The exploration of the spatiotemporal variations and influencing factors of grain yield in major grain-producing regions is greatly important to maintain stable and sustainable agriculture. Taking Henan Province and Heilongjiang Province as examples, this study reveals the spatiotemporal characteristics of grain yield at the county level by using multisource data on the economy, society, and natural geography from 2000 to 2021 and employing methods such as coefficients of variation, standard deviational ellipses, and spatial autocorrelation analysis. Moreover, geographical detector and geographically weighted regression models are combined to explore the differences in significant influencing factors between these provinces and the spatial heterogeneity of regression coefficients, respectively. The following findings are drawn: (1) Grain yield in both provinces gradually increased, with notable differences in the annual growth rate, the proportion, and at the county level. (2) The number of high-yield counties significantly increased and their spatial distribution became more concentrated, indicating a notable shift in the main regions. (3) The overall spatial correlation of grain yield steadily increased, and the local spatial correlation transitioned from random distribution to gradual aggregation. (4) There were significant differences in the influencing factors, where geographical environment, socio-economic factors, and input factors all affected both provinces. In summary, this study provides a scientific reference for governments worldwide to formulate rational and effective food production policies, thereby contributing to global food security and sustainable social development.

Governmental attention towards the high-quality development of the Yellow River basin has brought new development opportunities for the hotel industry. This study aims to reveal the spatial-temporal evolution patterns and influencing factors of hotels in the Yellow River Basin from 2012 to 2022, based on economic, social, and physical geographic data of 190,000 hotels in the Yellow River flowing. With the help of a GIS technology system, the spatial-temporal evolution patterns of all hotels, star hotels, and ordinary hotels were explored, respectively. Then, the significant influencing factors of these patterns were revealed by using geographic detector and Person correlation analysis. The following conclusions were drawn: (1) the overall scale of the hotel industry in the Yellow River Basin expanded year by year, achieving rapid growth from 2016, and fluctuating around 2020 due to the impact of the novel coronavirus epidemic; the overall spatial distribution had significant regional differences, showing the structural characteristics of “southeast more, northwest less”; (2) there was a great difference in the degree of spatial autocorrelation agglomeration among prefecture-level cities, and the degree of agglomeration of both the hotel industry as a whole and general hotels decreased year by year, showing a random distribution in 2022; star hotels were always distributed randomly. Additionally, a strong synergistic correlation was shown between the number of ordinary hotels and the number of star hotels in local space; (3) overall, the development of the hotel industry was significantly affected by seven factors: structural force, macro force, ecological force, internal power, consumption power, intermediary power, and external power. There were differences in the forces acting on different types of hotels, which gives a pattern recognition in-depth.

To promote sustainable agricultural development in small town areas during rapid industrialization, it is important to study the evolution of agricultural non-point source pollution (ANSP) and its influencing factors in small town areas in the context of rapid industrialization. The non-point source inventory method was used to study the characteristics of ANSP evolution in 14 small town areas in Gongyi City from 2002 to 2019. Using the spatial Durbin model and geographical detectors, the factors influencing ANSP in small town areas were analyzed in terms of spatial spillover effects and the spatial stratified heterogeneity. The results showed a zigzagging downward trend of ANSP equivalent emissions over time. Spatially, the equivalent emissions of ANSP showed a distribution pattern of being high in the west and low in the east. There was a significant positive global spatial autocorrelation feature and there was an inverted “U-shaped” Environmental Kuznets Curve relationship between industrialization and ANSP. Affluence, population size, and cropping structure positively contributed to the reduction of ANSP. Population size, land size, and industrialization were highly influential factors affecting the spatial variation of ANSP and the interaction of these factors was bivariate or nonlinearly enhanced. This study provides a feasible reference for policymakers and managers to develop reasonable management measures to mitigate ANSP in small town areas during rapid industrialization.