Computers Environment and Urban Systems最新文献

{"title":"Advancing population-targeted urban sensing: A comparative study on mobile and static sensing paradigms","authors":"Yuan-Qiao Hou ,&nbsp;Xiao-Jian Chen ,&nbsp;Zhou Huang ,&nbsp;Xia Peng ,&nbsp;Yu Liu","doi":"10.1016/j.compenvurbsys.2025.102288","DOIUrl":"10.1016/j.compenvurbsys.2025.102288","url":null,"abstract":"<div><div>To evaluate human exposure to environmental factors, sufficient population-targeted sensing power of sensor carriers is crucial. However, the traditional static sensing approach is constrained by its limited coverage. Recently, equipping moving vehicles with sensors has emerged as a new approach. However, a quantitative comparison between mobile and traditional static sensing is still lacking. Using empirical taxi trajectory and population data in Beijing and Xiamen, we found that while a small number of taxi-based mobile sensors can cover a larger portion of the population, well-located static sensors eventually surpass mobile sensors in coverage as their number increases. In addition, a higher required frequency reduces the coverage of mobile sensors, whereas a higher cost ratio between static and mobile sensors reduces the coverage of static sites. Taxis provide extensive spatial coverage but with some uncertainty, especially in peripheral areas, whereas static sensors ensure localized and stable coverage. Based on the advantage of taxis and static sites, we propose an effective greedy-add-guided strengthen elitist genetic algorithm to determine the optimal combination of static and mobile sensors. The key idea is to position static sensors in areas with relatively low taxi visit probabilities but high population density. The results indicate that this optimal combination achieves higher population coverage compared to using taxis alone. It addresses the uncertainty in taxi coverage and significantly reduces the number of sensors required. These results support the feasibility of using taxis as a sensing paradigm and further highlight the potential of combining these two sensing paradigms in population-targeted sensing applications.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102288"},"PeriodicalIF":7.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791196","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":"Corrigendum to “How do contributions of organizations impact data inequality in OpenStreetMap?” [Computers, Environment and Urban Systems, Volume 109 (2024), 102077]","authors":"Anran Yang ,&nbsp;Hongchao Fan ,&nbsp;Qingren Jia ,&nbsp;Mengyu Ma ,&nbsp;Zhinong Zhong ,&nbsp;Jun Li ,&nbsp;Ning Jing","doi":"10.1016/j.compenvurbsys.2025.102272","DOIUrl":"10.1016/j.compenvurbsys.2025.102272","url":null,"abstract":"","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102272"},"PeriodicalIF":7.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859090","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":"Modelling active travel accessibility at the micro-scale using multi-source built environment data","authors":"Corin Staves ,&nbsp;Irena Itova ,&nbsp;Belen Zapata-Diomedi ,&nbsp;Audrey de Nazelle ,&nbsp;Jenna Panter ,&nbsp;Lucy Gunn ,&nbsp;Alan Both ,&nbsp;Yuchen Li ,&nbsp;Ismail Saadi ,&nbsp;James Woodcock ,&nbsp;S.M. Labib","doi":"10.1016/j.compenvurbsys.2025.102270","DOIUrl":"10.1016/j.compenvurbsys.2025.102270","url":null,"abstract":"<div><div>Accessibility models explore how land use and transport systems interact to facilitate access to activities and daily needs. Existing applications generally model accessibility based on distance or travel time. For pedestrians and cyclists, the street-level environment (e.g., green visibility, streetside amenities, dedicated infrastructure) significantly influences people's willingness and ability to travel. Incorporating these features into accessibility models can help them to be more representative of active travellers' experienced environment.</div><div>This study presents a methodology for incorporating the street-level environment into active mode accessibility. First, micro-scale built environment data from multiple sources are harmonised into a high-resolution digital representation of the land use and transport system. Second, a compute-optimised framework is developed for modelling accessibility at the micro-scale (i.e., each dwelling separately) incorporating the street-level environment. The methods build upon the open geodatabase OpenStreetMap and open-source MATSim project, facilitating expandability and transferability to other contexts. We apply this methodology to develop policy-relevant accessibility indicators for Greater Manchester.</div><div>In the results, we observe that the street-level environment can cause accessibility indicators to vary at the micro-scale, especially in less connected neighbourhoods where the choice of routes is limited. We also observed that for cyclists, the accessibility advantage over walking reduces substantially when traffic stress is considered. Our findings support further adoption of micro-scale built environment data and high-resolution analysis methods for active travel accessibility modelling in research and practice.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102270"},"PeriodicalIF":7.