In the context of rapid urbanization, the phenomenon of spatial fragmentation in Chinese inland central cities is significant. The scientific measurement and evaluation of urban spatial fragmentation are conducive to its transformation, advancement, and sustainable development. Based on the fractal dimension index and Shannon index, this study measures urban spatial fragmentation in terms of form and function, respectively. In addition, multi-scale geographic weighted regression (MGWR) is used to study the influencing factors of spatial fragmentation. The conclusions are as follows: ① the measurement results of spatial form fragmentation and functional fragmentation of urban built-up areas are consistent. The fragmentation degree of the new urban area (new urban district and high-tech district) is higher than that of the old urban areas, and the urban space fragmentation degree around railways and rivers is high. The urban space fragmentation degree of coal resource concentrated distribution areas in the north is lower. The cold spot area of the fragmentation phenomenon appears in the old urban area, and the hot spot area is in the new urban area and along the railway. ② The positive influencing factors of urban spatial fragmentation in Pingdingshan city are the NDVI and the distance from CBD. The negative influencing factor is the number of bus stops per unit area. The DEM and population density have no significant impact on urban fragmentation in Pingdingshan city. ③ Among the variables with significance, its influence has a certain spatial heterogeneity. The spatial scale from small to large is the number of bus stops per unit area, NDVI, and the distance from CBD. The degree of urban fragmentation is very sensitive to the number of bus stops per unit area and the impact scale is quite small. The spatial impacts of the NDVI and the distance from CBD are relatively stable. This study provides a reference and basis for the spatial development of built-up areas of inland central cities and promotes the transformation, advancement, and sustainable development of inland central cities.
This study aims to investigate the spatial associations of luxury hotels by using geographical information system (GIS) tools and the multiscale geographically weighted regression (MGWR) model to examine the relationships between the distribution of luxury hotels and exogenous (regional) determinants of urban subdistricts in which the luxury hotels are located. Shanghai City is used as an example. The study first introduces the spatial-temporal characteristics of luxury hotels in Shanghai City, and the key exogenous determinants that contribute to luxury hotel location choice are identified with the MGWR model. The nearest neighbor index decreased from 1.01 to 0.47 and Moran’s I statistics increased from 0.268 to 0.452, revealing that the spatial-temporal evolution pattern of luxury hotels presents a cluster trend from 1995 to 2015. The significance level of the standard regression coefficient shows that the institutional proximity, room rate, green space and the World Expo are the primary determining factors that influence the distribution of luxury hotels in Shanghai City. The analysis is important theoretically, as it presents new and novel methodologies for shedding light on the influencing factors of the locational dynamics of luxury hotels. Meanwhile, it enriches the methodologies for analyzing the relationships between luxury hotels and urban structures, and it is important for practitioners, as it provides strategic information that would enable them to globally select appropriate locations for luxury hotels.
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