Computers Environment and Urban Systems最新文献_第5页

Optimizing urban green space spatial patterns for thermal environment improvement: A multi-objective approach in the context of urban renewal 基于热环境改善的城市绿地空间格局优化：城市更新背景下的多目标方法

IF 7.1 1区地球科学

Computers Environment and Urban Systems Pub Date : 2025-10-01 Epub Date: 2025-06-12 DOI: 10.1016/j.compenvurbsys.2025.102320

Liangguo Lin , Yaolong Zhao , Juchao Zhao

{"title":"Optimizing urban green space spatial patterns for thermal environment improvement: A multi-objective approach in the context of urban renewal","authors":"Liangguo Lin , Yaolong Zhao , Juchao Zhao","doi":"10.1016/j.compenvurbsys.2025.102320","DOIUrl":"10.1016/j.compenvurbsys.2025.102320","url":null,"abstract":"<div><div>The rapid and inevitable trend of urbanization has amplified urban thermal challenges, intensifying the urban heat island (UHI) effect. Given the constraints of limited urban land resources, optimizing the spatial patterns of urban green space (UGS) to maximize their cooling potential is essential for mitigating urban thermal environments and supporting effective urban renewal planning. This research integrates the XGBoost model with the NSGA-II algorithm to propose a multi-objective approach to optimize UGS spatial patterns for thermal environment improvement, using the central urban area of Guangzhou, China, as a case study in the context of urban renewal. To further assess the effectiveness of optimization, the Shapley additive explanation (SHAP) model was employed to examine how landscape pattern metrics, which characterize UGS spatial patterns, influence LST before and after optimization. The results demonstrate that optimized UGS spatial patterns, achieved through a controlled expansion of UGS area, significantly alleviated thermal stress by reducing the total LST by 2,799.82 °C and lowering its standard deviation by 0.04. Industrial zones, densely populated areas, and commercial districts exhibited the most pronounced LST reductions that spatially corresponded to changes in UGS spatial patterns. In addition, post-optimization analysis revealed notable changes in key landscape pattern metrics: patch cohesion index (COHESION), patch density (PD), landscape shape index (LSI), and percent of landscape (PLAND). Compared to pre-optimization conditions, their positive contributions to LST were weakened, while their cooling effects were enhanced. This research provides a “space-for-time” planning paradigm that offers intuitive and actionable decision-making support for urban renewal planners and policymakers.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"121 ","pages":"Article 102320"},"PeriodicalIF":7.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the power of CNNs to detect slums in Brazil 关于cnn探测巴西贫民窟的力量

IF 7.1 1区地球科学

Computers Environment and Urban Systems Pub Date : 2025-10-01 Epub Date: 2025-05-31 DOI: 10.1016/j.compenvurbsys.2025.102306

João P. da Silva , José F. Rodrigues-Jr , João P. de Albuquerque

{"title":"On the power of CNNs to detect slums in Brazil","authors":"João P. da Silva , José F. Rodrigues-Jr , João P. de Albuquerque","doi":"10.1016/j.compenvurbsys.2025.102306","DOIUrl":"10.1016/j.compenvurbsys.2025.102306","url":null,"abstract":"<div><div>The rapid expansion of slums poses a critical challenge for urban planning in Low- and Middle-Income Countries (LMICs), where traditional data collection methods like censuses are often outdated and insufficient. This study examines the transferability and generalization capabilities of deep learning models, specifically Convolutional Neural Networks (CNNs), for automated slum detection across six Brazilian cities with varying urban morphologies: São Paulo, Rio de Janeiro, Belo Horizonte, Brasília, Salvador, and Porto Alegre. Utilizing Very High Resolution (VHR) and High Resolution (HR) satellite imagery, we trained and evaluated models based on the EfficientNetV2L architecture. Our experimental results show that CNN models trained on data from a single city achieved high accuracy within that city (F1 scores exceeding 0.90 with VHR imagery), but their performance significantly decreased when applied to other cities (F1 scores dropping below 0.80), highlighting the impact of regional variations in urban morphology. Conversely, a generalized model trained on combined data from all six cities maintained robust performance across all cities, achieving F1 scores above 0.80 with VHR imagery. These findings indicate that while CNNs are effective for automated slum mapping, regional diversity necessitates training on diverse datasets to ensure generalization. We provide a comprehensive methodology over an openly shared dataset, and code to facilitate future research and applications in urban geoscience. The aim is to enhance the scalability and generalization of remote sensing and deep learning methods for slum identification across diverse urban environments.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"121 ","pages":"Article 102306"},"PeriodicalIF":7.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Renewable energy adoption in urban residential communities in China: An agent-based model for assessing intervention impact 中国城市居民社区采用可再生能源：基于主体的干预影响评估模型

