Computers Environment and Urban Systems最新文献_第7页

Quantifying seasonal bias in street view imagery for urban form assessment: A global analysis of 40 cities 量化城市形态评估中街景图像的季节偏差：对40个城市的全球分析

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-05-09 DOI: 10.1016/j.compenvurbsys.2025.102302

Tianhong Zhao , Xiucheng Liang , Filip Biljecki , Wei Tu , Jinzhou Cao , Xiaojiang Li , Shengao Yi

Street view imagery (SVI), with its rich visual information, is increasingly recognized as a valuable data source for urban research. Particularly, by leveraging computer vision techniques, SVI can be used to calculate various urban form indices (e.g., Green View Index, GVI), providing a new approach for large-scale quantitative assessments of urban environments. However, SVI data collected at the same location in different seasons can yield varying urban form indices due to phenological changes, even when the urban form remains constant. Numerous studies overlook this kind of seasonal bias. To address this gap, we propose a systematic analytical framework for quantifying and evaluating seasonal bias in SVI, drawing on more than 262,000 images from 40 cities worldwide. This framework encompasses three aspects: seasonal bias within urban areas, seasonal bias across cities on a global scale, and the impact of seasonal bias in practical applications. The results reveal that (1) seasonal bias is evident, with an average mean absolute percentage error (MAPE) of 54 % for GVI across all sampled cities, and it is particularly pronounced in areas with significant seasonal bias; (2) seasonal bias is strongly correlated with geographic location, with greater bias observed in cities with lower average rainfall and temperatures; and (3) in practical applications, ignoring seasonal bias may result in analytical errors (e.g., an ARI of 0.35 in clustering). By identifying and quantifying seasonal bias in SVI, this study contributes to improving the accuracy of urban environmental assessments based on street view data and provides new theoretical support for the broader application of such data on a global scale.

街景图像以其丰富的视觉信息，日益成为城市研究的重要数据来源。特别是，通过利用计算机视觉技术，SVI可用于计算各种城市形态指数（如绿色景观指数，GVI），为大规模定量评估城市环境提供了一种新的方法。然而，即使在城市形态保持不变的情况下，同一地点不同季节的SVI数据也会由于物候变化而产生不同的城市形态指数。许多研究都忽略了这种季节性偏见。为了解决这一差距，我们提出了一个系统的分析框架，用于量化和评估SVI的季节性偏差，利用来自全球40个城市的262,000多张图像。该框架包括三个方面：城市地区内的季节性偏差，全球范围内城市间的季节性偏差，以及季节性偏差在实际应用中的影响。结果表明：(1)季节偏差明显，所有样本城市的GVI平均绝对百分比误差（MAPE）为54%，且在季节偏差显著的地区尤为明显；(2)季节偏差与地理位置密切相关，平均降雨量和平均气温较低的城市偏差较大；(3)在实际应用中，忽略季节偏差可能导致分析误差（例如，聚类的ARI为0.35）。本研究通过识别和量化SVI的季节偏差，有助于提高基于街景数据的城市环境评价的准确性，为街景数据在全球范围内的更广泛应用提供新的理论支持。

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引用次数: 0

Incorporating environmental considerations into infrastructure inequality evaluation using interpretable machine learning 使用可解释机器学习将环境因素纳入基础设施不平等评估

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-05-03 DOI: 10.1016/j.compenvurbsys.2025.102301

