AN EXPLORATIVE STUDY ON THE IDENTIFICATION AND EVALUATION OF RESTORATIVE STREETSCAPE ELEMENTS

Yuting YIN, Yuhan SHAO, Zhenying XUE, Kevin THWAITES, Kexin ZHANG

PDF(5678 KB)
PDF(5678 KB)
Landsc. Archit. Front. ›› 2020, Vol. 8 ›› Issue (4) : 76-89. DOI: 10.15302/J-LAF-0-020005
PAPERS
PAPERS

AN EXPLORATIVE STUDY ON THE IDENTIFICATION AND EVALUATION OF RESTORATIVE STREETSCAPE ELEMENTS

Author information +
History +

Abstract

The street is a type of important urban public space with multiple social values, one of which is the restorative potential. Based on the “beingaway,” “extent,” “fascination,” and “compatibility” constructs of restorative environments proposed by the Attention Recovery Theory, this study elaborated the significance of restorativeness provided by street environments to people living in high-density cities. It used the traditional restorativeness scale with mobile eye trackers to explore the restorative experience provided by an urban street, and identified the specific streetscape elements related to restorativeness and the degree of their influences. The results show that “greenery,” “people,” and “cars” perform significant influences, and different streetscape elements have different degrees of influences on the 4 constructs of the restorative environment. For example, for the“being-away,” “extent,” and “fascination” constructs, the influence of “greenery” is the most important, while “people” plays the core role in “compatibility.” The findings can help professionals develop targeted design strategies to improve diverse street environments for a better restorativeness.

Keywords

Restorativeness / Streetscape Elements / Perception Evaluation Scale / Mobile Eye Tracker / Attention Recovery Theory

Cite this article

Download citation ▾
Yuting YIN, Yuhan SHAO, Zhenying XUE, Kevin THWAITES, Kexin ZHANG. AN EXPLORATIVE STUDY ON THE IDENTIFICATION AND EVALUATION OF RESTORATIVE STREETSCAPE ELEMENTS. Landsc. Archit. Front., 2020, 8(4): 76‒89 https://doi.org/10.15302/J-LAF-0-020005

RIGHTS & PERMISSIONS

2020 Higher Education Press
AI Summary AI Mindmap
PDF(5678 KB)

Accesses

Citations

Detail

Sections
Recommended

/