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A semi-supervised multi-criteria sorting approach to constructing social vulnerability composite indicators

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Abstract

IPVS (São Paulo Social Vulnerability index) was created by the State government of São Paulo, Brazil, with the identification and spatial location of the areas that contain the population segments most vulnerable to poverty. IPVS relies on a data-driven approach which is implemented by means of multivariate analysis techniques such as principal component analysis. A limitation of such a statistical approach is that it only considers information brought by data, as it does not take into consideration subjective information provided by decision makers. Motivated by this limitation, we propose an alternative approach based on multi-criteria sorting. For this purpose, we introduce a conceptual sorting framework based on the SMAA methodology and on the Choquet integral, which allows us to take into consideration interactions between criteria. The proposed sorting scheme classifies the municipality regions into groups characterized by reference values previously defined by the decision maker. As an important result, we show that our proposal provides more flexibility for vulnerability analysis in the sense that it allows decision makers to delve into different scenarios, opening the way for customized decision strategies.

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Notes

  1. https://www.ibge.gov.br/explica/pib.php.

  2. This approach can be seen as an extension of the weighted Euclidean distance when criteria interact.

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Acknowledgements

This work was supported by the São Paulo Research Foundation (FAPESP), Grant #2018/23447 and grant #2020/01089-9, and the Brazilian National Council for Scientific and Technological Development (CNPq). This project is also part of the Brazilian Institute of Data Science, Grant #2020/09838-0, São Paulo Research Foundation (FAPESP).

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  1. Sarah Ben Amor, Álvaro de Oliveira D’Antona, Eduardo José Marandola Júnior and Leonardo Tomazeli Duarte contributed equally to this work.

Authors and Affiliations

  1. School of Applied Sciences, UNICAMP, 1300 Pedro Zaccaria Street, Limeira, São Paulo, 13484-350, Brazil

    Renata Pelissari, Álvaro de Oliveira D’Antona, Eduardo José Marandola Júnior & Leonardo Tomazeli Duarte

  2. School of Electrical and Computer Engineering, UNICAMP, 400 Albert Einstein Avenue, Campinas, São Paulo, 13083-852, Brazil

    Renata Pelissari

  3. School of Engineering, Mackenzie Presbyterian University, 930 Consolação Street, São Paulo, São Paulo, 01302-907, Brazil

    Renata Pelissari

  4. Telfer School of Management, University of Ottawa, 55 Laurier Ave. E, Ottawa, ON, K1N 6N5, Canada

    Sarah Ben Amor

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  1. Renata Pelissari
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Pelissari, R., Amor, S.B., de Oliveira D’Antona, Á. et al. A semi-supervised multi-criteria sorting approach to constructing social vulnerability composite indicators. Ann Oper Res 337, 235–260 (2024). https://doi.org/10.1007/s10479-024-05900-1

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