In the context of the ongoing debates about the effects of vehicular traffic on urban environments, interest in street networks has increased across a broadening range of disciplines. With that increasing interest, there has been a proliferation of analytical methods and measures. A potential solution is to combine measures with types of urban tissue, seen as a common point of reference. The issue then arises that the definition of urban tissue is no less prone to proliferation of methods and attributes to consider. This paper proposes a general-purpose set of route and route aggregate types as a basis for the differentiation of street networks to facilitate comparative analysis, including correlations between network and urban tissue analysis as well as correlations with other aspects and attributes of human settlements. The method used starts with the abstraction of minimum topological attributes of streets, set within the mereological framework of the compositional hierarchy of built form, to arrive at a set of minimum elements. Possible combinations of the elements are then explored to identify distinct types at three levels: wide area inter-settlement routes, local area routes and local area route aggregates. The result is a consistent and coherent set of type families defined by their mereotopological attributes. The attributes in turn provide the basis for ordering the types and establishing the connection between network configurations and urban tissue. The types are applied in an analysis of the town of Leighton-Linslade to demonstrate the resulting differentiation of the street network and the relation between the route type and urban tissue. The paper concludes by assessing the potential for application of the types as well as further research.
The “15-minute city” has emerged as an influential urban-planning paradigm, promoting proximity to work and essential services. Its feasibility for journey-to-work commuting, however, has rarely been examined quantitatively. Here, we show that employment proximity is constrained by heterogeneity in the number of employees per firm. Combining urban geometry with empirically observed firm-size distributions, we derive a lower bound on commuting time that holds even under optimal workplace placement. This bound reveals a sharp transition: when employment is sufficiently unevenly distributed across firms, no spatial rearrangement of workplaces can provide uniformly short commutes. Applied to Paris and its near suburbs, with area A = 762 km 2 and empirical firm-size exponent γ ^ ≃ 1.38 , the model predicts that only about 37% of workers can reach their workplace within 15 minutes by walking. Cycling increases this share to about 67%, but still leaves roughly one third of the workforce beyond the 15-minute threshold. Achieving universal short commutes would therefore require economic restructuring, differentiated mobility strategies, remote work, or a longer target time. Our results suggest replacing the universal 15-minute target, for commuting, by a city-specific benchmark determined by firm-size heterogeneity and urban scale.
Currently, residents of large metropolitan areas in Latin America who do not own automobiles primarily rely on a multimodal mobility system to travel long distances. However, these transportation modes operate independently and do not interact effectively, resulting in high commuting costs that negatively impact the quality of life. This is particularly evident in the long travel times, necessary transfers, and the overall conditions of travel. In this context, this study assessed the feasibility of implementing a Mobility as a Service (MaaS) system in the city of Santiago (Chile), aiming to improve multimodal travel conditions and encourage the use of sustainable transportation options. To achieve this, we developed and applied a multi-criteria index for territorial suitability, which is structured around three dimensions: physical, temporal, and behavioral. The index was specifically used to assess the potential of Greater Santiago for future implementation of MaaS. The findings indicate that the city is moving away from the socioeconomic segregation that has shaped its growth model, highlighting a new East/West axis that is particularly beneficial for implementing a MaaS system.
Urban regeneration is crucial for cities adapting to growing populations and shifting socio-economic conditions. The regeneration process is often monitored through field surveys, permit records, or medium-resolution remote sensing, but these sources can be costly, inconsistent, or insufficient for identifying parcel-level morphological change. This study develops a Siamese network-based framework for classifying morphological urban change during regeneration from paired high-resolution aerial images. Using 2000 residential parcels from Portland, Oregon, we classify four operational change types: No Change, New Development, Redevelopment, and Demolition. We compare Siamese network variants using ResNet, UNet, and YOLO backbones, including a UNet variant with a local similarity attention module. Five-fold cross-validation shows that the UNet-based models achieve the most consistent performance, with an overall accuracy above 85%. A transfer-learning test using data from Charlotte, North Carolina, suggests that direct cross-city transfer is limited, but local fine-tuning substantially improves performance. This approach provides a cost-effective, replicable tool for parcel-level morphological change analysis, facilitating rapid, reliable assessment of urban regeneration even in data-limited environments.
