Computers Environment and Urban Systems最新文献

{"title":"Multi-modal contrastive learning of urban space representations from POI data","authors":"Xinglei Wang ,&nbsp;Tao Cheng ,&nbsp;Stephen Law ,&nbsp;Zichao Zeng ,&nbsp;Lu Yin ,&nbsp;Junyuan Liu","doi":"10.1016/j.compenvurbsys.2025.102299","DOIUrl":"10.1016/j.compenvurbsys.2025.102299","url":null,"abstract":"<div><div>Understanding and characterising urban environment is crucial for urban planning and geospatial analysis. One common approach to this process is through using point of interest (POI) data, which offers rich information about the spatial-semantic characteristics of urban spaces. Existing methods for learning urban space representations from POIs face several limitations, including reliance on predefined spatial units, ignorance of POI location information, underutilisation of POI semantic attributes, and computational inefficiencies. To address these gaps, we propose CaLLiPer (<u><strong>C</strong></u>ontr<u><strong>a</strong></u>stive <u><strong>L</strong></u>anguage-<u><strong>L</strong></u>ocat<u><strong>i</strong></u>on <u><strong>P</strong></u>r<strong>e</strong>-t<u><strong>r</strong></u>aining), a novel approach that directly embeds continuous urban spaces into vector representations that capture the spatial and semantic characteristics of urban environment. This model leverages multimodal contrastive learning to align location embeddings with textual descriptions of POIs, bypassing the need for complex training corpus construction and negative sampling. Applying CaLLiPer to learning urban space representations in London, UK, we demonstrate 5–15% improvement in predictive performance for land use classification and socioeconomic mapping tasks compared to state-of-the-art methods. Visualisations and correlation analysis of the learned representations further verify our model's ability to capture spatial variations in urban semantics with high accuracy and fine resolution. Moreover, CaLLiPer achieves reduced training time, showcasing its efficiency and scalability. Additional experiments demonstrate the robustness of our model across different spatial scales and urban context. Notably, the experiment on Singapore showed an improvement of over 20%. This work also provides a promising pathway for scalable, semantically rich urban space representation learning that can support the development of geospatial foundation models. The implementation code is available at <span><span>https://github.com/xlwang233/CaLLiPer</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"120 ","pages":"Article 102299"},"PeriodicalIF":7.1,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886865","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":"Simulation and exposure assessment of hourly traffic noise in Hong Kong using a minimal error iterative model based on diversion strategies","authors":"Kang Zou ,&nbsp;Xinyu Yu ,&nbsp;Coco Yin Tung Kwok ,&nbsp;Man Sing Wong ,&nbsp;Mei-Po Kwan ,&nbsp;Huiying (Cynthia) Hou","doi":"10.1016/j.compenvurbsys.2025.102300","DOIUrl":"10.1016/j.compenvurbsys.2025.102300","url":null,"abstract":"<div><div>Traffic noise poses a globally significant environmental threat to urban livability, particularly in high-density areas where conventional noise assessment methods struggle to capture dynamic spatio-temporal variations. The Minimal Error Iterative Model based on Diversion Strategies (MEI-DS) was proposed in this study to derive high-resolution traffic flow networks with overcoming temporal granularity limitations. A case study in Hong Kong, China, a high-density building environment city was conducted to examine the model performance, with an average relative error of 0.48 %. Afterwards, a novel noise assessment framework was developed by integrating MEI-DS-generated flows with noise source model and 3D noise propagation model. This approach reveals striking spatiotemporal heterogeneities: Peak noise levels occur between 08:00–09:00 on weekdays, while Saturdays show persistently high noise levels from 09:00 to 20:00. Sundays exhibit minimal diurnal noise fluctuations. Multi-scale assessments (city-district-building-individual) reveal 85.9 % of the population experiences noise exposure exceeding WHO-recommended thresholds. This study offers actionable insights to inform urban planning and develop health-centric strategies for mitigating traffic noise, and the proposed model can also be transferred to other regions with strong potential to address the impact of traffic noise on environmental health.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"120 ","pages":"Article 102300"},"PeriodicalIF":7.1,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878796","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":"Does co-development facilitate achieving useful planning tools? A socio-technical approach to the development of information model-based land use planning in Finland","authors":"Pilvi Nummi ,&nbsp;Anni Hapuoja","doi":"10.1016/j.compenvurbsys.2025.102291","DOIUrl":"10.1016/j.compenvurbsys.2025.102291","url":null,"abstract":"<div><div>The digitalization of urban planning entails a shift to information model-based planning, where plans are produced in a machine-readable and interoperable format. In Finland, a nationally interoperable information model for land use plans has been applied for the first time to digital planning tools in the recently completed project KAATIO. In this article, we apply socio-technical approach to assess how co-development in this project was perceived by municipal planners and software developers, and how did the tools developed meet the needs of planners and planning practice. The results show that a technology-driven culture dominates the national development and hampers the socio-technical approach. Despite the challenges, co-development is beneficial for both software developers and municipal actors. In conclusion, we argue that, in this context, empowering users, facilitating the discussion on information model-based planning, future-oriented understanding of planning tasks, and accepting the diversity of practices while harmonizing the plan data are essential for promoting human factors in the development.