Spatial autocorrelation and spatial heterogeneity of underground parking space development in Chinese megacities based on multisource open data

Abstract

Underground parking is prevalent in high-density megacities. Understanding the use patterns of underground parking spaces (UPSs) is important for sustainable and resilient urban development. As such, in this study, we employed multisource open data to investigate the spatial autocorrelation and spatial heterogeneity of the UPSs in seven representative Chinese megacities, determining the spatial distribution pattern of and forces driving UPS development. We used the underground parking ratio (UPR) and underground parking density (UPD) at the subdistrict level as critical indicators of UPS use. We found that the UPSs tend to be centrally clustered, whereas the UPR is high in the urban periphery. Both UPR and UPD showed positive univariate spatial autocorrelation, with UPD being much more spatially autocorrelated. The results of univariate spatial autocorrelation revealed the spatial mismatch between UPR and UPD, being high-low in the urban periphery and low-high in the urban centre, respectively. The results of spatial heterogeneity analysis indicated that urban function and land development intensity are common drivers of both UPR and UPD, and socioeconomic conditions are the specific drivers of UPD. Although traffic factors did not have a predominant influence on UPSs, they substantially enhanced the effects when integrated with other factors.