Search Results (1,473)

Household electricity consumption is a major and growing source of urban carbon emissions in high-density residential area. However, its influencing mechanisms operate across multiple nested spatial scales and are not fully captured by single-scale analyses. Using electricity consumption data and a household survey from 42 high-density communities in Beijing, this study applies a hierarchical linear model (HLM) to examine how spatial form, socioeconomic attributes, and behavioral factors affect household electricity carbon emissions across urban districts, Ten-Minute Living Circles, residential areas, and Individual dwellings. The results indicate that dwelling-level characteristics exert the most direct influence, while residential-area-level spatial morphology provides an important contextual effect. In contrast, Ten-Minute Living Circles indicators show limited direct associations after accounting for hierarchical structure. Several spatial factors influence emissions through cross-level moderating mechanisms rather than isolated effects. These findings provide a data-driven multiscale analytical framework to support low-carbon planning and retrofit strategies in high-density residential area. Full article

Under the background that urban–rural integrated development continuously deepens and the common prosperity goal continuously advances, systematically identifying the actual results of urban–rural integrated development and its influence mechanism on common prosperity holds important significance for understanding regional development differences and optimizing policy implementation paths. Based on land use data, NTL data, and POI facility data from 2013 to 2025, this study comprehensively employs spatial analysis and deep learning methods to conduct an empirical analysis on the spatiotemporal evolution characteristics and coupling relationship of urban–rural integrated development and common prosperity levels from dimensions including urban–rural spatial form evolution, economic activity intensity, and public service facility diversity. The research results indicate that urban–rural integration significantly promotes urban spatial expansion and the improvement in overall economic activity levels during the study period, but the difference in development magnitude among different regions remains obvious. The common prosperity level generally presents a rising trend, but it highly concentrates in the Pearl River Delta and city–county center areas in space, and the promotion effect of urban–rural integration on common prosperity exhibits obvious characteristics of regional heterogeneity, stages, time lags, and diminishing marginal effects. This study considers that urban–rural integration does not inevitably and synchronously transform into an elevation in common prosperity levels. Combining regional development basis and structural conditions to optimize urban–rural integration development paths by region and by stage and to improve the realization quality of common prosperity possesses important practical reference value. Full article

Under the fast development of urbanization, PM_2.5 pollution has become a prominent issue affecting the urban ecological environment and residents’ health. To investigate the impact of urban landscape patterns on PM_2.5 concentrations, this study applies the Local Climate Zone (LCZ) classification to Shanghai using the World Urban Database and Access Portal Tools (WUDAPT). LCZ-derived landscape metrics are adopted as predictor variables to focus on how urban form and spatial configuration affect PM_2.5 distribution and to identify the key landscape categories and types influencing PM_2.5 levels. The results reveal notable seasonal and spatial differences in the effects of different LCZ types and landscape metrics on PM_2.5 concentrations; on average, over 69% of the spatial variation in PM_2.5 across the four seasons can be explained by the Multi-scale Geographically Weighted Regression (MGWR) model. This research demonstrates that the LCZ framework effectively uncovers the seasonal and spatial mechanisms by which urban landscape patterns influence PM_2.5 concentrations in Shanghai. It offers a novel perspective for understanding the interplay between urban landscape and atmospheric pollution, and provides scientific guidance for sustainable urban planning and precise air pollution control strategies in other cities. Full article

