Urbanization level is a key indicator for socioeconomic development and policy making, but the measurement data and methods need to be discussed further due to the limitation of a single index and the availability and accuracy of statistical data. China is urbanizing rapidly, but the urbanization level at the county scale remains a mystery due to its complexity and lack of unified and effective measurement indicators. In this paper, we proposed a new urbanization index to measure the Chinese urbanization level at the county scale by integrating population, land, and economic factors; by fusing remote sensing data and traditional demographic data, we investigated the multi-dimensional unbalanced development patterns and the driving mechanism from 1995 to 2015. Results indicate that: The average comprehensive urbanization level at the Chinese county scale has increased from 31.06% in 1995 to 45.23% in 2015, and the urbanization level in the permanent population may overestimate China’s urbanization process. There were significant but different spatial and temporal dynamic patterns in population, land, and economic levels as well as at a comprehensive urbanization level. The comprehensive urbanization level shows the pattern of being high in the south-east and low in the north-west, divided by “Hu line”. The urbanization of registered populations presents high in the northern border and the eastern coastal areas, which is further strengthened over time. Economic urbanization based on lighting data presents high in the east and low in the west. Land urbanization based on remote sensing data shows high in the south and low in the north. The registered population urbanization level is lower than economic and land urbanization. County urbanization was driven by large population size, reasonable industrial structure, and strong government capacity; 38% and 59% of urbanization levels can be regarded as the key nodes of the urbanization process. When the urbanization rate is lower than 38%, the secondary industry plays a strong role in powering urbanization; when the urbanization rate is higher than 38% but less than 59%, the promotion effect of the tertiary industry is more obvious, and the secondary industry is gradually weakened. When the urbanization rate exceeds 59%, the tertiary industry becomes the major driver.

Cultural tourism is less seasonal than nature tourism. However, previous studies have mainly compared the tourist flow of scenic spots, and it is still unclear how cultural tourism affects regional tourism seasonality. This study investigated the seasonal patterns by analyzing the monthly inbound tourist flow of the 28 typical Chinese tourist cities from 2001 to 2012, and examined the effect of cultural tourism on weakening seasonality by using the random-response panel Tobit model. It was found that the seasonal patterns of inbound tourists present non-peak, one-peak, two-double, and three-peak regular fluctuations, and also have irregular fluctuations affected by emergencies and festivals. Cultural tourism can weaken the seasonality of regional tourism, while nature tourism products enhance tourism seasonality. Suitable travel times throughout the year and destination reception industry levels, locations, and external connections have a significant impact on the regional tourism seasonality, but climate comfort, foreign direct investment, and simply the number of hotels and international travel agencies are not significant for tourism seasonality.

Exploring the spatial distribution and influencing factors of firms’ location choices in the service-oriented digital industry is of great significance for achieving a new round of rapid development for the digital economy in the context of a post-epidemic situation. Based on the micro perspective of firms, this paper explores the spatial distribution and influencing factors of the location choice of the service-oriented digital industry in the Yangtze River Delta of China. The research shows that the number of the service-oriented digital industry firms varies greatly among cities in the Yangtze River Delta region of China, showing an obvious core–periphery structure. There are many firms in municipalities directly under the central government and provincial capitals, which are the core areas; the number of firms in non-provincial capital cities is small and they are marginal areas. The influencing factors include: per capita GDP, industrial structure upgrading, whether it is a municipality directly under the Central Government or a provincial capital city, whether it has opened high-speed rail, information transmission, number of employees in the computer service industry, number of patent applications, Internet infrastructure level, and scientific fiscal expenditure have a significant positive impact on its location choice, while the per capita total social consumer goods have a significant negative impact on its location choice.

The Sustainable Development Goals (SDGs) adopted by the United Nations in 2015 guide the important direction of high-quality urbanization in China’s main grain-producing areas (MGPAs), and improving the quality of urbanization is also crucial to achieve the SDGs. China’s MGPAs not only undertake the task of promoting urbanization but also of ensuring food security. The establishment of an index system based on SDGs can more effectively measure the urbanization quality of MGPAs. For the specific targets of the SDGs, this study established two sets of multidimensional indicator systems, whether including the goals of food and agriculture, and tracked the progress toward improving urbanization quality of China’s MGPAs, including 128 prefecture-level cities, during 2010–2018. We found that the comprehensive urbanization quality and the index of economic efficiency, urbanization level, and environmental quality showed an upward trend with significant regional differences and spatial agglomeration distributions, but the level of agricultural development and urban–rural co-ordination have declined in recent years; the ranking and distribution of urbanization quality, including agricultural development, varied significantly, and the number of cities belonging to the good co-ordination mode decreased as some cities changed to a lower level; and urbanization that does not sacrifice the agricultural capabilities of MGPAs could improve urbanization quality and implement the SDGs.