Assessing and Representing Livability through the Analysis of Residential Preference

Anna Kovacs-Györi, Pablo Cabrera-Barona, Bernd Resch, Michael Mehaffy and Thomas Blaschke
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Anna Kovacs-Györi: Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria
Pablo Cabrera-Barona: Department of Public Affairs, Latin American Social Sciences Institute–FLACSO, Quito 170135, Ecuador
Bernd Resch: Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria
Michael Mehaffy: Centre for the Future of Places, KTH Royal Institute of Technology, Drottning Kristinas väg 30, 114 28 Stockholm, Sweden
Thomas Blaschke: Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria

Sustainability, 2019, vol. 11, issue 18, 1-23

Abstract: 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.

Keywords: livability; relational-statistical learning; GIS; urban mobility; urban form; urban functions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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