Zhicheng Deng, Zhaoya Gong, Jean-Claude Thill
et al.
Current studies on activity space are limited by the conceptualization of absolute physical space that fails to consider the heterogeneity of relational spaces reconstructed from spatial interactions of human movements between locations and falls short in incorporating the inherent hierarchical property of human mobility. Consequently, these approaches cannot faithfully reflect how people interact with urban spaces through travels. From the lens of relational space, this study proposes the new Hierarchical Activity Region Model (HARM) to derive the space and hierarchical properties of activity spaces perceived by various urban groups. We demonstrate the enhanced validity of our model on travel behavior in Manhattan, New York City, before, during, and after Hurricane Sandy on the basis of taxi data. Empirical results show that intra-urban travel retains clear hierarchical organization, even under disruption of a major weather event. Yet, travel undergoes a compression effect in travel hierarchies, characterized by fewer hierarchical levels and enlarged characteristic scales, followed by a rebound. Clustering the derived hierarchies reveals pronounced heterogeneity that stems from differences in population profiles; some groups sustain deeper structures or recover quickly, while others experience a persistent loss of levels. This study provides valuable insights into the functional hierarchies of urban mobility, which could inform more sustainable, resilient and equitable urban planning. The proposed methodological framework is generic for studying human mobility in broader contexts.
Urban land cover doubled between 1985 and 2015, yet the spatial dynamics of urban form remain under-quantified, despite its importance for sustainability, infrastructure planning, and climate risk. Urban expansion is a non-equilibrium process shaped by interactions between population growth, infrastructure, institutions, and market failures -- rendering static and equilibrium models inadequate. We review key challenges and modeling approaches, focusing on partial differential equation (PDE) frameworks. Borrowed from statistical physics, PDEs capture spatial heterogeneity, anisotropy, stochasticity, and feedbacks between land use and transport networks. Integrating economic and institutional factors remains a major challenge for policy relevance. We propose a research agenda that bridges remote sensing, urban economics, and complexity science to develop dynamic, empirically grounded models of urban expansion.
How should cities invest to improve social welfare when individuals respond strategically to local conditions? We model this question using a game-theoretic version of Schelling's bounded neighbourhood model, where agents choose neighbourhoods based on concave, non-monotonic utility functions reflecting local population. While naive improvements may worsen outcomes - analogous to Braess' paradox - we show that carefully designed, small-scale investments can reliably align individual incentives with societal goals. Specifically, modifying utilities at a total cost of at most $0.81 ε^2 \cdot \texttt{opt}$ guarantees that every resulting Nash equilibrium achieves a social welfare of at least $ε\cdot \texttt{opt}$, where $\texttt{opt}$ is the optimum social welfare. Our results formalise how targeted interventions can transform supra-negative outcomes into supra-positive returns, offering new insights into strategic urban planning and decentralised collective behaviour.
Abstract In rapidly urbanizing cities, historical neighborhoods often experience drastic spatial transformation, leading to the erosion of urban form, memory, and identity. This study examines the morphological transformation of the Siwenli Lilong neighborhood in central Shanghai, tracing its evolution from 1948 to 2021. Drawing on a 70-year fine-scale GIS dataset at the lane-block level which is a rare longitudinal resolution in related urban research, the study integrates historical cartography, urban morphology, and heritage interpretation to identify three key phases: wartime densification, socialist consolidation, and market-driven redevelopment. Each phase reflects distinct governance rationales, cumulatively producing a shift from spatial continuity to fragmentation. The research introduces the concept of “interface rupture” to capture the disjunction between old and new typologies, particularly in façade logic and public–private transitions. Rather than treating transformation as incidental, it proposes a conceptual model linking governance regimes, development logics, and spatial consequences. While symbolic heritage elements are selectively retained, most morphological memory is weakened or erased. By integrating urban morphology with the Historic Urban Landscape (HUL) framework, the study contributes to heritage-led urbanism by moving beyond site-specific diagnosis toward transferable explanatory mechanisms. It calls for adaptive conservation frameworks that recognize spatial memory as a planning asset, promoting continuity during inevitable change. The Siwenli case thus serves as both empirical evidence and a theoretical lens for understanding structural dynamics behind morphological rupture in East Asian cities.
