Governing equations are fundamental for describing and predicting dynamic urban geographic systems. Unlike physical systems guided by first principles, urban spatiotemporal phenomena emerge from coupled geographic processes that lack deterministic theoretical foundations, making the discovery of governing equations elusive and largely heuristic. Spatiotemporal dynamics in urban systems are often observed as sequential snapshot data of spatial distribution, while the cause of such dynamics is often implied or unknown. In this study, we propose a unified differential equation formalism that decomposes urban dynamics into a time-invariant spatial interaction process and a self-dynamic component. Building on this formalism, we introduce the Urban Discovery Framework (U-Discovery), which integrates hypothesis generation, neural fitting, and governing equation identification for the discovery of governing spatial interaction laws. U-Discovery leverages Large Language Models and literature-based reasoning to propose differential equation candidates. Each candidate was calibrated from the observed spatiotemporal dynamics using a neural fitting method. The candidates are evaluated and ranked based on the fitting error and mathematical complexity. Our synthetic experiments prove that U-Discovery can find the sole governing equation from the simulated dynamics. Empirical experiments in Hennepin County, Minnesota, further demonstrate the potential of U-Discovery in identifying optimal governing laws from real-world human activity dynamics.
Urban scaling laws describe how an urban quantity $Y$ varies with city population $P$, typically as $Y \sim P^β$. These relations are usually obtained from cross-sectional comparisons across cities at a given time (transversal scaling), but their link to the temporal evolution of individual cities (longitudinal scaling) remains unclear. Here we derive explicit expressions for the transversal exponent from the longitudinal dynamics of cities. We show that the measured exponent does not directly reflect individual city dynamics, but instead arises from a snapshot of a heterogeneous ensemble of cities with distinct growth trajectories. As a result, transversal scaling combines intrinsic dynamics with statistical effects due to the distribution of city sizes and correlations between population and city-specific parameters. Consequently, cross-sectional scaling laws cannot, in general, be used to infer the dynamics of individual cities. In particular, apparent sub- or superlinear scaling can emerge even when all cities follow linear longitudinal dynamics, as we demonstrate for the area-population relation. Strikingly, the behavior associated with the transversal exponent is in general not observed in the trajectory of any individual city, underscoring its collective, rather than dynamical, nature. More broadly, the transversal exponent has a clear dynamical meaning only under restrictive conditions-when cities behave as scaled versions of one another and path dependence is weak. Outside of these limits, it is not a law of urban growth, but a statistical artefact of heterogeneity.
Recently, China’s central government initiated a series of social policies to alleviate social disparities, providing opportunities to revisit state entrepreneurialism, which is known to have long prevailed in China’s urban governance since the economic reform. By probing into a case of shantytown redevelopment in Luzhou, Sichuan Province, we assert the importance of considering state entrepreneurialism in relation to the state’s managerial pursuit. That is, an actually existing mode of urban governance may be characterised by the shifting dynamics between a managerial and entrepreneurial endeavour of the local state. Viewed this way, we argue for the manifestation of what we conceptualise as entrepreneurial neo-managerialism through the analysis of the shantytown redevelopment at the local scale. In the context of a shrinking discretional space under the power recentralisation of the central state that strives to avoid its legitimacy crisis, the local state, while still under the influence of its entrepreneurial logic of land-based accumulation, enhances its managerial role to respond to the top-down demands of social redistribution from the central state, devising a sophisticated redistributive mechanism of resource allocation. Through these findings, we hope to contribute not only to the literature on China’s state entrepreneurialism but also to the broader urban governance literature by resurrecting the importance of the managerial role of the state.
در روستای سولار، قاعدهای نانوشته وجود دارد که روستاییان را از فروش زمین به افراد غیربومی بازمیدارد. این قاعده ریشه در شبکهای از باورهای اخلاقی و اجتماعی دارد؛ تا آنجا که بسیاری از ساکنان، فروش زمین را نوعی «خیانت به میراث جمعی» قلمداد میکنند. قاعدۀ مذکور محدود به مالکیت زمین توسط فرد غیربومی نیست و مالکیت دولت در طرحهای توسعه را نیز منتفی میسازد. اینکه چه سازوکارهایی موجب تداوم این قاعده شده و چه نسبتی با بازار، قدرت و هویت در میدان روستا دارد، مهمترین پرسشهای پژوهش حاضر بهشمار میآیند.
پژوهش حاضر کیفی است و به روش مطالعۀ موردی اکتشافی انجام شده است. دادهها از طریق مصاحبۀ نیمهساختیافته با 13 نفر از فعالان محلی گردآوری شدند. همچنین از مشاهدۀ میدانی و ثبت گفتوگوهای غیررسمی برای درک بهتر زمینههای اجتماعی و عملکردهای پنهان این قاعده بهره گرفته شد.
