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predicted environment, one that is currently out of view, requires two information
qualities. Legibility helps facilitate understanding of the environment. A legible
environment is “easy to understand and to remember” (Kaplan and Kaplan 1989,
p. 55) and suggests that one can proceed further into the environment without get-
ting lost. Features of legibility include landmarks and trails. Mystery is the promise
of additional information with a change of vantage point, the possibility of more
information just around the corner. Mystery encourages future involvement (there is
some partially hidden information) and continued exploration of the environment
(to find out what it is, what is over there). Features of mystery include a bend in a
path, partial obstruction of a view, or a modest change in environmental features
(Kaplan and Kaplan 1989).
7.2.2.1 Connection to Biodiversity in the Preference Matrix
In the Preference Matrix, biodiversity is implicitly mentioned as complexity; Kaplan
and Kaplan (1989, p.Â
53) discuss an environment’s “diversity” and “richness” when
describing this information quality. Van den Berg etÂ
al. (2016) investigated whether
perceived complexity of natural and urban scenes would explain differences in
viewing times and ratings of mental restoration (a composite measure assessing
fascination, beauty, relaxation, positive affect) (see Sect. 7.3.2 for further discussion
on these concepts). Perceived complexity in this study was assessed as the number
of different elements to see in the environment. Participants rated natural scenes as
more complex than urban scenes. Further, within the type of environment, viewing
times and mental restoration differed according to the complexity of the environ-
ment. More complex natural scenes with “information-rich tree-tops and forest”
were viewed longer and rated as more restorative, than less complex natural scenes
with shrubs and fields (van den Berg et al. 2016, p. 400). The authors suggest that
complexity may be an important indicator of a scene’s restorative potential.
7.2.3 Fractal Geometry and Visual Fluency
The term fractal is used to describe shapes, processes or systems that contain repeat-
ing patterns that are reduced-size copies of the whole (Bourke 1991; Ibanez and
Bockheim 2013). As such, the defining feature of fractals is self-similarity; a “shape
is made of smaller copies of itself…same shape but different size” (Frame et al.
n.d.). This self-similarity can be identified and quantified by the fractal dimension,
D. The equation for fractal dimension, D, is log(NR)/log(1/SR), where N equals the
number of line segments in the pattern, S is the scale factor, and R is the number of
recursions of the pattern (Bies et al. 2016). For example, a fractal line will have a
fractal dimension D score that is between 1.0 and 2.0, whilst a fractal surface will
have a D score between 2.0 and 3.0 (Hagerhall et al. 2004). M. R. Marselle
Biodiversity and Health in the Face of Climate Change
- Titel
- Biodiversity and Health in the Face of Climate Change
- Autoren
- Melissa Marselle
- Jutta Stadler
- Horst Korn
- Katherine Irvine
- Aletta Bonn
- Verlag
- Springer Open
- Datum
- 2019
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-030-02318-8
- Abmessungen
- 15.5 x 24.0 cm
- Seiten
- 508
- Schlagwörter
- Environment, Environmental health, Applied ecology, Climate change, Biodiversity, Public health, Regional planning, Urban planning
- Kategorien
- Naturwissenschaften Umwelt und Klima