Street networks shape urban dynamics. However, at the important meso- and micro-scales, a research limitation remains in systematically linking the spatial logic of streets to the physical configuration of street-level commerce, in particular through an analytical lens that distinguishes between different urban network functions. With a view to overcoming this limitation and extending space syntax theory into the fine-grained analysis of commercial form, this study applies its dual-network logic, contrasting foreground networks and background networks. The spatial patterns of street stores were analyzed across eight street segments in four Chinese cities: Tianjin, Nanjing, Zhengzhou, and Hong Kong. Network types were distinguished using Normalized Angular Choice and patchwork pattern analysis. By using 2019 POI data, Street View imagery, and field surveys, a comparative quantitative analysis was conducted across three metrics: operation methods, functional diversity, and 100-m density. The results indicate differences: chain stores hold a clear advantage in high-value segments of the foreground network, a pattern supported by statistical tests. These segments also exhibit higher functional diversity (mean ENT = 5.12). In contrast, high-value street segments of the background network exhibit a consistently higher prevalence of sole stores. They also have a commercial density approximately 2.6 times greater than that of their foreground counterparts. These findings provide empirical evidence on how foreground and background networks support different kinds of commercial ecologies: one oriented toward micro-economy efficiency and standardized supply, the other toward socio-culturally embedded, high-intensity local exchange. Consequently, by linking specific street spatial configurations to measurable commercial outcomes, this research contributes methodologically by operationalizing the dual-network framework at a novel scale and offering a replicable analytical tool for diagnosing and guiding commercial spatial planning in cities. Full article

With the rapid urbanization in China, building energy consumption has become a critical challenge for sustainable urban development. Conventional simulation methods are computationally intensive and inefficient for large-scale urban layout optimization, highlighting the need for fast and reliable predictive approaches. Existing machine learning models often overlook spatial relationships among buildings and rely heavily on manual feature engineering, which limits their applicability at the urban block scale. To address these limitations, the study proposes a building energy consumption prediction model for urban blocks based on Generative Adversarial Networks (GANs), which preserves spatial information while significantly advancing computational speed. The optimal GAN model is further integrated with a Multi-Island Genetic Algorithm (MIGA) to form a GAN-MIGA optimization framework, which is applied to the layout optimization of a target urban block in Lanzhou. Key findings include: (1) the GAN model achieves an average prediction error of 6.8% compared with conventional energy simulations; (2) the GAN-MIGA framework reduces energy consumption by 48.78% relative to the worst-performing solution and by 22.53% compared with the original block layout; (3) the spatial distribution patterns of energy consumption predicted by the GAN are consistent with those obtained from traditional simulation methods; (4) the regression model derived from GAN-MIGA optimization results achieves an R² value exceeding 0.84; and (5) building layout design strategies are formulated based on key morphological indicators in the regression model. Overall, this study demonstrates the effectiveness of the GAN-based method for urban scale building energy prediction and layout optimization. The proposed GAN-MIGA framework provides practical tools and theoretical support for energy-efficient design, policy formulation, and smart city development, contributing to more sustainable urban energy planning. Full article

Ensuring equitable and inclusive access to children’s extracurricular activity facilities represents a profound manifestation of educational equity and is crucial for promoting children’s holistic development and societal sustainability. However, the underlying spatial mechanisms shaping their equity remain insufficiently explored. Using Tianjin’s central urban area as a case study, this study examines the spatial accessibility and equity of such facilities under dual government–market provision systems. The multi-mode Huff two-step floating catchment area model (MM-Huff-2SFCA) was employed to assess accessibility across walking, e-bike, public transport, and private car modes, integrating facility quality, household preference, and time-based distance decay. Equity was further evaluated using Lorenz curves and Gini coefficients across multiple spatial scales, while geographically weighted regression (GWR) identified spatial heterogeneity in factors such as child population density, transport infrastructure, household economic status, and basic education coverage. Results indicate that macro-level spatial balance masks substantial micro-scale inequities, particularly among transport-disadvantaged groups. Government and market systems exhibit contrasting spatial logics, forming a compensation–complementarity pattern across urban space. These findings underscore the need for refined and differentiated governance in extracurricular activity facilities planning, integrating spatial planning, transport accessibility, and social equity to advance child-friendly urban development and equitable public service provision. Full article

