Search Results (8)

This study investigates the relationship between intercity transportation accessibility and network centrality across South Korea by integrating Global Positioning System (GPS)-based mobility data with graph-theoretic centrality measures, including degree, PageRank, local clustering coefficient, harmonic, Katz, and information centrality. Employing both statistical modeling and machine learning techniques, this analysis uncovers key structural patterns and interaction effects within the national mobility network. The findings yield several important insights. First, the Seoul Metropolitan Area emerges as the dominant mobility hub, with Busan, Daegu, and Daejeon functioning as secondary centers, reflecting a polycentric urban configuration. Second, intermediary transfer hubs—despite having lower direct connectivity—substantially enhance overall network efficiency and interregional mobility. Third, transportation accessibility, particularly in relation to regional transit and highway infrastructure, exhibits a significant association with centrality measures and strong feature importance, identifying these modes as primary determinants of spatial connectivity. Fourth, the impact of accessibility on centrality is characterized by nonlinear relationships and threshold effects. By elucidating the complex interplay between mobility infrastructure and spatial network dynamics, this study contributes to a more comprehensive understanding of regional connectivity and network centrality and offers policy-relevant insights for future transportation planning. Full article

Understanding the effectiveness of different quarantine strategies is crucial for controlling the spread of COVID-19, particularly in regions with limited data. This study presents a SCIRD-inspired model to simulate the transmission dynamics of COVID-19 in medium-sized cities and their surrounding satellite towns. Unlike previous works that focus primarily on large urban centers or homogeneous populations, our approach incorporates intercity mobility and evaluates the impact of spatially differentiated interventions. By analyzing lockdown strategies implemented during the first year of the pandemic, we demonstrate that short, localized lockdowns are highly effective in reducing virus propagation, while intermittent restrictions balance public health concerns with socioeconomic demands. A key contribution of this study is the validation of the epidemic model using real-world data from the 2021 lockdown that occurred in a medium-sized city, confirming its predictive accuracy and adaptability to different contexts. Additionally, we provide a detailed analysis of how mobility patterns between municipalities influence infection spread, offering a more comprehensive mathematical framework for decision-making. These findings advance the understanding of epidemic control in regions with sparse data and provide evidence-based insights to inform public health policies in similar contexts. Full article

The phenomenon of patient mobility is becoming increasingly frequent, altering the actual service ranges of hospitals across various cities. However, its impact on the spatial equity of healthcare services at the national scale has yet to be fully explored. This paper aims to reveal the impact of intercity patient mobility on healthcare equity in China. Using one million patient mobility records from online healthcare platforms, we construct the 2023 Cross-City Patient Mobility Network in China and identify the patterns of cross-city patient mobility. Furthermore, we employ the Dagum Gini coefficient to measure the spatial disparities in per capita healthcare services before and after patient mobility. The results show that: (1) cross-city patient mobility exhibits administrative boundary effects and reflects the administrative hierarchy system, yet megacities extend their healthcare service ranges beyond provincial and urban agglomeration boundaries; (2) patient mobility enhances the equity of per capita healthcare services at both intra-provincial and inter-provincial levels, with inter-provincial disparities contributing significantly more than intra-provincial disparities—a trend further reinforced by patient mobility. This study not only provides a methodological framework for understanding the impact of patient mobility on the healthcare system but also offers empirical support for public health policymaking. Full article

Studies on urban shrinkage in eastern China lack relevant contributions. This study investigates population dynamics in Jiangsu Province, focusing on county-level cities to understand the factors influencing urban population size. It developed a multidimensional indicator system per 2014, 2017, 2020, and 2023 statistical data, incorporating road network accessibility, socio-economic levels, resident income, public services, and the built environment. Employing static fixed effects models and dynamic panel regression models, it analyses trends and evolutionary patterns of permanent population changes. The findings indicate an overall increase in the permanent population over the past decade, although the growth rate has slowed in the last five years. Notably, there is a significant spatial clustering of population growth and contraction, often aligned with high-speed transportation corridors. Unlike existing research findings, the urban shrinkage pattern in Jiangsu Province is unique. Some cities in northern Jiangsu exhibit a ‘relative contraction’ pattern, where population decline coexists with high economic growth and spatial expansion. Key short-term factors influencing population size include the city’s economic level and residents’ disposable income, while long-term factors encompass city level, industrial structure, and transportation networks. Small towns exhibit ‘close-range mobility’, with primary short-term influences stemming from residents’ income, public service quality, and intercity transportation accessibility. Indeed, long-term factors primarily reflect economic development quality and urban employment rates. Full article

