nach oben

Journal of Geographical Systems

Erschienen in:

29.12.2022 | Original Article

Effect of sub-indicator weighting schemes on the spatial dependence of multidimensional phenomena

verfasst von: Matheus Pereira Libório, João Francisco de Abreu, Petr Iakovlevitch Ekel, Alexei Manso Correa Machado

Erschienen in: Journal of Geographical Systems | Ausgabe 2/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The weighting of sub-indicators is widely debated in the composite indicator literature. However, these weighting schemes’ effects on the composite indicator’s spatial dependence property are still little known. This research reveals a direct relationship between the weighting scheme of sub-indicators and the spatial autocorrelation of the composite indicator. The Global Moran's Index (I) of composite indicators built using Data-driven (Moran’s I = 0.636) and Hybrid (Moran’s I = 0.597) weighting schemes is, on average, eleven percent higher than in the Equal-weights (Moran's I = 0.549) and Expert opinion (Moran's I = 0.560) weighting schemes. The average score of the composite indicator is higher when they are built by weighting schemes that better describe the spatial dependence. The spatial dependence of sub-indicators and composite indicators are not related. All fifteen sub-indicators show lower spatial autocorrelation than the composite indicators built by Expert opinion, Hybrid, and Data-driven weighting schemes. The spatial weighting matrix influences the spatial autocorrelation but does not change the robustness and quality parameters of the composite indicator. The research develops a Data-driven weighting scheme that allows individually or simultaneously considering the opinion of experts and parameters of quality and robustness of the composite indicator. It also offers the means to reduce judgment errors and evaluation biases in Expert opinion sub-indicator weighting schemes.

Vorheriger Artikel Induced earthquakes and house prices: the role of spatiotemporal and global effects

Nächster Artikel Geographically weighted regression with the integration of machine learning for spatial prediction

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

The most popular Expert opinion weighting methods are the Analytic hierarchy process, Conjoint analysis, Public opinion and Budget allocation process (Saisana & Tarantola 2002; Nardo et al. 2005; Otoiu et al. 2021).

Population Estimates carried out by the Brazilian Institute of Geography and Statistics in July 2021. Accessible at https://www.ibge.gov.br/estatisticas/sociais/populacao/9103-estimativas-de-populacao.

The nature of a sub-indicator is objective-quantitative when the variable is directly measurable, objective-qualitative when the variables are objectively verifiable by the presence or absence of something even if not directly measurable, and qualitative-subjective when the variables are obtained from Experts opinion (Libório et al. 2022a).

Abadie J (1969) Generalization of the Wolfe reduced gradient method to the case of nonlinear constraints. Optimization, 37–47

Adeleke R, Alabede O (2021) Understanding the patterns and correlates of financial inclusion in Nigeria. GeoJournal 87:1–18

Aljoufie M, Tiwari A (2020) Exploring housing and transportation affordability in Jeddah. Housing Policy Debate, 1–27

Anselin L (1996) The Moran scatterplot as an ESDA tool to assess local instability in spatial association. In: Fischer M, Scholten HJ, Unwin D (eds) Spatial analytical perspectives on GIS in environmental and socioeconomic sciences. Taylor and Francis, London, pp 111–125

Badea AC, Tarantola S, Bolado R (2011) Composite indicators for security of energy supply using ordered weighted averaging. Reliab Eng Syst Saf 96(6):651–662CrossRef

Becker W, Paruolo P, Saisana M, Saltelli A (2017) Weights and importance in composite indicators: mind the gap. Handbook of uncertainty quantification, pp 1187–1216

Bernardes P, Ekel PI, Rezende SF, Pereira Júnior JG, dos Santos AC, da Costa MA, Carvalhais RL, Libório MP (2021) Cost of doing business index in Latin America. Qual Quant 56:1–20

Carley S, Evans TP, Graff M, Konisky DM (2018) A framework for evaluating geographic disparities in energy transition vulnerability. Nat Energy 3(8):621–627CrossRef

Cartone A, Postiglione P (2021) Principal component analysis for geographical data: the role of spatial effects in the definition of composite indicators. Spat Econ Anal 16(2):126–147CrossRef

Chauhan N, Shukla R, Joshi PK (2020) Assessing impact of varied social and ecological conditions on inherent vulnerability of Himalayan agriculture communities. Hum Ecol Risk Assess Int J 26(10):2628–2645CrossRef

