Evidence map›Paper›PMID 38351040›Full record

ArticleScientific data2024

Mapping urban form into local climate zones for the continental US from 1986-2020.

Meng Qi, Chunxue Xu, Wenwen Zhang, Matthias Demuzere, Perry Hystad, Tianjun Lu, Peter James, Benjamin Bechtel, Steve Hankey

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Meng QiSchool of Public and International Affairs, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA.ORCID http://orcid.org/0000-0003-0700-2773
Chunxue XuCollege of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, 97331, USA.
Wenwen ZhangEdward J. Bloustein School of Planning and Public Policy, Rutgers University, New Brunswick, New Jersey, 08901, USA.
Matthias DemuzereUrban Climatology Group, Department of Geography, Ruhr-University Bochum, Bochum, 44801, Germany.ORCID http://orcid.org/0000-0003-3237-4077
Perry HystadCollege of Health, Oregon State University, Corvallis, OR, 97331, USA.
Tianjun LuDepartment of Epidemiology and Environmental Health, University of Kentucky, Lexington, KY, 40536, USA.
Peter JamesDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, 02215, USA.
Benjamin BechtelUrban Climatology Group, Department of Geography, Ruhr-University Bochum, Bochum, 44801, Germany.ORCID http://orcid.org/0000-0001-8802-7934
Steve HankeySchool of Public and International Affairs, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA. hankey@vt.edu.

Funding

Built Environment Assessment through Computer visiON (BEACON): Applying Deep Learning to Street-Level and Satellite Images to Estimate Built Environment Effects on Cardiovascular HealthR01HL150119 · NHLBI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI JAMES, PETER · 2020 to 2024
$3.9M
NHLBI NIH HHS R01 HL150119
6 · The paper itself

Abstract

Urbanization has altered land surface properties driving changes in micro-climates. Urban form influences people's activities, environmental exposures, and health. Developing detailed and unified longitudinal measures of urban form is essential to quantify these relationships. Local Climate Zones [LCZ] are a culturally-neutral urban form classification scheme. To date, longitudinal LCZ maps at large scales (i.e., national, continental, or global) are not available. We developed an approach to map LCZs for the continental US from 1986 to 2020 at 100 m spatial resolution. We developed lightweight contextual random forest models using a hybrid model development pipeline that leveraged crowdsourced and expert labeling and cloud-enabled modeling - an approach that could be generalized to other countries and continents. Our model achieved good performance: 0.76 overall accuracy (0.55-0.96 class-wise F1 scores). To our knowledge, this is the first high-resolution, longitudinal LCZ map for the continental US. Our work may be useful for a variety of fields including earth system science, urban planning, and public health.

Identifiers

PMID38351040
PMCPMC10864375

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.