Residents’ Spatial Image Perception of Urban Green Space through Cognitive Mapping: The Case of Beijing, China

Abstract

The fundamental purpose of future urban development is to meet residents’ yearning for a better city life with the rapid development of urbanization. This study uses a multinomial logit model and cognitive map to evaluate residents’ spatial image perception of urban green space. A field study and data collection were conducted from July to August 2019, using the typical urban green space area in Beijing as the research object. Based on 375 valid questionnaires and 139 cognitive maps, the study analyzed and evaluated the image characteristics and differences of residents to the urban green space under different conditions. The results show the following. First, there is a close relationship between residents’ preference and the characteristics of urban green spaces, especially the working and living environment and characteristics will have a great influence on it. Second, the cognitive map drawn by Beijing residents can be divided into sequential and spatial cognitive maps, and the image perception shows diversified characteristics. However, the perception is relatively superficial overall, and most are simple line maps. Besides, according to the analysis of the elements of the cognitive map, the advantages and disadvantages of each type of urban green space are closely related to their geographical location and internal structure. This study has two key findings. First, the construction of urban green spaces in various cities should be carried out according to local conditions, considering the scientific basis and reasonableness of urban green space in terms of structural setting. Second, the multinomial logit model and cognitive map can effectively quantify the subjective evaluation of respondents’ spatial perceptions in a relatively simple manner, which can be further expanded in the application system design of the method.

Residents’ Preference for Urban Green Space Types and Their Ecological-Social Services in China

“…For instance, mobile tracking data are biased across economic gradients as people with higher incomes often use more mobile-tracking-related software [35]; online reviews may not be representative of all types of user groups [36]. Overall, data source combined with web-based questionnaire is the most extensively applied data source by far in current studies [37,38].…”