Following the notion of the entrepreneurial city, this paper examines recent scholarship about China’s urban governance. Despite prevailing marketisation, the role of the state is visible in neighbourhood, cities and city-regions. The state necessarily deals with a fast changing society and deploys market-like instruments to achieve its development objectives. Through multi-scalar governance, the state involves social and market actors but at the same time maintains strategic intervention capacity. China’s contextualised scholarship provides a more nuanced understanding beyond the entrepreneurial city thesis, which is more state-centred.
Urban mobility models are essential tools for understanding and forecasting how people and goods move within cities, which is vital for transportation planning. The spatial scale at which urban mobility is analysed is a crucial determinant of the insights gained from any model as it can affect models' performance. It is, therefore, important that urban mobility models should be assessed at appropriate spatial scales to reflect the underlying dynamics. In this study, we systematically evaluate the performance of three popular urban mobility models, namely gravity, radiation, and visitation models across spatial scales. The results show that while the visitation model consistently performs better than its gravity and radiation counterparts, their performance does not differ much when being assessed at some appropriate spatial scale common to all of them. Interestingly, at scales where all models perform badly, the visitation model suffers the most. Furthermore, results based on the conventional admin boundary may not perform so well as compared to distance-based clustering. The cross examination of urban mobility models across spatial scales also reveals the spatial organisation of the urban structure.
Abstract The complexity of urban environments influences pedestrians’ walkability, which is especially significant for people living in mega cities. While many studies identify influential factors, how these factors shape pedestrian wayfinding through complex and spatially varied mechanisms remains underexplored. This study addresses this gap by using a novel pedestrian navigation dataset as a proxy to quantify the perceived complexity of walking environments. By integrating multi-scale urban features—four at the macro-level and 14 at the micro-level derived from Street View Imagery—we systematically uncover the key correlates of navigation demand and their underlying effects. The results reveal that a combination of factors such as the number of Points of Interest, transportation accessibility, proportion of people in view, and intersection count are positively associated with pedestrians’ navigation behavior. More importantly, we demonstrate that their relationship is profoundly non-linear and exhibits strong spatial heterogeneity. These results are further validated through population normalization, sensitivity tests, and temporal comparisons between weekdays and weekends. Such analyses confirm the robust and independent association between environmental complexity and navigation behavior. By operationalizing these complex interrelationships, our work advances the theoretical framework for urban environmental complexity. The findings provide crucial evidence for moving beyond a "one-size-fits-all" approach, offering targeted, context-aware insights to foster truly human-centered urban planning and design.
Nandini Iyer, Massimiliano Luca, Riccardo Di Clemente
Rural and urban areas exhibit distinct mobility patterns, yet a systematic understanding of how these trends differ across regions and contexts remains underexplored. By using origin-destination matrices from Location-Based Services data in Colombia, India, and Mexico, we delineate urban and rural boundaries through network percolation, reducing reliance on conventional urbanisation metrics tied to the built environment. We gauge mobility dynamics across regions developing a measure for routing inefficiency, which measures how much longer empirical trips are than their optimal shortest path. Our findings reveal that rural areas experience greater inefficiencies, particularly for longer trips made later in the day. At the urban level, we determine the misalignment between urban mobility efficiency and public transit accessibility, by measuring the difference between their respective vector fields. We observe that most cities experience misalignment during regular commuting hours, with Colombian cities exhibiting particularly high alignment. Meanwhile, mobility inefficiency in rural areas are associated with their orientation around their most proximate city. City-level analyses uncover disparities in the functions of rural and urban areas, with significant variations between weekdays and weekends, reflecting distinct roles in commuting and access to services. These findings highlight the importance of tailored, context-sensitive approaches to improving connectivity and reducing disparities. This study offers new insights into the spatial and temporal dynamics of mobility inefficiency, contributing to equitable regional planning and sustainable mobility solutions.
