Research on the persistence of regional entrepreneurship is of great significance for identifying the sustainable development capacity of regional economies and spatial imbalance issues. Based on long-term city-scale entrepreneurial data from 1989 to 2022, this study employed Pearson correlation coefficient and kernel density estimation methods to explore the duration, intensity, and spatiotemporal change characteristics of new firm formation rate (NFFR) in 261 Chinese cities. By constructing a four-dimensional analytical framework that includes institutional, historical, structural, and inherent factors, and applying a spatial Durbin error model (SDEM), the study examined the factors that had long influenced the NFFR in these cities and their spatial effects. The findings are as follows: 1) During the economic transition period, the NFFR at the prefecture-level in China exhibited a certain degree of persistence, with four distinct time periods, each spanning 8 to 15 years. 2) Over time, the persistence of entrepreneurship in Chinese cities showed a weakening trend. The strongest entrepreneurial persistence occurred between 2008 and 2013, during which more than half (54.02%) of the cities maintained the same entrepreneurial levels. 3) The phenomenon of entrepreneurial persistence mainly occurred in cities with low levels of entrepreneurship. 4) Factors that had long positively influenced NFFR include administrative hierarchy, entrepreneurial culture, major historical infrastructure, and coastal location, while historical population density and topographical variation had been significant factors in suppressing regional entrepreneurial levels. Over time, the effects of regional knowledge base and the proportion of state-owned enterprises had shifted from positive to negative. The conclusions of this study can serve as a reference for formulating policies aimed at promoting coordinated and sustainable regional economic development.

With the increasing competition in the field of technology, cities urgently need to improve their technological resilience to cope with the uncertainty of the external environment. Based on the China Urban Innovation Index and related data from 2001 to 2016, this study assessed the technological resilience levels of cities using an economic resilience measurement model, and analyzed the temporal and spatial change characteristics and influencing factors. The results indicate that: 1) The technological resilience levels of cities in China showed a continuous upward trend during the study period, with the urban technological resilience index turning positive after 2011 and increasing at a rapid pace. 2) High-resilience cities in China experienced a shift from regional concentration to a relatively balanced distribution, with the Yangtze River Delta emerging as a typical region where cities with high-high technological resilience cluster. 3) Besides educational human capital, advancement of industrial structures, economic development level, and degree of openness to the outside world all had significant positive impacts on the technological resilience of cities in China. The results of subgroup regression analysis suggest that in the eastern regions, educational human capital significantly suppressed technological resilience, while in the central and western regions, enhancing the level of openness was an effective way to boost technological resilience. 4) The influencing mechanisms analysis showed that technological resilience did not exist in isolation but continuously adapted and evolved dynamically within a complex network of risks and diverse influencing factors. The results of the study will help Chinese cities to better cope with technological changes and economic fluctuations, and provide theoretical support for the long-term sustainable development of cities in the global competition.

The burgeoning digital economy is a key driver in shaping urban and regional landscapes. The digital transformation of value chains is crucial for promoting high-quality growth in the digital creative sector. Based on the spatiotemporal production and service data of China's online gaming industry from 2010 to 2022, this study analyzed the spatial characteristics of China's online gaming industry network from the perspective of the digital creative value chain, and revealed its influencing factors using the valued Exponential Random Graph Models (ERGMs). The research findings provide several significant insights. First, the Chinese online gaming industry network features a hierarchical pyramid structure and a distinctive spatial configuration of "strong triangle" and "weak diamond". This is characterized by strong core connections and the integration of small to medium-sized cities. The network also displays path dependency and hierarchical attraction traits across cities. Second, there are distinct differences in the spatial layouts of industrial networks at different stages of the value chain. The production-operation network adopts a triangle + diamond configuration, while the operation-publishing network shifts to a radial structure centered around the Beijing-Tianjin-Hebei region. Last, the network can be influenced by factors such as administrative hierarchy, the growing new economy, institutional and social proximity, and contagion effects. It is important to recognize that these factors can vary across different developmental phases and segments of the value chain. This study contributes to the academic discussion by providing a strong theoretical framework and empirical evidence for analyzing urban networks in the digital era. It also offers strategic insights for the optimized allocation of the digital creative industry, promoting synergistic interactions, and facilitating the evolution and enhancement of value chains.

