北京城市认知的空间关联模式——城市地名认知率的空间分析

Spatial Correlation Patterns of Sites Cognition Rate in Beijing

论文通过空间聚类分析方法,在基于认知率和位置关系划分认知地点类型、街道类型的基础上,讨论了北京城市地名认知率的空间差异性、基于地点—街道的空间认知关联模式,并得出三方面的结论。第一,北京城市空间认知差异性明显。从地点类型来看,城区南部中心城区和近郊区的认知率低且分布集中,东北部近郊区认知率低且离散性强,西部中心城区和近郊区认知率高且集中,东南中心城区认知率高且离散。从街道类型看,中心城区街道认知率高,外围街道认知率低,表现为中心—边缘结构。第二,各类型街道对23地点的认知率与距离之间表现为不同的结合型式,有近距离—低认知率型、近距离—高认知率型、中距离—中认知率型、远距离—低认知率型、远距离—高认知率型。不同结合型式中距离对认知率的影响因结合型式而异。第三,北京城市空间的认知曲线存在双峰,一峰表现为近距离、高认知率特征,具体位置随认知地点而异;另一峰表现为远距离、高认知率特征,主要是城市中心区认知地点。

The research on cognition of city name, city image and cognition of distance and the cognitive distortions of urban space are the important parts of urban spatial cognitive study. Place names abroad include two aspects： One is to discuss the human emotion and the meaning of place names through its origin. The other is to study the differences of cognitive space in different spatial scales. In China, there are relatively few studies on urban spatial cognition. Most of the studies focus on the cognition of city image, while there is insufficient research on cognitive differences, cognition rate of names and correspondence. This paper aims to analyze the spatial cognition pattern in Beijing based on a large amount of cognition rate data by investigation. The objective is to set up the spatial association pattern between geographical locations and residents in different places. Spatial cognition data are obtained by questionnaires, which included properties of residents, cognition rate and so on. This paper uses the spatial analysis method to discuss spatial difference of recognition rate of place names of Beijing and spatial cognitive correlation pattern of cognition places and sub-districts based on different cognition places and sub-district types divided by cognition rate and position relations. Three conclusions can be drawn as follows. Firstly, the difference of spatial cognition in Beijing is obvious. Judged from different types of cognition place, the cognition rate in the southern part of urban and suburban areas is low and centralized, and it is low and divergent in the northeastern suburbs while high and centralized in the western urban and suburban areas, and it is high and divergent in the southeast of urban areas. Seen from the sub-district type, the cognition rate of sub-districts in urban areas is high and that of the periphery is low, which displays the central-marginal structures. Secondly, there are different associative patterns between distance and cognition rate of 23 cognition places by various types of streets, such as short distance-low cognition rate, short distance-high cognition rate, medium distance-medium cognition rate, long distance-low cognition rate, and long distance-high cognition rate. The influence of distance on cognition rate is changing for different associative patterns. Thirdly, there are twin peaks on the space cognition curve of Beijing. One represents short distance-high cognition rate, and the location is changed with the change of cognition places. The other represents long distance-high cognition rate, which mainly lies in the urban central area.