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Improving spatial clustering through a weight system on multilevel permanent museum attraction probability

Author

Listed:
  • Veronica Distefano

    (University of Salento
    Ca’ Foscari University of Venice)

  • Sandra De Iaco

    (University of Salento
    National Centre for HPC, Big Data and Quantum Computing
    National Biodiversity Future Center)

  • Sabrina Maggio

    (University of Salento)

Abstract

Museums are extensively distributed all over the Italian territory. In this context, the identification of spatial patterns, referred to specific characteristics of museums evaluated at regional level, can support the enhancement of the cultural and natural heritage as well as the social and economic growth. In the literature, many studies were focused on the visitors’ profile or on the managerial performance and economic efficiency of the museums. However, none of them analysed the effects of the permanent presence of museums and their spatial contiguity by using both spatial machine learning models and statistical models. To this aim an innovative approach, which combines multilevel binary model and spatial clustering, as a machine learning unsupervised technique, is proposed to investigate the pattern recognition of the permanent museums all over the Italian territory and provide relevant information in terms of similarity among the spatial cluster formed. The logit of the museums to remain open all over the year, also with respect to different types of institution (private/public) and a different spatial/geographical constraints are jointly considered. In addition, a weight system is defined in order to introduce a regional measure of museums prevalence with respect to other types of cultural institutions. The ISTAT microdata concerning the Italian survey on museums and cultural entities are considered. The results highlight the great potentiality of this spatial clustering approach in delivering a better understanding of the role of museums as factor of challenge of urban development, providing in the meantime suggestions for tourism providers and museum managers.

Suggested Citation

  • Veronica Distefano & Sandra De Iaco & Sabrina Maggio, 2025. "Improving spatial clustering through a weight system on multilevel permanent museum attraction probability," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(2), pages 1059-1088, April.
    • Handle: RePEc:spr:qualqt:v:59:y:2025:i:2:d:10.1007_s11135-024-01987-7
    DOI: 10.1007/s11135-024-01987-7
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    1. Thomas de Graaff & Jaap Boter & Jan Rouwendal, 2009. "On Spatial Differences in the Attractiveness of Dutch Museums," Environment and Planning A, , vol. 41(11), pages 2778-2797, November.
    2. Mossay, Pascal & Shin, Jong Kook & Smrkolj, Grega, 2022. "Quality Differentiation and Spatial Clustering among Restaurants," International Journal of Industrial Organization, Elsevier, vol. 80(C).
    3. Marie Chavent & Vanessa Kuentz-Simonet & Amaury Labenne & Jérôme Saracco, 2018. "ClustGeo: an R package for hierarchical clustering with spatial constraints," Computational Statistics, Springer, vol. 33(4), pages 1799-1822, December.
    4. Lorenzo Mizzau & Fabrizio Montanari, 2008. "Cultural districts and the challenge of authenticity: the case of Piedmont, Italy -super-†," Journal of Economic Geography, Oxford University Press, vol. 8(5), pages 651-673, September.
    5. Anders Skrondal & Sophia Rabe‐Hesketh, 2009. "Prediction in multilevel generalized linear models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(3), pages 659-687, June.
    6. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    7. Sandra De Iaco & Sabrina Maggio, 2022. "Using multilevel models to evaluate the attitude of separate waste collection in young people," METRON, Springer;Sapienza Università di Roma, vol. 80(1), pages 77-95, April.
    8. Sandra De Iaco & Sabrina Maggio & Donato Posa, 2019. "A Multilevel Multinomial Model for the Dynamics of Graduates Employment in Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 149-168, November.
    9. Roberto Cellini & Tiziana Cuccia & Domenico Lisi, 2020. "Spatial dependence in museum services: an analysis of the Italian case," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 44(4), pages 535-562, December.
    10. Francesca Petrei & Lorenzo Cavallo & Maria Teresa Santoro, 2020. "Cultural tourism: an integrated analysis based on official data," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(5), pages 1705-1724, December.
    11. Jan Ženka & Ondřej Slach & Igor Ivan, 2020. "Spatial Patterns of Knowledge-Intensive Business Services in Cities of Various Sizes, Morphologies and Economies," Sustainability, MDPI, vol. 12(5), pages 1-19, March.
    12. Juan Brida & Marta Disegna & Raffaele Scuderi, 2014. "The behaviour of repeat visitors to museums: review and empirical findings," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(5), pages 2817-2840, September.
    13. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    14. Cappello, Claudia & De Iaco, Sandra & Maggio, Sabrina, 2024. "A new approach for assessing the probability of museum opening choices and its spatial continuity," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    15. Guccio, Calogero & Martorana, Marco & Mazza, Isidoro & Pignataro, Giacomo & Rizzo, Ilde, 2020. "An analysis of the managerial performance of Italian museums using a generalised conditional efficiency model," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    16. Tony H. Grubesic & Ran Wei & Alan T. Murray, 2014. "Spatial Clustering Overview and Comparison: Accuracy, Sensitivity, and Computational Expense," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 104(6), pages 1134-1156, November.
    17. Enrico Bertacchini & Massimiliano Nuccio & Alessandra Durio, 2021. "Proximity tourism and cultural amenities: Evidence from a regional museum card," Tourism Economics, , vol. 27(1), pages 187-204, February.
    18. Roberto Dellisanti, 2023. "Spatial patterns of Cultural and Creative Industries: Creativity and filière behind concentration," Papers in Regional Science, Wiley Blackwell, vol. 102(3), pages 533-564, June.
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