I define big data with respect to its size but pay particular attention to the fact that the data I am referring to is urban data, that is, data for cities that are invariably tagged to space and time. I argue that this sort of data are largely being streamed from sensors, and this represents a sea change in the kinds of data that we have about what happens where and when in cities. I describe how the growth of big data is shifting the emphasis from longer term strategic planning to short-term thinking about how cities function and can be managed, although with the possibility that over much longer periods of time, this kind of big data will become a source for information about every time horizon. By way of conclusion, I illustrate the need for new theory and analysis with respect to 6 months of smart travel card data of individual trips on Greater London’s public transport systems.
In the last decade or so, inequality studies have assumed renewed prominence across the social sciences. In this introduction to a special issue of Applied Geography, we set out to articulate the importance of urban spatial context in broader present-day inequality debates. We argue that the information-based economy is emphatically urban-based and that it has forged new spatial inequalities in and between cities and among urban populations. Income gaps have widened, inter-city disparities have grown, suburbs have been re-sorted into a wide array on the basis of class and race or ethnicity, and many central cities have assumed a renewed importance within metropolitan areas. We argue that attention to urban spatial dimensions at various scales is critical to understanding current inequality trends, from intra-urban to regional and global scales. Contributions to this special issue from North America, Europe, South America, and China suggest that deepening urban inequalities are pervasive across the globe.
Fengze Sun, Egemen Tanin, Shanika Karunasekera
et al.
Recent advances in urban region representation learning have enabled a wide range of applications in urban analytics, yet existing methods remain limited in their capabilities to generalize across cities and analytic tasks. We aim to generalize urban representation learning beyond city- and task-specific settings, towards a foundation-style model for urban analytics. To this end, we propose UrbanVerse, a model for cross-city urban representation learning and cross-task urban analytics. For cross-city generalization, UrbanVerse focuses on features local to the target regions and structural features of the nearby regions rather than the entire city. We model regions as nodes on a graph, which enables a random walk-based procedure to form "sequences of regions" that reflect both local and neighborhood structural features for urban region representation learning. For cross-task generalization, we propose a cross-task learning module named HCondDiffCT. This module integrates region-conditioned prior knowledge and task-conditioned semantics into the diffusion process to jointly model multiple downstream urban prediction tasks. HCondDiffCT is generic. It can also be integrated with existing urban representation learning models to enhance their downstream task effectiveness. Experiments on real-world datasets show that UrbanVerse consistently outperforms state-of-the-art methods across six tasks under cross-city settings, achieving up to 35.89% improvements in prediction accuracy.
Este estudo avalia a dinâmica de uso e cobertura da terra em Dianópolis (TO), Brasil, utilizando dados da Coleção 9.0 do Projeto MapBiomas para os anos de 1985 e 2023. As informações foram derivadas de imagens Landsat classificadas com algoritmos de aprendizado de máquina no Google Earth Engine e analisadas no ArcGIS®. Observou-se redução da cobertura florestal de 68,35% para 54,40% e expansão agropecuária de 8,63% para 44,18%. A vegetação herbácea e arbustiva foi praticamente suprimida, indicando conversão de ecossistemas nativos. Houve ainda aumento de áreas urbanas, não vegetadas e de corpos hídricos, refletindo intensificação antrópica. Os resultados confirmam padrões nacionais e globais de conversão da vegetação natural, destacando a urgência de estratégias de manejo sustentável e conservação ambiental.
Innovation and entrepreneurship are core drivers of high-quality economic development; however, their inherent characteristics, high risk, heavy investment, and slow return, significantly constrain efficiency in the spatial allocation of financial capital. Difficult and expensive financing has long been a key bottleneck restricting the high-quality development of innovation and entrepreneurship in China. As the primary financing channel for enterprises, bank credit efficiency is strongly influenced by geographical distance: Bank-enterprise proximity can alleviate information asymmetry, reduce transaction costs, and ultimately strengthen financial support for innovation and entrepreneurship. However, against the backdrop of rapid digital finance development weakening spatial constraints and continuous government Research and Development (R&D) investment shaping the innovation ecosystem, the impact of bank-enterprise proximity on the quality of urban innovation and entrepreneurship is not a simple linear relationship. Its effectiveness may exhibit heterogeneity with changes in institutional environments and resource conditions. Existing studies have neither fully clarified the internal logic of this nonlinear relationship nor sufficiently addressed the boundary-setting role of government R&D investment. Based on this, from the perspective of financial geography, this study used panel data of 213 prefecture-level and above cities in China from 2003 to 2020. Various empirical methods, including baseline regression, threshold effect modeling, heterogeneity analysis, and robustness tests, were comprehensively applied to systematically explore the impact of bank-enterprise geographical proximity on the quality of urban innovation and entrepreneurship, verify the internal mechanisms of financial support, and identify the threshold effect of government R&D investment. Bank-enterprise proximity significantly promoted the quality of urban innovation and entrepreneurship, and this finding remained robust after controlling for endogeneity. Government R&D investment exerted a significant single-threshold moderating effect on this relationship, with a threshold value of 23.3% (measured as the sum of