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ZenSVI: An open-source software for the integrated acquisition, processing and analysis of street view imagery towards scalable urban science","authors":"Koichi Ito ,&nbsp;Yihan Zhu ,&nbsp;Mahmoud Abdelrahman ,&nbsp;Xiucheng Liang ,&nbsp;Zicheng Fan ,&nbsp;Yujun Hou ,&nbsp;Tianhong Zhao ,&nbsp;Rui Ma ,&nbsp;Kunihiko Fujiwara ,&nbsp;Jiani Ouyang ,&nbsp;Matias Quintana ,&nbsp;Filip Biljecki","doi":"10.1016/j.compenvurbsys.2025.102283","DOIUrl":"10.1016/j.compenvurbsys.2025.102283","url":null,"abstract":"<div><div>Street view imagery (SVI) has been instrumental in many studies in the past decade to understand and characterize street features and the built environment. Researchers across a variety of domains, such as transportation, health, architecture, human perception, and infrastructure have employed different methods to analyze SVI. However, these applications and image-processing procedures have not been standardized, and solutions have been implemented in isolation, often making it difficult for others to reproduce existing work and carry out new research. Using SVI for research requires multiple technical steps: accessing APIs for scalable data collection, preprocessing images to standardize formats, implementing computer vision models for feature extraction, and conducting spatial analysis. These technical requirements create barriers for researchers in urban studies, particularly those without extensive programming experience. We developed ZenSVI, a free and open-source Python package that integrates and implements the entire process of SVI analysis, supporting a wide range of use cases. Its end-to-end pipeline includes downloading SVI from multiple platforms (e.g., Mapillary and KartaView) efficiently, analyzing metadata of SVI, applying computer vision models to extract target features, transforming SVI into different projections (e.g., fish-eye and perspective) and different formats (e.g., depth map and point cloud), visualizing analyses with maps and plots, and exporting outputs to other software tools. We demonstrated its use in Singapore through a case study of data quality assessment and clustering analysis in a streamlined manner. Our software improves the transparency, reproducibility, and scalability of research relying on SVI and supports researchers in conducting urban analyses efficiently. Its modular design facilitates extensions of the package for new use cases. This package is openly available at <span><span><span>https://github.com/koito19960406/ZenSVI</span></span><svg><path></path></svg></span>, and it is supported by documentation including tutorials (<span><span><span>https://zensvi.readthedocs.io/en/latest/examples/index.html</span></span><svg><path></path></svg></span>).</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102283"},"PeriodicalIF":7.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684261","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":"GeoAvatar: A big mobile phone positioning data-driven method for individualized pseudo personal mobility data generation","authors":"Peiran Li ,&nbsp;Haoran Zhang ,&nbsp;Wenjing Li ,&nbsp;Dou Huang ,&nbsp;Zhiling Guo ,&nbsp;Jinyu Chen ,&nbsp;Junxiang Zhang ,&nbsp;Xuan Song ,&nbsp;Pengjun Zhao ,&nbsp;Jinyue Yan ,&nbsp;Shibasaki Ryosuke ,&nbsp;Noboru Koshizuka","doi":"10.1016/j.compenvurbsys.2025.102252","DOIUrl":"10.1016/j.compenvurbsys.2025.102252","url":null,"abstract":"<div><div>The importance of personal mobility data is widely recognized in various fields. However, the utilization of real personal mobility data raises privacy concerns. Therefore, it is crucial to generate pseudo personal mobility data that accurately reflects real-world mobility patterns while safeguarding user privacy. Nevertheless, existing methods for generating pseudo mobility data, mostly focusing on trip or trajectory generation, have limitations in capturing sufficient individual heterogeneity. To address these gaps, taking pseudo-person(avatar) as ground-zero, a novel individual-based human mobility generator named GeoAvatar has been proposed – which considering individual heterogeneity in spatial and temporal decision-making, incorporates demographic characteristics. Our method utilizes a deep generative model to generate heterogeneous individual life patterns, a variation inference model for inferring individual demographic characteristics, and a Bayesian-based approach for generating spatial choices considering individual demographic characteristics. Through our method, we have achieved generating realistic pseudo personal human mobility data - we evaluated the proposed method based on physical features – obeying common law of human mobility, activity features – showing diverse and realistic activities, and spatial-temporal characteristics – presenting high-accuracy in terms of temporal grid population and od-count, demonstrating its good performance, with both a big mobile phone GPS trajectory dataset from Tokyo Metropolis and a big mobile phone CDR dataset from Shanghai. Furthermore, this method maintains extensibility for broader applications, making it a promising framework for generating pseudo personal human mobility data.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102252"},"PeriodicalIF":7.