The escalating housing crisis and the uncontrolled proliferation of informal settlements in the Global South challenge the modernist ideal of the completed architectural object. While ‘Parasitic Architecture’ is conventionally coded as an act of illegal occupation, ‘Incremental Housing’ strategies propose a controlled evolution; however, a theoretical gap exists in defining the morphological mechanics where these two concepts intersect. This study aims to bridge this gap by proposing the concept of ‘Domesticated Parasitism’. Adopting an instrumental case study model, the research analyzes the morphological evolution of the Quinta Monroy housing complex in Chile. To mitigate interpretive bias and ensure analytical objectivity, the visual reading follows a structured coding protocol that categorizes the intervention zones into three distinct layers: (1) Fixed Structural Matrix, (2) Defined Expansion Zones, and (3) User-Generated Infill. Findings from the diachronic analysis comparing the initial state with current saturation levels reveal that the host structure functions as a ‘spatial cage’ that disciplines the growth of user additions. Unlike uncontrolled urban sprawl, the visual evidence confirms that the parasitic additions strictly adhere to the vertical void geometry defined by the architect. The research concludes that the architect’s role transforms from an author of static forms to an enabler, positioning domesticated parasitism as a sustainable spatial grammar for urban densification. Full article

Urban populations are exposed to elevated local temperatures compared to surrounding rural areas due to the urban heat island (UHI) effect, which increases health risks and energy demand. The literature highlights that accurately quantifying UHIs at broader territorial scales remains challenging because of limited microscale climate data availability and, at the same time, the difficulty of increasing the spatial coverage of the outcomes. Within the PRIN2022-PNRR CRiStAll (Climate Resilient Strategies by Archetype-based Urban Energy Modeling) project, this work addresses these limitations by coupling Urban Building Energy Modeling with archetype-based representation of urban form and high-resolution climatic data. Urban archetypes are defined as representative microscale configurations derived from combinations of urban canyon geometries and building typologies, accounting for different climatic zones, use categories, and construction periods. The proposed methodology was applied to the city of Turin (Italy), where representative urban blocks were identified and modeled to evaluate key urban context metrics under short-, medium-, and long-term climate scenarios. The UHI effect was assessed using Urban Weather Generator, while energy simulations were performed with CitySim. The urban archetype approach enables both fine spatial resolution and extensive spatial coverage, supporting urban-scale mapping. Full article

As an important industrial organizational form in the era of the digital economy, digital industry agglomeration exerts a profound impact on urban ecological resilience. Using panel data of 281 prefecture-level cities in China from 2011 to 2021, this study measures the level of digital industry agglomeration by means of the location entropy method, and constructs an urban ecological resilience evaluation system based on the “Pressure-State-Response (PSR)” model. It systematically examines the impact effects and action mechanisms of digital industry agglomeration on urban ecological resilience. The results show that: (1) The spatio-temporal evolution of the two presents a gradient pattern of “eastern leadership and central-western catch-up”, and their spatial correlation deepens over time, with the synergy maturity in the eastern region being significantly higher than that in the central and western regions. (2) Digital industry agglomeration significantly promotes the improvement in urban ecological resilience, and this conclusion remains valid after endogeneity treatment and robustness tests. (3) The promotional effect is more prominent in central cities, coastal cities, and key environmental protection cities, whose advantages stem from digital infrastructure and innovation endowments, industrial synergy and an open environment, and the adaptability of green technologies under strict environmental regulations, respectively. (4) Digital industry agglomeration empowers ecological resilience by driving green innovation and improving the efficiency of land resource allocation, while the construction of digital infrastructure plays a positive regulatory role. Full article