تحلیل نهایی نشان میدهد قاعدۀ منع فروش زمین، نه بازماندهای از سنت ایستا، بلکه نتیجۀ بازآرایی جدیدی از روابط اخلاقی، مناسبات قدرت، فشارهای اقتصادی و خاطرۀ جمعی است. در این معنا، میتوان آن را یک نهاد اخلاقی مدرن دانست که در برابر نیروهای بازار پدید آمده است. این قاعده برخلاف تصور اولیه، نهتنها همبستگی میآفریند، بلکه در مواردی به ابزار سلطه نیز بدل میشود.
قاعدۀ منع فروش زمین به غیربومیان درهمتنیدگی وجدان جمعی (دورکیم) و حکشدگی اقتصاد در بطن اجتماع محلی (پولانی) را در عین تبدیلشدن آن به ابزاری برای سلطۀ نمادین (بوردیو) بازتاب میدهد. این قاعده همزمان حافظ هویت جمعی روستا و عرصۀ بروز تنش میان معیشت، اخلاق و قدرت است.
László Zoltán Nádasy, Attila Gergely, Viktória Wenszky
A tájépítészet magyarországi elismertségének növeke-dése, a tájépítészeti tervezés egyre jelentősebb szerepeazzal jár, hogy a tájépítészeti növényalkalmazás, a tudato-san tervezett kiültetések, mesterséges növénytársulásokegyre erősebben képesek alakítani a hazai flóra összeté-telét és képét. Ugyanakkor az őshonos, és különösen avédett növényfajok meglehetősen csekély részaránybanjelennek meg a tájépítészeti alkotásokban, a tervezők szí-vesebben nyúlnak idegenhonos taxonokhoz. Az őshonosnövényfajok mellőzése a nagy léptékű tájépítészeti alko-tások (közparkok, ökoturisztikai létesítmények, városiszabadterek) esetében jelent különösen komoly problé-mát, hiszen ezeken a zöldfelületek mennyisége, ökoszisz-téma-szolgáltatásai és környezeti nevelési potenciálja iskiemelkedően magas.Kutatásunkban az őshonos fajok alkalmazásávalfoglalkozunk, illetve több szempontból megvizsgáljuka védett fajokkal kapcsolatos tájépítészeti lehetősége-ket. Áttekintjük a védett fajok alkalmazására vonatkozószabályozási környezetet, valamint vizsgáljuk a Magyar-országon védelem alatt álló fajok előfordulását a dísznö-vény-kereskedelemben is, azt áttekintendő, hogy a piaciszereplők valóban betartják-e a természetvédelmi jog-szabályokat. Az őshonos fajok tájépítészeti alkalmazásátkiemelten a BudaPart városrész mintáján vizsgáljuk, amintaterület kiültetéseinek fajszintű elemzésén keresztül.Mindemellett jó gyakorlatokon és megvalósult példákonkeresztül illusztráljuk, hogy milyen lehetőségek állnakrendelkezésre az őshonos és védett növényfajok alkalma-zására a tájépítészetben. Eredményeink reményeink sze-rint rávilágítanak arra, hogy különösen nagy kiterjedésű,illetve természetvédelmi szempontból kiemelten jelen-tős projektek esetében nagy szükség lenne az őshonosnövényfajok szélesebb körű alkalmazására, illetve arra,hogy fontos és időszerű lenne, hogy elkezdődjön a szak-mai párbeszéd bizonyos védett fajok ellenőrzött körülmé-nyek között történő kultúrába vonásának lehetőségeiről.
Escalating urban heat, driven by the convergence of global warming and rapid urbanization, is a profound threat to billions of city dwellers. The science directing urban heat adaptation is strongly influenced by studies that use satellite-based land surface temperature (LST), which is readily available globally and address data gaps in cities, particularly in the Global South. LST, however, is a poor surrogate for near-surface air temperature, physiologically relevant human thermal comfort, or direct human heat exposure. This flawed practice leads to issues for several downstream use cases by inflating adaptation benefits, distorting the magnitude and variability of urban heat signals across scales, and thus misguiding urban adaptation policy. We argue that satellite-based LST must be treated as a distinct indicator of surface climate, which, though relevant to the urban surface energy budget, can be frequently decoupled from human-relevant thermal impacts especially during daytime. Only by a disciplined application of this variable, combined with complementary datasets, process-based and data-driven models, as well as interdisciplinary collaboration, can urban adaptation design and policy be effectively advanced.
