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.

As a pillar industry carrying China’s ambition in manufacturing upgrades and energy transformation, the new energy vehicle (NEV) industry has received much attention from the government and investment institutions. The spatial pattern of the industry, which is undergoing dramatic changes, urgently needs to be studied retrospectively. This paper explores the spatial distribution pattern of related industries and their influencing factors using data related to NEV industry listed companies from 2008 to 2021. Spatial statistical analysis and stepwise regression analysis were conducted in this study. At the national level, the study found that an “8” industrial axis was formed, with the Yangtze River Delta, the Pearl River Delta, and the Beijing–Tianjin–Hebei city cluster as the core. At the provincial level, it was found that the traditional auto industry-dominant regions do not have a competitive advantage in the NEV field. After stepwise regression of the potential factors, five key factors determining the number of listed NEV enterprises in each province were identified, namely, policy strength, patents, per capita wage, tertiary industry share, and road density. The research results improve the understanding of NEV industry development rules in related disciplines and provide a reference for the spatial arrangement of the NEV industry to be coordinated and optimized at the regional level.

The diversified innovative strategies adopted by the new retail format in urban spaces have significantly driven retail transformation and innovation. The combination of online platforms and physical stores provides a substantial advantage in market competition. This paper takes “Freshippo”, a typical representative of China’s new retail, as an example. Based on multi-source data and using tools such as GIS spatial analysis, statistical analysis, and geographical detectors, this study comprehensively examines the spatial clustering characteristics and influencing factors of Freshippo physical stores in Shanghai. The findings show that Freshippo has significantly expanded in the Shanghai fresh food market by innovatively opening various types of stores. However, there are substantial differences in the proportions of different types of stores, with 94% of the stores having online retail capabilities. Each offline store in the new retail format presents a multi-level “complementary” spatial distribution feature across the urban space, with distinctive clusters in the urban central districts, urban periphery areas, and outer suburban districts. The radiation range of logistics and distribution services exhibits characteristics of “central agglomeration and multi-point distribution”, providing residents with diverse and accurate services. Additionally, the comparison of multiple model results shows that the location selection of various types of new retail stores is significantly influenced by multiple factors, especially the nonlinear amplification effect of factor interactions on store agglomeration. These findings provide an important scientific reference for understanding the development of new retail formats and offer new ideas that promote the transformation and innovation of the retail industry, thereby achieving sustainable development.

This study delves into the factors influencing the willingness of rural land transfers in different terrain areas, aiming to promote the improvement of land transfer institutions and accelerate the process of scale farming. Based on rural survey data from Anhui and Qinghai provinces in China, this research uses geographical detector and Binary Logistic Model to explore the differential factors affecting the willingness of farmers to participate in land contract transfer in the first and third terrain areas of China. The study examines four dimensions, including individual characteristics, family endowments, social support strategies, and geographical environment. The findings reveal the following: (1) By comparing the mean values, standard deviations, and coefficients of variation of the data from both provinces, it is evident that the indicators of individual characteristics, family endowments, social support strategies, and geographical environment differ significantly between the two provinces. This indicates substantial disparities in the basic attributes of farmers and their living environments. (2) The single-factor explanatory power significantly influencing farmers’ willingness to engage in land transfer varies considerably and is statistically significant at the 1% level. The types of interaction between two factors mainly include dual-factor enhancement, nonlinear enhancement, single-factor nonlinear attenuation, and nonlinear attenuation. (3) There are commonalities and differences in the factors that significantly influence farmers’ willingness to participate in land transfer in the two provinces. Common factors influencing farmers’ land transfer willingness in both provinces include: the educational level of household heads, the health status of household heads, the number of family laborers, the arable land area, the differentiation of agricultural management objectives, the proportion of agricultural operating income, labor service economy, and relocation policies. Factors showing different influences include: the age of household heads, school-age children, the number of family members engaged in different occupations, the proportion of income from off-farm employment, minimum guarantee policies credit support, location distance, and terrain undulation. Therefore, in formulating land transfer policies, the government should prioritize significant driving factors influencing farmers’ decision-making behavior in different regions. It is essential to develop and implement land transfer policies tailored to local conditions with the primary goal of safeguarding the rights and interests of the principal stakeholders, thus achieving sustainable land utilization.

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.

Building cycling-friendly street environments is crucial for promoting sustainable urban mobility. However, existing studies exploring the influence of the built environment on cycling have paid limited attention to the three-dimensional characteristics of street landscapes and have mostly relied on linear assumptions. To address these gaps, this study employs street view imagery and interpretable machine learning methods to investigate the nonlinear and interaction effects of street landscape elements on residents’ cycling preferences in Xiamen Island, China. The results reveal that the visual indices of buildings, sky, vegetation, and roads are the most influential variables affecting cycling preferences. These factors exhibit pronounced nonlinear relationships with cycling preference. For instance, buildings exhibit a threshold effect, with positive influences on cycling preference when the building index is below 0.12 and negative effects when it exceeds 0.12. A low sky index significantly suppresses cycling preference, whereas higher values offer only limited additional benefits, with an optimal range of 0.1–0.25. Vegetation contributes positively only at relatively high levels, suggesting that its index should ideally exceed 0.3. The road index shows a V-shaped relationship: values between 0.15 and 0.25 reduce cycling preference, whereas values below 0.15 or above 0.25 enhance it. Moreover, clear interaction effects among these variables are observed, suggesting that the combined visual composition of the streetscape plays an important role in shaping cycling preferences. These findings deepen the understanding of how street landscape characteristics influence cycling behavior and provide nuanced, practical insights for designing cycling-friendly streets and promoting sustainable travel in urban environments.