Activity space represents the spatiotemporal interaction between individuals and their environment. While most studies measure potential activity space using short-term data, few have defined or measured its actual internal structure. This study introduces “anchor regions” as the core areas where daily activities are concentrated, and conceptualizes the structure of an individual’s activity space by incorporating the concept of regular locations, anchor regions, potential regular activity space, and potential activity space. Using three months of mobile signaling data from 10,848 residents in Shenzhen, we detected anchor regions via a weighted density-based spatial clustering for applications with noise (DBSCAN) method and categorized individuals into six typical activity space structures based on a rule-based taxonomy. We also figured out the intra- and inter-anchor region mobility pattern of each type. Our results show the following: (1) A total of 80% of activities and 87% of time are concentrated in just 26% of locations, forming anchor regions—with 95% of individuals having no more than five such regions. (2) The total area of anchor regions is merely 0.1% of the potential activity space. (3) Six typical structures of activity space are derived with different combinations of several functional anchor regions, including home, weekday anchors, and daily activity anchors. (4) The spatial patterns of the six types are different, while intra-anchor region mobilities dominate daily movement in all six types. This study provides a region-based, instead of a point-based, perspective interpretation of the anchor points theory, helping to better understand the regularities and internal structure of human activity space. Our conceptual framework and methodology have the potential to help urban and transportation planning practice and policy making. Full article

The urban physical environment is composed of multiple elements that collectively influence the spatial pattern of crime. Existing research has predominantly focused on the relationship between individual types of facilities and crime, yet there remains a gap in comprehensively examining the integrated effects of the urban physical environment. This study, taking 87 police precincts in the central city of Changchun as units of analysis, innovatively constructs an integrated “Factor Analysis–Negative Binomial Regression” framework. First, factor analysis is applied to reduce the dimensionality of 14 categories of Points of Interest (POI) data, extracting three comprehensive factors that characterize the macro-level functional structure of the city: the “Business and Economic Activities Factor,” the “Residential, Educational, and Transportation Factor,” and the “Leisure and Entertainment Factor.” This approach effectively addresses the issue of multicollinearity among variables and uncovers the underlying macro-level functional factors. Subsequently, a negative binomial regression model is employed to analyze the impact of each factor on crime counts. The results indicate that: (1) The spatial distribution of urban crime is markedly heterogeneous and is systematically driven by the urban functional structure; (2) Both the “Business and Economic Activities Factor” and the “Leisure and Entertainment Factor” exhibit significant positive effects on crime, with each unit increase in their scores associated with an approximately 20% increase in the relative risk of crime; (3) The influence of the “Residential, Educational, and Transportation Factor” is not significant. Collectively, the findings demonstrate that shifting the perspective from “micro-level facilities” to “macro-level functional dimensions” can provide deeper insights into the fundamental formative mechanisms underlying the spatial pattern of crime. Full article

Accurate spatiotemporal forecasting underpins high-stakes decision making in smart urban systems, from traffic control and energy scheduling to environment monitoring. Yet two persistent gaps limit current models: (i) spatial modules are often biased toward low-pass smoothing and struggle to reconcile slow global trends with sharp local dynamics; and (ii) the graph structure required for forecasting is frequently latent, while learned graphs can be unstable when built from temporally derived node features alone. We propose SpeQNet, a query-enhanced spectral graph filtering framework that jointly strengthens node representations and graph construction while enabling frequency-selective spatial reasoning. SpeQNet injects global spatial context into temporal embeddings via lightweight learnable spatiotemporal queries, learns a task-oriented adaptive adjacency matrix, and refines node features with an enhanced ChebNetII-based spectral filtering block equipped with channel-wise recalibration and nonlinear refinement. Across twelve real-world benchmarks spanning traffic, electricity, solar power, and weather, SpeQNet achieves state-of-the-art performance and delivers consistent gains on large-scale graphs. Beyond accuracy, SpeQNet is interpretable and robust: the learned spectral operators exhibit a consistent band-stop-like frequency shaping behavior, and performance remains stable across a wide range of Chebyshev polynomial orders. These results suggest that query-enhanced spatiotemporal representation learning and adaptive spectral filtering form a complementary and effective foundation for effective spatiotemporal forecasting. Full article

