The construction of new towns is one of the main measures to evacuate urban populations and promote regional coordination and urban–rural integration in China. Mining the spatio-temporal pattern of new town hot spots based on multivariate data and analyzing the influencing factors of new town construction hot spots can provide a strategic basis for new town construction, but few researchers have extracted and analyzed the influencing factors of new town internal hot spots and their classification. In order to define the key points of Wuhan’s new town construction and promote the construction of new cities in an orderly and efficient manner, this paper first constructs a space-time cube based on the luminous remote sensing data from 2010 to 2019, extracts hot spots and emerging hot spots in Wuhan New City, selects 14 influencing factor indicators such as population density, and uses bivariate Moran’s index to analyze the influencing factors of hot spots, indicating that the number of bus stops and vegetation coverage rate are the most significant. Secondly, the disorderly multivariate logistic regression model is used to analyze the influencing factors of emerging hot spots. The results show that population density, vegetation coverage, road density, distance to water bodies, and distance to train stations are the most significant factors. Finally, based on the analysis results, some relevant suggestions for the construction of Wuhan New City are proposed, providing theoretical support for the planning and policy guidance of new cities, and offering reference for the construction of new towns in other cities, promoting the construction of high-quality cities.

Achieving effective integration of urban–rural relationships and promoting the flow of resources between urban and rural areas in megacities are a key priority in the development of China’s new urbanization efforts. As a transitional zone between urban and rural areas, the urban fringe is the frontier of urban–rural integration. The specific research object of this paper is the urban fringe areas of Wuhan City. This paper quantifies the neighborhood vitality of the fringe areas by the short-stay visitors in the fringe areas and selects the 5D elements of the built environment and social media data from multiple sources to construct the indicator system assessing the neighborhood vitality of the urban fringe areas. This paper analyzes the spatial distribution characteristics of neighborhood vitality and its influencing factors in urban fringe areas and investigates the connection between neighborhood vitality and its influencing factors through the application of the multi-scale geographically weighted regression (MGWR) model. Based on the regression results, relevant planning recommendations are made on how to enhance the vitality of neighborhoods in urban fringe areas. The results show that the index system constructed by “5D” elements of built environment and social media data can well explain the spatial distribution of neighborhood vitality in urban fringe areas. Among the influencing factors, the absolute value of the correlation coefficient of network exposure is the largest, followed by road density and functional density. Thanks to the different bandwidths given by MGWR to the influencing factors, the global influencing factors are only two indicators—development intensity and functional mixing degree—while the other influencing factors are all local, and the influence degree of different regions is different, so it is necessary to analyze and put forward different planning suggestions accordingly.