Bo Li, Ali Mostafavi

A growing body of literature has recognized the importance of characterizing infrastructure inequality in cities and provided quantified metrics to inform urban development plans. However, the majority of existing approaches suffered from two limitations. First, prior research has provided empirical evidence of negative environmental impacts that infrastructure can incur, while infrastructure provision inequality assessment has not taken those environmental concerns into consideration. Second, comprehensive provision assessment for multi-infrastructure system calls for a proper weight assignment, while current studies either determine the infrastructure components as equal weights or rely on subjective methods (e.g. AHP), which may be affected by potential biases. This study proposes a novel approach for incorporating environmental considerations into quantifying and assessing infrastructure provision in cities based on a data-driven method. We applied an interpretable machine learning method (XGBoost + SHAP) to capture the relationship between infrastructure features and environmental hazards (i.e., air pollution and urban heat), and then determined feature weights as their relative contributions towards environmental hazards when calculating infrastructure provision. The implementation of the model in five metropolitan areas in the U.S. demonstrates the capability of the proposed approach in characterizing inequality in infrastructure. Further the study reveals both spatial and income inequality regarding infrastructure provision. Environmentally integrated infrastructure provision proposed in this study can better capture the intersection of infrastructure development and environmental justice in measuring and characterizing infrastructure inequality in cities. This study could be used effectively to inform integrated urban design strategies to promote infrastructure equity and environmental justice based on data-driven and machine learning-based insights.

越来越多的文献认识到描述城市基础设施不平等的重要性，并为城市发展规划提供了量化指标。然而，现有的大多数方法都有两个局限性。首先，先前的研究提供了基础设施可能产生负面环境影响的经验证据，而基础设施提供不平等评估并未考虑到这些环境问题。其次，多基础设施系统的综合供应评估需要适当的权重分配，而目前的研究要么将基础设施组成部分确定为相等的权重，要么依赖于可能受到潜在偏差影响的主观方法（如AHP）。本研究提出了一种基于数据驱动的方法，将环境因素纳入量化和评估城市基础设施供应的新方法。我们应用了一种可解释的机器学习方法（XGBoost + SHAP）来捕捉基础设施特征与环境危害（即空气污染和城市热量）之间的关系，然后在计算基础设施供应时确定特征权重作为它们对环境危害的相对贡献。该模型在美国五个大都市地区的实施证明了所提出的方法在描述基础设施不平等方面的能力。此外，研究还揭示了基础设施提供方面的空间和收入不平等。本研究提出的环境一体化基础设施提供可以更好地捕捉基础设施发展与环境正义在衡量和表征城市基础设施不平等方面的交叉点。这项研究可以有效地用于为综合城市设计策略提供信息，以促进基于数据驱动和机器学习的见解的基础设施公平和环境正义。

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引用次数: 0

Hedonic price models, social media data and AI – An application to the AIRBNB sector in us cities 享乐价格模型、社交媒体数据和人工智能——美国城市AIRBNB部门的应用

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-04-30 DOI: 10.1016/j.compenvurbsys.2025.102303

John Östh , Umut Türk , Karima Kourtit , Peter Nijkamp

The Airbnb sector has experienced exponential growth over the past decade and has led to extensive research in fields such as hospitality sciences, urban geography, tourism economics, and information management. This paper contributes to quantitative research in the Airbnb sector by focusing on the integration of digital platform data at the neighborhood level. It explores innovative methodologies for analyzing urban attractiveness by combining insights from hedonic pricing models with large-scale digital data sourced through AI-based approaches. This novel framework compares user-based valuations of accommodations derived from hedonic pricing with subjective, AI-generated neighborhood descriptions, offering new perspectives on data quality and reliability in information systems. The study also critically examines the challenges of integrating AI-generated content in information science, referencing also ‘Garbage-in Garbage-out’ and ‘Bullshit-in Bullshit-out’ concepts. Employing a multi-scalar modeling approach, the research examines Airbnb pricing dynamics across several U.S. cities, starting with Manhattan (USA) as an illustrative case. A subsequent large-scale application to additional metropolitan areas utilizes a combination of hedonic price modeling, social media data, and AI-generated urban descriptions, including a Shapley decomposition analysis. This interdisciplinary integration provides actionable insights into neighborhood attractiveness and pricing mechanisms, while highlighting methodological and empirical contributions to the broader field of information management. By employing the relationship between AI-driven textual data and quantitative modeling, this research provides added value in analyzing urban information systems and their application to digital platforms.

