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The Influences of Actual and Perceived Familiarity on Environmental Preferences for the Design of a Proposed Urban Square

Bibliographic Data

ID23030914
AuthorsTony Craig (0000-0002-2509-3632, Social, Economic and Geographical Sciences Group, The James Hutton Institute, Aberdeen AB15 8QH, UK), Anna Conniff (James Hutton Institute), Carlos Galán-Díaz (0000-0002-9006-5670, James Hutton Institute)
Year2012
Volume2012
Pages1-9
Publication date2012-10-22
Open AccessYes
TypeARTICLE
VenueUrban Studies Research (JOURNAL)
Journal identifiersISSN: 2090-4185 • E-ISSN: 2090-4193
PublisherWiley (PUBLISHER • GB)
DOI10.1155/2012/767049
OpenAlexW2124202084
LanguageEN
Citations received1
References cited22

People gain actual familiarity through direct experience of environments, but environments we have never visited can still seem familiar. To date, few academic studies have investigated this phenomenon of perceived familiarity. This paper discusses the concept of perceived familiarity and environmental preference from the perspective of people who may be asked to make judgements of future urban designs as part of the planning process. A sample of local ( n = 102 ) and nonlocal ( n = 176 ) people were asked to rate images of two versions (existing environment and proposed redesign) of an urban square on scales of preference and perceived familiarity. Results showed that the mean ratings for the proposed design were similar for both local and non-local samples. However, we found a clearly discernible difference in the way psychological antecedents are associated with environmental preference. For nonlocals, preference for the existing design is significantly associated with preference for the proposed design, but for local people this is not the case. In addition, for non-locals perceived familiarity of the proposed design is associated with perceived familiarity of the existing environment, but for the local sample this is not the case. Implications for public participation processes in urban design, as well as limitations and future lines of research, are discussed.

Algorithm · Machine learning · Preference · Statistics · Artificial Intelligence · Computer Science · Environmental Education and Sustainability · Mathematics · Place Attachment and Urban Studies · Urban Green Space and Health

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Citations per year0.25
Citation span2022 - 2022 (1)
Highly citedNo

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