This study aims to explain transit-induced commercial gentrification by quantifying the impact of opening a new metro on the closure of nearby businesses. We conducted survival analysis and Kernel Density Estimation comparing closure differences between existing and new businesses around the new metro line, focusing on periods before and after the opening. Furthermore, to identify regional heterogeneity in transit-induced commercial gentrification, we conducted a comparative analysis by dividing regions based on socioeconomic characteristics. The analysis results show that transit-induced commercial gentrification occurred around new metro stations located in areas undergoing development and socioeconomic growth and differed by land use. In particular, rapid demographic changes, an increase in commercial land price, and an increase in the overall number of businesses were observed around metro stations where commercial gentrification occurred. The framework proposed in this study is expected to be applicable to other regions and contribute to advancing the understanding of transit-induced commercial gentrification.
This paper presents a global comparative analysis of the long-term transformation dynamics of industrial cities. It addresses a critical gap in urban studies by systematically examining the coupled industrial-demographic trajectories of 297 cities in the United States, Japan, and South Korea over a 70-year period from 1950 to 2020. The study introduces a novel methodological contribution: a unified typology that classifies cities based on their industrial characteristics (type and composition) and their empirically derived demographic pathways. The analysis reveals a significant temporal lag in the peak and subsequent decline of manufacturing-based economies across the three nations, corresponding to their distinct stages of national industrialization. Key findings identify divergent transformation pathways, including path-dependent decline, particularly prevalent in early-industrializing US cities with specialized heavy industry; resilient growth, common among diversified late-industrializers in Japan and Korea; and widespread stagnation in Japan, driven by the dual pressures of industrial maturation and national demographic decline. This research advances the theoretical understanding of post-industrial urban change by empirically grounding concepts of urban resilience and path dependence. Its findings provide crucial policy insights for managing sustainable urban transitions in industrial regions globally.
Amid the wave of the digital revolution, the openness of public data served as a crucial measure to advance the construction of Digital China and promote enterprise digital transformation. Based on data from A-share listed enterprises between 2007 and 2023, this study employed the quasi-natural experiment of local governments launching public data platforms. Using a staggered difference-in-differences (DID) model and drawing on innovation ecosystem theory, the study explored the relationship between public data openness and enterprise digital transformation. The findings indicated that public data openness facilitated enterprise digital transformation, and this effect remained robust after various tests. The innovation ecosystem functioned as an important intermediary in this relationship. Specifically, public data openness promoted digital transformation by encouraging enterprises to increase innovation resource investment, optimise the innovation environment, and strengthen innovation collaboration. Further analysis revealed that absorptive and adaptive capacities reinforced the positive impact of public data openness on digital transformation, with the effect of absorptive capacity being more pronounced. Additionally, the promotion effect was stronger among non-state-owned enterprises and firms with lower operational efficiency. Public data openness was also found to promote digital transformation from multiple dimensions, with a greater effect on the transformation of underlying technological applications than on the application of digital technologies. Among these, cloud computing transformation benefited the most. Finally, the digital transformation driven by public data openness simultaneously enhanced enterprises' economic performance and optimised their ESG ratings. The research findings contributed to a deeper understanding of the value creation role of public data openness in facilitating enterprise digital transformation.
This paper aims to shed some light on the relationship between poverty and economic inequality, as well as to explain the current dynamics leading to the perpetuation of poverty and the rise of inequalities in peripheral regions of developed countries. Thus, the impact of economic inequality on poverty and the possible existence of regional dynamics of wealth concentration are explored. For that purpose, a quantitative analysis based on Robust Least Squares regression is conducted for Galician municipalities in Spain, using proxy variables for poverty, economic inequality and per capita income. In particular, three poverty levels are considered - severe, moderate, and the poverty line - proxied by the percentage of individuals with income per consumption unit below 5.000 & euro;, 7.500 & euro;, and 10.000 & euro; per year, respectively. Moreover, the Gini index and the S80/S20 ratio are used as proxies for income inequality, while average net income per capita is used as a proxy for per capita income. The results suggest that the proportion of poor people is greater in places with higher income inequality. Moreover, once the GDP per capita surpasses a certain threshold, economic inequality increases as GDP per capita rises further. Therefore, persistent 'poverty traps' may happen in case these dynamics are not externally reverted.
