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Assessing and representing livability through the analysis of residential preference
KTH, School of Architecture and the Built Environment (ABE), Centres, Center for the Future of Places.
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2019 (English)In: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 11, no 18, article id 4934Article in journal (Refereed) Published
Abstract [en]

Livability reflects the quality of the person-environment relationship, namely how well the built environment or the available services in a city fulfill the residents' needs and expectations. We argue that livability assessment can aid the implementation of certain New Urban Agenda (NUA) goals by providing a flexible way to assess urban environments and their quality. However, a reliable and transferable assessment framework requires the key elements of livability to be defined in such a way that measurable factors adequately represent the person-environment relationship. As an innovative approach, we determined key livability elements accordingly and asked over 400 residents worldwide to evaluate their urban environments using these parameters. Thereby, we could calibrate the livability assessment workflow by including personal aspects and identifying the most relevant livability factors through an ordinal regression analysis. Next, we performed relational-statistical learning in order to define the individual and combined contribution of these statistically significant factors to the overall livability of a place. We found that urban form and mobility-related factors tend to have the highest influence on residential satisfaction. Finally, we tested the robustness of the assessment by using geospatial analysis to model the livability for the city of Vienna, Austria. We concluded that the workflow allows for a reliable livability assessment and for further utilization in urban planning, improving urban quality by going beyond simple city rankings.

Place, publisher, year, edition, pages
MDPI AG , 2019. Vol. 11, no 18, article id 4934
Keywords [en]
GIS, Livability, Relational-statistical learning, Urban form, Urban functions, Urban mobility
National Category
Other Civil Engineering
Identifiers
URN: urn:nbn:se:kth:diva-262499DOI: 10.3390/su11184934ISI: 000489104700120Scopus ID: 2-s2.0-85072594947OAI: oai:DiVA.org:kth-262499DiVA, id: diva2:1366037
Note

QC 20191028

Available from: 2019-10-28 Created: 2019-10-28 Last updated: 2019-12-04Bibliographically approved

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Mehaffy, Michael W.

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CiteExportLink to record
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  • apa
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