It is of great significance to study the spatial and temporal distribution characteristics and influencing factors of national key cultural relics protection units in the Yangtze River Delta region. Taking the national key cultural relics protection units in the Yangtze River Delta as an example, the spatial and temporal distribution characteristics and influencing factors of the sites were studied by using the methods of nearest proximity index, spatial density, standard deviation ellipse, and geographic detector. It can be concluded that the number of key cultural relics under protection in the Yangtze River Delta region is concentrated in the Song, Yuan, Ming and Qing Dynasties and since modern times, with the main types of ancient buildings, and the largest number in the 100-year time scale since modern times. On the whole, the spatial density of the national insurance unit has formed contiguous areas dominated by “Shanghai, Suzhou, and Wuxi,”“Nanjing, Yangzhou,”“Hangzhou and Shaoxing” and single core areas dominated by Ningbo and Huangshan, with the spatial distribution characteristics of “three contiguous areas and two single cores.” During the eight periods from prehistoric period to modern times, the national security units showed a state of agglomeration in spatial distribution, showing the transfer trend of “Southwest-northwest-southeast-southeast-southeast-northwest-northeast” and the evolution process of “agglomeration—dispersion—agglomeration—dispersion—agglomeration—agglomeration.” Natural environmental factors such as altitude and river network density, and socio-economic factors such as economic development level, abundance of scenic spots, abundance of intangible cultural heritage resources and total population have a significant impact on the spatial distribution of national protection units in the Yangtze River Delta.

Based on the theory of cultural ecology and the spatial perspective of geography, this paper studies the spatial distribution characteristics and influencing factors of 320 key rural tourism villages in China by using spatial analysis methods such as the nearest neighbor index, equilibrium measure, kernel density analysis, hot spot analysis, geographical linkage rate, and quantitative geography research methods. The results show that: 1) The spatial distribution type of the key rural tourism villages in China is condensed, but the distribution is relatively balanced in the provinces. 2) Overall, the spatial distribution pattern of “two cores, three plates, and the rest close clusters” is presented, and the spatial distribution characteristics are more in the east and less in the west, and more in the edges and borderlands of provinces and regions. 3) On the provincial scale, the hot spots and cold spots of the key rural tourism villages in China show obvious distribution characteristics of “blocky clusters” from east to west. 4) Tourist market, resource endowment, social economy, population density, macro policy, and road traffic are the main factors affecting the spatial distribution of key rural tourism villages in China.

Based on the data from high-level scenic spots in Jiangsu Province in 2020 after the outbreak of the epidemic, this paper uses the geographic space method to display the temporal and spatial change process of the number of tourists in scenic spots during COVID-19, and establishes a multivariate lag nonlinear regression model to empirically study the mechanism of changes in the number of tourists in tourist attractions during the epidemic. The results show that: (1) Except for the restrictions such as closing the park, after the outbreak of COVID-19, the number of tourists in the scenic spots has recovered, and the recovery speed of different types of scenic spots is different. Tourists in cultural scenic spots and modern amusements are recovering slowly, and they are less resilient due to the impact of the epidemic. (2) The situation of the COVID-19 epidemic and related public opinions have an impact on the number of tourists in the scenic spot. The degree of impact from large to small is the number of newly diagnosed domestically, the public opinion index of COVID-19, the number of newly imported overseas, and the number of newly diagnosed overseas. From the perspective of time, the number of newly diagnosed domestic and foreign epidemics has a significant inhibitory effect on the number of tourists in the scenic spot before June 2020, and the impact is not obvious after June, while the number of newly imported overseas cases and the epidemic public opinion index have a significant longterm negative effect on the number of tourists in the scenic spot (3) The time lag days for the impact of COVID-19 on the number of tourists in scenic spots are usually 1-2 days, and as the event progresses, the time lag is extended to 3-4 days, reflecting that tourists are less sensitive to COVID-19. Events such as holidays and other events can also dramatically alter the impact of the COVID-19 pandemic. (4) The differentiation, volatility, and lag of the impact of COVID-19 on tourist attractions stem from the impact of the epidemic on tourists’ travel motivation. In the repeated epidemic environment, tourists’ travel is affected by the distance between the epidemic site and the tourist location, and tourists are more inclined to open, and close-up scenic spots. To sum up, natural and ecological scenic spots should actively seize the opportunity for recovery and speed up the pace of recovery. Historical, cultural, and modern recreational scenic spots need to wait for the opportunity and make good use of holidays to drive recovery through marketing strategies; areas far away from the epidemic should pay attention to tourists. It is necessary to take decisive measures to close the park during the outbreak of the epidemic in order to avoid the danger caused by lag.

Hypsometric integral (HI) has been widely used in Geomorphology study as an index to depict the recent and active structures for its sensitivity to the influence of tectonic and different lithology. The traditional methods to study HI often use drainage basins, this may be dependent on basin geometry and drainage area. To avoid this influence, HIs using two digital elevation models of 30 and 90 m of pixels resolution were calculated in Bailongjiang basin. The relations between relief amplitude, mean elevation and HI are also analysed in different analytical window. Meanwhile, spatial pattern analysis using Global Moran’s index and Getis-Ord Gi* index was applied to measure the spatial autocorrelation of HI and the distribution of hot spots. It can be concluded that spatial distributions of HI do not show clear spatial patterns and correlation with mean elevation and relief amplitude. HI are independent of the resolution of DEM and lithology but are scale influenced. The hot spots define the upliftment of the region and active faults in the upstream of Bailongjiang basin, the cold spots define tectonic inactivity or subsidence in the downstream of Bailongjiang and some river valley regions.