Urban Digital Twin (UDT) technology is increasingly recognised as a promising tool for designing and developing sustainable, resilient urban environments. Nonetheless, the current literature lacks a comprehensive understanding of UDTs’ current applications in the built environment. Therefore, this study addresses the identified gap by analysing scholarly literature and industry reports connected to UDT implementations. The results of scientometric analysis revealed five key research fields including: (i) UDT for urban monitoring and controlling, (ii) UDT for smart urban planning, (iii) UDT for environmental management, (iv) UDT for decision-making, and (v) UDT for smart and sustainable cities. Further, this study analysed 10 industry reports on UDT technology to identify practical insights and evaluate industry-driven approaches for implementing UDT solutions in urban environments. Despite promising progress, the findings indicate the absence of a clear, structured process to facilitate consistent implementation, scalability, and interoperability in UDT technology. This further highlights the need for globally recognised guidelines and well-defined KPIs to fully realise its potential in urban environments. The study also presents a new classification model developed from analysing the research flow to elaborate on the main outcomes from five clusters towards UDT pathways. The new proposed model reintroduces the structure of UDT literature with a new flow to interpret and correlate the content identified in previous studies. Based on these insights, the study offers recommendations to support the advancement of UDT technology for building resilient, sustainable cities.

The digital economy drives economic growth and regional competitiveness. Understanding the evolution of county-level digital economies is essential for regional economic transformation, upgrading, and long-term development. Traditional assessment methodologies have several shortcomings for representing the county digital economy, especially data availability and reliability. In this paper, we develop a multi-scale analytic framework using complex network indicators including average clustering coefficient, k-core, and weighted degree at macro, meso, and micro scales. The framework allows us to establish a county-level network using enterprise investment data from Fujian Province, China, to study digital economy investment development from 2000 to 2021. The outcomes are: 1) The digital economy's investment scale and connection in the county grew in stages, with network expansion aligning with the concept of"the rich leading the whole, and the whole leading the poor."2) The interconnectivity hot zones, which made up less than 9% of counties, had a major impact on the network and have gotten stronger. Investment linkage control increased from 24.64% in 2000 to 41.56% in 2021, and the focus of hot areas shifted from outside the province to within the province. 3) Over time, the top six key counties have increasingly controlled more than 30% of the total investment quota. In 2021, when 2% of counties controlled 60% of investment, developmental imbalances became more important.

Urban areas globally have become home to over half of the world's population, leading to the intensification of the urban heat island (UHI) effect, where cities experience higher temperatures than their rural counterparts. The current study develops a new model predicting UHI intensity for 216 cities across all climate zones for both the Global North and Global South using machine learning techniques, focusing on the years 2019 to 2023. Utilising a novel dataset, integrating climate, economic, population, and land use data from 216 cities worldwide, the model, trained using Support Vector Regression (SVR), demonstrates a mean absolute error (MAE) of 0.86 °C. Results reveal that wind speed significantly mitigates UHI intensity, while cities in temperate climates exhibit more pronounced UHI effects compared to those located within tropical climbs. Additionally, results show the crucial role of coastal proximity in reducing UHI intensity and find no significant differences in UHI intensity between cities in the Global North and Global South. Findings offer important empirical actionable insights alongside a robust tool for urban planners and policymakers to measure, map, and monitor the UHI effect, contributing to the development of more liveable and sustainable urban environments.

Digital twins are enjoying widespread and growing success in both theoretical and practical applications. A recent development that is gaining increasing traction is the application of digital twins to cities. The aim of this article is to discuss whether there are inherent limitations in this case. At present, the scientific literature on urban digital twins is dominated by “technical” approaches. Critical investigation of digital twins – especially from a philosophical perspective – is still at its beginnings. This article aims to contribute to this line of inquiry. It is mainly theoretical and analytical. On the basis of a specific conceptual framework, it examines digital twins and their applications in urban contexts. It starts by distinguishing among simple, complicated and complex systems, and reaches the conclusion that, while using digital twins is generally appropriate (and often helpful) in the first two of these systems, there are some structural limitations on their use in the case of complex systems. In the latter case, inherent limitations depend on certain distinctive aspects of complex systems, such as their emergent and unpredictable nature, and the role played in this regard by “dispersed knowledge” (that is, is a form of diffused practical knowledge that is crucial for the functioning of large urban systems but that cannot be collected and re-unified because, as a coherent and integrated whole, it does not and cannot exist anywhere).

