This study proposes a framework for classifying urban form typologies at the block level and examining changes in their prevalence in Seoul since 1960. The study identifies major typologies aligned with urban planning trends and analyzes their morphological characteristics. Key questions addressed include: how many distinct urban form types can be identified in Seoul; what unique features define each type; and how changes in the prevalence of these types over time reflect broader urban development trends. The analytical procedure consisted of three steps. First, block units were defined based on infrastructure boundaries such as arterial roads. Second, eight indicators were established to capture the morphological features of blocks, parcels, streets, and buildings—the fundamental components of urban structure. Third, Gaussian Mixture Model (GMM) clustering was applied to classify blocks with similar numerical indicators into five distinct types. The result effectively captures how many morphological features aligned with Seoul’s urban planning objectives by era. Compared with previous typologies, the study integrates multiple scales of urban form and examines changes in typology prevalence across different periods, providing a basis for interpreting the historical process of urbanization.
Crime geographers are attentive to how the spatial characteristics of the environment affect criminal behaviors. Recently, scholars have found that aside from physical and social attributes, the perception of the environment, such as safety perception, is also associated with crime occurrence. However, perceived liveliness, a visual perceptual proxy for potential routine activity settings that are closely linked to routine activity theory and crime pattern theory, is rarely explored in prior research. This study examines the association between perceived liveliness and street thefts in the City of Cincinnati, the United States. The liveliness perception scores are derived from street view imagery using a support vector machine model trained on the MIT Place Pulse V2 crowdsourcing image survey. Negative binomial regression models are used to test the relationships between perceived liveliness and street theft, with social and physical environmental attributes controlled. Results show that perceived liveliness significantly improves model performance and is positively associated with street theft. This study provides the first evidence that perceived liveliness helps explain crimes. The positive relationship between perceived liveliness of the built environment and crime adds a new dimension to the routine activity theory and the crime pattern theory. It also offers a scalable and cost-effective measure that can assist urban planners and law enforcement in identifying potential theft hotspots using readily available street view imagery.
Urban morphology has long recognized plot patterns as fundamental units shaping urban form and supporting heritage-sensitive regeneration. Yet, existing plot-based studies largely focus on flatland contexts, leaving mountainous historic cities-where steep terrain fundamentally constrains land subdivision and development logic-insufficiently examined. This study adapts established morphometric approaches to mountainous historic urban areas by integrating K-means clustering with a multi-scale typo-morphological framework. Using the Yuzhong Peninsula in Chongqing as a representative case, 688 plots were analyzed based on four geometric indicators: plot area, frontage, compactness, and slope. Six plot types and five dominant plot pattern categories were identified. Rather than tracing diachronic evolution, the study examines the contemporary spatial distribution of plot patterns and interprets their differentiation through a temporal-institutional lens. The results reveal pronounced east-west differentiation, center-periphery layering within the former walled city, and ring-road-related pattern transitions in the western area, reflecting the combined effects of topography, land tenure, urban policy, and construction modes. Building on these findings, the study synthesizes the morphological specificity of mountainous historic cities, characterized by topography-responsive plot forms, terrain-driven consolidation, and three-dimensional spatial organization. It further proposes a plot-pattern-based, multi-tiered morphological control approach to support organic regeneration while preserving historic urban character. This research contributes a slope-sensitive analytical framework and offers transferable insights for managing regeneration in topographically complex historic cities.
Geogames are increasingly recognized as playful systems based on real-world geographic data and spatial reasoning, yet open-ended geographic play remains underexplored. This study examines immersive virtual fieldwork as exploratory geoplay, where participants engage with geographic environments through autonomous observation, movement, and interaction rather than scores, competition, or fixed missions. We developed a multiplayer metaverse environment based on a life-scale 3D reconstruction of the Byobugaura Sea Cliff in Japan. Sixty-two participants explored it in two rounds: one unguided and one supported by light interpretive prompts. Avatar positions were recorded using the YAIBA behavior-logging system and analyzed through space-time clustering, PCA, trajectory-grid aggregation, and cluster-duration analysis. During unguided exploration, participants clustered around geomorphologically significant areas, while interpretive supports shifted visits toward breakwater and terrace zones. Cluster durations increased in the smaller sub-instance but remained stable in the larger main instance. Questionnaire responses reflected positive experiences and perceived behavioral changes. By framing avatar movement as learning data, while recognizing that movement does not directly measure knowledge acquisition, this study proposes exploratory geoplay as a model for studying locational attention and geographic inquiry within immersive environments.
Pressing environmental challenges present significant obstacles to cities’ sustainable development and underscore the need for effective resource management. However, resource management studies often overlook or oversimplify the spatial and social dimensions of resource use, limiting their ability to target interventions to specific population groups and integrate them into urban planning. Addressing this requires disaggregated data that capture spatial variation and interdependencies between socio-economic factors and resource consumption. Yet such data are often scarce in Global South contexts, where data limitations frequently hinder meaningful resource management research. This research develops a hybrid multi-scale analysis framework that integrates Spatial Microsimulation, Multi-Scale Geographically Weighted Regression (MGWR), and Geodemographic Classification to help researchers address data limitations and generate policy-relevant insights. The framework offers three contributions: methodological, empirical, and policy translation. The research provides the first systematic, household-level analysis of food-water-energy consumption patterns across Greater Cairo. Spatial microsimulation creates robust synthetic household datasets by integrating multiple surveys, while MGWR reveals spatially varying drivers of consumption. The geodemographic classification delivers the first empirically grounded typology of Greater Cairo households based on demographic, socio-economic, and consumption characteristics. Lastly, the applied probability-based allocation of clusters to neighbourhoods offers an indicative tool for exploring differentiated tariff structures and area-specific demand management, validated through expert workshops. By addressing Greater Cairo’s data limitations, the framework offers a replicable approach for other cities facing similar constraints, supporting the transition to evidence-based, spatially differentiated resource management in the Global South.
