and a raster time series of built-up intensity representing the sum of building areas in a pixel. Several accompanying uncertainty surfaces are provided to ensure the user is informed about inherent spatial, temporal and thematic uncertainty in the data. A validation study using high quality reference data confirms high levels of accuracy of the resulting data products. 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