Understanding human movement and city dynamics has always been challenging. From traditional methods of manually observing the city's inhabitant, to using cameras, to now using sensors and more complex technology, the field of urban monitoring has evolved greatly. Still, there are more that can be done to unlock better practices for understanding city dynamics. This paper surveys how the landscape of urban dynamics studying has evolved with a particular focus on event-based cameras. Event-based cameras capture changes in light intensity instead of the RGB values that traditional cameras do. They offer unique abilities, like the ability to work in low-light, that can make them advantageous compared to other sensors. Through an analysis of event-based cameras, their applications, their advantages and challenges, and machine learning applications, we propose event-based cameras as a medium for capturing information to study urban dynamics. They offer the ability to capture important information while maintaining privacy. We also suggest multi-sensor fusion of event-based cameras and other sensors in the study of urban dynamics. Combining event-based cameras and infrared, event-LiDAR, or vibration has to potential to enhance the ability of event-based cameras and overcome the challenges that event-based cameras have.
Urban research involves a wide range of scenarios and tasks that require the understanding of multi-modal data. Current methods often focus on specific data types and lack a unified framework in urban field for processing them comprehensively. The recent success of multi-modal large language models (MLLMs) presents a promising opportunity to overcome this limitation. In this paper, we introduce $\textit{UrbanLLaVA}$, a multi-modal large language model designed to process these four types of data simultaneously and achieve strong performance across diverse urban tasks compared with general MLLMs. In $\textit{UrbanLLaVA}$, we first curate a diverse urban instruction dataset encompassing both single-modal and cross-modal urban data, spanning from location view to global view of urban environment. Additionally, we propose a multi-stage training framework that decouples spatial reasoning enhancement from domain knowledge learning, thereby improving the compatibility and downstream performance of $\textit{UrbanLLaVA}$ across diverse urban tasks. Finally, we also extend existing benchmark for urban research to assess the performance of MLLMs across a wide range of urban tasks. Experimental results from three cities demonstrate that $\textit{UrbanLLaVA}$ outperforms open-source and proprietary MLLMs in both single-modal tasks and complex cross-modal tasks and shows robust generalization abilities across cities. Source codes and data are openly accessible to the research community via https://github.com/tsinghua-fib-lab/UrbanLLaVA.
Matt Franchi, Maria Teresa Parreira, Fanjun Bu
et al.
This paper introduces the Robotability Score ($R$), a novel metric that quantifies the suitability of urban environments for autonomous robot navigation. Through expert interviews and surveys, we identify and weigh key features contributing to R for wheeled robots on urban streets. Our findings reveal that pedestrian density, crowd dynamics and pedestrian flow are the most critical factors, collectively accounting for 28% of the total score. Computing robotability across New York City yields significant variation; the area of highest R is 3.0 times more "robotable" than the area of lowest R. Deployments of a physical robot on high and low robotability areas show the adequacy of the score in anticipating the ease of robot navigation. This new framework for evaluating urban landscapes aims to reduce uncertainty in robot deployment while respecting established mobility patterns and urban planning principles, contributing to the discourse on harmonious human-robot environments.
Zsófia Varga-Szilay, K. Fetykó, G. Szövényi
et al.
