Abstract Digital 3D city models support urban planning, simulation, and immersive applications, but common production methods such as manual CAD modelling, photogrammetry/LiDAR, and GIS-based extrusion are often slow, costly, difficult to scale, and hard to update. Procedural modelling offers a scalable alternative, yet practitioners still need clear guidance on when to use it and how to evaluate its outputs. This paper presents and evaluates a CityEngine workflow that combines open geospatial inputs with rule-based generation of road networks, blocks and parcels, and buildings. Using a commodity laptop (Intel Core i5‑3210 M, 6 GB RAM) and CityEngine, we generated three canonical morphologies (organic, raster/grid, and radial) over an area of approximately 2 km 2 under both default and dense scenarios. We report computational performance metrics, including generation time, peak RAM, CPU seconds, exported file size, and polygon count, and we complement these with output checks aimed at plausibility and visual realism for each morphology. Compared with a traditional modelling workflow, procedural generation reduces production time by one to two orders of magnitude while keeping resource use within desktop limits. Based on the case study results, we derive a decision matrix (Table 3) that compares procedural modelling with photogrammetry/LiDAR, GIS extrusion, and manual/CAD approaches across criteria such as time, scalability, update cadence, and required visual detail. This synthesis positions procedural modelling as a practical middle ground and motivates hybrid workflows that combine procedural background fabric with data-driven and manual elements when projects must balance fidelity, cost, and the frequency of updates.

Emergency groundwater supply is crucial for maintaining regional stability and supporting the livelihoods of both urban and rural populations during disaster events. Given the variety of natural disasters in China and the existing deficiencies in rapid water source detection, this paper focuses on developing advanced groundwater surveying technologies and decision support systems to expedite post-disaster water restoration. We propose a Space-Aerial-Ground (SAG) integrated technology framework that synergizes multi-source remote sensing (RS), geographic information systems (GIS), and geophysical prospecting. By incorporating multiple data sources, including RS, geological, and hydrological datasets, a rapid assessment model targeting groundwater potential was created through multi-criteria decision analysis. Coupled with a targeted approach combining unmanned aerial vehicle (UAV) reconnaissance and geophysical profiling, this allows for precisely identifying groundwater storage spaces and strategically placing emergency wells in disaster-affected areas. To facilitate its operation, a software platform called the Intelligent Survey and Rapid Analysis System of Groundwater Sources (GISRAS), featuring analytical positioning and field adaptability, was engineered with modular capabilities. Extensive testing of this framework and technical suite across a range of typical disaster scenarios has produced promising results in terms of reliability and performance. This research provides a validated, rapid, and accurate solution for groundwater exploration under extreme conditions, significantly enhancing China’s emergency water supply capabilities.

Abstract The pedestrian volume, a critical indicator of urban vitality, has been broadly investigated in urban streets, particularly in China. However, how the street environment impacts the pedestrian volume has not received much attention in current Chinese cities. This study has adopted a machine learning model of LightGBM to test the association between 22 typical street environmental factors (spatial elements & spatial proximity) and the pedestrian volume (two models: the average value & the distance weighted value), and the SHAP approach to interpret and visualize the results achieved using the model. Guangzhou, a mega city in southern China, was chosen as the location studied. Key findings achieved are as follows: 1) Several environmental factors, particularly the density of commercial points of interest (POIs) and functional density, could significantly affect the average pedestrian volume. While most paired environmental factors may also produce important interaction effects, the strongest one was specifically observed between commercial and transportation POIs. 2) The distance-weighted pedestrian volume was significantly impacted by all 22 environmental factors, with commercial POIs having the highest impact. Furthermore, although most paired environmental factors had significant interaction effects, these effects were generally small. 3) Most paired environmental factors had significant interaction effects on both average and distance-weighted pedestrian volumes. However, the size of these effects was smaller relative to their main effects.

Abstract Deep learning (DL) methods are an emerging tool that can be well-suited to uncover the complexity of urban vitality at a greater scale and depth. However, the use of DL in the field, even beyond vitality, remains underexplored, with no established state of the art to inform further research. This paper assesses DL’s potential as a research tool, critically examining its current status, benefits, and challenges in urban vitality studies. Through a systematic review, we analysed 208 publications that examine qualities under the dimensions of the built environment and urban activity, tying big data resources and DL methods to the study of urban vitality. A thematic analysis was conducted, identifying research contributions, big data types, resources, and DL architectures. Our findings indicate that DL shows significant promise for urban vitality research due to its scalability, multimodal capability, and fine-grained analysis, particularly of visual data. While it is well-established in analysing the built environment, its applications in human activity and spatio-temporal dynamics are only emerging. Multi-source data use is growing, enhancing robustness, but challenges such as geographic bias and high data requirements persist. Notably, over half of studies originate from China, limiting the generalizability of findings to contexts with different data availability, urban morphologies, and socio-cultural dynamics. Nonetheless, this synthesis serves as a critical assessment of DL’s possible future role in urban vitality studies.