Transit buses are vital for safe, sustainable urban mobility, yet fatal crashes involving these vehicles remain a critical concern for city planners and policymakers. This study applies Association Rule Mining (ARM) with the Lift Increase Criterion (LIC) to examine multiyear patterns of fatal transit bus crashes in the United States from 2016 to 2023, using data from the Fatality Analysis Reporting System (FARS). The analysis distinguishes between intersection-level and segment-level environments, revealing context-specific risk profiles shaped by driver demographics, roadway design, lighting conditions, and traffic controls. At intersections, weekday crashes often involved elderly and middle-aged drivers under dark-lighted conditions on county roads, while summer crashes on U.S. highways were frequently linked to younger drivers and rear-end impacts. Segment-level crashes were associated with winter conditions, uncontrolled or rural roadways, and frontal collisions among drivers aged 45-64. Additional rules highlighted risks for young male drivers in dense urban areas and weekday pedestrian crashes during daylight hours. By applying LIC, the study identifies high-strength co-occurrence patterns that traditional models often obscure, offering actionable insights for targeted interventions. The findings underscore the need for improved lighting, stronger traffic control, roadway redesign, and driver-focused safety programs. These measures directly support Vision Zero and other urban safety frameworks, providing evidence-based pathways to reduce fatalities and strengthen the reliability of transit within cities.
Smart cities, which integrate information and communication technologies (ICTs) into urban management systems, can differ from traditional cities in their approach to spatial hierarchy because they are realized through domains that combine physical spaces and virtual networks. Similar to conventional urban studies, the spatial hierarchy of smart cities has been examined primarily along two axes: city size and scale. However, prior research has tended to treat these concepts as separate analytical dimensions, and studies addressing either size or scale in smart cities have been conducted in a fragmented manner. To develop a more multidimensional understanding of smart city spatial hierarchies, it is therefore necessary to synthesize convergent and contrasting perspectives on size and scale and to clarify their relationships. Accordingly, this study aims to identify the key themes within smart city size and scale discourse and examine their relationships through a systematic literature review of 51 peer-reviewed articles. The findings indicate that size discourse mainly focuses on how a city's physical magnitude influences investment efficiency and managerial complexity. In contrast, scale discourse reconceptualizes smart cities not as fixed territorial units but as multi-layered relational networks and technological spheres of operation spanning from the hyperlocal to the global. Furthermore, discussions on the size-scale relationship in smart cities revolve around three main issues: (1) the influence of urban size on technological scale-up, (2) the reconfiguration of functional scales through right-sizing strategies, and (3) tensions between universal expansion models and size-specific urban particularities.
With rising levels of fine particulate matter in urban areas, further intensified by climate change, it becomes critical to understand how risk communication influences adaptive behaviors. This study investigates the structural pathways linking risk communication, risk perception, and adaptive behaviors in response to particulate matter exposure, using the Health Belief Model as a theoretical framework. Specifically, this study examines the diversity of risk communication channels - media, government real-time alerts, and social ties - in shaping individuals' risk perceptions and prompting their adaptive behaviors. The research was conducted in Seoul, South Korea, where air pollution levels are particularly high. The findings demonstrate that among factors shaping risk perception, positive reinforcement, especially highlighting the direct health benefits of adaptive behaviors, was identified as a key motivator for individual adaptive behaviors. Furthermore, social ties play the most significant role in shaping risk perception, followed by media and government alerts. The results suggest that enhancing the quality and frequency of risk communication can improve public health outcomes and increase community resilience to environmental risks.