The expansion of urban areas threatens biodiversity, disrupts essential ecological relationships and jeopardises fragile ecological networks, thereby impedes key ecosystem services. To avert irreversible consequences, there is a focus on improving the biodiversity value of domestic gardens for both human well-being and conservation and a global imperative for well-planned and sustainable urban environments. Here, we employ machine learning and network analysis and examine gardening practices and garden owners’ environmental consciousness in Hungary through a questionnaire-based study to untangle the interplay among socio-demographic factors, garden management, and garden characteristics. We found that the activities determined as biodiversity-positive were widespread among respondents, but a lack of undisturbed areas (n = 624, 49.52%), mowing several times a month (n = 404, 32.06%) and ubiquitous pesticide use (n = 783, 62.14%) were also present. Middle-aged respondents demonstrated more biodiversity-supporting activities than those over 55, who had long-term gardening experience and were predominantly conventional gardeners. Residents of towns showed the least biodiversity-positive activities, whereas those living in cities and the countryside fared better. Additionally, multiple interconnected garden characteristics revealed various types of gardens distinguished by care practices and use, such as gardens for food self-provisioning, ornamental gardens, or those prioritizing biodiversity support. Our results show that garden owners use pesticides, and within them herbicides, independently of socio-demographic parameters, gardening practices, or garden characteristics, suggesting a widespread pesticide use in Hungary. Our findings suggest that strategies, to promote biodiversity-friendly gardening practices may not be equally suitable for all European countries with different cultural backgrounds, environmental consciousness and pesticide use. In particular, factors like differences between societal groups underscore the preference for in-person programs over online information transfer in several cases, for instance, among the elderly and those living in the countryside. This study offers fresh perspectives on the intricate connections between garden diversity, characteristics, and practices, and it lays the groundwork for future research into the sociological drivers of gardening practices in Eastern Europe. Our work also emphasises that optimizing gardens for multiple ecosystem services, including biodiversity conservation and enhancing well-being across diverse societal groups, requires a nuanced understanding of both ecological and socio-demographic factors. Highlights Biodiversity-friendly domestic gardens are key for urban sustainability Gardening practices of Hungarian gardeners varied with socio-demographic factors Lack of undisturbed areas, frequent mowing and pesticide use were the most harmful Elderly gardeners showed less biodiversity support and environmental awareness Tailored strategies are crucial for enhancing Eastern European garden biodiversity
City digital twins (CDTs), as digital replica of urban systems and development processes, have been heralded as the next-generation technology for urban planning and management. Arguably, the concept of CDTs is not new. Prior to CDTs, applied urban modelling has been playing a pivot role in supporting city and infrastructure planning since the 1960s. Examining CDTs in relation to conventional urban models can thus offer valuable insights into their nature, potential, and challenges. Such a comparative, reflective exercise, however, remains rare. This commentary aims to share insights and reflections from a dedicated applied urban modelling (AUM) community. It is argued that to substantiate the power of CDTs, a theory-driven modelling strategy is essential for both practical policy analysis and knowledge discovery. Modellers must think beyond the technical perspective and exploring novel use of CDTs beyond optimisation. A blind pursuit for data without building on and expanding existing domain knowledge remains an existential risk for CDTs.
Rahman Aulia Fuad, Agusti Rosalita Rachma, Kurniawati Desi Tri
The mounting environmental concerns have become a pressing issue across industries. Nevertheless, the banking sector has a distinct influence in shaping economic growth and development. This study sought to evaluate the impact of green banking in strengthening corporate value through its level of sustainability reporting in order to address these concerns. A research framework was developed based on theoretical support. The sampled data was collected from banks listed on the Indonesian Stock Exchange from 2018-2021. An empirical analysis was performed through hierarchical regression. The study’s findings indicated that green banking positively and significantly impacts firm value. Furthermore, there is a mediating effect between green banking and business value due to the quality of sustainability reporting. The empirical test revealed that the quality of sustainability reporting has a mediating effect to some extent. The results also showed that there is an interaction between business size (assets) and correlations between firm value and green banking. By undertaking a data-driven research that explains the impact of green banking on business value, this study aims to fill a significant gap in the body of knowledge on green banking and sustainability reporting.
Regional economics. Space in economics, Economics as a science
Junio Soares dos Santos, Jaqueline Guimarães Santos, Mariana Teodoro Santos
et al.