scientific and educational expenditure as a share of total general public budget expenditure). Only when R&D investment exceeded this threshold did the enabling effect of bank-enterprise proximity became significantly amplified; when investment was insufficient, the effect was insignificant. The level of digitalization also presented a single-threshold characteristic: Below the threshold, traditional bank-enterprise geographical proximity played a dominant role, whereas above the threshold, digital finance supplemented geographical proximity by improving information transmission efficiency and replacing its core position. In terms of heterogeneity, in the context of high R&D investment, the promoting effect of bank-enterprise proximity was the most prominent in eastern regions and in super-large or mega cities, followed by central regions and large cities, while it was relatively weak in western regions and medium and small cities owing to weak economic foundations and insufficient resource agglomeration. Further mechanism tests confirmed that alleviating corporate financing constraints was the key channel through which bank-enterprise proximity operated. The academic value of this study is reflected in three dimensions: First, focusing on the new characteristics of the financial geography structure in the digital era, this study verifies the continued importance of bank-enterprise proximity against the background of weakened spatial constraints, enriching the interdisciplinary research in financial geography and innovation economics. Second, this study is the first to identify the single-threshold moderating effect of government R&D investment, clarifying the boundary conditions of the nonlinear relationship between bank-enterprise proximity and innovation and entrepreneurship quality, and providing new empirical support for reconciling divergent conclusions in existing studies. Third, this study constructs a multi-dimensional integrated analytical framework of "government factor (R&D investment)-technological factor (digitalization)," deepening the systematic understanding of the driving mechanism of urban innovation and entrepreneurship quality. At the practical level, this study provides clear implications for local governments to formulate relevant policies. Governments should shorten bank-enterprise distance by optimizing the spatial layout of bank branches, increase R&D investment to exceed the critical threshold of 23.3%, promote the deep integration of digital finance and traditional banking, and strengthen policy support for central and western regions and for medium or small cities. These measures can jointly enhance the synergistic effectiveness of financial support and government intervention in boosting the high-quality development of innovation and entrepreneurship.
Migration plays a crucial role in urban growth. Over time, individuals opting to relocate led to vast metropolises like London and Paris during the Industrial Revolution, Shanghai and Karachi during the last decades and thousands of smaller settlements. Here, we analyze the impact that migration has on population redistribution. We use a model of city-to-city migration as a process that occurs within a network, where the nodes represent cities, and the edges correspond to the flux of individuals. We analyze metrics characterizing the urban distribution and show how a slight preference for some destinations might result in the observed distribution of the population.
Understanding how urban systems and traffic dynamics co-evolve is crucial for advancing sustainable and resilient cities. However, their bidirectional causal relationships remain underexplored due to challenges of simultaneously inferring spatial heterogeneity, temporal variation, and feedback mechanisms. To address this gap, we propose a novel spatio-temporal causality framework that bridges correlation and causation by integrating spatio-temporal weighted regression with a newly developed spatio-temporal convergent cross-mapping approach. Characterizing cities through urban structure, form, and function, the framework uncovers bidirectional causal patterns between urban systems and traffic dynamics across 30 cities on six continents. Our findings reveal asymmetric bidirectional causality, with urban systems exerting stronger influences on traffic dynamics than the reverse in most cities. Urban form and function shape mobility more profoundly than structure, even though structure often exhibits higher correlations, as observed in cities such as Singapore, New Delhi, London, Chicago, and Moscow. This does not preclude the reversed causal direction, whereby long-established mobility patterns can also reshape the built environment over time. Finally, we identify three distinct causal archetypes: tightly coupled, pattern-heterogeneous, and workday-attenuated, which map pathways from causal diagnosis to intervention. This typology supports city-to-city learning and lays a foundation for context-sensitive strategies in sustainable urban and transport planning.
With the emergence of a green environment and green business, the banking sector has also enforced green practices. This study aims to explore the impact of motivational factors and green behaviors on the environmental performance of banking sector employees. This is a quantitative study and data has been collected through a cross-sectional survey of the questionnaire in the banking sector. 300 questionnaires were distributed to the bank employees. PLS-SEM was used to find the statistical results. The study finds a positive impact of Extrinsic motivation and Intrinsic motivation on Employee Environmental Performance, the mediating effect of Task-related Green Behaviors was also found to be positive. The study does not support the effect of Voluntary Green Behaviors on Employee Environment Performance and its mediating effect was also not supported. The study findings and deep knowledge of the impact of motivational and behavioral employee environmental performance on banking sector employees have provided new directions for researchers and policymakers. This study will help the policymakers in strategically developing rewarding policies for the employees that would definitely create a positive impact on performance. The results of the study have provided empirical confirmation of employees’ motivational needs and their impact on green behaviors that collectively impact employee environmental performance.
