Assessing the perceptions of urban parks and understanding the relationships between environmental features and park perceptions are critical to the design and management of urban parks. However, it is challenging to quantify park perceptions at a large spatial-temporal scale. In addition, little is known about which environmental features contribute to different perceptions in cross-cultural comparison. This study collected 21462 and 28398 online reviews from 2015 to 2020 and applied Natural Language Processing (NLP) methods to understand how people perceive urban parks and which environmental features contribute to positive and negative park perceptions in Shanghai and Brussels. We quantified park perceptions through sentiment analysis and transformed unstructured high-frequency words into structured environmental feature clusters corresponding to positive and negative perceptions using the word2vec models and machine learning. In addition, we created Chinese-based and English-based environmental feature lexicons for standardized measurement of the perceived frequencies of different environmental features and their associations with park perceptions. The results of our sentiment analysis showed that urban parks were generally perceived positively in both cities. We also identified 15 environmental feature clusters that were related to positive and negative perceptions. Beta regression results showed that the perceived frequencies of water bodies and colorfulness could be used as positive predictors for park perceptions in Shanghai. In contrast, the perceived frequency of tranquility had a positive impact on park perceptions in Brussels. We found that the perceived frequency of crowdedness was also positively associated with park perceptions in Brussels, suggesting the potential use conflicts between different user groups in urban parks. This study can provide insights into park design and management from cross-cultural comparison through systematic identification and standardized assessment of landscape preferences using online reviews and NLP methods.
Bibliographical noteFunding Information:
This research was funded by the China Scholarship Council (Grant number: 201906260287). We thank editor and anonymous reviewers for their constructive comments.
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- Cross-cultural comparison
- Landscape perceptions
- Landscape preferences
- Machine learning
- Natural Language Processing (NLP)
- Urban parks
- User-generated data