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"120 ","pages":"Article 102291"},"PeriodicalIF":7.1,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847917","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":"2025-04-10","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":"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":"2025-04-08","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":"A segmented approach to modeling building height: Delineating high-rise and low-rise buildings for enhanced height estimation","authors":"Clinton Stipek,&nbsp;Daniel Adams,&nbsp;Philipe Dias,&nbsp;Taylor Hauser,&nbsp;Viswadeep Lebakula,&nbsp;Alexander Sorokine,&nbsp;Justin Epting,&nbsp;Jessica Moehl,&nbsp;Robert Stewart","doi":"10.1016/j.compenvurbsys.2025.102287","DOIUrl":"10.1016/j.compenvurbsys.2025.102287","url":null,"abstract":"<div><div>Understanding building height is imperative to the overall study of energy efficiency, population distribution, urban morphologies, emergency response, among others. Currently, existing approaches for modeling building height at scale are hindered by two pervasive issues. First, there is no consistent approach to quantify what a high-rise building is at a macro scale, leaving researchers unable to accurately compare results across geographies and domains. Second, high-rise buildings represent a small fraction of the built environment, implying data imbalance challenges that negatively affect current approaches. This is a problem of practical relevance since information on high-rise buildings is important for studies on urban heat islands, population dynamics, and pollution dispersion. Here, we introduce a novel approach to map building height which first identifies two distinct distributions within the built environment, with one being composed of low-rise buildings and one composed of high-rise buildings. We then develop an ensemble scheme where discrete specialist models are trained for each subset of low-rise buildings and high-rise buildings to infer building height from morphology features. For experiments mapping heights of 4.85 million buildings in Japan, we show an increase of 34 % in accuracy within <span><math><mn>3</mn><mi>m</mi></math></span> error when compared to the current state-of-the-art when modeling high-rise buildings, which based on KNN experimentation we define as any building <span><math><mo>&gt;</mo><mn>12</mn><mi>m</mi></math></span>. Our findings show that such an ensemble framework outperforms the current state-of-the-art approaches, which is especially relevant in relation to inferring height for high-rise buildings, a prominent issue of existing approaches for mapping the built environment.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102287"},"PeriodicalIF":7.1,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791057","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":"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":"2025-04-03","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}

{"title":"Route planning of mobile sensing fleets for repeatable environmental monitoring tasks","authors":"Wen Ji ,&nbsp;Ke Han ,&nbsp;Qian Ge","doi":"10.1016/j.compenvurbsys.2025.102285","DOIUrl":"10.1016/j.compenvurbsys.2025.102285","url":null,"abstract":"<div><div>Vehicle-based mobile sensing is a new paradigm for urban data collection. Certain urban sensing scenarios require sensing vehicles for highly targeted monitoring, such as air pollutant and accident site investigation. A hallmark of these scenarios is that the points of interest (POIs) need to be repeatedly visited by a set of agents, whose routes should provide sufficient sensing coverage with coordinated overlap at certain important POIs. For these applications, this paper presents the <em>open team orienteering problem with repeatable visits</em> (OTOP-RV) and specifically tailors an adaptive large neighborhood search (ALNS) algorithm to address it. Test results on randomly generated datasets show that the ALNS significantly outperforms the greedy algorithm (by 7.2 % to 32.4 %), and a heuristic based on sequential orienteering problem (by about 6 %). Finally, a real-world air pollution sensing case study demonstrates the unique applicability of the OTOP-RV and the effectiveness of the proposed algorithms in enhancing sensing capabilities.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102285"},"PeriodicalIF":7.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714890","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":"Physical urban change and its socio-environmental impact: Insights from street view imagery","authors":"Yingjie Liu ,&nbsp;Zeyu Wang ,&nbsp;Siyi Ren ,&nbsp;Runying Chen ,&nbsp;Yixiang Shen ,&nbsp;Filip Biljecki","doi":"10.1016/j.compenvurbsys.2025.102284","DOIUrl":"10.1016/j.compenvurbsys.2025.102284","url":null,"abstract":"<div><div>Urban transformation not only reshapes physical spaces but also impacts public perception, influencing how people experience their environments. This study utilizes Street View Imagery (SVI) as an emerging, human-level data source to assess urban changes, providing perspective beyond traditional datasets. Existing studies often focus on either urban physical changes or human perception changes, without bridging the two. This research integrates both aspects by combining a change detection model, trained on a self-labeled dataset, and a human perception model based on the crowdsourced Place Pulse 2.0 dataset with input from 81,630 online volunteers, to analyze urban transformation in New York City and Memphis from 2007 to 2023. Our findings reveal differences between the two cities: New York City exhibited small, isolated changes often driven by community needs, while Memphis transitioned from concentrated to more dispersed development patterns. This study provides insights into how physical changes influence public perception within these two cities. It demonstrates how thoughtful, well-planned urban transformation can improve neighborhood's perception such as safety and livability, while also pointing out potential challenges like gentrification or social fragmentation. These findings provide policymakers with valuable insights into human perception, aiding in the creation of more inclusive, vibrant, and resilient urban transformation. This helps ensure that urban transformation efforts are based on community desires and align with long-term sustainability goals.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102284"},"PeriodicalIF":7.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714949","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":"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":"2025-03-20","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}