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577239","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 urban villages in China: Progress and challenges","authors":"Rui Cao ,&nbsp;Wei Tu ,&nbsp;Dongsheng Chen ,&nbsp;Wenyu Zhang","doi":"10.1016/j.compenvurbsys.2025.102282","DOIUrl":"10.1016/j.compenvurbsys.2025.102282","url":null,"abstract":"<div><div>The shift toward high-quality urbanization has brought increased attention to the issue of “urban villages”, which has become a prominent social problem in China. However, there is a lack of available geospatial data on urban villages, making it crucial to prioritize urban village mapping. In order to assess the current progress in urban village mapping and identify challenges and future directions, we have conducted a comprehensive review, which to the best of our knowledge is the first of its kind in this field. Our review begins by providing a clear context for urban villages and elaborating the method for literature review, then summarizes the study areas, data sources, and approaches used for urban village mapping in China. We also address the challenges and future directions for further research. Through thorough investigation, we find that current studies only cover very limited study areas and periods and lack sufficient investigation into the scalability, transferability, and interpretability of identification approaches due to the challenges in concept fuzziness and variances, spatial heterogeneity and variances of urban villages, and data availability. Future research can complement and further the current research in the following potential directions in order to achieve large-area mapping across the whole nation: 1) establish a unified standard of urban villages in China to accommodate significant variances and fuzzy concepts, 2) address the challenges of data availability via flexible use of available multisource data and explore potential use of data-driven image super-resolution approaches, 3) create public benchmarks to ensure fair comparison and focus on the scalability, transferability, and interpretability of urban village recognition approaches, 4) initiate a crowdsourcing program to enable effective and efficient data collection and validation as well as application. This review not only supports urban village-related research in China, but also contributes valuable knowledge from a Chinese perspective to global informal settlements mapping research and the achievement of the United Nations' Sustainable Development Goals (SDGs).</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102282"},"PeriodicalIF":7.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628243","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":"From theory to deep learning: Understanding the impact of geographic context factors on traffic violations","authors":"Hao Yang ,&nbsp;X. Angela Yao ,&nbsp;Farnoosh Roozkhosh ,&nbsp;Ruowei Liu ,&nbsp;Gengchen Mai","doi":"10.1016/j.compenvurbsys.2025.102268","DOIUrl":"10.1016/j.compenvurbsys.2025.102268","url":null,"abstract":"<div><div>Traffic violations pose significant threats to road safety, often leading to accidents, injuries, fatalities, and property damage. While driving behavior results from a dynamic interaction among drivers, vehicles, and the environment, most studies have primarily focused on human factors, neglecting the critical influence of social and environmental elements of the geographical context. To address this gap, we first develop a theoretical framework that explores how vehicles, drivers, and environmental factors interact, with particular emphasis on the environmental impact on driving behavior and road safety. Building on this framework, we introduce TraVio, a multitask deep learning model that combines Graph Convolutional Networks (GCNs) with Multilayer Perceptrons (MLPs) to detect and classify traffic violations. Through a case study in Montgomery County, Maryland, we demonstrate that TraVio outperforms traditional machine learning baselines in both binary and multi-label classification tasks. The results underscore the critical role of geographic context and time factors in shaping traffic violations. This study not only provides a robust modeling technique for predicting traffic violations but also offers a theoretical foundation that can be extended to other driving-related themes, contributing to safer road environments through informed urban planning and policymaking.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102268"},"PeriodicalIF":7.