A gestão ordinária é um tema importante no campo da gestão e seu uso tem sido difundido nas ciências sociais aplicadas. Assim, esse estudo teve como objetivo analisar como a gestão ordinária se caracteriza no cotidiano dos artesãos vinculados a associação ARTESAL e suas contribuições para o desenvolvimento local e regional. No que se refere aos procedimentos metodológicos, foi realizada uma pesquisa de abordagem qualitativa, utilizando a entrevista semiestruturada e observação não participante, além do grupo focal, como as técnicas de coleta de dados. Os dados, por sua vez, foram analisados a partir da técnica de análise de conteúdo. Os principais resultados da pesquisa apontam para a utilização de algumas práticas de gestão ordinária por artesãos e artesãs vinculadas a associação de artesanato estudada, tais como reciprocidade, solidariedade e valorização das trocas. Contudo, a partir de um olhar crítico para o fenômeno, observamos que a forma como a associação é conduzida exerce alguma força de padronização de alguns processos de gestão, condizentes aos modelos de produção capitalistas caracterizados pela padronização e massificação.
Social inequality, defined as “unequal rewards and opportunities for different individuals within a group or groups within a society” is multi-dimensional, including legal status, opportunities and outcomes, and different sociological perspectives approach it differently (Scott, 2014, p. 352). Inequalities of access to opportunities and resources have deeper structural inequalities—social class, gender, locality, etc.—underneath (Hamnett, 2019). These structural inequalities are reproduced through social systems, such as education, across generations. One aspect of social inequality in today’s cities concerns transport inequality. This simply refers to the transport advantages of the rich compared with the poor (Gebresselassie and Sanchez, 2019). Mobility research is connected to social and environmental sustainability ideals. This line of research emphasizes the fact that marginal urban communities are disadvantaged in multiple ways: income, employment, health, education, environment, housing and mobility, such as reduced access to public transport. The transport inequality intersects with other forms of marginalization as well, based on gender, age, disability, and ethnicity. Yet for the mobile or kinetic elite (Andreotti, Le Gallès, and Moreno-Fuentes, 2013), all places and transport means are readily available. Furthermore, transport-related mega-projects accentuate the existing social inequalities of the neoliberal city. However, urban policy makers have begun to realize the importance of transport inequality and develop inclusive policies, such as “accessibility planning” in the UK (Lucas, 2012). Urban citizens are also forming mobility justice movements to protest against the increasing transport costs, as in Latin America (Díaz Pabón and Palacio Ludeña, 2021) and France. Hence, this paper will study the relationship between mobility and inequality.
Nadezhda Yashina, Oksana Kashina, Sergey Yashin
et al.
The need to take into account imbalances among regional indicators in the development of state policy for financing national projects makes it necessary to develop a methodology that will enable objective assessment of the effectiveness of socially significant projects in Russia. This paper reports the development of a methodology for financial monitoring of national project implementations in the constituent entities of the Russian Federation, taking into account the correlation of their target indicators and using cluster analysis and methods in mathematical statistics. The proposed methodology was tested on health and demography national project data obtained from the Federal Treasury of Russia, the Federal State Statistics Service and the Accounts Chamber for 2020–2021. The analysis of public funding for national projects based on centralization indices and target indicators for their implementation enabled classifying the regions of Russia according to the levels of effectiveness and the financial risks of implementing the projects. The results of the study correspond to the actual effectiveness of national projects and can be used in the development of flexible state policy in financing national projects, taking into account the level of the target indicators achieved.
