Pub Date : 2025-08-20DOI: 10.1016/j.compenvurbsys.2025.102338
Huan Chen , Zhipeng Gui , Dehua Peng , Yuhang Liu , Yuncheng Ma , Huayi Wu
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.
{"title":"ScaleFC: A scale-aware geographical flow clustering algorithm for heterogeneous origin-destination data","authors":"Huan Chen , Zhipeng Gui , Dehua Peng , Yuhang Liu , Yuncheng Ma , 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}
Pub Date : 2025-08-04DOI: 10.1016/j.compenvurbsys.2025.102331
Luoan Yang , Wei Huang , Xintao Liu , Wanglin Yan
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.
{"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":"2025-08-04","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}
Pub Date : 2025-08-03DOI: 10.1016/j.compenvurbsys.2025.102328
Xiao Qian, Shangjia Dong, Rachel Davidson
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.
{"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":"2025-08-03","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}
Pub Date : 2025-08-01DOI: 10.1016/j.compenvurbsys.2025.102332
Xukai Zhao , He Huang , Tao Yang , Yuxing Lu , Lu Zhang , Ruoyu Wang , Zhengliang Liu , Tianyang Zhong , Tianming Liu
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.
{"title":"Urban planning in the age of large language models: Assessing OpenAI o1's performance and capabilities across 556 tasks","authors":"Xukai Zhao , He Huang , Tao Yang , Yuxing Lu , Lu Zhang , Ruoyu Wang , Zhengliang Liu , Tianyang Zhong , 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}
Pub Date : 2025-08-01DOI: 10.1016/j.compenvurbsys.2025.102333
Zihui Ma , Guangxiao Hu , Ting-Syuan Lin , Lingyao Li , Songhua Hu , Loni Hagen , Gregory B. Baecher
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.
{"title":"Analyzing public response to wildfires: A socio-spatial study using SIR models and NLP techniques","authors":"Zihui Ma , Guangxiao Hu , Ting-Syuan Lin , Lingyao Li , Songhua Hu , Loni Hagen , 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}
Pub Date : 2025-07-26DOI: 10.1016/j.compenvurbsys.2025.102330
Wenqi Qian , Fujie Rao , Xiaoyu Li , Dayi Lai
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.
{"title":"Mapping priority zones for urban heat mitigation in Shanghai: Heat risk vs. shelter provision","authors":"Wenqi Qian , Fujie Rao , Xiaoyu Li , 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}
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.
{"title":"An agent-based model for estimating daily face-to-face contact networks in large urban systems","authors":"Ismaïl Saadi , Etienne Côme , Liem Binh Luong Nguyen , 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}
Pub Date : 2025-07-11DOI: 10.1016/j.compenvurbsys.2025.102327
Devin Yongzhao Wu , Jue Wang
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.
{"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":"2025-07-11","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}
Pub Date : 2025-07-01DOI: 10.1016/j.compenvurbsys.2025.102326
Sebastian Kohl , Bo Li , Can Cui
Most studies on residential segregation in China have primarily relied on decennial population census data, which lacks the granularity and timeliness needed to capture segregation dynamics with higher frequency. Drawing on georeferenced housing market transaction data between 2012 and 2023 in Shanghai and Beijing, and employing fine-grained spatial segregation analysis techniques, including k-nearest neighbor approaches (k−NN) and modifiable grids, we find that housing segregation by price and size increased between 2012 and 2018, followed by a decline thereafter, particularly in the larger-sized and higher-priced market segments. While segregation levels are generally comparable between the two cities, Shanghai exhibits higher segregation for the top 20 % of apartments, while Beijing shows greater segregation for the bottom 20 %. Segregation is highest for prices, followed by rents, with housing size showing the lowest segregation. Expanding the analysis to 11 major Chinese cities, we suggest that high and rising housing prices are associated with increasing segregation, particularly in cities with lower initial segregation. Methodologically, this paper demonstrates that leveraging big transaction and listing data, alongside utilizing fine-grained spatial analysis, can advance our understanding of urban inequalities.
{"title":"Housing segregation in Chinese major cities: A K-nearest neighbor analysis of longitudinal big data","authors":"Sebastian Kohl , Bo Li , Can Cui","doi":"10.1016/j.compenvurbsys.2025.102326","DOIUrl":"10.1016/j.compenvurbsys.2025.102326","url":null,"abstract":"<div><div>Most studies on residential segregation in China have primarily relied on decennial population census data, which lacks the granularity and timeliness needed to capture segregation dynamics with higher frequency. Drawing on georeferenced housing market transaction data between 2012 and 2023 in Shanghai and Beijing, and employing fine-grained spatial segregation analysis techniques, including k-nearest neighbor approaches (<em>k</em>−NN) and modifiable grids, we find that housing segregation by price and size increased between 2012 and 2018, followed by a decline thereafter, particularly in the larger-sized and higher-priced market segments. While segregation levels are generally comparable between the two cities, Shanghai exhibits higher segregation for the top 20 % of apartments, while Beijing shows greater segregation for the bottom 20 %. Segregation is highest for prices, followed by rents, with housing size showing the lowest segregation. Expanding the analysis to 11 major Chinese cities, we suggest that high and rising housing prices are associated with increasing segregation, particularly in cities with lower initial segregation. Methodologically, this paper demonstrates that leveraging big transaction and listing data, alongside utilizing fine-grained spatial analysis, can advance our understanding of urban inequalities.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"121 ","pages":"Article 102326"},"PeriodicalIF":7.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522231","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}
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