在过去的十年里，Airbnb行业经历了指数级的增长，并在酒店科学、城市地理、旅游经济学和信息管理等领域引发了广泛的研究。本文通过关注社区层面的数字平台数据整合，为Airbnb领域的定量研究做出了贡献。它通过将享乐定价模型的见解与基于人工智能的方法获取的大规模数字数据相结合，探索了分析城市吸引力的创新方法。这一新颖的框架将基于用户的享乐定价与人工智能生成的主观社区描述进行了比较，为信息系统的数据质量和可靠性提供了新的视角。该研究还批判性地考察了将人工智能生成的内容整合到信息科学中的挑战，并引用了“垃圾中垃圾”和“废话中废话”的概念。该研究采用多标量建模方法，考察了美国几个城市的Airbnb定价动态，并以美国曼哈顿为例进行了说明。随后在其他大都市地区的大规模应用结合了享乐价格模型、社交媒体数据和人工智能生成的城市描述，包括Shapley分解分析。这种跨学科的整合为社区吸引力和定价机制提供了可操作的见解，同时突出了对更广泛的信息管理领域的方法和经验贡献。通过利用人工智能驱动的文本数据与定量建模之间的关系，本研究为分析城市信息系统及其在数字平台上的应用提供了附加价值。

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引用次数: 0

Multi-modal contrastive learning of urban space representations from POI data 基于POI数据的城市空间表征的多模态对比学习

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-04-30 DOI: 10.1016/j.compenvurbsys.2025.102299

Xinglei Wang , Tao Cheng , Stephen Law , Zichao Zeng , Lu Yin , Junyuan Liu

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 (Contrastive Language-Location Pre-training), 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 https://github.com/xlwang233/CaLLiPer.

了解和描述城市环境对城市规划和地理空间分析至关重要。实现这一过程的一种常见方法是使用兴趣点（POI）数据，这些数据提供了关于城市空间空间语义特征的丰富信息。现有的从POI中学习城市空间表示的方法面临一些限制，包括依赖预定义的空间单元、忽略POI位置信息、未充分利用POI语义属性以及计算效率低下。为了解决这些差距，我们提出了CaLLiPer（对比语言-位置预训练），这是一种新颖的方法，它直接将连续的城市空间嵌入到矢量表示中，从而捕捉城市环境的空间和语义特征。该模型利用多模态对比学习将位置嵌入与poi的文本描述对齐，从而绕过了复杂的训练语料库构建和负采样的需要。将CaLLiPer应用于学习英国伦敦的城市空间表示，我们证明，与最先进的方法相比，土地利用分类和社会经济制图任务的预测性能提高了5-15%。对学习表征的可视化和相关性分析进一步验证了我们的模型以高精度和高分辨率捕获城市语义空间变化的能力。此外，CaLLiPer实现了更短的训练时间，展示了其效率和可扩展性。其他实验证明了我们的模型在不同空间尺度和城市背景下的稳健性。值得注意的是，在新加坡的实验显示，改善幅度超过20%。这项工作还为可扩展的、语义丰富的城市空间表示学习提供了一条有希望的途径，可以支持地理空间基础模型的开发。实现代码可从https://github.com/xlwang233/CaLLiPer获得。

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引用次数: 0

Simulation and exposure assessment of hourly traffic noise in Hong Kong using a minimal error iterative model based on diversion strategies 利用基于改道策略的最小误差迭代模型模拟及评估香港每小时交通噪音

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-04-28 DOI: 10.1016/j.compenvurbsys.2025.102300

Kang Zou , Xinyu Yu , Coco Yin Tung Kwok , Man Sing Wong , Mei-Po Kwan , Huiying (Cynthia) Hou

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.