Developing the tourism industry is a key pathway to improving people's livelihood and well-being and an important force for consolidating the foundation of common prosperity in the Yangtze River Economic Belt. Taking the 108 prefecture-level cities in the Yangtze River Economic Belt as the research area, this study constructed a theoretical framework for the impact of tourism development on people's well-being, and examined the direct and spatial spillover effects of tourism on well-being based on the measurement of tourism development and the level of people's well-being. The results show that: 1) The development of the tourism industry and the well-being of people in the Yangtze River Economic Belt both showed a trend of increase over the study period, with serious spatial polarization in the development of the tourism industry and a convergence trend in the spatial polarization of people's livelihood and well-being. 2) The spatial differentiation characteristics and trends of change of tourism development and people's well-being showed a certain continuity, with a gradual spread from east to west and an increase in the southwest-northeast direction. 3) Tourism is an effective way to enhance people's well-being, and the positive direct effect of tourism development is manifested as "material well-being>cultural well-being>social well-being", while it has a negative effect on ecological well-being. 4) There is a significant nonlinear relationship between tourism development and livelihood and well-being, the direct and indirect effects of tourism development on people's livelihood and well-being both showed an inverted "U" characteristic, and the impacts showed significant regional heterogeneity. The research conclusion can provide scientific basis for optimizing the spatial layout of regional tourism industry and formulating differentiated strategies for improving people's livelihood and well-being, and has practical reference value for promoting the high-quality coordinated development and common prosperity goals of the Yangtze River Economic Belt.

As a rail transit infrastructure linking cities in a certain region, intercity railways play an important role in promoting regional integrated social and economic development. In the formulation of intercity railway construction plans, after determining the scale and layout of the line network, it is also necessary to preliminarily divide the construction phases of each line. Due to the different roles and positions of intercity railways in an urban agglomeration, scientifically planning their construction sequence is conducive to forming a reasonable spatial and temporal layout, as well as achieving the best socioeconomic benefits. Based on the forecasted long-term distribution of population and economy, this study formulated the spatial interactive accessibility of intercity railways, constructed a value model for intercity railway connections, and proposed a "round by round comparison-dynamic progressive" evaluation method for the construction sequence of intercity railways. Taking the Guangdong-Hong Kong-Macao Greater Bay Area urban agglomeration as a case study, we evaluated and ranked the intercity railway lines in the medium and long-term development plans. The study identified some key routes with high value and some ordinary routes with small value increment, which generally conforms to the recent construction project arrangement of the Greater Bay Area urban agglomeration, but there are also some differences. The proposed connection value model can provide a reference basis for the formulation and implementation of planning schemes.

China has fully entered a stage of moderate aging, and the elderly migration population has become a focal group in contemporary urban and rural governance. Supported by the data of interprovincial migration flows of China's elderly population from 2000 to 2020, we used social network analysis and the eigenvector spatial filtering Poisson pseudo-maximum likelihood estimation (ESF PPML) gravity model to explore the spatial-temporal variability of influencing factors of interprovincial elderly migration in China. The following conclusions were drawn: 1) From 2000 to 2020, the scale of China's interprovincial elderly migration population continued to grow, and the main destinations remained basically stable. 2) The migration pattern of the elderly population remained stable but also with some changes: Generally, a large number of elderly people migrated from the northeastern, central, southwestern, and northwestern regions to the northern, eastern, and southern regions; however, from 2015 to 2020, there was a reversal trend in the outflow of elderly people from the southwestern and northwestern regions. In addition, the density of the elderly migration network first decreased and then increased, and the source areas became increasingly dispersed while the destination areas remained concentrated. The main migration flows occurred more frequently between adjacent provinces over time. 3) The ESF PPML model indicated that traditional gravity factors (population size, geographical distance), living costs, natural environment factors, health service facilities, resource depletion level, and social network factors jointly drove the interprovincial elderly migration pattern between 2000 and 2020. 4) Over the 20-year period, the hindering effect of living costs on interprovincial elderly migration showed a weakening trend, while the promoting effect of social network factors on elderly migrants had gradually increased. The impact of health service facilities on the elderly migrants was relatively weak and fluctuated, while natural environment amenity only significantly affected the scale of out-migration of the elderly population. The findings of this study provide a scientific and empirical foundation for actively addressing population aging and promoting high-quality population development in the new era.

In the process of suburbanization, the consumption life of residents in large residential areas has developed new characteristics in the context of urban socio-spatial reconstruction. Based on the theoretical perspective of spatial behavior, this study constructed a multidimensional analytical framework of "social group-consumption place-consumption behavior" to deeply analyze the internal relationship between social, behavioral, and spatial factors in the consumption behavior of residents in large residential areas. The study adopted the method of questionnaire survey, and analyzed the spatial and temporal characteristics and influencing factors of the consumption behaviors of the residents in the Wangjing area of Beijing through the binary logistic regression model. The following results were obtained: 1) The consumption behavior of Wangjing area residents showed the characteristics of high proportion of food and education, cultural, and entertainment spending, and at the same time showed four varied types of consumption demand, namely, personal basic consumption type, self-development consumption type, family development consumption type, and leisure and health consumption type. 2) In the Wangjing area, the loss of the centrality of the municipal shopping malls is persisting. Residents' social consumption demand and spatial cognitive evaluation of places jointly promoted the formation of differentiated consumption behavior choices of neighborhood-level, commercial-level, and city-level consumption places. 3) The consumption behavior of residents in large residential areas presented the pattern of "localization" in time and "near-residence" in space. The service functions of job-housing, urban and virtual consumption spaces exhibited characteristics of balance, differentiation, and convenience. 4) The length of work-related commute, length of residence, and age of the residents had a stronger explanatory power for the behavior of job-housing consumption spaces, and the influencing factors related to monthly income and type of occupation had a stronger influence on the behavior of urban consumption spaces.