IF 7.1 1区地球科学

Computers Environment and Urban Systems Pub Date : 2025-10-01 Epub Date: 2025-06-19 DOI: 10.1016/j.compenvurbsys.2025.102323

Hua Du , Qi Han , Bauke de Vries , Jun Sun

{"title":"Renewable energy adoption in urban residential communities in China: An agent-based model for assessing intervention impact","authors":"Hua Du , Qi Han , Bauke de Vries , Jun Sun","doi":"10.1016/j.compenvurbsys.2025.102323","DOIUrl":"10.1016/j.compenvurbsys.2025.102323","url":null,"abstract":"<div><div>Designing effective policy interventions is an essential instrument to promote the widespread adoption of photovoltaic (PV) systems in the residential sector. Designing such policies and evaluating their effectiveness requires an approach that allows for simulation in the complex system setting of the built environment. In this study we applied Agent-Based Modelling to evaluate the effectiveness of two policies (i.e., information campaign and demonstration projects) and two community factors (i.e., community size and required agreement rate) to promote the adoption of residential community PV diffusion in Chinese cities. This model is developed based on the empirical results of a previous discrete choice experiment. The results show that lowering the required agreement rate for community decisions contributes to an increase in PV adoption, while community size has little impact on adoption diffusion. We found that combining the two policy interventions or combining them with a community factor (i.e., lowering the required agreement rate) can effectively promote the adoption of community PV. Policy intervention implications and suggestions are presented.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"121 ","pages":"Article 102323"},"PeriodicalIF":7.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling spatial and temporal urban environmental noise using street view imagery and machine learning 利用街景图像和机器学习建模时空城市环境噪声

IF 7.1 1区地球科学

Computers Environment and Urban Systems Pub Date : 2025-10-01 Epub Date: 2025-07-11 DOI: 10.1016/j.compenvurbsys.2025.102327

Devin Yongzhao Wu , Jue Wang

{"title":"Modeling spatial and temporal urban environmental noise using street view imagery and machine learning","authors":"Devin Yongzhao Wu , Jue Wang","doi":"10.1016/j.compenvurbsys.2025.102327","DOIUrl":"10.1016/j.compenvurbsys.2025.102327","url":null,"abstract":"<div><div>This study proposes a framework for modeling environmental noise pollution by integrating land use regression (LUR) with machine learning models and street built environments. Using noise data collected from 128 locations over nine consecutive days in Mississauga, Ontario, Canada, the research demonstrates that incorporating finer-scale street built environment features derived from street view images significantly improves noise prediction accuracy. The model using XGBoost and street view-derived variables significantly outperforms traditional LUR-based models. The results indicate that street-level characteristics, particularly terrain, play a critical role in modeling noise levels, complementing traditional land use and NDVI-based greenness. Furthermore, the research highlights the importance of using non-linear models like XGBoost to capture complex relationships between noise and urban features. This approach offers valuable insights for advancing environmental noise modeling, which further supports future public health studies investigating the impact of noise exposure on population health.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"121 ","pages":"Article 102327"},"PeriodicalIF":7.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing transparency in land use change modeling: Leveraging eXplainable AI techniques for urban growth prediction with spatially distributed insights 提高土地利用变化建模的透明度：利用可解释的人工智能技术进行具有空间分布洞察力的城市增长预测

IF 7.1 1区地球科学

Computers Environment and Urban Systems Pub Date : 2025-10-01 Epub Date: 2025-06-28 DOI: 10.1016/j.compenvurbsys.2025.102322