We propose SpatialLLM, a novel approach advancing spatial intelligence tasks in complex urban scenes. Unlike previous methods requiring geographic analysis tools or domain expertise, SpatialLLM is a unified language model directly addressing various spatial intelligence tasks without any training, fine-tuning, or expert intervention. The core of SpatialLLM lies in constructing detailed and structured scene descriptions from raw spatial data to prompt pre-trained LLMs for scene-based analysis. Extensive experiments show that, with our designs, pretrained LLMs can accurately perceive spatial distribution information and enable zero-shot execution of advanced spatial intelligence tasks, including urban planning, ecological analysis, traffic management, etc. We argue that multi-field knowledge, context length, and reasoning ability are key factors influencing LLM performances in urban analysis. We hope that SpatialLLM will provide a novel viable perspective for urban intelligent analysis and management. The code and dataset are available at https://github.com/WHU-USI3DV/SpatialLLM.
Congestion and extreme events in transportation networks are emergent phenomena with significant socio-economic implications. In this work, we study congestion and extreme event properties on real urban street (planar) networks drawn from four cities and compare it with that on a regular square grid. For dynamics, we employ three variants of random walk with additional realistic transport features. In all the four urban street networks and 2D square grid and with all dynamical models, phase transitions are observed from a free flow to congested phase as a function of birth rate of vehicles. These transitions can be modified by traffic-aware routing protocols, but congestion cannot be entirely mitigated. In organically evolved street networks, we observe a semi-congested regime which has both congested and free-flow components. In the free-flow regime, the extreme event occurrence probability is larger for small degree nodes than for hubs, a feature originally observed in non-planar scale-free networks. In general, with respect to congestion and extreme events, the urban street networks and regular square grid display similar properties.
Urban space is highly heterogeneous, with population and human activities concentrating in localized centers. However, the global organization of such intra-urban centers remains poorly understood due to the lack of consistent, comparable data. Here we develop a scalable geospatial framework to identify intra-urban activity centers worldwide using nighttime light observations. Applying this approach to more than 9,500 cities, we construct a high-resolution global dataset of over 15,000 centers. We uncover a striking regularity: despite vast differences in city size, regional development, and population density, the built-up area associated with individual centers remains remarkably consistent. Across cities, total urban area scales proportionally with the number of centers, yielding a stable mean spatial footprint. This regularity holds at the micro-scale, where Voronoi-based service areas exhibit a characteristic size that is persistent across countries and independent of local population concentration. As a geometric consequence, this polycentric multiplication maintains stable average distances to the nearest center as cities expand, preventing the accessibility decay inherent in monocentric growth. These findings reveal a universal organizing principle whereby urban expansion is accommodated through the replication of activity centers with a consistent spatial extent, providing a new empirical foundation for understanding the nature of urban growth.
As digital twins become central to the transformation of modern cities, accurate and structured 3D building models emerge as a key enabler of high-fidelity, updatable urban representations. These models underpin diverse applications including energy modeling, urban planning, autonomous navigation, and real-time reasoning. Despite recent advances in 3D urban modeling, most learning-based models are trained on building datasets with limited architectural diversity, which significantly undermines their generalizability across heterogeneous urban environments. To address this limitation, we present BuildingWorld, a comprehensive and structured 3D building dataset designed to bridge the gap in stylistic diversity. It encompasses buildings from geographically and architecturally diverse regions -- including North America, Europe, Asia, Africa, and Oceania -- offering a globally representative dataset for urban-scale foundation modeling and analysis. Specifically, BuildingWorld provides about five million LOD2 building models collected from diverse sources, accompanied by real and simulated airborne LiDAR point clouds. This enables comprehensive research on 3D building reconstruction, detection and segmentation. Cyber City, a virtual city model, is introduced to enable the generation of unlimited training data with customized and structurally diverse point cloud distributions. Furthermore, we provide standardized evaluation metrics tailored for building reconstruction, aiming to facilitate the training, evaluation, and comparison of large-scale vision models and foundation models in structured 3D urban environments.
Biophilic design, which integrates nature into the built environment, has shown positive impacts on human well-being. However, the benefits of indirect experiences of nature remain less explored, particularly in relation to the sense of place (SoP). This study investigates the relationship between nature-based architectural beauty and SoP through a comparative case study of two educational buildings with different aesthetic qualities: one classical and naturalistic, the other modernist and mechanistic. Using a quasi-experimental design, the study employs a survey-based quantitative analysis, measuring 1) SoP among users, 2) perceptions of naturalness through image-based judgments, and 3) objective measures of naturalistic beauty using image analysis. Findings indicate that users of the naturalistic building report a significantly higher sense of place, supported by perceptions of naturalness and objective measures. This suggests that environments embodying naturalistic beauty foster positive connections and place attachment. The study contributes to architectural and urban design strategies by highlighting the socio-economic benefits of nature-inspired aesthetics. By enhancing emotional and psychological well-being, such designs can lead to increased productivity, reduced stress, and stronger community ties, ultimately contributing to socio-economic development through improved quality of life and sustainable urban planning.
