Sustainable Urban Green Blue Space (UGBS) and Public Participation: Integrating Multisensory Landscape Perception from Online Reviews

Jiao Zhang, Danqing Li, Shuguang Ning and Katsunori Furuya ()
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Jiao Zhang: Graduate School of Horticulture, Chiba University, Chiba 271-8510, Japan
Danqing Li: Institute of Landscape Architecture, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
Shuguang Ning: Power Grid Information Division 1, Shandong Lusoft Digital Technology Co., Ltd., Jinan 250001, China
Katsunori Furuya: Graduate School of Horticulture, Chiba University, Chiba 271-8510, Japan

Land, 2023, vol. 12, issue 7, 1-29

Abstract: The integration of multisensory-based public subjective perception into planning, management, and policymaking is of great significance for the sustainable development and protection of UGBS. Online reviews are a suitable data source for this issue, which includes information about public sentiment, perception of the physical environment, and sensory description. This study adopts the deep learning method to obtain effective information from online reviews and found that in 105 major sites of Tokyo (23 districts), the public overall perception level is not balanced. Rich multi-sense will promote the perception level, especially hearing and somatosensory senses that have a higher positive prediction effect than vision, and overall perception can start improving by optimizing these two senses. Even if only one adverse sense exists, it will seriously affect the perception level, such as bad smell and noise. Optimizing the physical environment by adding natural elements for different senses is conducive to overall perception. Sensory maps can help to quickly find areas that require improvement. This study provides a new method for rapid multisensory analysis and complementary public participation for specific situations, which helps to increase the well-being of UGBS and give play to its multi-functionality.

Keywords: urban green–blue space; multisensory perception; natural language processing (NLP); fully convolutional networks; public participation; sustainable management (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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