Use of the Analytic Hierarchy Process (AHP) method to assess the urban quality of life of seniors in terms of architectural and urban planning aspects

• Autorzy: Ptak-Wojciechowska Agnieszka

Identyfikatory

DOI

10.37190/arc240409

Warianty tytułu

Wykorzystanie metody Analytic Hierarchy Process (AHP) do oceny jakości życia osób starszych w miastach pod względem aspektów architektoniczno-urbanistycznych

• Języki publikacji: EN

Abstrakty

EN

Cities should aim to provide a high quality of life (QoL) for all residents. However, the urban structure often fails to meet the spatial needs of senior citizens, despite ongoing demographic changes. Furthermore, there is a lack of scientific assessment instruments that could be used to evaluate the architectural and urban aspects of a city and guide improvements. Although popular urban rankings may be used for urban policy development, their results are often misinterpreted by their recipients. The use of multi-criteria decision-making (MCDM) methods can facilitate the process of comparing city areas, increase the transparency of the evaluation, and involve different stakeholders in the evaluation process. Machine learning (ML) could be an interesting extension to commonly used statistical methods. This paper presents the latest research methods on the urban QoL of seniors, using a multi-criteria analysis of five neighbourhoods in Poznan as an example. The Analytic Hierarchy Process (AHP) method is discussed as part of the author’s tool for measuring the perceptual assessment of senior citizens and the expert assessment of architects and urban planners, in terms of functional and spatial aspects. The AHP method’s effectiveness is demonstrated, and the results can support city authorities, designers, and researchers. Additionally, this research presents directions for its development using ML methods.

PL

Wykorzystanie metody Analytic Hierarchy Process (AHP) do oceny jakości życia osób starszych w miastach pod względem aspektów architektoniczno-urbanistycznych Miasta powinny zapewniać wysoką jakość życia wszystkim mieszkańcom. Mimo zachodzących zmian demograficznych tkanka urbanistyczna odpowiada w sposób niewystarczający na potrzeby przestrzenne seniorów. Brakuje ponadto naukowych instrumentów oceny, które mogłyby posłużyć za wsparcie w ocenie aspektów architektoniczno-urbanistycznych miasta, a w konsekwencji także w ich poprawie. Chociaż popularne rankingi miejskie mogą być wykorzystywane w rozwoju polityki miejskiej, ich wyniki są często nieprawidłowo interpretowane przez odbiorców. Zastosowanie metod wielokryterialnego wspomagania decyzji może ułatwić proces porównywania obszarów miasta, zwiększyć transparentność ewaluacji oraz zaangażować różnych interesariuszy w proces oceny. Uczenie maszynowe natomiast może stanowić interesujące rozszerzenie dla stosowanych powszechnie metod statystycznych. W artykule zaprezentowano najnowsze metody w badaniach nad miejską jakością życia seniorów na przykładzie wielokryterialnej analizy pięciu osiedli miasta Poznania. Omówiono wykorzystanie metody analytic hierarchy process (AHP) jako części autorskiego narzędzia do oceny percepcyjnej przez starszych mieszkańców, a także oceny eksperckiej przez architektów i urbanistów pod względem aspektów funkcjonalno-przestrzennych. Dowiedziono skuteczności metody AHP – rezultaty mogą stanowić wsparcie dla władz miasta, projektantów i badaczy. Przedstawiono ponadto kierunki rozwoju niniejszych badań z zastosowaniem metod uczenia maszynowego.

Słowa kluczowe

EN

participatory methods; multi-criteria methods; quality of life in cities; machine learning; aging societies

PL

metody partycypacyjne; metody wielokryterialne; jakość życia w miastach; uczenie maszynowe; starzejące się społeczeństwo

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Architectus
• Rocznik: 2024
• Tom: Nr 4 (80)
• Strony: 85--98
• Opis fizyczny: Bibliogr. 62 poz., rys.

Twórcy

autor

Ptak-Wojciechowska Agnieszka

• ORCID. Faculty of Architecture, Poznan University of Technology, Poland

• https://orcid.org/0000-0002-4833-905X

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