Rural living space is a social space formed by the overlapping daily activities of rural residents. Clarifying the characteristics and influencing factors of rural living space is helpful for understanding the reality of rural reconstruction and responding to the national strategy of vigorously and effectively promoting rural revitalization. This study constructed an analytical framework of radiation range-central place structure, grounded in the concept of "dependency", to elucidate the characteristics and influencing factors that shape rural living space. Drawing from extensive firsthand research conducted between 2021 and 2023, including surveys, interviews, and village registration data from rural residents in Zengcheng District, Guangzhou City, this study examined the rural living behaviors of these residents. Employing linear and binary logistic regression models, the study investigated the social and physical determinants of rural living space. We concluded that: 1) The core of rural living space consistently resides within the village-town-county nexus, albeit exhibiting an uneven spatial distribution with villages and towns as primary fields. 2) Evolving urban amenities attract residents to move to multiple towns or cities based on individual needs, yet certain groups such as women, individuals with limited education, elderly, and households with minors exhibit constrained activity patterns due to personal or family factors. 3) Forward-looking rural development strategies should prioritize enhancing residents' actual living standards by focusing on spatial quality rather than sheer construction scale, mandating proactive integration into planning and public policy frameworks.

Mobile phone signaling data have been widely used in spatiotemporal behavior research, but their potential application in urban planning is limited due to the lack of information on activity purposes. To address this issue, this study proposed a method to infer the purposes of non-work activities from mobile phone signaling data. The method integrates multisource data, and employs a multinomial logit model to explore the relationship between activity purposes and individual attributes, temporal characteristics, and spatial properties of activities. The key innovation lies in the introduction of a location visit frequency variable that reflects long-term behavioral patterns. Empirical results show that incorporating visit frequency significantly improves the model's goodness of fit from 0.265 to 0.442, and increases the overall prediction accuracy from 58.0% to 69.2%. While maintaining interpretability, this method substantially enhances the accuracy of non-work activity identification, providing new insights into residents' spatiotemporal behavior patterns. The findings offer valuable data support and decision-making references for public facility planning, traffic demand forecasting, and business spatial layout optimization.

A scientific understanding of the spatial pattern change and formation mechanisms of Africa's territorial functions is crucial for fundamental research on Africa's land resources. It also aligns with China's Going Global strategy and supports the development of a China-Africa community with a shared future. Using China's territorial dominant functions of urbanization, food security, and ecological security as spatial reference benchmarks, this study classified Africa's territorial space into three major functional areas: urban-rural construction, agricultural production, and ecological protection. By applying spatiotemporal cubes, spatial transformation intensity models, and interpretable machine learning, this research analyzed the spatial evolution characteristics and regional disparities of Africa's territorial functions from 2002 to 2022. Furthermore, the study examined the mechanisms behind these changes by exploring the importance and interaction strengths of key driving factors. The results indicate that: 1) Over the study period, approximately 3.97% of Africa's territorial functional spaces had undergone structural transformation. Urban-rural construction space had expanded significantly by 9.71%, while the per capita scale of construction land had decreased by 33.78%, highlighting an increasing imbalance between population and available land. Agricultural production space had shown a fluctuating decline, with a distinct zone of drastic change forming along the southern edge of the Sahara Desert, while the internal structure of ecological protection space had significantly changed, albeit in an optimizing trend overall. 2) A clear high-dynamic transformation zone had emerged between 5°N and 15°N, where the dynamic mutual transformation between agricultural production and ecological protection spaces was most pronounced. Despite ongoing desert greening efforts, significant land degradation persisted, leading to the expansion-related structural transformations of urban-rural construction space being less prominent on a continental scale. 3) Socioeconomic development and population growth had limited effects on the structural changes of Africa's territorial functional spaces at a continental scale. Instead, variations in hydrological conditions such as atmospheric vapor pressure, temperature, and precipitation, as well as geographical factors like latitude and topographic relief, played a decisive role. Africa's territorial functional space patterns are determined by spatial transformation processes with varying scales, intensities, and driving mechanisms. Future Chinese investments and spatial development strategies in Africa must not only take into account Africa's own unique spatial evolution processes and the underlying patterns that drive these transformations, but also incorporate a thorough understanding of the continent's diverse geographical and socioeconomic contexts. It is crucial to fully leverage the spatial differentiation characteristics, including regional disparities in land use, ecological zones, and development potentials, as well as the emerging trends of its territorial functional spaces. By doing so, these strategies can maximize the comprehensive spatial benefits, fostering sustainable development outcomes that align with both African realities and Chinese strategic objectives in the region, ultimately fostering a mutually beneficial partnership.

