Computers Environment and Urban Systems最新文献

{"title":"Reassessing air pollution exposure: How daily mobility and activities shape individual risk in greater Paris","authors":"Taos Benoussaïd ,&nbsp;Isabelle Coll ,&nbsp;Hélène Charreire ,&nbsp;Inès Makni ,&nbsp;Malo Costes ,&nbsp;Arthur Elessa Etuman","doi":"10.1016/j.compenvurbsys.2025.102340","DOIUrl":"10.1016/j.compenvurbsys.2025.102340","url":null,"abstract":"<div><div>Understanding individual exposure to air pollution is essential for tackling environmental inequalities and informing public policies aimed at reducing disparities. Traditional approaches often focus on residential locations, but exposure is intrinsically linked to daily mobility, activities and socio-economic profiles. This study presents new results based on a dynamic exposure modelling approach that takes these dimensions into account, offering a more realistic assessment of air pollution risk. By integrating high-resolution air quality data with detailed information on individual mobility, activities and socio-economic characteristics, we quantify the exposure of 400,000 individuals in the Île-de-France region. Our approach takes into account all the environments that individuals visit during the day and the time spent in each of them, going beyond static exposure assessments based on residential location. We compare this dynamic model with traditional exposure calculations, revealing significant differences in the spatial distributions of PM10 and NO2 exposure. Our analysis highlights how mobility patterns and daily activities contribute to total exposure, demonstrating that place of residence is only one part of reality. For example, commuting, workplaces and leisure activities play a key role in determining individual exposure levels. The results of our study show that dynamic exposure calculation provides a better understanding of exposure factors and offers a framework for understanding environmental inequalities. By shifting the focus from home-based to person-based exposure, our approach makes it possible to identify levers for action to reduce disparities and support targeted public health action. Our study redefines the way in which we assess the risks associated with air pollution, by highlighting the need to take into account mobility behaviour and individual trajectories.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"122 ","pages":"Article 102340"},"PeriodicalIF":8.3,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144894924","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}

{"title":"Toward satisfactory public accessibility: A crowdsourcing approach through online reviews to inclusive urban design","authors":"Lingyao Li ,&nbsp;Songhua Hu ,&nbsp;Yinpei Dai ,&nbsp;Min Deng ,&nbsp;Parisa Momeni ,&nbsp;Gabriel Laverghetta ,&nbsp;Lizhou Fan ,&nbsp;Zihui Ma ,&nbsp;Xi Wang ,&nbsp;Siyuan Ma ,&nbsp;Jay Ligatti ,&nbsp;Libby Hemphill","doi":"10.1016/j.compenvurbsys.2025.102329","DOIUrl":"10.1016/j.compenvurbsys.2025.102329","url":null,"abstract":"<div><div>As urban populations grow, the need for accessible urban design has become urgent. Traditional methods for assessing public perceptions of accessibility, such as surveys and interviews, are often resource-intensive and geographically limited in scope. Crowdsourcing via online reviews offers a valuable alternative to understanding public perceptions, and advancements in large language models (LLMs) can facilitate their use. In this study, we examine over one million Google Maps reviews from points of interests (POIs) across the United States and fine-tune the Llama 3 model using the Low-Rank Adaptation (LoRA) technique to identify public sentiment toward accessibility. At the POI level, most categories – restaurants, retail, hotels, and healthcare – show negative sentiments, indicating persistent barriers across key sectors. Socio-spatial regression analysis reveals that more positive sentiment is associated with areas that have higher proportions of white residents and greater socioeconomic advantage. Conversely, more negative sentiment is expressed in areas with higher concentrations of elderly and highly-educated populations. Interestingly, no clear link is found between the presence of disabilities and public sentiments, but a significant positive relationship does exist between disability-friendly scores and public perception. Overall, our findings demonstrate the value of crowdsourcing with LLM-enhanced analysis in identifying accessibility challenges and informing inclusive urban design, offering actionable insights for planners, policymakers, and advocates striving toward more equitable cities.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"122 ","pages":"Article 102329"},"PeriodicalIF":8.3,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889251","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}

