Evidence map›Paper›PMID 39217157›Full record

ArticleScientific data2024

A synthetic vulnerable population dataset for fine scale geographical equity analysis and urban planning.

Jérémy Gelb, Philippe Apparicio, Hamzeh Alizadeh

Abstract read
In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Jérémy GelbAutorité Régionale de Transport Métropolitain - équipe Recherche et Valorisation des Données, 1001 Boulevard Robert-Bourassa, Montréal, QC, H3B 4L4, Canada. jgelb@artm.quebec.ORCID http://orcid.org/0000-0002-7114-2714
Philippe ApparicioUniversité de Sherbrooke - département de géomatique, 2500 Boulevard de l'université, Sherbrooke, QC, J1K 2R1, Canada.
Hamzeh AlizadehAutorité Régionale de Transport Métropolitain - équipe Recherche et Valorisation des Données, 1001 Boulevard Robert-Bourassa, Montréal, QC, H3B 4L4, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Assessing the social and economic vulnerability of populations within a given area is essential for conducting environmental equity evaluations and devising effective public policies to mitigate disparities. However, prevailing indicators used to measure socio-economic vulnerability exhibit several shortcomings. Primarily relying on factor analysis, these indicators face challenges in terms of comparability over time, lack of standardized scales, and inherent limitations associated with composite indicators. To address these shortcomings, we propose a novel approach that estimates the number of potentially vulnerable individuals by constructing a synthetic population. Our methodology, developed using open tools and datasets, offers a scalable solution applicable to the entire Canadian context. The resulting percentage of potentially vulnerable populations demonstrates strong correlations with traditional vulnerability indicators commonly used in Canada, while overcoming their inherent limitations. The generated dataset holds significant potential and serves as a valuable resource for both researchers and governmental organizations. It provides a robust foundation for conducting equity analyses, assessments, and policy evaluations, thereby facilitating evidence-based decision-making processes aimed at promoting social and economic inclusivity.

Identifiers

PMID39217157
PMCPMC11366005

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.