Evidence map›Paper›PMID 37303974›Full record

ReviewFrontiers in big data2023

Crime, inequality and public health: a survey of emerging trends in urban data science.

Massimiliano Luca, Gian Maria Campedelli, Simone Centellegher, Michele Tizzoni, Bruno Lepri

Abstract readReview
In one paragraph

Review in Frontiers in big data, 2023. 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. Review
  2. 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

5 authors.

Massimiliano LucaMobile and Social Computing Lab, Bruno Kessler Foundation, Trento, Italy.
Gian Maria CampedelliDepartment of Sociology and Social Research, University of Trento, Trento, Italy.
Simone CentellegherMobile and Social Computing Lab, Bruno Kessler Foundation, Trento, Italy.
Michele TizzoniDepartment of Sociology and Social Research, University of Trento, Trento, Italy.
Bruno LepriMobile and Social Computing Lab, Bruno Kessler Foundation, Trento, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale.

Indexed as

citiescrimedigital datapublic healthsegregation and inequalities

Identifiers

PMID37303974
PMCPMC10248183

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.