Cinelli M, Spada M, Kim W, Zhang Y, Burgherr P (2021) MCDA Index Tool: An Interactive Software To Develop Indices And Rankings. Environ Syst Decis 41(1):82–109CrossRef

Cutter SL, Finch C (2008) Temporal and spatial changes in social vulnerability to natural hazards. Proc Natl Acad Sci 105(7):2301–2306CrossRef

Davino C, Gherghi M, Sorana S, Vistocco D (2021) Measuring social vulnerability in an urban space through multivariate methods and models. Soc Indic Res 157(3):1179–1201CrossRef

Dialga I, Le Giang TH (2017) Highlighting methodological limitations in the steps of composite indicators construction. Soc Indic Res 131(2):441–465CrossRef

Ekel P, Pedrycz W, Pereira J Jr (2020) Multicriteria decision-making under conditions of uncertainty: a fuzzy set perspective. Wiley, Chinchester

Ekel P, Bernardes P, Vale GMV, Libório MP (2022) South American business environment cost index: reforms for Brazil. Int J Bus Environ 13(2):212–233CrossRef

El Gibari S, Gómez T, Ruiz F (2019) Building composite indicators using multicriteria methods: a review. J Bus Econ 89(1):1–24

Fusco E, Vidoli F, Sahoo BK (2018) Spatial heterogeneity in composite indicator: a methodological proposal. Omega 77:1–14CrossRef

Geary RC (1954) The contiguity ratio and statistical mapping. Inc Stat 5(3):115–146

Getis A (1999) Spatial statistics. Geogr Inf Syst 1:239–251

Goodchild MF (1991) Geographic information systems. Prog Hum Geogr 15(2):194–200CrossRef

Greco S, Ishizaka A, Tasiou M, Torrisi G (2019) On the methodological framework of composite indices: a review of the issues of weighting, aggregation, and robustness. Soc Indic Res 141(1):61–94CrossRef

Harris P, Clarke A, Juggins S, Brunsdon C, Charlton M (2015) Enhancements to a geographically weighted principal component analysis in the context of an application to an environmental data set. Geogr Anal 47(2):146–172CrossRef

IBGE (2010) Censo demográfico 2010. https://censo2010.ibge.gov.br/. Accessed 23 Dec 2022

Jha RK, Gundimeda H (2019) An integrated assessment of vulnerability to floods using composite index–a district level analysis for Bihar, India. Int J Disaster Risk Reduct 35:101074CrossRef

Kallio M, Guillaume JH, Kummu M, Virrantaus K (2018) Spatial variation in seasonal water poverty index for Laos: an application of geographically weighted principal component analysis. Soc Indic Res 140(3):1131–1157CrossRef

Katumba S, Cheruiyot K, Mushongera D (2019) Spatial change in the concentration of multidimensional poverty in Gauteng, South Africa: evidence from quality of life survey data. Soc Indic Res 145(1):95–115CrossRef

Kuc-Czarnecka M, Lo Piano S, Saltelli A (2020) Quantitative storytelling in the making of a composite indicator. Soc Indic Res 149(3):775–802CrossRef

Lasdon LS, Fox RL, Ratner MW (1974) Nonlinear optimization using the generalized reduced gradient method. Revue française d’automatique, informatique, recherche opérationnelle. Recherche Opérationnelle 8(V3):73–103CrossRef

Libório MP, Martinuci ODS, Ekel PI, Hadad RM, Lyrio RDM, Bernardes P (2021) Measuring inequality through a non-compensatory approach. GeoJournal 87:1–18

Libório MP, Laudares S, Abreu JFD, Ekel PY, Bernardes P (2020a) Property tax: dealing spatially with economic, social, and political challenges. Urbe. Revista Brasileira de Gestão Urbana, p 12

Libório MP, Martinuci ODS, Laudares S, Lyrio RDM, Machado AMC, Bernardes P, Ekel P (2020b) Measuring intra-urban inequality with structural equation modeling: a theory-grounded indicator. Sustainability 12(20):8610CrossRef

Libório M, Abreu JF, Martinuci ODS, Ekel PI, Lyrio RDM, Camacho VAL, Melazzo ES (2022a) Uncertainty analysis applied to the representation of multidimensional social phenomena. Papers in Applied Geography, 1–24

Libório MP, Ekel PY, Martinuci ODS, Figueiredo LR, Hadad RM, Lyrio RDM, Bernardes P (2022b) Fuzzy set based intra-urban inequality indicator. Qual Quant 56(2):667–687CrossRef