Urban green infrastructure (UGI) is recognized as a promising strategy for enhancing urban environmental and social sustainability. However, effective planning requires more than expanding green coverage; it must reflect diverse user preferences and underlying perceived value dimensions. This study investigates how perceived value dimensions shape generational preferences for UGI in Seoul, a densely built city facing environmental challenges. By integrating behavioral heterogeneity into urban environmental planning, the study identifies how intergenerational differences in perceived value dimensions are associated with urban greening preferences, with implications for inclusive and adaptive policy design. A two-stage framework captures the role of perceived value dimensions in linking demographic characteristics and preference structures, offering insights for generation-specific planning strategies that support sustainable urban development. Results indicated distinct associations: rooftop and vertical greening aligned with physical and economic benefits; podium gardens aligned with social benefits; balcony gardens showed more mixed associations. Between-wave comparisons showed more multidimensional perceived value patterns in the more recent wave, particularly among younger generations, while multiple value dimensions were also observed within individual age groups. These findings suggest the utility of flexible, user-centered design strategies. By linking value dimensions to design strategies grounded in preference patterns, the study provides practical insights into green infrastructure planning in high-density cities with heterogeneous generational preference structures.
This study explores how psychological anxiety shaped individual transport mode choices during the early stages of the COVID-19 outbreak, prior to the implementation of nationwide restriction policies. Focusing on the intersection of public health and urban mobility, we investigate how factors such as awareness, fear, and coronaphobia influenced modal shift decisions in the absence of formal restrictions. A web-based survey was conducted in Daegu, South Korea - one of the first metropolitan areas to experience a major outbreak. Data were collected from 417 residents, covering psychological perceptions, transport usage before and after the outbreak, and preferred safety measures. We applied an ensemble classification model using the AdaBoost algorithm to predict modal shifts across various trip purposes, leveraging both sociodemographic and psychological indicators. Results show that heightened psychological anxiety led to significant reductions in public transit use, with a corresponding increase in the use of private cars, bicycles, and walking. These shifts occurred even without policy-enforced restrictions, suggesting that behavioural response to health risk perception plays a critical role in shaping urban transport demand. The study highlights the importance of integrating psychological dimensions into transport planning, particularly during crisis conditions. Findings support the development of adaptive, health-sensitive mobility strategies capable of responding not only to government interventions but also to individual psychological reactions in times of public health emergencies.HighlightsPsychological anxiety shaped urban travel choices during the coronavirus outbreak.Fear of infection reduced public transport use and encouraged safer travel modes.A two-stage framework assessed whether and how travelers changed modes.Machine learning predicted pandemic transport mode shifts with high overall accuracy.Findings support health-sensitive planning for resilient urban transport systems.
Urban park green spaces (UPGS) are essential components of equitable and resilient urban environments, yet high-density cities often experience mismatches between public green space provision and socially differentiated demand. This study develops a governance-oriented supply-demand coupling framework to evaluate UPGS equity in Tianjin's high-density central districts. By integrating the Criteria Importance Through Intercriteria Correlation (CRITIC) method with the Coupling Coordination Degree Model (CCDM), the framework quantifies multi-dimensional interactions between park supply and residents' demand. Results reveal a pronounced governance paradox across 63 subdistricts: 69.84% exhibit optimal/good coupling, yet none reach good or optimal coordinated development, and 92.06% fall into supply-deficit categories. Spatially, supply advantages cluster in the southwest, whereas higher composite demand concentrates in northern and eastern corridors, producing widespread high-coupling-low-coordination conditions. These findings indicate that spatial inequity arises not only from constrained service capacity, uneven quality and connectivity, and fragmented implementation under land-scarcity constraints, but also from the decoupling between spatial interaction (proximity) and functional adequacy in meeting vulnerability-weighted demand. The study contributes theoretically by linking spatial-justice principles to a governance-oriented diagnostic-to-translation chain, and methodologically by integrating objective weighting and multi-source data to improve reproducibility in assessing urban green equity. Practically, the results highlight that achieving just and inclusive park provision requires governance innovation rather than spatial expansion alone. Differentiated strategies - including micro-interventions and adaptive land reuse, network-based accessibility repair and quality upgrading, safeguarding rare balanced pockets, and regional-hub activation for cross-boundary benefits - are proposed to support adaptive, justice-oriented green governance in high-density cities of the Global South.