The world economic crisis that began in 2007 is a phenomenon comparable only to the crisis of 1929. The depth of the crisis, which is still ongoing, is hidden by state interventions and its effects are more visible on the employment side in relation to the fall in the gross social product (GDP). The costs of the crisis are enormous and are reflected in the lost GDP growth, in the tremendous growth of public debts and the huge monetary expansion, the consequences of which are not clearly visible for now. Developed countries are re-examining the prevailing monetary practice and showing much greater tolerance towards inflation than at any time in the past three decades. The introduction of stronger financial regulation is underway, especially in the countries of the European Union. There is no agreement on the world stage regarding the degree of control that should be introduced in the financial sphere, and developing countries are always much more vulnerable in financial crises because they cannot be financed by printing money. The risk of crisis is more clearly seen through the current balance deficit and the level of external debt, than through the level of the budget deficit and the level of public debt.
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.
Dillip Kumar Das, Sanjaykumar G Sonar, Mohamed Mostafa Hassan Mostafa
ABSTRACTInformation and Communication Technology (ICT) is advocated to contribute to sustainable transportation by prompting a change in travel patterns. Therefore, using an ICT-enabled city region in India as the study context, the objectives of the study were to examine the influence of ICT on travel patterns in city regions and explore the strategies to enhance ICT usage for positively changed travel patterns. Findings suggest that definite linkages between ICT usage and travel patterns exist. An increase in ICT usage in daily activities of people will enable a significant reduction in trip generation, travel distance and travel time. Strategic interventions including enhancing networking and ICT connectivity, provisioning free Wi-Fi hot spots at important nodal points, and enabling the availability of real-time road transportation-related information can enhance ICT usage leading to a positively changed travel pattern. This is likely to improve traffic movement and reduce congestion contributing to sustainable urban road transportation.
The aim of this paper is to review the current international competitiveness of the Republic of Serbia, as well as the conditions and indicators of business in the Republic of Serbia, in order to determine progress compared to the previous period, discover obstacles and present ways to overcome them. The paper is based on the fact that Serbia's competitiveness, although it has been increasing in recent years, is still relatively low as a result of a number of gaps and that, among other things, it is necessary to increase the inflow of FDI, which in the past was significant for GDP growth, as the competitiveness of the economy of the Republic of Serbia would increase.
Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Conventionally, urban mobility data has been structured as spatiotemporal videos, treating longitude and latitude grids as fundamental pixels. Consequently, video prediction methods, relying on Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have been instrumental in this domain. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex multivariate time series. This perspective involves treating the time-varying values of each grid in each channel as individual time series, necessitating a thorough examination of temporal dynamics, cross-variable correlations, and frequency-domain insights for precise and reliable predictions. To address this challenge, we present the Super-Multivariate Urban Mobility Transformer (SUMformer), which utilizes a specially designed attention mechanism to calculate temporal and cross-variable correlations and reduce computational costs stemming from a large number of time series. SUMformer also employs low-frequency filters to extract essential information for long-term predictions. Furthermore, SUMformer is structured with a temporal patch merge mechanism, forming a hierarchical framework that enables the capture of multi-scale correlations. Consequently, it excels in urban mobility pattern modeling and long-term prediction, outperforming current state-of-the-art methods across three real-world datasets.
Against the backdrop of the current hyperconnected and highly virialised post-COVID-19 societies, we, ‘pandemic citizens’, wherever we are located now, have already become tiny chips inside an algorithmic giant system that nobody really understands. Furthermore, over the last decade, the increasing propagation of sensors and data collections machines and data collections machines in the so-called Smart Cities by both the public and the private sector has created democratic challenges around AI, surveillance capitalism, and protecting citizens’ digital rights to privacy and ownership. Consequently, the demise of democracy is clearly already one of the biggest policy challenges of our time, and the undermining of citizens’ digital rights is part of this issue, particularly when many ‘pandemic citizens’ will likely be unemployed during the COVID-19 crisis. Amidst the AI-driven algorithmic disruption and surveillance capitalism, this book review sheds light on the way citizens take control of the Smart City, and not viceversa, by revolving around the new book entitled Smart City Citizenship recently published by Elsevier. The book review introduces nine key ideas including how to (1) deconstruct, (2) unplug, (3) decipher, (4) democratise, (5) replicate, (6) devolve, (7) commonise, (8) protect, and (9) reset Smart City Citizenship.