Understanding intercity mobility patterns is important for future urban planning, in which the intensity of intercity mobility indicates the degree of urban integration development. This study investigates the intercity mobility patterns of the Greater Bay Area (GBA) in China. The proposed workflow starts by analyzing intercity mobility characteristics, proceeds to model the spatial-temporal heterogeneity of intercity mobility structures, and then identifies the intercity mobility patterns. We first conduct a complex network analysis, based on weighted degrees and the PageRank algorithm, to measure intercity mobility characteristics. Next, we calculate the Normalized Levenshtein Distance for Population Mobility Structure (NLPMS) to quantify the differences in intercity mobility structures, and we use the Non-negative Matrix Factorization (NMF) to identify intercity mobility patterns. Our results showed an evident ‘Core-Periphery’ differentiation characterized by intercity mobility, with Guangzhou and Shenzhen as the two core cities. An obvious daily intercity commuting pattern was found between Guangzhou and Foshan, and between Shenzhen and Dongguan cities at working time. This pattern, however, changes during the holidays. This is because people move from the core cities to peripheral cities at the beginning of holidays and return at the end of holidays. This study concludes that Guangzhou and Foshan have formed a relatively stable intercity mobility pattern, and the Shenzhen–Dongguan–Huizhou metropolitan area has been gradually formed. Full article

Recent research has attempted to document large-scale emotional contagion on online social networks. Despite emotional contagion being primarily driven by in-person mechanisms, less research has attempted to measure large-scale emotional contagion in in-person contexts. In this paper, I operationalize the temporal emotions associated with a particular city at particular points in time using sentiment analysis on Twitter data. Subsequently, I study how emotions converge between seven proximal cities in the state of Virginia, using two-way fixed effect models. I find that positive emotions tend to be synchronous between cities, but that effect is conditional on the level of contact between city residents at that period of time, as indicated by cell phone mobility data. I do not find any synchrony based on other types of emotions or general sentiment. I discourage drawing causal conclusions based on the presumed existence of several unmeasured sources of bias. Full article

Estimation of economic development in advance is benefit to test the validity of economic policy or to take timely remedial measures for economic recession. Due to the inevitable connections between human mobility and economic status, estimation of economic trend in advance from easily observable big data in human mobility has the superiority of authenticity, timeliness, and convenience. However, high-precision quantitative relations between human mobility and economic growth remain an outstanding question. To this issue, we firstly analyzed and compared the general patterns of human mobility and economic development; then, a novel, simple, and effective hybrid human mobility indicator ( $HHM{I}_i$ ) of weighted human mobility networks was proposed to quantitatively estimate economic growth. $HHM{I}_i$ contained two parts, that is, the interaction volumes of a given city with all participation cities and only top hub cities, respectively. This implied that the economic growth of a city is affected by not only its own strength, but also the cooperation with hub cities. Several empirical experiments demonstrated that the proposed $HHM{I}_i$ had an exceedingly high estimation ability of economic growth, especially for the tertiary industry. Compared with other complex network indicators, $HHM{I}_i$ had a distinct advantage and its best accuracy reached 0.9543. These results can provide policy-making supports for inter-city sustainable coordinated development. Full article

Spatial patterns of tourist mobility are important for tourism management and planning. A large number of traveler-generated content accumulated on the internet provide a unique opportunity for revealing comprehensive spatial patterns of tourist movements. Instead of concentrating on a single city or attraction in previous research, this work investigates the intercity travel flows extracted from the online travel blogs in China from 2012 to 2016. The descriptive statistics of travel flows are first analyzed. The distribution of travel volume is found to satisfy the power-law distribution. Based on the intercity travel flows, a network structure is then constructed to investigate tourism interactions between cities. After four communities and 14 sub-communities being detected from the network, a tourism spatial layout with regional agglomeration effects are recognized. This research concludes that distance is essential in determining tourist movements based on a spatial interaction model. Intercity travel flows decline with distance under a power-law function. These results reveal the spatial patterns of tourist movements at an intercity scale. It will be helpful for arranging tourism resources, predicting tourist flows, and maintaining sustainable tourism. Full article