Cargando…

Combining expert and crowd-sourced training data to map urban form and functions for the continental US

Although continental urban areas are relatively small, they are major drivers of environmental change at local, regional and global scales. Moreover, they are especially vulnerable to these changes owing to the concentration of population and their exposure to a range of hydro-meteorological hazards...

Descripción completa

Detalles Bibliográficos
Autores principales: Demuzere, Matthias, Hankey, Steve, Mills, Gerald, Zhang, Wenwen, Lu, Tianjun, Bechtel, Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7421904/
https://www.ncbi.nlm.nih.gov/pubmed/32782324
http://dx.doi.org/10.1038/s41597-020-00605-z
_version_ 1783569995586863104
author Demuzere, Matthias
Hankey, Steve
Mills, Gerald
Zhang, Wenwen
Lu, Tianjun
Bechtel, Benjamin
author_facet Demuzere, Matthias
Hankey, Steve
Mills, Gerald
Zhang, Wenwen
Lu, Tianjun
Bechtel, Benjamin
author_sort Demuzere, Matthias
collection PubMed
description Although continental urban areas are relatively small, they are major drivers of environmental change at local, regional and global scales. Moreover, they are especially vulnerable to these changes owing to the concentration of population and their exposure to a range of hydro-meteorological hazards, emphasizing the need for spatially detailed information on urbanized landscapes. These data need to be consistent in content and scale and provide a holistic description of urban layouts to address different user needs. Here, we map the continental United States into Local Climate Zone (LCZ) types at a 100 m spatial resolution using expert and crowd-sourced information. There are 10 urban LCZ types, each associated with a set of relevant variables such that the map represents a valuable database of urban properties. These data are benchmarked against continental-wide existing and novel geographic databases on urban form. We anticipate the dataset provided here will be useful for researchers and practitioners to assess how the configuration, size, and shape of cities impact the important human and environmental outcomes.
format Online
Article
Text
id pubmed-7421904
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-74219042020-08-18 Combining expert and crowd-sourced training data to map urban form and functions for the continental US Demuzere, Matthias Hankey, Steve Mills, Gerald Zhang, Wenwen Lu, Tianjun Bechtel, Benjamin Sci Data Data Descriptor Although continental urban areas are relatively small, they are major drivers of environmental change at local, regional and global scales. Moreover, they are especially vulnerable to these changes owing to the concentration of population and their exposure to a range of hydro-meteorological hazards, emphasizing the need for spatially detailed information on urbanized landscapes. These data need to be consistent in content and scale and provide a holistic description of urban layouts to address different user needs. Here, we map the continental United States into Local Climate Zone (LCZ) types at a 100 m spatial resolution using expert and crowd-sourced information. There are 10 urban LCZ types, each associated with a set of relevant variables such that the map represents a valuable database of urban properties. These data are benchmarked against continental-wide existing and novel geographic databases on urban form. We anticipate the dataset provided here will be useful for researchers and practitioners to assess how the configuration, size, and shape of cities impact the important human and environmental outcomes. Nature Publishing Group UK 2020-08-11 /pmc/articles/PMC7421904/ /pubmed/32782324 http://dx.doi.org/10.1038/s41597-020-00605-z Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.
spellingShingle Data Descriptor
Demuzere, Matthias
Hankey, Steve
Mills, Gerald
Zhang, Wenwen
Lu, Tianjun
Bechtel, Benjamin
Combining expert and crowd-sourced training data to map urban form and functions for the continental US
title Combining expert and crowd-sourced training data to map urban form and functions for the continental US
title_full Combining expert and crowd-sourced training data to map urban form and functions for the continental US
title_fullStr Combining expert and crowd-sourced training data to map urban form and functions for the continental US
title_full_unstemmed Combining expert and crowd-sourced training data to map urban form and functions for the continental US
title_short Combining expert and crowd-sourced training data to map urban form and functions for the continental US
title_sort combining expert and crowd-sourced training data to map urban form and functions for the continental us
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7421904/
https://www.ncbi.nlm.nih.gov/pubmed/32782324
http://dx.doi.org/10.1038/s41597-020-00605-z
work_keys_str_mv AT demuzerematthias combiningexpertandcrowdsourcedtrainingdatatomapurbanformandfunctionsforthecontinentalus
AT hankeysteve combiningexpertandcrowdsourcedtrainingdatatomapurbanformandfunctionsforthecontinentalus
AT millsgerald combiningexpertandcrowdsourcedtrainingdatatomapurbanformandfunctionsforthecontinentalus
AT zhangwenwen combiningexpertandcrowdsourcedtrainingdatatomapurbanformandfunctionsforthecontinentalus
AT lutianjun combiningexpertandcrowdsourcedtrainingdatatomapurbanformandfunctionsforthecontinentalus
AT bechtelbenjamin combiningexpertandcrowdsourcedtrainingdatatomapurbanformandfunctionsforthecontinentalus