{"title":"ScaleFC: A scale-aware geographical flow clustering algorithm for heterogeneous origin-destination data","authors":"Huan Chen ,&nbsp;Zhipeng Gui ,&nbsp;Dehua Peng ,&nbsp;Yuhang Liu ,&nbsp;Yuncheng Ma ,&nbsp;Huayi Wu","doi":"10.1016/j.compenvurbsys.2025.102338","DOIUrl":"10.1016/j.compenvurbsys.2025.102338","url":null,"abstract":"<div><div>Exploring the cluster pattern of geographical flow facilitates the understanding of the underlying process of geographical phenomena among spatial locations. Despite recent advancements in identifying flow clusters, challenges remain when handling flow data with uneven length, heterogeneous density and weak connectivity. To solve the issues, this study proposes a Scale-aware Flow Clustering algorithm (ScaleFC). It identifies flow clusters of arbitrary lengths by employing an analytical scale to generate an adjustable neighborhood range of each flow. Meanwhile, inspired by the idea of boundary-seeking clustering, ScaleFC introduces partitioning flows to identify flow clusters with different densities, and separate the weakly-connected clusters. To validate the effectiveness, we compared ScaleFC with three mainstream baselines, i.e., AFC, FlowLF and FlowDBSCAN, on six synthetic datasets. The results presented that ScaleFC can accurately identify the clusters with complex structures, achieving an average accuracy improvement of 27 %, 17 %, and 15 % over the three competitors, respectively. The application on bike-sharing data with 16,140 flow pairs from Shanghai City demonstrated that ScaleFC is capable to capture both long-distance and short-distance movements, thereby providing a more comprehensive understanding to multi-scale human mobility patterns in geographical space.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"122 ","pages":"Article 102338"},"PeriodicalIF":8.3,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863804","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}

{"title":"Digital planning for sustainable urban future","authors":"Yanliu Lin ,&nbsp;Stan Geertman ,&nbsp;Patrick Witte ,&nbsp;Nuno Pinto","doi":"10.1016/j.compenvurbsys.2025.102334","DOIUrl":"10.1016/j.compenvurbsys.2025.102334","url":null,"abstract":"","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"122 ","pages":"Article 102334"},"PeriodicalIF":8.3,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265536","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}

{"title":"Wheelchair accessibility to public facilities via transits and analysis of delay factors—A case study of Shanghai, China","authors":"Luoan Yang ,&nbsp;Wei Huang ,&nbsp;Xintao Liu ,&nbsp;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":"2025-08-04","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}

{"title":"Deep contrastive learning for feature alignment: Insights from housing-household relationship inference","authors":"Xiao Qian,&nbsp;Shangjia Dong,&nbsp;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":"2025-08-03","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}

{"title":"Urban planning in the age of large language models: Assessing OpenAI o1's performance and capabilities across 556 tasks","authors":"Xukai Zhao ,&nbsp;He Huang ,&nbsp;Tao Yang ,&nbsp;Yuxing Lu ,&nbsp;Lu Zhang ,&nbsp;Ruoyu Wang ,&nbsp;Zhengliang Liu ,&nbsp;Tianyang Zhong ,&nbsp;Tianming Liu","doi":"10.1016/j.compenvurbsys.2025.102332","DOIUrl":"10.1016/j.compenvurbsys.2025.102332","url":null,"abstract":"<div><div>Integrating Large Language Models (LLMs) into urban planning presents significant opportunities to enhance efficiency and support data-driven city development strategies. Despite their potential, the specific capabilities of LLMs within the urban planning context remain underexplored, and the field lacks standardized benchmarks for systematic evaluation. This study presents the first comprehensive evaluation focused on OpenAI o1's performance and capabilities in urban planning, systematically benchmarking it against GPT-3.5 and GPT-4o using an original open-source benchmark comprising 556 tasks across five critical categories: urban planning documentation, examinations, routine data analysis, AI algorithm support, and thesis writing. Through rigorous testing and manual analysis of 170,627 words of generated output, OpenAI o1 consistently outperformed its counterparts, achieving an average performance score of 84.08 % compared to 69.30 % for GPT-4o and 45.27 % for GPT-3.5. Our findings highlight o1's strengths in domain knowledge mastery, basic operational competence, and coding capabilities, demonstrating its potential applications in information retrieval, urban data analytics, planning decision support, educational assistance, and LLM-based agent development. However, significant limitations were identified, including inability in urban design, susceptibility to fabricating information, moderate academic writing quality, challenges in high-level professional examinations, and spatial reasoning, and limited support for specialized or emerging AI algorithms. Future optimizations should prioritize enhancing multimodal integration, implementing robust validation mechanisms, adopting adaptive learning strategies, and enabling domain-specific fine-tuning to meet urban planners' specialized needs. These advancements would enable LLMs to better support the evolving demands of urban planning, allowing professionals to focus more on strategic decision-making and the creative aspects of the field.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"121 ","pages":"Article 102332"},"PeriodicalIF":8.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144748888","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}