Libório MP, Martinuci ODS, Machado AMC, Ekel PI, Abreu JFD, Laudares S (2022c) Representing multidimensional phenomena of geographic interest: benefit of the doubt or principal component analysis?. The Professional Geographer, pp 1–14

Lindén D, Cinelli M, Spada M, Becker W, Gasser P, Burgherr P (2021) A framework based on statistical analysis and stakeholders’ preferences to inform weighting in composite indicators. Environ Model Softw 145:105208CrossRef

Maricic M, Egea JA, Jeremic V (2019) A hybrid enhanced Scatter Search—Composite I-Distance Indicator (eSS-CIDI) optimization approach for determining weights within composite indicators. Soc Indic Res 144(2):497–537CrossRef

Marzi S, Mysiak J, Essenfelder AH, Amadio M, Giove S, Fekete A (2019) Constructing a comprehensive disaster resilience index: the case of Italy. PLoS ONE 14(9):e0221585CrossRef

Mavhura E, Manyangadze T, Aryal KR (2021) A composite inherent resilience index for Zimbabwe: an adaptation of the disaster resilience of place model. Int J Disaster Risk Reduct 57:102152CrossRef

Mazziotta M, Pareto A (2017) Synthesis of indicators: the composite indicators approach. In: Complexity in society: from indicators construction to their synthesis. Springer, Cham, pp 159–191

Mazziotta M, Pareto A (2018) Measuring well-being over time: the adjusted Mazziotta-Pareto index versus other non-compensatory indices. Soc Indic Res 136(3):967–976CrossRef

Moran PA (1950) Notes on continuous stochastic phenomena. Biometrika 37(1/2):17–23CrossRef

Musa HD, Yacob MR, Abdullah AM (2019) Delphi exploration of subjective well-being indicators for strategic urban planning towards sustainable development in Malaysia. J Urban Manag 8(1):28–41CrossRef

Nardo M, Saisana M, Saltelli A, Tarantola S (2005) Tools for composite indicators building. Eur Com Ispra 15(1):19–20

Otoiu A, Pareto A, Grimaccia E, Mazziotta M, Terzi S (2021) Open issues in composite indicators. A starting point and a reference on some state-of-the-art issues, vol 3. Roma TrE-Press, Rome

Pedrycz W, Ekel P, Parreiras R (2011) Fuzzy multicriteria decision-making: models, methods and applications. Wiley, Chichester

Powell SG, Batt RJ (2008) Modeling for insight. Wiley, HobokenCrossRef

Rufat S, Tate E, Emrich CT, Antolini F (2019) How valid are social vulnerability models? Ann Am Assoc Geogr 109(4):1131–1153

Saaty TL (1988) What is the analytic hierarchy process?. In: Mathematical models for decision support. Springer, Berlin, Heidelberg, pp 109–121

Saisana M, Tarantola S (2002) State-of-the-art report on current methodologies and practices for composite indicator development, vol 214. Ispra: European Commission, Joint Research Centre, Institute for the Protection and the Security of the Citizen, Technological and Economic Risk Management Unit

Saisana M, Saltelli A, Tarantola S (2005) Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators. J R Stat Soc A Stat Soc 168(2):307–323CrossRef

Saltelli A (2007) Composite indicators between analysis and advocacy. Soc Indic Res 81(1):65–77CrossRef

Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ Geogr 46(sup1):234–240CrossRef

Van Laarhoven PJ, Pedrycz W (1983) A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst 11(1–3):229–241CrossRef

Titel: Effect of sub-indicator weighting schemes on the spatial dependence of multidimensional phenomena
verfasst von: Matheus Pereira Libório
João Francisco de Abreu
Petr Iakovlevitch Ekel
Alexei Manso Correa Machado
Publikationsdatum: 29.12.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Geographical Systems / Ausgabe 2/2023
Print ISSN: 1435-5930
Elektronische ISSN: 1435-5949
DOI: https://doi.org/10.1007/s10109-022-00401-w

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 2/2023

Evaluating multiscale and multimodal transport inequalities in Chinese cities with massive open-source path data

A new Voronoi diagram-based approach for matching multi-scale road networks

JGS Editors’ choice article

Geographically weighted regression with the integration of machine learning for spatial prediction

Spatial shopping behavior during the Corona pandemic: insights from a micro-econometric store choice model for consumer electronics and furniture retailing in Germany

Induced earthquakes and house prices: the role of spatiotemporal and global effects

Premium Partner