What factors make cities 'smart' and how they should be ranked in terms of smartness have been essential focuses of smart city research. Valid and reliable answers to this question can be achieved by creating valid and reliable criterion sets, supporting them with high-quality empirical studies, and enriching them with local contributions from diverse countries and cities. This study develops a new set of criteria for ranking the smart cities and establishes a context-specific, evidence-based framework by evaluating implemented projects. The approach incorporates both objective and subjective data, utilizing the WENSLO and BWM methods to determine the weights of criteria and the AROMAN method for ranking alternatives in smart cities. The results indicate that 'environment and health monitoring' has the highest priority and that the importance of environmental sustainability coincides with many studies in the literature. Although the priority rankings of the criteria 'mobility and transportation' and 'energy and utilities' are supported by previous studies, significant inter-study deviations show up in these criteria. The study also reveal that non-metropolitan cities implementing smart city projects are ranked higher, showing that concrete projects, rather than population density, are decisive in the ranking of smart cities. The results imply that more importance should be given to cities' project performances on their way to becoming smart and that not only their demographic size or geographical location, but also smartprojects are vital determining factors. Based on the Turkish sample, the study contributes to the smart city ranking process by providing project-based insights towards becoming smart, offering a more realistic, scientifically backed ranking system.HighlightsDevelopes a new set of criteria for smart cities.Offers an MCDM model including WENSLO, BWM, and AROMAN methods for analysis.Evaluates Turkish smart city candidates to show the model's applicability.The driver 'environment and health monitoring' has the highest priority.
Seoul is actively considering the introduction of UAM; however, the plans thus far have been developed using a top-down, supplier-centred approach. Since UAM services are still in their early stages, with limited experience, and individual environments and preferences vary, it is essential to develop plans from the users' perspective. In this study, latent class analysis (LCA) was performed based on the benefits and sacrifices variables from the value-based adoption model (VAM). Then, using location quotient (LQ) analysis, areas in Seoul with high acceptance tendencies and the characteristics of the groups most concentrated in those areas were identified. The analysis revealed that, for Airport Access Trips, individuals in their 20s and long-distance car users exhibited positive responses to UAM, whereas for Commuting Trips, highly educated, high-income individuals in their 30s and long-distance users showed positive attitudes towards UAM services. These groups are characterized by a high understanding and adaptability to new technologies, a preference for convenience and comfort during travel and relatively low sensitivity to fare burdens for repetitive trips. The southeastern and southwestern regions of Seoul were identified as areas with high user specialization, which coincided with the locations of vertiports in the pilot route plans. However, additional consideration for the northeastern region, which lacks public transportation infrastructure, is suggested for mid- to long-term planning.HighlightsUser-centred planning is needed for urban air mobility in Seoul.Young adults and long-distance drivers showed stronger preferences for airport access trips.High-income commuters in their 30s showed strong potential for adoption.Perceived benefits and risks helped identify distinct urban air mobility user groups.The identified high-acceptance areas largely matched the pilot sites, while highlighting the need for further review of areas with limited public transportation.
Formal national policy documents are key instruments for enhancing state governance capacity. Differential textual adjustments made by local governments at different administrative tiers to central documents - i.e. policy content reproduction - can lead to heterogeneous implementation and thereby affect policy outcomes. Using China's Role Model City for Safe Development policy, initiated in 2010, as a case, this paper employs an LDA-based weighted approach to compare the extent and driving factors of content updating and elaboration by prefectural and provincial governments, thus contributing to the existing literature. The study finds a structurally differentiated pattern in the drivers of policy content reproduction - 'similarity in form, divergence in substance' - between the two tiers. Both are jointly pulled by pressure from production accidents and signals in central policy documents, yet they diverge markedly with respect to factors such as per capita GDP and fiscal self-sufficiency. The study further confirms a U-shaped relationship between prefectural policy adoption speed and the level of policy content reproduction, with a turning point at 0.81 years. This study proposes a conceptual analytical framework for understanding central - local differences in policy texts and their operating mechanisms, and provides empirical evidence to inform the formulation of context-appropriate strategies for enhancing urban safety and resilience.HighlightsA weighted latent Dirichlet allocation topic-model captures the policy content reproduction.Policy content reproduction differs between provincial and prefectural governments.Central signals and accident pressure shape urban safety policy texts.Prefectural adoption speed shows a U-shaped pattern in policy content reproduction.Provincial fiscal autonomy supports stronger adaptation of central texts.