Abstract The sociolinguistic situation in Morocco is complex, with a diversity of Arabic varieties that interact daily. Existing linguistic ideologies have created a hierarchical relationship between these varieties that has been forged for political and socio-economic reasons. Thus, the variety of Casablanca, the economic capital of the country, leads the linguistic levelling in the country and therefore has more social value, but others have less social value, even reaching stigmatization. This is the case of the northwest Moroccan variety that is stigmatized for most Moroccans, but shapes the collective identity in this region. One of the most important cities in the region is Tetouan, a medium-sized city, where the arrival of influences from other Moroccan varieties has created a new-urban variety and has contributed to shaping a new Tetouan identity. This article focuses on folk metalinguistic discourse on the Tetouan Arabic variety. The purpose is to evaluate the metalinguistic beliefs used by speakers to build a collective identity at local and regional level. To do this, the role of social media as a space for ideological language discussions is shown, and chats posted on a Facebook group are examined as an interactive source of data. Specifically, the focus will be placed on an open chat called “Tetouan”, founded in 2012, and in which people from this city write mainly. The results reveal that the social meanings of the different varieties produce antagonistic ideas about them, depending on the context and the participants in the linguistic interaction.
The a rticle presents a n a nalysis of religious identity depending on the t ype of locality, region, confessional affiliation, age of respondents. Global trends are taken into account, which affects identification processes, secularization urbanization, religious diversity in large cities, and leads to the formation of a special type of urban culture, characterized by the simultaneous decline and growth of religious consciousness, blurring the boundaries of the religious and non-religious way of life of citizens. Based on the data of a sociological survey conducted in 2021 by the Institute of Sociology of the National Academy of Sciences of Belarus, a comparative analysis of the confessional, religious structure of various population groups has been carried out, the level of trust in the main Christian churches has been revealed. The conclusion has been made about the specifics of the own way of development of the Belarusian society, which is characterized by wide opportunities for worldview and identification choice in large cities, where a certain part of the inhabitants consider themselves free from religious influences.
Yury A. Doroshenko, Maria S. Starikova , Viktoriya N. Ryapukhina
New industrialisation challenges, turbulent economic environment and opening market niches change the structure of competitiveness factors and determine the innovativeness of industrial development. In the current context, it is necessary to deepen the analysis of industrialisation and innovation performance of regions. Therefore, this study aims to identify industrial and innovative development models present in Russian regions. To this end, we propose a methodology based on assessing the localisation coefficients of both regional industrialisation and innovation performance. Calculation of these indicators resulted in the creation of four models: Model 1 (low industrial development and low innovation performance), Model 2 (low industrial development and high innovation performance), Model 3 (high industrial development and high innovation performance), Model 4 (high industrial development and low innovation performance). The classification of the constituent entities of the Russian Federation according to the industrial and innovative development model shows that more than 40 % of regions use Model 1 and about 12 % of territories use Model 2. Simultaneously, approximately 27 % of regions (including Tula, Lipetsk, Chelyabinsk, Vladimir oblasts, Republic of Bashkortostan) chose Model 3, which most fully meets the new industrialisation challenges. The high stability of this disproportionate structure indicates the absence of positive dynamics and poor balance of industrial and innovation policy measures in most Russian regions in the period 2015–2019. The study results can be used to create an alternative ranking of innovative development of regions. Further research can apply these findings to assess the efficiency of regional industrial and innovation policies.
The differences in travel times of passenger cars, traffic stream, and trucks depend on the area type, temporal factors, reference speed, and traffic condition. These explanatory variables account for the effect of geometric conditions and variations in the traffic flow. The focus of this research is to examine the correlations and estimate truck travel time to passenger car or traffic stream travel time ratio of a road link (dependent variable) as a function of these explanatory variables. Travel time data for Mecklenburg County and Iredell County in North Carolina, USA were gathered for the year 2017 to examine correlations, develop generalized estimating equations (GEE) models, and identify explanatory variables influencing the ratios. Gamma log-link distribution-based models are the best-fitted models to estimate the average travel time (ATT) of trucks to the ATT of passenger cars or traffic stream ratios. Notable differences in the coefficients were observed when the ATT of trucks was compared with the ATT of passenger cars or traffic stream. The area type (urban or rural) was observed to influence the ratios differently. The influence of traffic condition, reference speed (or free-flow speed), day-of-the-week (DOW) and time-of-the-day (TOD) on the ratios also varied with the area type.