Zelin Wang , Tianshu Feng , Abolfazl Safikhani , Emre Tepe

{"title":"Enhancing transparency in land use change modeling: Leveraging eXplainable AI techniques for urban growth prediction with spatially distributed insights","authors":"Zelin Wang , Tianshu Feng , Abolfazl Safikhani , Emre Tepe","doi":"10.1016/j.compenvurbsys.2025.102322","DOIUrl":"10.1016/j.compenvurbsys.2025.102322","url":null,"abstract":"<div><div>Recent applications of machine learning (ML) and deep learning (DL) techniques in land-use change modeling have demonstrated significant success in capturing the intricate dynamics of land development. However, their “black-box” nature restricts their utility in various contexts, such as uncovering the underlying drivers of urban expansion. To mitigate this issue, we propose to utilize eXplainable AI (XAI) techniques in ML/DL methods, which presents a promising solution to this primary constraint. To that end, we introduce DL methods to investigate and predict the non-linear dynamics of land use changes. These methods achieved notably high accuracy scores and were more computationally viable than traditional statistical approaches. Moreover, the proposed approach employed in this study surpassed the parameter interpretation capabilities of statistical methods. More specifically, the proposed XAI approach not only highlights the average effects of features on the outcome but also elucidates the factors influencing specific decisions regarding land use changes, including the number of vacant parcels, the share of single-family parcels, and certain time-lagged neighborhood features. Such analyses provide invaluable insights for researchers, practitioners, and policymakers.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"121 ","pages":"Article 102322"},"PeriodicalIF":7.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Social areas revisited through the lens of mobility: A comparative study of the traditional and mobility approaches 通过流动性视角重新审视社会领域：传统方法与流动性方法的比较研究

IF 7.1 1区地球科学

Computers Environment and Urban Systems Pub Date : 2025-10-01 Epub Date: 2025-06-29 DOI: 10.1016/j.compenvurbsys.2025.102325

Run Shi , Anthony Gar-On Yeh , Fang Bian

{"title":"Social areas revisited through the lens of mobility: A comparative study of the traditional and mobility approaches","authors":"Run Shi , Anthony Gar-On Yeh , Fang Bian","doi":"10.1016/j.compenvurbsys.2025.102325","DOIUrl":"10.1016/j.compenvurbsys.2025.102325","url":null,"abstract":"<div><div>Social area analysis is a framework for understanding residential social structure as a product of urbanization and economic development. Building on our previous findings that socioeconomically similar residents exhibit different mobility patterns, this study examines urban socio-spatial structure by incorporating commuting patterns from mobile phone data with census in Shenzhen, China. We conduct a comparative analysis to explore differences between the traditional and mobility approaches. Principal Component Analysis (PCA) results reveal that mobility is an essential dimension of socio-spatial differentiation at the aggregated neighborhood committee level. The derived residential social structure explicitly highlights mobility disparities, providing evidence for possible segregation and potential improvements in urban planning. By analyzing the interplays of economic, political, and social forces, we conceptualize mobility as a sub-dimension of social space. The contribution of this study lies in two folds. First, we propose a framework for integrating mobile phone data with census data to capture mobility disparities at the aggregated level with the concept of activity space. Second, we explore the role of mobility in delineating urban socio-spatial structure, providing a novel perspective for examining the internal spatial structure of cities.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"121 ","pages":"Article 102325"},"PeriodicalIF":7.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144510637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep contrastive learning for feature alignment: Insights from housing-household relationship inference 特征对齐的深度对比学习：来自住房-家庭关系推断的见解

IF 8.3 1区地球科学

Computers Environment and Urban Systems Pub Date : 2025-10-01 Epub Date: 2025-08-03 DOI: 10.1016/j.compenvurbsys.2025.102328