The use of ride-hailing services, online shopping, and telecommuting are behaviors which have recently increased dramatically in popularity, due in part to technological advancement and global events such as the Covid-19 pandemic. In theory, these behaviors may have the potential to shift people towards more sustainable travel. This study aims to explore the influences of ride-hailing, online shopping, and telecommuting on household vehicle miles traveled (VMT) and walking trip generation using the 2017 and 2022 U.S. National Household Travel Surveys (NHTS). Results reveal that the frequencies of all three activities increased between 2017 and 2022, online shopping and telecommuting showing positive correlations with VMT generation, and higher mean VMT associated with all three activities in 2017 and with online shopping and telecommuting in 2022. Regression models further indicate that telecommuting is most strongly associated with more sustainable travel, with seven of the eight models estimated indicating lower VMT generation and more walking trips associated with telecommuting. Ride-hailing service usage was also associated with lower VMT and more walking trips in six models. The results for online shopping are mixed, with our models showing that online shopping leads to more walking trips, but also higher VMT. The results of this study indicate that ride-hailing services and telecommuting may play an important role in shifts towards more sustainable travel behavior. Suggestions are presented for maximizing shifts to sustainable travel modes and minimizing potential inequitable effects, including designing for increased walkability, particularly in predominately minority areas, and the promotion of transit-oriented development.

The evidence on the relationship between built environment factors and obesity in primary school children is limited, and this study is the first to investigate this relationship in Iran. This study utilizes Geographical Information Systems (GIS) techniques to assess built environment indices for geographical addresses based on the street network. A school-based survey was conducted in ten neighborhoods in Tehran from January to April 2019, collecting socio-demographic information and home addresses from 2,677 primary school children (6–13 years). School nutrition experts measured children's height and weight, and their obesity status was calculated based on the BMI z-score adjusted for age and gender. Logistic regression analysis showed that higher accessibility to parks within 2 km was associated with lower odds of obesity, even after adjusting for age, gender, family income, and parental educational level in the model (OR = 0.919, 95% CI = 0.848–0.996). Living in an area less than 400 m from a park was also associated with lower odds of obesity (OR = 0.811, 95% CI = 0.665–0.989). Access to sports facilities and the percentage of major streets were inversely associated with childhood obesity (highest vs. lowest tertile OR = 0.766; 95% CI = 0.597, 0.985 and OR = 0.739, 95% CI = 0.582, 0.938 respectively). However, no significant relationships were identified for residential density, intersection density, land-use diversity, and the effective walkable area index. Similar to findings from other international studies, these results suggest that addressing spatial disparities in access to parks and sports facilities as an amenable environmental factor is important for reducing children's obesity. This information is valuable for creating local policies and intervention programs. Further investigations with a longitudinal design may provide a better understanding of these relationships.

Excess commuting, defined as the inefficiency resulting from spatial mismatches between residential and employment locations, poses significant challenges for urban planning and transportation systems. This study uses big data from individual vehicle trips collected in Tampa, Florida, to quantify excess commuting more accurately than traditional zonal approaches. Through the application of Linear Programming (LP) and Integer Linear Programming (ILP) models, this research measures minimum and actual commuting patterns across different spatial scales—census tract, block group, and individual trip levels. The findings reveal a clear scale effect associated with the Modifiable Areal Unit Problem (MAUP), as smaller spatial units consistently yield shorter minimum commuting distances and times and the ILP model at the individual trip level yields the least. By directly analyzing actual trips rather than simulated data, this approach provides a more precise and realistic assessment of excess commuting. The results underscore the values of methodological improvements and individual-level data in refining our understanding of excess commuting and supporting more efficient urban planning and policymaking.