Depicting the usage characteristics of a bike-sharing system can contribute to more efficient planning and operation. Station-level imbalances are typically treated as operational defects to be corrected. However, the systematic asymmetries between pick-ups and drop-offs may reflect stable behavioural and spatial patterns. The study aims to reframe imbalance as a behavioural and spatial signal that can be systematically profiled. This paper proposes an integrated framework to analyze station-level asymmetry in a station-based bike-sharing system and to derive interpretable behavioural station profiles. The framework combines four steps: temporal analysis, calculation of absolute and relative asymmetry indices, asymmetry-based K-means station clustering into specific profiles, and GIS-based mapping of profiles. The approach is applied to trip records from MOL Bubi public bike-sharing system in Budapest. The results reveal clear contrasts between weekdays and weekends, as well as between weekday commuting peaks, and robust source–sink patterns between the inner city and surrounding residential areas during weekdays. Five behavioural station profiles are identified, capturing urban contexts. Stations in residential areas act as sources in the morning and sinks in the afternoon; stations in downtown areas act oppositely. Spatial analysis highlights recurring features, including changes in usage patterns near railway and metro stations. By explicitly linking asymmetry-based station profiles to urban structure and public transport interfaces, the framework supports data-driven planning, rebalancing strategies, and station placement. The Budapest case study provides new empirical evidence from an underrepresented Central and Eastern European context, demonstrating that asymmetry-based profiling yields stable and operationally relevant insights.
Tactile paving is vital infrastructure for safe mobility among 2.2 billion visually impaired individuals worldwide, but in complex urban environments it faces both static damage and dynamic encroachment. This study develops an intelligent evaluation framework that integrates visual-language models (VLMs) with pedestrian-view street imagery to assess tactile paving usability around urban metro stations. Using GPT-4o and GoPro-collected imagery, we built a three-tier risk detection system covering the tactile paving body, a 250 mm proximity zone, and the surrounding environment. The framework includes 26 structural and 24 situational indicators with differentiated risk-scoring thresholds. Based on 110 metro stations within Beijing’s Third Ring Road, we analyzed the spatial distribution of tactile paving obstructions. The 250 mm proximity zone showed the highest obstruction rate (34.46%), exceeding the tactile body (33.03%) and environment (19.79%), mainly due to spatial pressure from wall attachments, poles, and adjacent facilities. Structural obstacles reflected persistent damage and encroachment, whereas situational obstacles showed greater temporality, peak intensity, and spatial variability, especially within the proximity zone. AI evaluations closely matched expert ratings (Pearson r = 0.943), and iterative scoring reduced false positives from 54% to 11%, confirming the reliability of VLMs in complex urban contexts. Fengtai District scored poorest in both indicator categories, with Majiapu Station as a key case. We recommend introducing a “proximity buffer zone” and improving fine-scale maintenance in high-density areas. The resulting intelligent platform is scalable and transferable for nationwide monitoring and governance of accessible infrastructure.
The relationship between urban form and urban activity has long been debated but remains difficult to operationalize empirically. This study presents a computational pipeline integrating urban morphometrics and place-based activity data to examine how distinct urban form types associate with functional composition and visit intensity. Five north-western German cities serve as an analytical laboratory. Their post-war reconstruction produced a spatial coexistence of pre-war compact fabrics and modernist reconstructed areas within the same urban tissue, enabling direct morphological comparison across planning paradigms. Results reveal recurring spatial-functional configurations across urban form types and consistently show that high ground-floor density and functional diversity concentrate in morphological types corresponding to pre-war fabrics, while post-war reconstructed areas show systematically lower activity levels, reflecting the enduring impact of historical planning decisions on present-day urban environments. Functional diversity proves a stronger predictor of visit intensity than physical density alone. These findings contribute empirical grounding to theoretical claims about density and urban vitality and demonstrate a transferable methodology applicable to other urban contexts combining morphologically distinct fabrics.
Agricultural education is highly experience-based and context-dependent, which limits the effectiveness of conventional teaching methods for conveying practical applications. To support more immersive and applied learning, we introduce two educational escape games on vision-related animal welfare aspects in poultry production. One game is implemented in 2D and uses text, pictures, and simple animations, while the other takes place on a virtual 3D farm that allows free movement and spatial arrangement of riddles and puzzles. Both versions cover the same educational content. A comparative user study with university students showed that both games improved knowledge levels in reference to a conventional lecture baseline. The 2D version achieved slightly higher user acceptance as a tool for university teaching, whereas the 3D version led to better quantitative learning outcomes and superior time efficiency. Both games were perceived as motivating and suitable for agricultural knowledge transfer.
