Designer's approach for scene selection in tests of preference and restoration along a continuum of natural to manmade environments

Abstract

It is well-established that the experience of nature produces an array of positive benefits to mental well-being. Much less is known about the specific attributes of green space which produce these effects. In the absence of translational research that links theory with application, it is challenging to design urban green space for its greatest restorative potential. This translational research provides a method for identifying which specific physical attributes of an environmental setting are most likely to influence preference and restoration responses. Attribute identification was based on a triangulation process invoking environmental psychology and aesthetics theories, principles of design founded in mathematics and aesthetics, and empirical research on the role of specific physical attributes of the environment in preference or restoration responses. From this integration emerged a list of physical attributes defining aspects of spatial structure and environmental content found to be most relevant to the perceptions involved with preference and restoration. The physical attribute list offers a starting point for deciphering which scene stimuli dominate or collaborate in preference and restoration responses. To support this, functional definitions and metrics—efficient methods for attribute quantification are presented. Use of these research products and the process for defining place-based metrics can provide (a) greater control in the selection and interpretation of the scenes/images used in tests of preference and restoration and (b) an expanded evidence base for well-being designers of the built environment.

Image Feature Types and Their Predictions of Aesthetic Preference and Naturalness

“…Supplementary Table 1 provides a breakdown of the investigated theories and the structure-content properties they embody that Hunter and Askarinejad (2015) theorized predict preference. Based on the 10 structure-content properties, the authors then derived 62 measurable high-level semantic features that can be used to predict scene aesthetic preference.…”

A systematic information problem-solving process

“…The result shown: (1) in stage part, consistency is 100.0%; (2) in step part, consistency is 75.0%; (3) in key task part, consistency is 84.2%. The "final table of SIPSP" (Shown in Table. 1) was confirmed by Delphi questionnaire survey and Q method, apparently, which is reliable and acceptable for both experts and business managers.…”

“…To aware risk things, human had to notice the changing of information environment, and determine decision [3]. Human was deal with information in following process: (1) Brain providing information; (2) Information processing; (3) Information activation; (4) Connecting networks of association; (5) Illustrating the structure of thinking.…”

“…In initial state, (1) Step 1 "Perceiving problem areas and factors" was defined as "Recognizing and describing all essential knowledge and related area of problem ; (2) Step 2 "Recognizing background and status of problem" was defined as "Identifying the status of current problem and presenting expected result and situation"; (3) Step 3 "Checking factors and conditions" was defined as "Analyzing and classifying current problem, narrowing goal and collecting basic data, even establishing the restriction and limitation of current problem.…”

Transit versus Nature. Depreciation of Environmental Values of the Road Alleys. Case Study: Gamerki-Jonkowo, Poland

“…Alleys have a potential to enhance connectivity and ecosystem services, and as such can facilitate forming a network to aid the dispersal and movement of populations [17][18][19][20][21][22][23][24][25][26]. Habitat connectivity contributes to ecosystem function and resilience.…”