Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 292

The DOI matches its Crossref record; 134 of the 134 references it cites were found in a registry or an index.

A review of urban physical environment sensing using street view imagery in public health studies

Open access
Kang, Yuhao, Zhang, Fan, Gao, Song, Lin, Hui, Liu, Yu·2020·Annals of GIS

Abstract

Urban physical environments are the physical settings and built environments in neighbourhoods and cities which provide places for human activities. Evidence suggests that there are substantial associations between urban physical environments and various health outcomes, e.g. people’s physical activities might be influenced by surrounding physical environments, thereby affecting their health behaviours; more exposure to urban physical environments may benefit human mental health. Street view imagery enables us to capture the landscape at eye-level, making it a promising data source for observing and analysing the realistic dynamics of urban physical environments. Compared with traditional in-person assessments and field observations, street view imagery-based data collection is relatively time-effective and cost-effective. Researchers from epidemiology, psychology, and geography have used street view imagery to quantify the built environment and understand its impacts on public health. To summarize current research trends, this paper systematically reviews the use of street view images for sensing urban environments in public health studies. Specifically, we describe the characteristics of street view imagery and review the methodology for image processing and semantic understanding. We then summarize the challenges that remain for quantifying urban environments in terms of data and methodology. Several future research directions that would benefit public health research and practices are recommended in urban environment research.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/19475683.2020.1791954

Cites

134 of 134 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 134 references

Cited by

See all 292

Metadata

Authors
Kang, Y., Zhang, F., Gao, S., Lin, H., & Liu, Y.
Venue
Annals of GIS
Published
2020-07-02
Volume
26
Issue
3
Pages
261-275
ISSN
  • 1082-4006 (ISSN-L)
  • 1947-5683
  • 1947-5691
OpenAlex
W3046751713
Cited by
292
References
134
FWCI
10.53
Type
journal article
Language
en
Open access
gold

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A review of urban physical environment sensing using street view imagery in public health studies. https://app.cochat.ai/papers/10.1080/19475683.2020.1791954
The paper
Kang, Y., Zhang, F., Gao, S., Lin, H., & Liu, Y. (2020). A review of urban physical environment sensing using street view imagery in public health studies. Annals of GIS. https://doi.org/10.1080/19475683.2020.1791954

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.