Cadastral systems are central to property taxation, urban planning, and territorial governance, yet in many cities—particularly in the Global South—they suffer from outdated information, poor data quality, and inconsistent valuation. These limitations undermine both horizontal and vertical equity, weakening fiscal capacity and public trust. This paper proposes an integrated framework that combines machine learning and bootstrap-based simulation to improve the internal consistency and dynamic updating of cadastral values. First, we use Extreme Gradient Boosting (XGBoost) to estimate Expected Cadastral Values (ECV) based on observable property and location characteristics. Deviations between observed and predicted values are then analyzed to systematically detect anomalies associated with data errors, outdated records, or inconsistent application of valuation criteria. Second, we implement a bootstrap-based simulation to estimate Simulated Cadastral Values (SCV) under hypothetical changes in urban infrastructure, thereby isolating and quantifying the impact of improved accessibility on land values. The methodology is applied to Aracataca, Colombia, a data-constrained context characterized by limited institutional capacity for frequent updates. Results show that anomalies follow clear spatial and structural patterns, particularly in transition areas between valuation zones and in locations with low-quality data. The simulation further demonstrates that infrastructure improvements can generate significant increases in land values, highlighting the need for timely cadastral updates. By shifting the focus from prediction alone to consistency and adaptability, this study contributes to the literature on automated valuation models and provides a scalable, evidence-based tool to strengthen equity, transparency, and fiscal performance in cadastral systems.
Income-based unequal access to schooling threatens inclusive urban growth and fair education in the rapidly urbanizing Global South, but measuring it remains difficult due to limited data. This study addresses the issue by using a new multi-source data method, analyzing the residence-school relationship of over one million students through mobile phone population mobility data in Chinese megacities. We reveal a complex pattern of inequality: students at top schools travel long distances, while lower-quality schools cluster low-income students in isolated groups. Top schools, although showing greater income diversity within their student bodies, are still predominantly attended by high-income students. Segregation decreases from primary to high-school levels, driven by the shift from proximity-based school districting to merit-based admission. However, urban expansion, despite the increase in school provisioning, reproduces these inequalities and segregation instead of solving them. This paper validates the Effectively Maintained Inequality theory in the Global South context and provides empirical evidence on unequal access to schooling in urban China.
Women’s care trips in Santiago (Chile) form a denser and more triangulated aggregate network than men’s. We aggregated survey trips into zone-to-zone flows, built gender- and purpose-specific subgraphs using Pointwise Mutual Information, and tested the difference with a gender-permutation null model ( p < 0.005). These differences show that network and geographical methods can reveal structural patterns in care mobility that trip counts miss. A denser, more triangulated care network points to the need for stronger within-neighborhood connectivity, and transport planning informed by these methods can help identify where care-related infrastructure and local connections are most needed.
Conventional high-resolution population maps typically represent long-term averages and overlook the temporal rhythms of urban life. Using Tencent User Location Data and Amap points of interest (POI), we visualized the intensity and timing of hour-of-day population variation across functional zones in Beijing at 0.01 degrees resolution. For each grid cell, we quantified diurnal fluctuation using the standard deviation of hourly population and identified the hour of peak population presence. These metrics were integrated with POI-derived functional zones to produce a three-dimensional visual representation. The results revealed that commercial areas exhibit the highest fluctuation intensity and tend to peak around midday, whereas residential zones showed the lowest variability and peak at mid-night. This featured graphic highlights the value of time-aware and function-sensitive population representations for urban analytics and planning.
Urban housing markets exhibit pronounced spatial heterogeneity shaped by multiple spatial factors, resulting in scaling characteristics and hierarchical structures typical of complex systems. These properties provide an alternative perspective for delineating housing market areas and interpreting inter-market linkages. This study introduces the concept of natural submarkets, defined as spatially contiguous housing clusters exhibiting both hierarchical scaling in price patterns and consistent hedonic price determinants. We implement this concept through a bottom-up segmentation framework that combines an improved head/tail breaks with hedonic model fit, enabling data-driven delineation of market boundaries and comparative regional analysis. Applied to 26 cities in the Yangtze River Delta (YRD), China, the proposed method yields a more objective and interpretable representation of price differentiation and scaling properties than conventional classifications. The results indicate that economically advanced cities may exhibit either multi-level submarket structures with well-defined hedonic relationships or more complex hierarchical structures with weakly defined pricing mechanisms. Further submarkets clustering by shared pricing mechanism reveals two dominant spatial patterns, comprising interconnected development corridors and isolated market structures. These morphologies are generally driven by network centrality and accessibility to amenities. As a result, the YRD housing price distribution is characterized by a polycentric structure and ongoing cross-boundary integration. The proposed framework complements traditional regional analyses by revealing structural patterns of mechanical similarity and supporting differentiated policy interventions aligned with housing market dynamics.
Instant delivery has emerged as a critical component of urban mobility, with its spatiotemporal dynamics significantly shaping street congestion, emissions, and gig labor conditions. Here, I developed a 3D interactive animation to vividly visualize the spatiotemporal trajectories of on-demand deliveries based on 2508 real-world orders delivered by 113 unique riders in Beijing. This visualization transforms fragmented origin-destination (OD) order data into a dynamic exploration of street-level delivery mobility, revealing riders' multi-stop routing patterns, street flow distributions, and collective urban delivery rhythms. This case demonstrates the potential of dynamic visualization in urban trajectory analytics.