Understanding and predicting pedestrian dynamics has become essential for shaping safer, more responsive, and human-centered urban environments. This study conducts a comprehensive scientometric analysis of research on data-driven pedestrian trajectory prediction and crowd simulation, mapping its intellectual evolution and interdisciplinary structure. Using bibliometric data from the Web of Science Core Collection, we employ SciExplorer and Bibliometrix to identify major trends, influential contributors, and emerging frontiers. Results reveal a strong convergence between artificial intelligence, urban informatics, and crowd behavior modeling--driven by graph neural networks, transformers, and generative models. Beyond technical advances, the field increasingly informs urban mobility design, public safety planning, and digital twin development for smart cities. However, challenges remain in ensuring interpretability, inclusivity, and cross-domain transferability. By connecting methodological trajectories with urban applications, this work highlights how data-driven approaches can enrich urban governance and pave the way for adaptive, socially responsible mobility intelligence in future cities.
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.
Oladiran Johnson Abimbola, Taiwo Adewumi, Musa Abubakar
As the global climate changes, urban heat island (UHI) is a critical factor in ever expanding urban landscape, studying and mitigating the UHI is important for remediating climate change and providing for the human and ecosystem health within the urban area. This study has aimed to study the UHI in Lafia, a tropical city in Nigeria and its other impacted factors such as the land surface temperature (LST) and normalized difference vegetation index (NDVI), with the aim of mitigating the UHI effect. Landsat 4, 5, 7 and 8 together with Sentinel data has been used for this study, through the public archive of the Google Earth Engine data catalog, used also is the ERA5 data from the same data catalog. The result showed that the expanding city of Lafia is experiencing significant UHI with increase in temperatures in the city and adjoining areas, it was found that the vegetation cover in Lafia city is rapidly disappearing as a result of urbanization leading to more UHI and greater discomfort to the inhabitant of the city. Several remediation steps were suggested to mitigate the UHI effect in Lafia.
Reconstruction of buildings in the Christchurch central business district following the 2011 earthquake has been a massive undertaking that is not yet completed. Interviews have been conducted with representatives of the consulting engineering companies who designed 55 of these buildings from 2017 until 2025 to determine: (i) the building construction materials and structural system types used, and (ii) the drivers for the selection of these systems. The information obtained is compared with a 2017 survey, by the authors, with the same design companies for buildings constructed from 2012 to 2017, as part of the Christchurch rebuild after the 2010–2011 Canterbury earthquakes. It is found that 47 % and 45 % of the buildings constructed had steel and concrete lateral force resisting systems, respectively, with the remainder using timber. In terms of floor space areas, the steel buildings were typically larger and the ratios were 70 % and 24 %, respectively. The most popular structural steel seismic systems were MRFs and BRBFs with 29 % and 20 % of the floor areas, respectively. Gravity systems, when needed, were generally steel. Although slightly different, these numbers are similar in magnitude to those reported in the prior study. However, comparing the factors driving choice of structure systems reported in the previous study, many of the engineers interviewed commented that, as the Canterbury earthquakes became further away in time, fewer of their clients requested resilient designs that would help achieve functionality (e.g., maintain business continuity) following future earthquakes, requesting instead lowest-cost designs. Nonetheless, it is expected that much of the newer construction will provide improvements in seismic performance given that many buildings were designed for significantly higher strength and lower drift than permitted in the standards.
Disasters and engineering, Cities. Urban geography
The territory of Mato Grosso, divided in 1979 to form the federal state of Mato Grosso do Sul, was for decades the reference point for the identity of the latter, invariably confused either due to outdated information or similarities, as there were few differences between the ‘distant’ Midwest, even though its first urban centres were already recorded in the 18th century. Mato Grosso do Sul, land of yerba mate and extensive cattle ranching, became known in the late 1970s for its meat-grain chain, with the expansion of wheat, soybeans and corn, associated with the herds of the plateau, the cattle fields and the Pantanal wetlands of Mato Grosso do Sul. The meat-grain binomial was significantly altered by the speed of globalisation, which transformed the platinum frontier erected on supposed boundaries that gradually became border areas. The international border was occupied by an urban phenomenon known as ‘twin cities’ and, more recently, the integration proposal has been updated with the initiative to build the Bioceanic Corridor, which gives the southern Mato Grosso do Sul territory a border identity. What seemed, in a way, ‘homogeneous’ has been revealed to be full of differences by various studies from the UFGD Postgraduate Programme in Geography, whose area of concentration is precisely ‘Regional Space Production and Borders’. This text presents some characteristics of the transition underway in several aspects: from a predominantly primary-export economy to the emergence of agro-industrialisation strongly anchored in natural resources, with diversified animal protein, consisting of cattle, pigs, poultry and fish; transition from local and regional capital groups to the presence of international corporations; activities in the sugar-energy sector; exports that include trade in captive fish, variety in the biofuel chain, with production of corn ethanol and the pulp economic complex, in addition to the recent location of data centres in the south of the state. Contrary to national deindustrialisation, industry is expanding its share of state GDP, and all this is happening alongside ongoing land conflicts, the precarious social reproduction of indigenous peoples, and migration, which has historically been from Paraguay and, more recently, from Haiti and Venezuela, as well as the strengthening of border practices. This text highlights processes of transition and continuity based on research carried out by the Postgraduate Programme in Geography at UFGD and a review of the literature and primary and secondary data.