交通噪声对城市宜居性构成了全球性的重大环境威胁，特别是在传统噪声评估方法难以捕捉动态时空变化的高密度地区。本文提出了基于导流策略的最小误差迭代模型（MEI-DS），克服了时间粒度的限制，获得了高分辨率的交通流网络。以中国香港高密度建筑环境城市为例，对模型的性能进行了检验，平均相对误差为0.48%。然后，将mei - ds生成的流与噪声源模型和三维噪声传播模型相结合，建立了新的噪声评价框架。该方法揭示了显著的时空异质性：峰值噪声水平出现在工作日的08:00-09:00之间，而周六的09:00 - 20:00持续显示高噪声水平。星期天的噪音波动最小。多尺度评估（城市-地区-建筑物-个人）显示，85.9%的人口经历的噪声暴露超过了世卫组织建议的阈值。该研究为城市规划和制定以健康为中心的交通噪声缓解策略提供了可操作的见解，并且所提出的模型也可以推广到其他有潜力解决交通噪声对环境健康影响的地区。

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引用次数: 0

Does co-development facilitate achieving useful planning tools? A socio-technical approach to the development of information model-based land use planning in Finland 共同开发是否有助于实现有用的规划工具？芬兰基于信息模型的土地利用规划发展的社会技术方法

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-04-17 DOI: 10.1016/j.compenvurbsys.2025.102291

Pilvi Nummi , Anni Hapuoja

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.

城市规划的数字化需要向基于信息模型的规划转变，其中规划以机器可读和可互操作的格式生成。在芬兰，在最近完成的KAATIO项目中，土地利用计划的全国互操作信息模型首次应用于数字规划工具。在本文中，我们运用社会技术方法来评估市政规划者和软件开发商如何看待该项目中的共同发展，以及开发的工具如何满足规划者和规划实践的需求。结果表明，技术驱动型文化主导了国家发展，阻碍了社会技术途径。尽管存在挑战，但共同开发对软件开发人员和市政参与者都是有益的。总之，我们认为，在这种背景下，赋予用户权力，促进基于信息模型的规划讨论，面向未来的规划任务理解，在协调规划数据的同时接受实践的多样性，对于促进发展中的人为因素至关重要。

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引用次数: 0

Analysing local spatial density of human activity with quick density clustering (QDC) algorithm 基于快速密度聚类算法的局部人类活动空间密度分析

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-04-10 DOI: 10.1016/j.compenvurbsys.2025.102289

Katarzyna Kopczewska

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

本文研究人类活动的局部空间密度。通过在微观层面上理解和量化商业地点和人口定居等相关现象的空间分布，就有可能跟踪地方社会经济发展中的空间代表性和空间代表性。使用点数据的空间密度建模对于有地域针对性的政策和商业决策至关重要。这一领域研究的薄弱是缺乏方法的结果。本文提出了一种快速密度聚类（QDC）算法，用于将定位点数据分为低、中、高密度聚类。QDC使用两个空间特征——到k个最近邻的距离之和（kNN）和固定半径内的邻居数量（frNN）——来生成参数鲁棒的、可解释的聚类。通过规范化这些指标并应用K-means聚类，QDC捕获了局部和全球密度变化，使其适用于分析城市和区域尺度上的人类活动。实验验证了该方法对点数据进行密度聚类划分和网格密度组比较的准确性和有效性。QDC为推进社会经济研究、环境科学和空间统计方面的基于密度的研究提供了一个强有力的框架

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引用次数: 0

Advancing population-targeted urban sensing: A comparative study on mobile and static sensing paradigms 推进以人口为目标的城市感知：移动与静态感知范式的比较研究

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-04-08 DOI: 10.1016/j.compenvurbsys.2025.102288

Yuan-Qiao Hou , Xiao-Jian Chen , Zhou Huang , Xia Peng , Yu Liu

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.

