Exploring the pattern of block vitality within cities and its influencing factors is an important topic in urban vitality research, which has great significance in promoting high-quality urban development. However, existing research has paid little attention to the spatial difference characteristics of daytime and night urban vitality and the differences in their influencing factors. Using the urban area of Kunming, China, as a case study, this study explores the differences in spatial patterns of daytime and night vitality at the intra-city block scale and analyses the differences in the main influencing factors of daytime and night block vitality using spatial regression models. The study obtains the following findings: (1) daytime block vitality in Kunming’s urban district (the urban Dist. of KM) is higher than night block vitality. The First Ring Area has the highest daytime block vitality, whereas the Second Ring Area has the highest night block vitality. (2) Block vitality in the urban Dist. of KM presents a polycentric structure. (3) There are obvious differences in the factors influencing urban vitality between daytime and night, with work accessibility, functional mixing, daily consumption accessibility, floor area ratio, and average building age, having different directions of influence on daytime and night urban vitality. (4) Daytime urban vitality in the urban Dist. of KM relies on work activities, while night urban vitality relies on residential activities. The results provide a scientific basis for urban land use layout, facility construction, and higher-quality urban planning and management, thus promoting sustainable urban development.

The evaluation of housing conditions is a crucial aspect of determining the well-being of residents and the sustainable development of settlements. Assessing housing conditions at a macro-level is imperative to understand the differences in well-being and livability among residents in various regions within a country. Unfortunately, the spatial variation characteristics of housing conditions in China have not been extensively studied at the county scale. Thus, this study examines the housing conditions in China by using 2846 counties as the basic research unit. A housing condition evaluation index system, comprising seven indicators, is constructed based on three aspects: housing spaciousness, internal facilities, and elevator configuration. The entropy value method is used to determine the weights of the indicators, and the spatial difference patterns and spatial autocorrelation characteristics of the housing conditions and types of housing conditions in China are analyzed. The correlation analysis method is used to analyze the correlation between the subtypes of housing conditions and county fundamentals (population density, urbanization, foreign population, and rental housing). The results show that: (1) the configuration of elevators is the most important indicator of the differences in housing conditions in China; (2) the better housing conditions in China are distributed on the southeast side of the “Hu Line”, while the worse areas are distributed on the northwest side of the “Hu Line”, showing significant spatial clustering characteristics, while the distribution of the different subtypes of housing conditions and their distribution in the H–H and L–L zones also have significant variability; (3) housing conditions in China’s urban areas are generally better than those in non-urban areas, and the internal infrastructure conditions of urban housing and the degree of elevator configuration are better than those in non-urban areas; and (4) the correlation between housing conditions and county fundamentals varies depending on the regional level. At the national and urban levels, a negative correlation exists between county fundamentals and housing spaciousness, although a positive correlation exists with internal infrastructure and elevator configuration. Urbanization has the greatest impact on housing conditions in these areas. In non-urban areas, there is significant variability in the correlation between county fundamentals and housing conditions.