Cities. Urban geography, Urbanization. City and country
Margarita Mishina, Mingyi He, Venu Garikapati
et al.
Urban development is shaped by historical, geographical, and economic factors, presenting challenges for planners in understanding urban form. This study models commute flows across multiple U.S. cities, uncovering consistent patterns in urban population distributions and commuting behaviors. By embedding urban locations to reflect mobility networks, we observe that population distributions across redefined urban spaces tend to approximate log-normal distributions, in contrast to the often irregular distributions found in geographical space. This divergence suggests that natural and historical constraints shape spatial population patterns, while, under ideal conditions, urban organization may naturally align with log-normal distribution. A theoretical model using preferential attachment and random walks supports the emergence of this distribution in urban settings. These findings reveal a fundamental organizing principle in urban systems that, while not always visible geographically, consistently governs population flows and distributions. This insight into the underlying urban structure can inform planners seeking to design efficient, resilient cities.
The generation of large-scale urban layouts has garnered substantial interest across various disciplines. Prior methods have utilized procedural generation requiring manual rule coding or deep learning needing abundant data. However, prior approaches have not considered the context-sensitive nature of urban layout generation. Our approach addresses this gap by leveraging a canonical graph representation for the entire city, which facilitates scalability and captures the multi-layer semantics inherent in urban layouts. We introduce a novel graph-based masked autoencoder (GMAE) for city-scale urban layout generation. The method encodes attributed buildings, city blocks, communities and cities into a unified graph structure, enabling self-supervised masked training for graph autoencoder. Additionally, we employ scheduled iterative sampling for 2.5D layout generation, prioritizing the generation of important city blocks and buildings. Our approach achieves good realism, semantic consistency, and correctness across the heterogeneous urban styles in 330 US cities. Codes and datasets are released at https://github.com/Arking1995/COHO.
Abstract Using firm birth records and startup data matched with cities' characteristics, this paper analyzes nearly 300 prefecture-level cities to examine the role of human capital and market access in shaping the economic geography of innovation-driven entrepreneurship in China. We document strong positive entrepreneurial effects of local human capital resources and market size as well as market integration and human capital spillovers from mega-urban agglomerations of integrated cities. Our estimates point to an elasticity of innovation-driven entrepreneurship with respect to human capital spillovers of 0.50–0.79. The elasticity with respect to market integration is 0.53–0.89. Our results also suggest heterogeneous human capital spillover and market integration effects across urban agglomerations. These effects are more robust in first-tier urban agglomerations because first-tier urban agglomerations have a stronger economic base and greater connectivity. Strong human capital spillover and large gains from access to surrounding economic mass jointly highlight the integrated development of mega-urban agglomerations in China. We discuss policy implications that concern promotion of local innovation-driven entrepreneurship by strengthening intercity coordination, building transportation and social infrastructure, and improving urban management.
Are institutional trust and interpersonal trust threatened by globalisation? For nineteen countries in Europe, using a fixed effects model for a panel data set relating globalisation to several economic and social macro variables, like income inequality and diversity, to average institutional and interpersonal trust derived from responses in European Social Surveys, we do not find any significant relation between the relatively moderate globalisation of the first two decades of the 21st century on average interpersonal and institutional trust. At the same time, occurrences of economic decline in a country are negatively related to institutional trust. GDP has a positive effect on both institutional and interpersonal.Combining the macro factors with the individual traits of respondents using pooled repeated cross-sectional data demonstrate the dominance of personal characteristics in individual levels of trust, with only institutional quality emerging as a macro variable which is significantly and positively related to trust, especially for the Socio-Economic Groups 3 to 7 (of the eight groups distinguished). Those who are born in the country exhibit higher levels of interpersonal trust, in particular in the higher SES groups 4–7, but show significantly lower institutional trust for the SES groups 0–2. Age is negatively related to institutional trust for all SES groups, but positively related to interpersonal trust for SES groups 4–7.These findings appear to imply that those who are concerned with the level of institutional trust in the population as a basic requirement for democracy in Europe should focus on the quality of institutions and not on globalisation.
Cities. Urban geography, Urbanization. City and country
Fan Zhang, Arianna Salazar Miranda, Fábio Duarte
et al.
The visual dimension of cities has been a fundamental subject in urban studies, since the pioneering work of scholars such as Sitte, Lynch, Arnheim, and Jacobs. Several decades later, big data and artificial intelligence (AI) are revolutionizing how people move, sense, and interact with cities. This paper reviews the literature on the appearance and function of cities to illustrate how visual information has been used to understand them. A conceptual framework, Urban Visual Intelligence, is introduced to systematically elaborate on how new image data sources and AI techniques are reshaping the way researchers perceive and measure cities, enabling the study of the physical environment and its interactions with socioeconomic environments at various scales. The paper argues that these new approaches enable researchers to revisit the classic urban theories and themes, and potentially help cities create environments that are more in line with human behaviors and aspirations in the digital age.