The Middle East has consistently served as a crucial geopolitical arena in the strategic maneuvers of major powers. Analyzing the evolution of geopolitical relations between China, the United States (US), Russia, and Middle Eastern countries is essential for comprehending both regional dynamics and the broader global geopolitical landscape. This study constructed a geo-position evaluation model to examine the spatiotemporal evolution of geo-relations between China, US, Russia, and five countries within the Shia Crescent from 2010 to 2022. It employed an event data analysis to validate and analyze results derived from this evaluation model, leading to several key conclusions: 1) The event data analysis aligns well with the geopotential model in capturing shifts in geographical relations. The geopolitical power dynamics involving China, US, and Russia in the Shia Crescent have undergone three distinct phases: US dominance (2010-2012), Russian engagement (2013-2017), and a phase characterized by a Sino-US-Russian balance of power (2018-2022). 2) In a comparative analysis between these countries, US geopolitical influence over the five countries within the Shia Crescent exhibited a trend of fluctuating decline; Russian influence remained generally stable but experienced localized volatility. Meanwhile, China's role was evolving from that of a passive participant to an active architect shaping regional circumstances. 3) Factors driving changes in geopolitical relations between China, US, Russia, and these five countries can be categorized into external influences and internal barriers. External influences include geographic location as a foundational context; geo-economics as a driving force; geo-military aspects as manifestations; and geo-culture serving as a coordination mechanism. These latter three factors impact changes in geo-relations based on their positive or negative directional forces relative to geographic positioning. Internal barriers primarily consist of cultural fragmentation and political instability. Together these two sets of factors interact within a dynamic system that forms a "one body with two sides" mechanism influencing geographical relation change.

The accurate simulation of ice and snow melt runoff is crucial for flood disaster warning and water resource optimization management in inland river basins. The long short-term memory (LSTM) deep learning model can be effectively used in the field of hydrological prediction. However, The LSTM model frequently falls short in fully harnessing the spatial characteristics of meteorological elements. In this study, the headwaters of the Yarkant River were taken as the research area. The spatial characteristics of air temperature were extracted using the spatial moments of catchment (SMC) method. The long time series process rule was learned using LSTM, and a daily runoff prediction model called SMC-LSTM was developed. This model integrates the spatial and temporal characteristics of air temperature. The strengths and limitations of the improved LSTM model in predicting runoff from ice and snow melt recharge type rivers were evaluated by comparing its results with both the LSTM model and the hydrological model—the soil and water assessment tool (SWAT). The results indicate that the Nash efficiency coefficient of the SMC-LSTM runoff model is approximately 20% higher than that achieved by SWAT's daily scale hydrological model. Moreover, during forecast periods ranging from 1-7 days, the SMC-LSTM runoff prediction model outperforms the LSTM model by 5%-15% in terms of Nash efficiency coefficient, while also exhibiting improved peak flow predictions by 2%-22%. Notably, these advantages become more pronounced when forecasting runoff for shorter periods. Overall, incorporating SMC enhances the accuracy of runoff prediction within an LSTM framework.

Palaeoflood events were the response of river systems to regional climatic variability. Through extensive and detailed field investigations in the Lihe River Basin, a TZC profile containing typical palaeoflood sediments was found at the riverbank platform in the middle reaches of the river, and the profile was observed, measured, stratified, and sampled. The macroscopic and microscopic characteristics of sediments (magnetic susceptibility, grain-size composition, and end-members) indicate that the three groups of palaeoflood sediments are typical overbank flood deposits (OFD) in the plain area, and record three phases of palaeoflood events. The results of sample OSL datings and Bacon age-depth model simulations reveal that the three palaeoflood events occurred in the Late Pleistocene 12.0-11.6 kaBP, Middle Holocene 4.2-3.8 kaBP, and Late Holocene 1.8-1.5 kaBP, respectively. These periods correspond to global climate transitions or abrupt climate events, during which the monsoon climate changed abnormally, its variability increased, and both flood and drought events occurred. In the same periods, there were also large climate variability and frequent extreme climate events in the Lihe River Basin, which caused sudden heavy rainfall and led to flood disasters in the basin. This study is of great significance for flood prediction, flood disaster reduction in the Lihe River Basin and even the Huaihe River Basin, as well as for revealing the response pattern of regional river hydrology to global change.