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Normalization methods for spatio‐temporal analysis of environmental performance: Revisiting the Min–Max method

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  • Matteo Mazziotta
  • Adriano Pareto

Abstract

Over the last few years, composite indices for ranking or assessing country performance in a wide variety of complex phenomena, such as environmental sustainability, have gained a lot of popularity. However, not all indices allow for detailed spatio‐temporal analyses. For instance, if individual indicators to be aggregated are normalized by the Min–Max method, they are converted to a common scale with a range of [0, 1] and it can be difficult to appreciate any absolute change in country performance at the extremes of the range (i.e., when a country scores 0 to 1). In this article, an alternative method for normalizing data in a three‐way array of the type units × variables × times, is considered. It normalizes the range of individual indicators, similarly to the Min–Max method, but uses a common reference that allows to “center” them, without forcing them into a closed range. An application to renewable energy consumption data is also shown.

Suggested Citation

  • Matteo Mazziotta & Adriano Pareto, 2022. "Normalization methods for spatio‐temporal analysis of environmental performance: Revisiting the Min–Max method," Environmetrics, John Wiley & Sons, Ltd., vol. 33(5), August.
    • Handle: RePEc:wly:envmet:v:33:y:2022:i:5:n:e2730
    DOI: 10.1002/env.2730
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    1. Matteo Mazziotta & Adriano Pareto, 2021. "Everything you always wanted to know about normalization (but were afraid to ask)," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 75(1), pages 41-52, January-M.
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    4. Enrico Casadio Tarabusi & Giulio Guarini, 2013. "An Unbalance Adjustment Method for Development Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 112(1), pages 19-45, May.
    5. Salvatore Greco & Alessio Ishizaka & Menelaos Tasiou & Gianpiero Torrisi, 2019. "On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 61-94, January.
    6. Pasquale De Muro & Matteo Mazziotta & Adriano Pareto, 2011. "Composite Indices of Development and Poverty: An Application to MDGs," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 104(1), pages 1-18, October.
    7. Michael Freudenberg, 2003. "Composite Indicators of Country Performance: A Critical Assessment," OECD Science, Technology and Industry Working Papers 2003/16, OECD Publishing.
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    2. Arthur C. Santos & Wesley A. Souza & Gustavo V. Barbara & Marcelo F. Castoldi & Alessandro Goedtel, 2023. "Diagnostics of Early Faults in Wind Generator Bearings Using Hjorth Parameters," Sustainability, MDPI, vol. 15(20), pages 1-17, October.
    3. Salvatore Flavio Pileggi, 2023. "Walking Together Indicator (WTI): Understanding and Measuring World Inequality," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    4. Wenhui Zhang & Yajing Song & Ge Zhou & Ziwen Song & Cong Xi, 2023. "Multiobjective-Based Decision-Making for the Optimization of an Urban Passenger Traffic System Structure," Sustainability, MDPI, vol. 15(18), pages 1-20, September.

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