关键词: 网络空间点模式, K函数, 核密度, 空间格局, 餐饮店

Abstract:

The catering industry has been considered as one of the most important indicators concerning economic development of a city. Using appropriate methods to study catering industry has been playing an important part in research fields such as city planning, business location and economic development. Many restaurants in a city can be abstracted as point objects in the study of geography. It is one of the most commonly used methods to study the spatial layout of facility events by using spatial point patterns. The traditional point pattern analysis methods are basically based on the Euclidean distance and assume that the plane space is a homogeneous and isotropic space. However, many geo-objects are usually distributed on the road network or along the road network, such as restaurants, banks, supermarkets and road traffic accidents. If the traditional method of plane space point analysis is applied to the trend events occurring along the road network, wrong aggregation mode may occur. By using the network spatial point pattern analysis method, the shortest path distance instead of the Euclidean distance can be used to study the distribution characteristics of the event points, and more accurate spatial analysis results can be obtained. Take Haizhu District of Guangzhou city as an example, on the basis of restaurants POI (point of interest) data, Kernel density estimation is adopted to analyze spatial distribution characteristics of restaurants. The network kernel density method is used to investigate the distribution characteristics of the hot roads, and double variable K function method is applied to analyze the relations between distribution of restaurants and bus stations and residential areas. The spatial pattern of Haizhu District restaurants shows much more dense in the West and comparatively sparser in the East. The restaurant hot spots are mainly concentrated along the streets of Jiangnan West and Jiangnan Zhong, and the density of the restaurants decreases with the increase of the distance from the hot spots. The degree of aggregation of restaurants, bus stations and residential areas is also investigated under the road network structure. The results show that restaurants have strong aggregation relations with bus stations, which indicates that the restaurant tends to close to the traffic stations, but have no significant aggregation relationship with the residential areas. As far as the spatial point objects along streets are concerned, better results can be obtained by using network analysis of spatial point pattern.

Key words: network space point pattern, K function, kernel density, spatial pattern, restaurant