The recent COVID-19 pandemic has underscored the need for effective public health interventions during infectious disease outbreaks. Understanding the spatiotemporal dynamics of urban human behaviour is essential for such responses. Crowd-sourced geo-data can be a valuable data source for this understanding. However, previous research often struggles with the complexity and heterogeneity of such data, facing challenges in the utilisation of multiple modalities and explainability. To address these challenges, we present a novel approach to identify and rank multimodal time series features derived from mobile phone and geo-social media data based on their association with COVID-19 infection rates in the municipality of Rio de Janeiro. Our analysis spans from April 6, 2020, to August 31, 2021, and integrates 59 time series features. We introduce a feature selection algorithm based on Chatterjee’s Xi measure of dependence to identify relevant features on an Área Programática da Saúde (health area) and city-wide level. We then compare the predictive power of the selected features against those identified by traditional feature selection methods. Additionally, we contextualise this information by correlating dependence scores and model error with 15 socio-demographic variables such as ethnic distribution and social development. Our results show that social media activity related to COVID-19, tourism and leisure activities was associated most strongly with infection rates, indicated by high dependence scores up to 0.88. Mobility data consistently yielded low to intermediate dependence scores, with the maximum being 0.47. Our feature selection approach resulted in better or equivalent model performance when compared to traditional feature selection methods. At the health-area level, local feature selection generally yielded better model performance compared to city-wide feature selection. Finally, we observed that socio-demographic factors such as the proportion of the Indigenous population or social development correlated with the dependence scores of both mobility data and health- or leisure-related semantic topics on social media. Our findings demonstrate the value of integrating localised multimodal features in city-level epidemiological analysis and offer a method for effectively identifying them. In the broader context of GeoAI, our approach provides a framework for identifying and ranking relevant spatiotemporal features, allowing for concrete insights prior to model building, and enabling more transparency when making predictions.

Human dynamics research has undergone a significant transformation over the past decade, driven by interdisciplinary collaboration and technological innovation. This opinion paper examines the evolution of the field in the past ten years, focusing on its integration of GIScience (Geographic Information Science), social science, and public health to tackle spatial and societal challenges such as urban sustainability, disaster response, and epidemics. Key advancements include the adoption of living structure theory, which redefines space as a dynamic and interconnected entity linked to human well-being and ecological sustainability, and the application of cutting-edge technologies like GeoAI (Geospatial Artificial Intelligence) and digital twins for adaptive modeling and informed decision-making. Despite these advancements, challenges persist, including incomplete data, mismatched scales, and barriers to equitable access to geospatial information. Addressing these issues necessitates innovative approaches such as multiscale modeling, open data platforms, and inclusive methodologies. Increased funding opportunities offer pathways for accelerating translational research. By integrating advanced theories, user-centered technologies, and collaborative frameworks, human dynamics research is poised to transform urban systems into sustainable, resilient, and equitable environments. This paradigm shift underscores the importance of ethical considerations and inclusivity, offering a holistic approach that aligns with human and ecological needs.

The concept of Spatially Integrated Social Sciences (SISS) emerged in the 1990s, introducing a spatial dimension to social science research through Geographic Information Science (GIS)-based tools. This movement, known as the "spatial turn," integrates space and place into the analysis of social phenomena and human behaviour. Although SISS has not yet fully permeated mainstream social science, its application continues to expand, offering a broader understanding of social processes by examining them within spatial and temporal contexts. Initially rooted in quantitative methods, SISS now includes qualitative approaches, enriching its analytical scope. Central to SISS is the recognition of geographical concepts such as distance, distribution, and location, which shape societal dynamics and human interactions. Supported by diverse theoretical paradigms and advanced analytical tools, we discuss nine opportunities embedded within SISS to contribute to new theories and techniques for capturing, mapping, and modelling spatial data. It offers deeper insights into social inequalities and the spatial factors influencing human behaviour and society, enriching the scope of computational urban science.