Landscape and Urban Planning 157 (2017) 63–74
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Landscape and Urban Planning
journal homepage: www.elsevier.com/locate/landurbplan
Research paper
Comparing text-only and virtual reality discrete choice experiments
of neighbourhood choice
Zachary Patterson
a,*
, Javad Mostofi Darbani
a
, Ali Rezaei
a
, John Zacharias
b
,
Ali Yazdizadeh
a
a
Transportation Research for Integrated Planning (TRIP) Laboratory, Department of Geography, Planning and Environment, Concordia University, 1455 de
Maisonneuve W., H 1255-15 (Hall Building), Montreal, QC H3G 1M8, Canada
b
College of Architecture and Landscape, Peking University, Beijing 200080, China
h i g h l i g h t s
•
Virtual reality model had more significant coefficients.
•
Virtual reality platform appears to have better focused respondent attention.
•
Visually attributes did not gain importance relative to text-only attributes.
•
LPS visuals best employed when they are accurate descriptions of possible outcomes.
•
Visual LPSs well suited to use in public consultations on planning interventions.
a r t i c l e i n f o
Article history:
Received 12 June 2015
Received in revised form 13 May 2016
Accepted 25 May 2016
Keywords:
Discrete choice experiments
Landscape preference studies
Neighborhood choice
Attribute visualization
Gaming engines
a b s t r a c t
Stated Preference (SP) surveys are used in many disciplines including: marketing; transportation-,
environmental- and health-economics; and landscape and urban planning. The Landscape Preference
Study (LPS) is a common SP technique in landscape and urban planning, defined by the presentation
of landscapes through images, something uncontroversial in this literature. The use of visual attributes
in SP surveys in the marketing and economics literatures has, however, aroused controversy. Poten-
tial benefits are evoked (greater realism in tasks), but drawbacks (e.g. unintended information affecting
respondent choices) are also discussed. At the same time, the use of visualization and simulation is said to
be “outstripping” understanding of how best to use them in planning contexts. We adopt “the economic
approach” to LPSs (the Discrete Choice Experiment) to better understand how presentational methods
affect results in the context of neighborhood choice. We compare two experiments; one administered as
a virtual reality simulation, and the other as a text-only survey. We conclude that in essence, respondent
preferences in the text-only survey were based on respondent mental images of building types, whereas
in the visual survey, preferences were based on the displayed images. As such, we propose that LPS visuals
are best employed when the visual representations provided to respondents are accurate descriptions of
possible outcomes, as they could be in public consultations related to landscape and urban planning. In
so doing we make one step toward Lovett et al.’s (2015) call to help evaluate the increasing number of
options available in landscape visualization.
© 2016 Elsevier B.V. All rights reserved.
1. Introduction
The Stated Preference (SP) survey encompasses a large number
of research tools designed to help understand people’s prefer-
*
Corresponding author.
E-mail addresses: zachary.patterson@concordia.ca, zak.patterson@gmail.com
(Z. Patterson), jj.mostofi@gmail.com (J.M. Darbani), a.rezaaei@gmail.com
(A. Rezaei), zachariasjohn478@gmail.com (J. Zacharias),
ali.yazdizadeh.pres@gmail.com (A. Yazdizadeh).
ences, and is used in many disciplines, including: marketing;
transportation-, environmental- and health-economics; and land-
scape and urban planning. One particularly common SP tool in
landscape and urban planning is the Landscape Preference Study
(LPS). LPSs take various forms, but a defining feature is the rep-
resentation of landscapes as images, and increasingly, computer
simulations. Images play a fundamental role in LPSs and their use
in landscape and urban planning is uncontroversial. The visual
presentation of attributes in SP surveys in the marketing and
economics literatures, however, has aroused controversy. While
http://dx.doi.org/10.1016/j.landurbplan.2016.05.024
0169-2046/© 2016 Elsevier B.V. All rights reserved.