Evidence map›Paper›PMID 36968094›Full record

ArticleComputers, environment and urban systems2023

How regularly do people visit service places?

Shiran Zhong, Ling Bian

Abstract read
In one paragraph

Article in Computers, environment and urban systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Who Gets the Flu? Individualized Validation of Influenza-like Illness in Urban Spaces.International journal of environmental research and public health · 2023
    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

2 authors.

Shiran ZhongDepartment of Geography, University at Buffalo, the State University of New York, 105 Wilkeson Quad, Buffalo, NY 14261, USA.
Ling BianDepartment of Geography, University at Buffalo, the State University of New York, 105 Wilkeson Quad, Buffalo, NY 14261, USA.

Funding

A three-population three-scale social network model to assess disease dispersionR01GM108731 · NIGMS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI BIAN, LING, CHEN, CYNTHIA · 2015 to 2019
$2.7M
NIGMS NIH HHS R01 GM108731
6 · The paper itself

Abstract

It is often believed that regularities are embedded in mobile behaviors. Highly regular mobile behaviors, such as daily commutes between home and workplace, have been actively investigated in the context of health risks. Less regular mobile behaviors, such as visits to service places (e.g., supermarkets and healthcare facilities), have not received much attention. This study explores the regularity in service place visits using a deep learning method and the effect of place type on the stability of recurring visits using an entropy assessment. Results reveal both periodic and bursty visit behaviors to service places. The periodic visits are prominent on the weekly and bi-weekly scales, and the bursty visits dominate the multi-day scales. Service place type indeed affects the stability of recurring visits, and certain place types have the strongest effect. The research findings substantially expand the knowledge of mobile behaviors and are valuable in informing both visitor-based and place-based health risks.

Indexed as

Human mobilityService place typesService place visits behaviors

Identifiers

PMID36968094
PMCPMC10034970

What OpenQuestion holds

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Registered trials

None linked

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.