Abstract This study introduces an integrative approach to forecasting urban morphogenesis and housing allocation dynamics in rapidly developing cities. Doha metropolitan is examined as the case study, with projections extending to the year 2035. Based on a robust theoretical framework, the study considers three factors: (i) residential land capacity and regulatory policies, (ii) housing supply and demand, and (iii) housing preferences. An integrative methodological framework was adopted, including content analysis of housing regulatory frameworks, a survey of housing preferences, and Machine Learning (ML). A Convolutional Long Short-Term Memory (ConvLSTM) network was utilized to simulate parametric urban morphogenesis. The integrated model was then employed to project housing allocation patterns for the year 2035. Spatial stratification in ArcGIS-Pro revises the predicted parametric urban morphogenesis, focusing on the residential growth within regulated growth boundary. The findings show that housing demand and supply patterns in Doha metropolitan are shaped by regulatory policies and socioeconomic factors. Predicted growth is concentrated in suburban areas, dominated by low-density villas, while the waterfront will require inclusionary zoning to balance housing needs. The study offers policymakers, urban planners, and developers a robust tool for regulatory adaptation that aligns urban morphology with housing market dynamics. By providing a nuanced prediction, the model supports sustainable urban growth by balancing housing needs for a diverse population and addressing supply and demand imbalances. This study provides a valuable reference for cities facing similar growth challenges worldwide.

The COVID-19 pandemic and restrictions had an unprecedented impact on human society. Recent studies have shown that the negative effects of pandemic lockdown measures on crime are significant. However, the impact of strict lockdown measures on the spatial distribution of crime at the community level remains underexplored. This study employs Bayesian Structural Time Series to estimate the community-level impact of a complete lockdown (from January 21 to February 24, 2020) and a refined lockdown (from May 21 to June 24, 2021) on theft in ZG City, China. Additionally, based on social disorganization theory and crime pattern theory, we utilize a logistic regression model to explore how social characteristics influence the significant reduction in theft observed in the BSTS analyses. The key findings are: (1) Complete lockdowns achieved a 57% reduction in theft, compared to 15% for refined lockdowns, indicating a stronger suppressive effect of complete lockdowns. (2) Communities experienced significant reductions in theft during the complete and refined lockdowns, while thefts in some communities have experienced a marked increase during the refined lockdown. (3) Under complete lockdowns, the proportion of migrant population significantly positively impacted the reduction in theft. The main contribution of this study is that it provides a comprehensive assessment of the contrasting impacts of the complete and refined lockdown on the temporal and spatial patterns of theft. These insights provide a scientific basis for effective crime prevention and urban governance strategies during emergencies.

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.

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.

Understanding the impact of population mobility on the spread of infectious diseases is crucial for designing effective interventions. Traditional models, such as origin-destination matrices, often lack the spatial and temporal resolution needed to accurately capture these dynamics. This study addresses this gap by introducing a novel methodology to estimate time-varying occupancy patterns across urban zones (patches) using geospatial data from mobile phones. By leveraging Brownian bridge models at an inhabitant-patch level, we construct a residence-occupation matrix (ROM) that represents the fraction of time individuals spend in each urban patch. We apply this approach to real-world data from Hermosillo, Sonora, Mexico, during the COVID-19 pandemic. Our findings show that even small shifts in local mobility patterns can significantly alter the epidemic’s trajectory, highlighting the importance of high-resolution mobility data in modeling infectious disease spread. These changes can be patch-specific, and their contribution to the overall evolution depends on the mobility dynamics and population sizes within each patch. The proposed ROM serves as a key input for multi-patch epidemiological models, that in turn, can provide more realistic epidemic forecasts, facilitating the evaluation of the effectiveness of patch-specific and global mobility restrictions, and improving the estimation of epidemiological parameters of such models in related research. Additionally, the ROM framework can be adapted to other patch-based models modeling various phenomena influenced by human mobility.

In contemporary society, music art plays a pivotal role in enhancing spiritual quality-of-life and optimizing talent cultivation models. The spatial distribution of music educational resources undoubtedly plays a promotional role in enhancing the quality of music education. However, existing research has not conducted specialized and in-depth studies on this topic. This study uses a geographic information system (GIS) and classical geographical research methods such as the nearest neighbor index, kernel density analysis, and geographic detectors to analyze the spatial distribution and influencing factors of music training institutions in Beijing, Shanghai, Guangzhou, and Shenzhen. The results indicate that the distribution of these institutions is obviously clustered, with a "northeast‒southwest" trend in Beijing and Shenzhen and a "northwest‒southeast" trend in Shanghai and Guangzhou. Among the categories of urban facilities, which encompass sports, leisure, shopping, and dining, sports venues exert the most substantial influence on the spatial distribution characteristics of music training institutions. The location of music training institutions should be closely integrated with the spatial distribution of urban facilities. This study provides theoretical foundations and practical guidance for policy-makers in the refined adjustment of urban layouts, the rational allocation of artistic educational resources, and the promotion of cultural industry development.