Evidence map›Paper›PMID 38715320›Full record

ArticleJournal of the Royal Society, Interface2024

Mobility census for monitoring rapid urban development.

Gezhi Xiu, Jianying Wang, Thilo Gross, Mei-Po Kwan, Xia Peng, Yu Liu

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Mobility census for monitoring rapid urban development.Journal of the Royal Society, Interface · 2024
    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

6 authors.

Gezhi XiuInstitute of Remote Sensing and GIS, Peking University, Beijing, People's Republic of China.ORCID 0000-0003-0475-6617
Jianying WangInstitute of Space and Earth Information Science, The Chinese University of Hong Kong (CUHK), Hong Kong, People's Republic of China.
Thilo GrossHelmholtz Institute for Functional Marine Biodiversity (HIFMB), Oldenburg, Germany.ORCID 0000-0002-1356-6690
Mei-Po KwanInstitute of Space and Earth Information Science, The Chinese University of Hong Kong (CUHK), Hong Kong, People's Republic of China.
Xia PengTourism College, Beijing Union University, Beijing, People's Republic of China.
Yu LiuInstitute of Remote Sensing and GIS, Peking University, Beijing, People's Republic of China.ORCID 0000-0002-0016-2902

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monitoring urban structure and development requires high-quality data at high spatio-temporal resolution. While traditional censuses have provided foundational insights into demographic and socio-economic aspects of urban life, their pace may not always align with the pace of urban development. To complement these traditional methods, we explore the potential of analysing alternative big-data sources, such as human mobility data. However, these often noisy and unstructured big data pose new challenges. Here, we propose a method to extract meaningful explanatory variables and classifications from such data. Using movement data from Beijing, which are produced as a by-product of mobile communication, we show that meaningful features can be extracted, revealing, for example, the emergence and absorption of subcentres. This method allows the analysis of urban dynamics at a high-spatial resolution (here 500 m) and near real-time frequency, and high computational efficiency, which is especially suitable for tracing event-driven mobility changes and their impact on urban structures.

Indexed as

CensusesBeijingHumansPopulation DynamicsUrban PopulationUrban Renewalcitieshuman mobilitymanifold learning

Identifiers

PMID38715320
PMCPMC11077011

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.