Modeling China’s Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods

The Influence Mechanism of Urban Spatial Structure on Urban Vitality Based on Geographic Big Data: A Case Study in Downtown Shanghai

How can we construct the multidimensional evaluation framework of urban vitality from the perspective of urban spatial structure? Under the current intensity of land development, does the population density in the downtown area of Shanghai have the potential to be improved? Can a subdistrict be an appropriate spatial scale for evaluating urban vitality in central Shanghai? First, based on multisource geospatial big data, a comprehensive theoretical framework of the influence mechanism of urban spatial structure on urban vitality was constructed from the three dimensions of population, land use, and traffic, and 11 indicators were selected to form an evaluation index system. Second, the influence mechanism of urban spatial structure on urban vitality was analyzed from the six aspects of economic vitality, cultural vitality, quality of life, social governance, air quality, and nocturnal vitality, using binary spatial correlation analysis and a regression model based on eigenvector space filtering (ESF). The results show the following: (1) According to the combined impact analysis, the job–housing balance degree, floor area ratio, open space ratio, and road network density are positively correlated with urban vitality, while population density, land use mix degree, average block area, rail transit station density, and rail transit station accessibility are negatively correlated with urban vitality. (2) The correlation between population density and the urban comprehensive vitality index is related to the floor area ratio. Under the current land development intensity, the population density of the central urban area of Shanghai has approached the theoretical “ceiling”. (3) Different spatial scales have different effects on urban vitality. Finally, some policy suggestions are proposed to optimize the urban spatial structure and enhance urban vitality.

A novel classification regression method for gridded electric power consumption estimation in China

Spatially explicit information on electric power consumption (EPC) is crucial for effective electricity allocation and utilization. Many studies have estimated fine-scale spatial EPC based on remotely sensed nighttime light (NTL). However, the spatial non-stationary relationship between EPC and NTL at prefectural level tends to be overlooked in existing literature. In this study, a classification regression method to estimate the gridded EPC in China based on imaging NTL via a Visible Infrared Imaging Radiometer Suite (VIIRS) was described. In addition, owing to some inherent omissions in the VIIRS NTL data, the study has employed the cubic Hermite interpolation to produce a more appropriate NTL dataset for estimation. The proposed method was compared with ordinary least squares (OLS) and geographically weighted regression (GWR) approaches. The results showed that our proposed method outperformed OLS and GWR in relative error (RE) and mean absolute percentage error (MAPE). The desirable results benefited mainly from a reasonable classification scheme that fully considered the spatial non-stationary relationship between EPC and NTL. Thus, the analysis suggested that the proposed classification regression method would enhance the accuracy of the gridded EPC estimation and provide a valuable reference predictive model for electricity consumption.