Kamiba I. Kabuya, Olasupo O. Ajayi, Anotine B. Bagula
The "Smart City" (SC) concept has been around for decades with deployment scenarios revealed in major cities of developed countries. However, while SC has enhanced the living conditions of city dwellers in the developed world, the concept is still either missing or poorly deployed in the developing world. This paper presents a review of the SC concept from the perspective of its application to cities in developing nations, the opportunities it avails, and challenges related to its applicability to these cities. Building upon a systematic review of literature, this paper shows that there are neither canonical definitions, models or frameworks of references for the SC concept. This paper also aims to bridge the gap between the "smart city" and "smart village" concepts, with the expectation of providing a holistic approach to solving common issues in cities around the world. Drawing inspiration from other authors, we propose a conceptual model for a SC initiative in Africa and demonstrate the need to prioritize research and capacity development. We also discuss the potential opportunities for such SC implementations in sub-Saharan Africa. As a case study, we consider the city of Lubumbashi in the Democratic Republic of Congo and discuss ways of making it a smart city by building around successful smart city initiatives. It is our belief that for Lubumbashi, as with any other city in Sub-Saharan Africa, the first step to developing a smart city is to build knowledge and create an intellectual capital.
Kazi Shahrukh Omar, Gustavo Moreira, Daniel Hodczak
et al.
Sunlight and shadow play critical roles in how urban spaces are utilized, thrive, and grow. While access to sunlight is essential to the success of urban environments, shadows can provide shaded places to stay during the hot seasons, mitigate heat island effect, and increase pedestrian comfort levels. Properly quantifying sunlight access and shadows in large urban environments is key in tackling some of the important challenges facing cities today. In this paper, we propose Deep Umbra, a novel computational framework that enables the quantification of sunlight access and shadows at a global scale. Our framework is based on a conditional generative adversarial network that considers the physical form of cities to compute high-resolution spatial information of accumulated sunlight access for the different seasons of the year. We use data from seven different cities to train our model, and show, through an extensive set of experiments, its low overall RMSE (below 0.1) as well as its extensibility to cities that were not part of the training set. Additionally, we contribute a set of case studies and a comprehensive dataset with sunlight access information for more than 100 cities across six continents of the world. Deep Umbra is available at https://urbantk.org/shadows.
Ayad Ghany Ismaeel, S. J. Jereesha Mary, C. Anitha
et al.
Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating these rich and diverse data To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large scale data sets.
Propõe-se que a governança colaborativa é o centro do desenvolvimento das redes, influenciando nos resultados. Realizou-se pesquisa qualitativa e quantitativa, analisando-se o caso único do Arranjo Produtivo Local (APL) de moda íntima de Juruaia, no Estado de Minas Gerais, no Brasil. Construíram-se indicadores para as entrevistas e os questionários. Os dados sustentaram a associação de influência da governança colaborativa nos resultados comerciais e sociais, especialmente por causa dos laços fortes de um subgrupo de atores que decidem os rumos do negócio na cidade. O artigo sustenta e reforça a importância de pesquisas sobre governança colaborativa e oferece uma matriz de indicadores operacionais e confiáveis.
Cities. Urban geography, Urban groups. The city. Urban sociology
Felix Wagner, Florian Nachtigall, Lukas Franken
et al.