The optimization of historic district form, given the coordinated relationship between global urbanization and sustainable development, faces the core contradiction between preservation and development. Taking Shenyang’s Nanshi area as a case study, this study aimed to construct a sustainable urban form evaluation system comprising 7 dimensions and 23 indicators by integrating multi-source geographic Big Data. A combination of a weighting approach in rank-order analysis and the entropy weight method was adopted, followed by spatial quantitative analysis conducted based on ArcGIS. The results showed that the sustainability of the area exhibited significant spatial differentiation: historic blocks became high-value areas due to their “small blocks, dense road network” fabric and high functional mix. However, newly built residential areas were low-value zones, constrained by factors such as fragmented green spaces, single-functional land use, and other limitations. Road network density and functional mixing were identified as the primary driving factors, while green coverage rate served as a secondary factor. Based on these findings, a three-tier “element–structure–system” optimization strategy was proposed, providing quantitative decision support for the low-carbon renewal of high-density historic urban districts. Full article

GNN approaches to property valuation typically rely on spatial proximity, assuming that nearby properties exhibit similar price patterns. In practice, this assumption often fails as neighborhood and administrative boundaries create sharp price discontinuities, a form of spatial heterophily. This study proposes a Boundary-Aware Dual-Path Graph Neural Network (BD-GNN), a heterophily-oriented GNN specifically designed for continuous regression tasks. The model uses a dual and adaptive message passing design, separating inter- and intra-boundary pathways and combining them through a learnable gating parameter $\alpha$. This allows it to capture boundary effects while preserving spatial continuity. Experiments conducted on three structurally contrasting housing datasets, namely Bangkok, King County (USA), and Singapore, demonstrate consistent performance improvements over strong baselines. The proposed BD-GNN reduces MAPE by 7.9%, 4.4%, and 4.5% and increases $R^2$ by 3.2%, 0.7%, and 5.0% for the respective datasets. Beyond predictive performance, $\alpha$ provides a clear picture of how spatial and administrative factors interact across urban scales. GNN Explainer provides local interpretability by showing which neighbors and features shape each prediction. BD-GNN bridges predictive accuracy and structural insight, offering a practical, interpretable framework for applications such as property valuation, taxation, mortgage risk assessment, and urban planning. Full article

With the continuous expansion of high-density urban forms, residents’ opportunities for daily contact with natural environments have been increasingly reduced, making the equity of urban green space allocation a critical challenge for sustainable urban development. Existing studies have largely focused on green space quantity or accessibility at single time points, lacking systematic investigations into the spatiotemporal evolution of green space exposure (GSE) and its equity from the perspective of residents’ actual environmental experiences. GSE refers to the integrated level of residents’ contact with urban green spaces during daily activities across multiple dimensions, including visual exposure, physical accessibility, and spatial distribution, emphasizing the relationship between green space provision and lived environmental experience. Based on this framework, this study takes the central urban area of Hangzhou as the study area and integrates multi-temporal remote sensing imagery with large-scale street view data. A deep learning–based approach is developed to identify green space exposure, combined with spatial statistical methods and equity measurement models to systematically analyze the spatiotemporal patterns and evolution of GSE and its equity from 2013 to 2023. The results show that (1) GSE in Hangzhou increased significantly over the study period, with accessibility exhibiting the most pronounced improvement. However, these improvements were mainly concentrated in peripheral areas, while changes in the urban core remained relatively limited, revealing clear spatial heterogeneity. (2) Although overall GSE equity showed a gradual improvement, pronounced mismatches between low exposure and high demand persisted in densely populated areas, particularly in older urban districts and parts of newly developed residential areas. (3) The spatial patterns and evolutionary trajectories of equity varied significantly across different GSE dimensions. Composite inequity characterized by “low visibility–low accessibility” formed stable clusters within the urban core. This study further explores the mechanisms underlying green space exposure inequity from the perspectives of urban renewal patterns, land-use intensity, and population concentration. By constructing a multi-dimensional and temporally explicit analytical framework for assessing GSE equity, this research provides empirical evidence and decision-making references for refined green space management and inclusive, sustainable urban planning in high-density cities. Full article