With the rapid construction of urban areas, it is necessary to make assessment of the built-up areas. As the city develops and expands, many of the build-up areas may show the phenomenon of vacancy with a small population, few businesses, and seriously low productivity.It is a serious obstacle to urban development and leads to the decrease of street vitality. In order to promote the healthy development of cities, it is necessary to evaluate the existing phenomenon of vacancy in cities. This paper proposes a method to quantify and evaluate the phenomenon of vacancy at the city block level with avacancy index, as well as investigate its influencing factors. Firstly, a vacancy index is determined by the characteristics of a vacant town based on nighttime light data and land cover information. The nighttime light data of Luojia-1A satellite whose resolution is higher than DMSP-OLS and NPP-VIIR, reflect the intensity of nocturnal activity. In SAR interferometry, coherence is the criteria of phase stability. Stabilized objects in urban areas can be identified as buildings according to the coherence, which shows the external characteristics of vacant towns. By visualizing the vacancy index in the study area, failed construction in urban areas can be identified. Then, based on classical urban design theories on spatial vitality promotion, a series of factors of urban vitality are summarized as the internal influencing factors of the vacancy phenomenon, including the distance to the street, integration, choice, the crowd density, function of street-block, mixture of primary use, and mixture of building ages. These internal influencing factors of the vacancy phenomenon are quantified by social sensing data and road information. Lastly, the extrinsic and intrinsic features of vacancy are combined, a Random Forest model is employed to refine the vacancy index and rank the importance of the influencing factors. We use the urban area of Wuhan as a case study. Wuhan develops and expands fast in recent years. The areas with severe vacancy can be identified by visualizing the vacancy index with this proposed method. Meanwhile, the influencing factors that have the most impact on the degree of vacancy, accessibility of road network, and population density, are also identified. In this paper, the phenomenon of vacancy is analyzed from two perspectives, multi-source remote sensing data and multi-source social sensing data, both of which have time series correspondence. Thus, the results of this paper are reliable and can assist with the assessment and analysis of the phenomen on of vacancy in urban areas.