As one of the most important criteria for measuring the quality of urban life and the environment, urban vitality has become the focus of urban-related research and related disciplines with an increasing number of advocates for the rapid and harmonious development of urban cities. Urban takeaway can represent urban vitality, but studies have not investigated this in a quantitative manner. Furthermore, current studies rarely focus on or even mention the urban food takeaway vitality generated by the spatial distribution of urban takeaway. This study first calculated the vitality of urban takeaways based on the urban takeaway distribution, building footprint, Open Street Map (OSM) data, and the Rapidly Exploring Random Tree (RRT). Then, the urban vitality was obtained using Tencent-Yichuxing data and night-time light data, followed by a spatial correlation analysis between the urban takeaway vitality and urban vitality. Finally, the results for Beijing, Shanghai, and Guangzhou were compared, and the following conclusions were drawn: (1) there is a significant spatial correlation between the urban takeaway vitality and urban vitality, but the correlation varies in different cities at different times; and (2) even in the same city, different road and building densities have an impact on the correlation. The urban takeaway vitality proposed in this study can be used as a new index to evaluate the urban vitality, which has important theoretical and practical significance for the sustainable development of future urban cities.

Nighttime light (NTL) is a critical indicator of urban vitality and development. Using NTL as a representation of urban vitality and development, the study explores how different fresh-made beverage shops, namely boba and coffee shops, proxy various facets of urban vitality and development in four megacities in China. Existing studies mostly discuss urban vitality as a broad concept and seldom investigate the diverse urban vitality and development represented by different indicators. This study selects Beijing, Shanghai, Guangzhou, and Shenzhen as case study regions and explores (1) their urban vitality pattern represented by NTL. (2) the heterogeneous spatial distribution of boba and coffee shops; (3) how boba and coffee shops represent urban vitality differently; and (4) how boba and coffee shops portray the economy and population growth aspect of urban development differently. We acquired NTL data from remote sensing images to measure urban vitality and development. Cross-sectionally, the majority of urban vitality and development represented by NTL concentrates in urban centers. The distribution of coffee shops assimilates the spatial pattern of urban vitality represented by NTL while boba shops have a greater spatial extent in metropolitan fringes. Longitudinally, from 2012 to 2020, the global and local bivariate Moran’s I analysis between NTL and beverage shops shows that the coffee shops capture urban vitality and development better than boba shops in Beijing, while the pattern is reversed in Guangzhou and Shenzhen. Examining the evolving spatial dynamics between beverage shops’ growth and urban development using bivariate Moran’s I and Getis–Ord Gi/Mann–Kendall emerging hot spot analysis, we found that the locations with the most intense economic growth have seen the most spatial expansion of coffee shops. In contrast, those with the fastest population growth have seen the greatest spatial development of boba businesses. These results indicate that coffee shops represent the economic aspect of urban vitality while boba shops emphasize the population growth aspects. By examining the dynamic spatio–temporal relationship between small beverage shops and urban vitality and development represented by NTL data, this study broadens the usage of remote sensing data in urban studies and expands on previous research and offers insights for urban planners and geographers to reference when choosing indicators of urban vitality and growth.

This study quantitatively investigated the relationship between climate change (proxied by CO₂ emissions), air pollution (proxied by PM2.5 concentration levels and PM10 and SO₂ emissions), and tourism flows (proxied by inbound and domestic tourist arrivals) using panel data for 30 Chinese provinces from 2010 to 2017. The results demonstrate a long-term equilibrium relationship between CO₂ emissions, air pollution variables, and tourism flows (including the number of inbound and domestic tourists). The panel data model results show that CO₂ emissions have an opposite effect on inbound and domestic tourist arrivals, while domestic and inbound tourists positively affect CO₂ emissions. PM2.5 level and PM10 and SO₂ emissions all have a negative effect on the number of tourists. There is bidirectional causality between CO₂ emissions and domestic tourist arrivals, which means CO₂ emissions and domestic tourist arrivals have a two-way effect. A one-way causality running from PM2.5 to inbound tourist arrivals and SO₂ emissions to domestic tourist arrivals was found. Moreover, we also found bidirectional causality between PM10 and inbound tourist arrivals and PM10 and domestic tourist arrivals. Variance decomposition function results suggest that PM10 and SO₂ emissions have stronger effects on inbound tourist arrivals in the long term, while CO₂ emissions and PM10 have stronger power in explaining innovations in domestic tourist arrivals. The movements in the domestic tourist arrivals do significantly affect CO₂ emissions in the long run. The study provides theoretical implications and guidance for achieving a healthy and sustainable tourism industry.

The distribution pattern of high-level tourist attractions is crucial for the sustainable development of the tourism industry. However, few studies have explored the spatial distribution and dominant influencing factors of tourist attractions of different levels from a macro perspective in China. This study, which was based on large-scale multi-source data, involved the use of kernel density analysis, local spatial autocorrelation, and geographical detector analysis to explore the spatial distribution, spatial correlation, and dominant influencing factors of high-level tourist attractions in China. The study’s results show that the spatial distribution of tourist attractions of different levels is polarized and regionally clustered, and there exist some spatial correlation effects among attractions of the same level. Additionally, different influencing factors play a different role in determining the spatial distribution of attractions of different levels. Based on market demand and tourism resources, it is necessary to regulate attractions of different levels to promote the sustainable development of high-level tourist attractions and provide a reference for the development of China’s tourism industry.

The development of the tourism industry serves as a crucial pathway for guiding urban spatial vitality, making the study of the matching relationship between the spatial distribution characteristics of tourist attractions and regional spatial vitality particularly important for the advancement of the tourism sector. This study combines Amap POI data and Weibo sign-in data, employing various quantitative methods, including Kernel Density Estimation (KDE), Hotspot Analysis (Getis-Ord Gi*), and the Geographically Weighted Regression (GWR) model, to thoroughly explore the distribution characteristics of different grades of tourist attractions in Xinjiang and their matching relationship with spatial vitality. The findings indicate that AAAAA attractions are primarily concentrated in Urumqi and its surrounding areas, where spatial vitality highly matches the distribution of attractions. The distribution of AAAA attractions shows regional differences, exhibiting higher matching degrees in certain areas of southern and western Xinjiang, while some regions in northern Xinjiang demonstrate lower matching degrees. Conversely, AAA attractions are more widely distributed in remote areas, where the matching between vitality and attraction distribution is low, particularly in southern and eastern Xinjiang, revealing a notable mismatch between tourism resources and spatial vitality. By analyzing the matching relationship between tourism resources and spatial vitality, this study provides a scientific basis for optimizing the allocation of tourism resources in Xinjiang and enhancing regional tourism spatial vitality. Additionally, this study also offers valuable insights for tourism managers and planners to formulate more precise tourism development policies.