为了评估人类对环境因子的暴露，传感器载体具有足够的人群感知能力至关重要。然而，传统的静态传感方法受限于其有限的覆盖范围。最近，为移动车辆配备传感器已成为一种新方法。然而，移动传感与传统静态传感之间的定量比较仍然缺乏。利用北京和厦门的出租车轨迹和人口数据，我们发现，虽然少量基于出租车的移动传感器可以覆盖大部分人口，但位置良好的静态传感器最终会随着数量的增加而覆盖范围超过移动传感器。此外，更高的频率要求降低了移动传感器的覆盖范围，而更高的静态和移动传感器之间的成本比降低了静态站点的覆盖范围。出租车提供了广泛的空间覆盖，但也有一些不确定性，尤其是在周边地区，而静态传感器则确保了局部和稳定的覆盖。基于出租车和静态站点的优势，我们提出了一种有效的贪婪添加导向强化精英遗传算法来确定静态和移动传感器的最优组合。关键思想是将静态传感器定位在出租车访问概率相对较低但人口密度较高的地区。结果表明，与单独使用出租车相比，这种优化组合实现了更高的人口覆盖率。它解决了出租车覆盖范围的不确定性，并显著减少了所需传感器的数量。这些结果支持了出租车作为一种感知范式的可行性，并进一步强调了将这两种感知范式结合起来用于人群目标感知应用的潜力。

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引用次数: 0

A segmented approach to modeling building height: Delineating high-rise and low-rise buildings for enhanced height estimation 建筑高度建模的分段方法：描绘高层和低层建筑以增强高度估计

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-04-08 DOI: 10.1016/j.compenvurbsys.2025.102287

Clinton Stipek, Daniel Adams, Philipe Dias, Taylor Hauser, Viswadeep Lebakula, Alexander Sorokine, Justin Epting, Jessica Moehl, Robert Stewart

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

3 m

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

> 12 m

. 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.

了解建筑高度对能源效率、人口分布、城市形态、应急响应等方面的整体研究至关重要。目前，现有的大规模建筑高度建模方法受到两个普遍问题的阻碍。首先，没有一致的方法来量化宏观尺度上的高层建筑，这使得研究人员无法准确地比较不同地域和领域的结果。其次，高层建筑只占建筑环境的一小部分，这意味着数据不平衡的挑战会对当前的方法产生负面影响。这是一个具有实际意义的问题，因为高层建筑的信息对于研究城市热岛、人口动态和污染扩散非常重要。在这里，我们引入了一种新的方法来绘制建筑高度，该方法首先在建筑环境中识别出两种不同的分布，一种由低层建筑组成，另一种由高层建筑组成。然后，我们开发了一个集成方案，其中为低层建筑和高层建筑的每个子集训练离散的专家模型，以从形态特征推断建筑高度。对于绘制日本485万幢建筑高度的实验，我们显示，与目前最先进的高层建筑建模相比，在3米误差范围内的精度提高了34%，基于KNN实验，我们将其定义为任何12米的建筑。我们的研究结果表明，这样的集成框架优于当前最先进的方法，这在推断高层建筑的高度方面尤其相关，这是现有的绘制建筑环境方法的一个突出问题。

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引用次数: 0

Measuring evacuation rates from mobility data during the McDougall Creek wildfire in British Columbia, Canada 从加拿大不列颠哥伦比亚省麦克杜格尔河野火期间的移动数据测量疏散率

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-04-03 DOI: 10.1016/j.compenvurbsys.2025.102286

Hui Jeong Ha, Jed A. Long

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.

近年来，在气候变化引发极端干燥天气条件的推动下，全球野火的强度和发生率都有所上升。这项研究的重点是2023年不列颠哥伦比亚省的麦克杜格尔河野火，突出了城市社区对严重野火的脆弱性。使用来自加拿大电信提供商的聚合和去识别网络移动数据，我们量化了社区一级的疏散率，并检查了野火事件期间的区域间旅行模式。我们应用空间差分模型来了解社区特征如何影响疏散率。我们的研究结果表明，正式的疏散命令与疏散率呈正相关。我们还发现，到野火周边的距离是疏散率的一个强大而重要的预测因子，而以前被确定为疏散率的强预测因子的社会人口变量在这种特殊情况下并不显著。对森林火灾发生前和发生期间的旅行模式进行分析，揭示了区域间旅行在空间尺度上的明显方向性模式和变化。本研究有助于了解野火疏散动态，并将人员流动数据应用于灾害管理，提高我们对人类应对自然灾害的认识。

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