Evidence map›Paper›PMID 39962223›Full record

ReviewNature human behaviour2025

Using human mobility data to quantify experienced urban inequalities.

Fengli Xu, Qi Wang, Esteban Moro, Lin Chen, Arianna Salazar Miranda, Marta C González, Michele Tizzoni, Chaoming Song, Carlo Ratti, Luis Bettencourt and 2 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature human behaviour, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

12 authors.

Fengli XuDepartment of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, P. R. China. fenglixu@tsinghua.edu.cn.ORCID 0000-0002-5720-4026
Qi WangDepartment of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA.ORCID 0000-0001-6804-1173
Esteban MoroMedia Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0003-2894-1024
Lin ChenDepartment of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, P. R. China.ORCID 0000-0002-2605-749X
Arianna Salazar MirandaSenseable City Lab, Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0003-4093-9250
Marta C GonzálezDepartment of Civil and Environmental Engineering, University of California, Berkeley, CA, USA.ORCID 0000-0002-8482-0318
Michele TizzoniDepartment of Sociology and Social Research, University of Trento, Trento, Italy.ORCID 0000-0001-7246-2341
Chaoming SongDepartment of Physics, University of Miami, Coral Gables, FL, USA.
Carlo RattiSenseable City Lab, Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA.
Luis BettencourtMansueto Institute for Urban Innovation, University of Chicago, Chicago, IL, USA.ORCID 0000-0001-6176-5160
Yong LiDepartment of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, P. R. China. liyong07@tsinghua.edu.cn.ORCID 0000-0001-5617-1659
James EvansSanta Fe Institute, Santa Fe, NM, USA. jevans@uchicago.edu.ORCID 0000-0001-9838-0707

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The lived experience of urban life is shaped by personal mobility through dynamic relationships and resources, marked not only by access and opportunity, but also inequality and segregation. The recent availability of fine-grained mobility data and context attributes ranging from venue type to demographic mixture offer researchers a deeper understanding of experienced inequalities at scale, and pose many new questions. Here we review emerging uses of urban mobility behaviour data, and propose an analytic framework to represent mobility patterns as a temporal bipartite network between people and places. As this network reconfigures over time, analysts can track experienced inequality along three critical dimensions: social mixing with others from specific demographic backgrounds, access to different types of facilities, and spontaneous adaptation to unexpected events, such as epidemics, conflicts or disasters. This framework traces the dynamic, lived experiences of urban inequality and complements prior work on static inequalities experience at home and work.

Indexed as

Population DynamicsSocioeconomic FactorsUrban PopulationHumans

Identifiers

What OpenQuestion holds

Textmetadata
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.