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509561","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":"Analysing local spatial density of human activity with quick density clustering (QDC) algorithm","authors":"Katarzyna Kopczewska","doi":"10.1016/j.compenvurbsys.2025.102289","DOIUrl":"10.1016/j.compenvurbsys.2025.102289","url":null,"abstract":"<div><div>This paper deals with the local spatial density of human activity. By understanding and quantifying the spatial distribution of interrelated phenomena such as business location and population settlement at the micro level, it is possible to track local under- and over- spatial representation in socio-economic development. The modelling of spatial density using point data is crucial for territorially targeted policies and business decisions. Weak stream of studies in this field is a consequence of lack of methods. This study presents quick density clustering (QDC), a novel algorithm for classifying geolocated point data into low, medium and high density clusters. QDC uses two spatial features - the sum of distances to k-nearest neighbours (kNN) and the number of neighbours within a fixed radius (frNN) - to generate parameter robust, interpretable clusters. By normalising these metrics and applying K-means clustering, QDC captures both local and global density variations, making it suitable for analysing human activity at urban and regional scales. Empirical validation demonstrates its accuracy and effectiveness in partitioning point data into density clusters and comparing density groups in grids. The QDC provides a robust framework for advancing density-based studies in socio-economic research as well as environmental science and spatial statistics</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815252","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":"Quantify relationships between bike network connectivity and bike safety: A comparative analysis of connectivity metrics conducted in two California cities","authors":"Jiahua Chen ,&nbsp;Peter Kedron ,&nbsp;Trisalyn Nelson ,&nbsp;Dan Willett ,&nbsp;Achituv Cohen ,&nbsp;Colin Ferster","doi":"10.1016/j.compenvurbsys.2025.102271","DOIUrl":"10.1016/j.compenvurbsys.2025.102271","url":null,"abstract":"<div><div>To motivate people to use bikes for transportation, cities are shifting their focus from constructing isolated bike lanes to building interconnected bike networks. The effectiveness of these networks is measured by their level of connectivity, specifically how easily individuals of all ages and abilities can reach their destinations by bike. While most researchers and policymakers hypothesize that well-connected bike networks will reduce crash risk by offering bicyclists extended protection from traffic, most studies find positive or null associations between network connectivity and bike crashes. This discrepancy may arise either from actual processes, such as increased ridership in high-traffic areas, or from variability in how connectivity is measured. Our study aims to understand relationships between bike safety and various connectivity metrics at the neighborhood level by deconstructing and comparing different metrics. We critique previous constructs of density-based metrics rely solely on bike infrastructure and introduce new density-based and routing-based metrics derived from low-stress networks. Using a negative binomial regression model, we examine the association between bike crashes and connectivity metrics across 125 block groups in Santa Barbara and Goleta, California. We find that increased density-based connectivity in both bike infrastructure and low-stress networks correlates with fewer crashes. In contrast, routing-based connectivity measures, which reflect bike access to key destinations, are positively associated with crashes. We conclude that different connectivity metrics can alter the direction of connectivity-safety associations. Our proposed metrics, which incorporate low-stress networks and routing algorithms, provide a more nuanced understanding of how connectivity is related to bicycling safety.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102271"},"PeriodicalIF":7.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring evacuation rates from mobility data during the McDougall Creek wildfire in British Columbia, Canada","authors":"Hui Jeong Ha,&nbsp;Jed A. Long","doi":"10.1016/j.compenvurbsys.2025.102286","DOIUrl":"10.1016/j.compenvurbsys.2025.102286","url":null,"abstract":"<div><div>In recent years, the intensity and occurrence of wildfires have risen globally, driven by climate change triggering extreme dry weather conditions. This study focuses on the 2023 McDougall Creek wildfire in British Columbia, highlighting the vulnerability of urban communities to severe wildfires. Using aggregated and de-identified network mobility data from a Canadian telecommunications provider, we quantified neighborhood-level evacuation rates and examined inter-regional travel patterns during the wildfire event. We applied a spatial difference-in-difference (DID) model to understand how neighborhood characteristics influenced evacuation rates. Our findings suggest that formal evacuation orders were positively associated with evacuation rates. We also found that the distance to the wildfire perimeter was a strong and significant predictor of evacuation rates, while socio-demographic variables previously identified as strong predictors of evacuation rates were not significant in this particular context. The analysis of travel patterns before and during the wildfire event reveals distinct directional patterns and variations in inter-regional travel across spatial scales. This research contributes to the understanding of wildfire evacuation dynamics and the application of human mobility data into disaster management, enhancing our knowledge of the human response to natural disasters.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102286"},"PeriodicalIF":7.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}