Xiao Qian, Shangjia Dong, Rachel Davidson

{"title":"Deep contrastive learning for feature alignment: Insights from housing-household relationship inference","authors":"Xiao Qian, Shangjia Dong, Rachel Davidson","doi":"10.1016/j.compenvurbsys.2025.102328","DOIUrl":"10.1016/j.compenvurbsys.2025.102328","url":null,"abstract":"<div><div>Housing and household characteristics are key determinants of social and economic well-being, yet our understanding of their interrelationships remains limited. This study addresses this knowledge gap by developing a deep contrastive learning (DCL) model to infer housing-household relationships using the American Community Survey (ACS) Public Use Microdata Sample (PUMS). More broadly, the proposed model is suitable for a class of problems where the goal is to learn joint relationships between two distinct entities without explicitly labeled ground truth data. Our proposed dual-encoder DCL approach leverages co-occurrence patterns in PUMS and introduces a bisect K-means clustering method to overcome the absence of ground truth labels. The dual-encoder DCL architecture is designed to handle the semantic differences between housing (building) and household (people) features while mitigating noise introduced by clustering. To validate the model, we generate a synthetic ground truth dataset and conduct comprehensive evaluations. The model further demonstrates its superior performance in capturing housing-household relationships in Delaware compared to state-of-the-art methods. A transferability test in North Carolina confirms its generalizability across diverse sociodemographic and geographic contexts. Finally, the post-hoc explainable AI analysis using SHAP values reveals that tenure status and mortgage information play a more significant role in housing-household matching than traditionally emphasized factors such as the number of persons and rooms.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"121 ","pages":"Article 102328"},"PeriodicalIF":8.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144764047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wheelchair accessibility to public facilities via transits and analysis of delay factors—A case study of Shanghai, China 公共设施的轮椅可及性及其延迟因素分析——以上海为例

IF 8.3 1区地球科学

Computers Environment and Urban Systems Pub Date : 2025-10-01 Epub Date: 2025-08-04 DOI: 10.1016/j.compenvurbsys.2025.102331

Luoan Yang , Wei Huang , Xintao Liu , Wanglin Yan

{"title":"Wheelchair accessibility to public facilities via transits and analysis of delay factors—A case study of Shanghai, China","authors":"Luoan Yang , Wei Huang , Xintao Liu , Wanglin Yan","doi":"10.1016/j.compenvurbsys.2025.102331","DOIUrl":"10.1016/j.compenvurbsys.2025.102331","url":null,"abstract":"<div><div>The pursuit of egalitarian and sustainable communities represents a collective aspiration and aligns with the United Nations’ Sustainable Development Goals. The public transit system, as the primary mode of mobility for wheelchair users in China, often imposes barriers that hinder travel or prolong travel times. It is essential to evaluate the spatial accessibility of public transit for wheelchair users to mitigate their social exclusion and enhance their participation within the community. However, there is a paucity of research on wheelchair transit accessibility and the factors contributing to prolonged travel times. This study introduces a wheelchair-accessible public transit route planning algorithm utilizing an online map API to acquire travel time and identify delay factors using the city of Shanghai as the study area, then evaluates spatial accessibility differences between wheelchair users and the general population in accessing public service facilities. Key findings include: (1) 73.9% of wheelchair transit routes encounter delays due to insufficient wheelchair facilities. (2) Parks show the largest accessibility gap, with wheelchair users’ accessibility at only 45% of that of the general population within the same time threshold. (3) Walking segment obstacles cause the longest delays, the most frequent delay factor is the lack of accessible metro station entrances, and SHAP values from the machine learning model furnish localized explanations regarding the impact of each delay factor. These findings reveal disparities in wheelchair transit accessibility and investigate factors causing delays, informing urban planning and infrastructure improvements in Shanghai and providing a reference for barrier-free development in other cities.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"121 ","pages":"Article 102331"},"PeriodicalIF":8.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144766830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Torque work of origin-destination flows: Quantifying urban place centrality from a physical perspective 起点-终点流的扭矩功：从物理角度量化城市场所中心性

IF 7.1 1区地球科学

Computers Environment and Urban Systems Pub Date : 2025-10-01 Epub Date: 2025-05-31 DOI: 10.1016/j.compenvurbsys.2025.102311

Xiaorui Yan , Tao Pei , Ci Song , Zidong Fang , Xiaohan Liu , Tianyu Liu , Linfeng Jiang , Ying Gao , Guangdong Li , Jie Huang , Yaqin Sun