{"title":"Analyzing public response to wildfires: A socio-spatial study using SIR models and NLP techniques","authors":"Zihui Ma ,&nbsp;Guangxiao Hu ,&nbsp;Ting-Syuan Lin ,&nbsp;Lingyao Li ,&nbsp;Songhua Hu ,&nbsp;Loni Hagen ,&nbsp;Gregory B. Baecher","doi":"10.1016/j.compenvurbsys.2025.102333","DOIUrl":"10.1016/j.compenvurbsys.2025.102333","url":null,"abstract":"<div><div>The increasing frequency and severity of wildfires pose significant risks to communities, infrastructure, and the environment, especially in Wildland-Urban Interface (WUI) areas. Effective disaster management requires understanding how the public perceives and responds to wildfire threats in near-real-time. This study uses social media data to assess public responses (including collective perceptions/reactions) and explores how these responses are linked to city-level community characteristics. Specifically, we leveraged a transformer-based topic modeling technique called BERTopic to identify wildfire response-related topics and then utilized the Susceptible-Infectious-Recovered (SIR) model to compute two key metrics — public response awareness and resilience indicators. Additionally, we used GIS-based spatial analysis to map wildfire responses and the relationships with four groups of city-level factors (racial/ethnic, socioeconomic, demographic, and wildfire-specific). Our findings reveal significant geographic and socio-spatial differences in public responses. Southern California cities with larger Hispanic populations demonstrate higher wildfire awareness and resilience. In contrast, urbanized regions in Central and Northern California exhibit lower awareness levels. Furthermore, resilience is negatively correlated with unemployment rates, particularly in southern regions where higher unemployment aligns with reduced resilience. These findings highlight the need for targeted and equitable wildfire management strategies to improve the adaptive capacity of WUI communities.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"121 ","pages":"Article 102333"},"PeriodicalIF":8.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144748887","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}

{"title":"Mapping priority zones for urban heat mitigation in Shanghai: Heat risk vs. shelter provision","authors":"Wenqi Qian ,&nbsp;Fujie Rao ,&nbsp;Xiaoyu Li ,&nbsp;Dayi Lai","doi":"10.1016/j.compenvurbsys.2025.102330","DOIUrl":"10.1016/j.compenvurbsys.2025.102330","url":null,"abstract":"<div><div>Global climate change has intensified heat wave events, raising their intensity, duration, and frequency. Outdoor urban green spaces and indoor air-conditioned spaces serve as critical ‘heat shelters’, providing crucial cooling relief to extreme heat. However, there is a lack of studies focused on the spatial distribution of potential heat shelters and how shelters in different urban areas match varying degrees of heat risk. To address this research gap, we quantify and map heat risks and shelter provisions of administrative neighborhoods (often the smallest level of urban governance) and walkable grids of 500 × 500 m (a commonly-used comfortable walking distance for vulnerable groups such as the elderly people), and identify vulnerable areas where heat mitigation interventions should be prioritized. We select Shanghai – a metropolis of around 25 million people experiencing increasingly extreme heat wave events - for the case study. We measure heat risk by a composite index incorporating heat hazard, exposure and vulnerability. We largely measure heat provision by the number of indoor air-conditioned venues and outdoor green spaces, weighted by their time availability. Our findings reveal a general decrease in heat mitigation priority levels from the urban core to the suburbs, a pattern that is consistent between neighborhoods and grids at the metropolitan scale. This said, at smaller scales, significant differences between these two types of spatial units emerged in the degree and distribution of heat mitigation priority levels, revealing nuanced, inequitable capacities of different urban areas to tackle extreme heat. Our study provides a novel and systematic lens for assessing heat mitigation priority levels, informing more effective strategies for planning and managing heat shelters and allocating heat mitigation resources.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"121 ","pages":"Article 102330"},"PeriodicalIF":7.1,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704836","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}

{"title":"An agent-based model for estimating daily face-to-face contact networks in large urban systems","authors":"Ismaïl Saadi ,&nbsp;Etienne Côme ,&nbsp;Liem Binh Luong Nguyen ,&nbsp;Mahdi Zargayouna","doi":"10.1016/j.compenvurbsys.2025.102321","DOIUrl":"10.1016/j.compenvurbsys.2025.102321","url":null,"abstract":"<div><div>Detailed contact data is important to model disease transmission in dense urban areas, as human mobility and social interactions significantly impact spread. However, linking mobility, activities, and social contacts in large cities is challenging. Current models often rely on contact surveys and overlook travel behaviors. Here we present a novel modeling framework for estimating large-scale, multi-setting contact networks by leveraging high-resolution synthetic activity-travel data. Our approach introduces a new mathematical formalism to construct multi-setting contact networks from spatiotemporal co-location patterns, enabling constraints based on key statistics (e.g., contact rates per setting, proportions of each contact type), incorporation of location types, and individual activity purposes. Efficient algorithms extract co-presence events, generating validated, individual-based contact networks, from which age-specific contact matrices are derived. The approach is tested using EQASIM, an open and reproducible activity-based travel demand model that relies on publicly available data for France’s Île-de-France region. We also evaluated the spatial effects of work-from-home policies on contact patterns by modifying individuals’ activity-travel diaries. Results show that multi-setting contact networks — representing 12 million individuals distributed across 1,714,920 unique locations — can be generated in minutes while accurately reproducing setting- and age-specific spatial contact patterns.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"121 ","pages":"Article 102321"},"PeriodicalIF":7.1,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653395","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}