Climate change mitigation in urban mobility requires policies reconfiguring urban form to increase accessibility and facilitate low-carbon modes of transport. However, current policy research has insufficiently assessed urban form effects on car travel at three levels: (1) Causality -- Can causality be established beyond theoretical and correlation-based analyses? (2) Generalizability -- Do relationships hold across different cities and world regions? (3) Context specificity -- How do relationships vary across neighborhoods of a city? Here, we address all three gaps via causal graph discovery and explainable machine learning to detect urban form effects on intra-city car travel, based on mobility data of six cities across three continents. We find significant causal effects of urban form on trip emissions and inter-feature effects, which had been neglected in previous work. Our results demonstrate that destination accessibility matters most overall, while low density and low connectivity also sharply increase CO$_2$ emissions. These general trends are similar across cities but we find idiosyncratic effects that can lead to substantially different recommendations. In more monocentric cities, we identify spatial corridors -- about 10--50 km from the city center -- where subcenter-oriented development is more relevant than increased access to the main center. Our work demonstrates a novel application of machine learning that enables new research addressing the needs of causality, generalizability, and contextual specificity for scaling evidence-based urban climate solutions.
Shengjie Hu, Zhenlei Yang, Sergio Andres Galindo Torres
et al.
Urban land growth presents a major sustainability challenge, yet its growth patterns and dynamics remain unclear. We quantified urban land evolution by analyzing its statistical distribution in 14 regions and countries over 29 years. The results show a converging temporal trend in urban land expansion from sub-country to global scales, characterized by a coherent shift of urban area distributions from initial power law to exponential distributions, with the consequences of reduced system stability and resilience, and increased exposure of urban populations to extreme heat and air pollution. These changes are attributed to the increased influence from external economies of scale associated with globalization and are predicted to intensify in the future. The findings will advance urban science and direct current land urbanization practices toward sustainable development, especially in developing regions and medium-size cities.
Yuxiang Li, Jens-Christian Svenning, Weiqi Zhou
et al.
Heat extremes are projected to severely impact humanity and with increasing geographic disparities. Global South countries are more exposed to heat extremes and have reduced adaptation capacity. One documented source of such adaptation inequality is a lack of resources to cool down indoor temperatures. Less is known about the capacity to ameliorate outdoor heat stress. Here, we assess global inequality in green infrastructure, on which urban residents critically rely to ameliorate lethal heat stress outdoors. We use satellite-derived indicators of land surface temperature and urban green space area to quantify the daytime cooling capacity of urban green spaces in the hottest months across ~500 cities with population size over 1 million per city globally. Our results show a striking contrast with an about two-fold lower cooling capacity in Global South cities compared to the Global North (2.1 degrees Celsius vs. 3.8 degrees Celsius). A similar gap occurs for the cooling adaptation benefits received by an average urban resident (Global South 1.9 degrees Celsius vs. North 3.6 degrees Celsius), i.e., accounting for relative spatial distributions of people and urban green spaces. This cooling adaptation inequality is attributed to the discrepancies in urban green space quantity and quality between Global North and South cities, jointly shaped by natural and socioeconomic factors. Our analyses suggest vast potential for enhancing outdoor cooling adaptation while reducing its global inequality through expanding and optimizing urban green infrastructure.
Building on near-real-time and spatially explicit estimates of daily carbon dioxide (CO2) emissions, here we present and analyze a new city-level dataset of fossil fuel and cement emissions. Carbon Monitor Cities provides daily, city-level estimates of emissions from January 2019 through December 2021 for 1500 cities in 46 countries, and disaggregates five sectors: power generation, residential (buildings), industry, ground transportation, and aviation. The goal of this dataset is to improve the timeliness and temporal resolution of city-level emission inventories and includes estimates for both functional urban areas and city administrative areas that are consistent with global and regional totals. Comparisons with other datasets (i.e. CEADs, MEIC, Vulcan, and CDP) were performed, and we estimate the overall uncertainty to be 21.7%. Carbon Monitor Cities is a near-real-time, city-level emission dataset that includes cities around the world, including the first estimates for many cities in low-income countries. A more complete description of this dataset is published in Scientific Data (https://doi.org/10.1038/s41597-022-01657-z).