{"title":"Torque work of origin-destination flows: Quantifying urban place centrality from a physical perspective","authors":"Xiaorui Yan , Tao Pei , Ci Song , Zidong Fang , Xiaohan Liu , Tianyu Liu , Linfeng Jiang , Ying Gao , Guangdong Li , Jie Huang , Yaqin Sun","doi":"10.1016/j.compenvurbsys.2025.102311","DOIUrl":"10.1016/j.compenvurbsys.2025.102311","url":null,"abstract":"<div><div>Quantifying urban place centrality, defined as its relative importance in serving its peripheral areas, provides insights into urban structures, optimizes resource allocation, and supports strategic urban planning. Centrality is shaped by three aspects: service volume, spatial reach, and directional diversity. However, existing measures often assess these factors separately and few integrate them simultaneously. Additionally, centrality analyses often overlook local perspectives and intra-day dynamics. To this end, we propose a novel origin-destination flow-based centrality measure, namely Total Torque Work (TTW), that integrates these three aspects into a single value, conceptualized as “Torque work of flow”, where flow volume, length, and direction correspond to the force magnitude, lever arm, and angular displacement. The effectiveness of the TTW is validated by simulation experiments. We apply this measure to analyze macro- and micro-centralities in Beijing, using taxi and shared bike flow data. Macro-centrality shows a monocentric structure, with higher values near railway stations, airports, and business and commercial centers. Micro-centrality is more polycentric, with subway stations exhibiting higher centrality. Time series clustering identifies three temporal patterns in both macro- and micro-centralities: two “daytime-dominant” patterns linked to multifunctional activities and commuting, and a “nighttime-dominant” pattern in residential areas. The study concludes with several implications for urban planning, emphasizing the importance of incorporating multi-spatiotemporal scales.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"121 ","pages":"Article 102311"},"PeriodicalIF":7.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From grids to dendrites: Quantifying spatial heterogeneity in urban road networks 从网格到树突：量化城市道路网络的空间异质性

IF 7.1 1区地球科学

Computers Environment and Urban Systems Pub Date : 2025-10-01 Epub Date: 2025-06-03 DOI: 10.1016/j.compenvurbsys.2025.102309

Lin Zhang , Shenhong Li , Yaolin Liu , Haosheng Huang , Nico Van de Weghe

{"title":"From grids to dendrites: Quantifying spatial heterogeneity in urban road networks","authors":"Lin Zhang , Shenhong Li , Yaolin Liu , Haosheng Huang , Nico Van de Weghe","doi":"10.1016/j.compenvurbsys.2025.102309","DOIUrl":"10.1016/j.compenvurbsys.2025.102309","url":null,"abstract":"<div><div>Road network spatial heterogeneity significantly influences urban development and infrastructure efficiency. We present a novel approach using Relational Graph Convolutional Networks (RGCN) to analyze road networks across 58 global cities from 2020 to 2024, introducing Hits@1 as a comprehensive measure of spatial heterogeneity. When nodes (Road intersections) exhibit high spatial heterogeneity, they are more diverse and distinct from each other, making the embedding process more straightforward for the RGCN model. A higher Hits@1 score indicates RGCN can better differentiate between nodes, directly correlating with greater spatial heterogeneity in the road network. Our analysis demonstrates that Hits@1 can effectively distinguish four road network typologies (Dendritic, Grid, Mixed, and Polygonal), with Dendritic networks showing the highest heterogeneity (Hits@1<span><math><mo>≈</mo></math></span>0.57) and Grid networks the lowest (Hits@1<span><math><mo>≈</mo></math></span>0.42). Statistical analysis reveals strong correlations between heterogeneity and urban metrics, including traffic index (R = 0.36), CO2 emissions (R = 0.43), and road density (R = 0.48). Temporal analysis of road evolution shows distinct regional patterns: developing regions trend toward higher heterogeneity, while Western cities demonstrate increasing uniformity. Chinese coastal cities exhibit increasing complexity, contrasting with inland cities’ movement toward organized patterns. These findings validate Hits@1 as an effective metric for understanding road network evolution and provide valuable insights for urban planning.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"121 ","pages":"Article 102309"},"PeriodicalIF":7.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0