Computers Environment and Urban Systems最新文献_第6页

Torque work of origin-destination flows: Quantifying urban place centrality from a physical perspective 起点-终点流的扭矩功：从物理角度量化城市场所中心性

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-05-31 DOI: 10.1016/j.compenvurbsys.2025.102311

Xiaorui Yan , Tao Pei , Ci Song , Zidong Fang , Xiaohan Liu , Tianyu Liu , Linfeng Jiang , Ying Gao , Guangdong Li , Jie Huang , Yaqin Sun

Quantifying urban place centrality, defined as its relative importance in serving its peripheral areas, provides insights into urban structures, optimizes resource allocation, and supports strategic urban planning. Centrality is shaped by three aspects: service volume, spatial reach, and directional diversity. However, existing measures often assess these factors separately and few integrate them simultaneously. Additionally, centrality analyses often overlook local perspectives and intra-day dynamics. To this end, we propose a novel origin-destination flow-based centrality measure, namely Total Torque Work (TTW), that integrates these three aspects into a single value, conceptualized as “Torque work of flow”, where flow volume, length, and direction correspond to the force magnitude, lever arm, and angular displacement. The effectiveness of the TTW is validated by simulation experiments. We apply this measure to analyze macro- and micro-centralities in Beijing, using taxi and shared bike flow data. Macro-centrality shows a monocentric structure, with higher values near railway stations, airports, and business and commercial centers. Micro-centrality is more polycentric, with subway stations exhibiting higher centrality. Time series clustering identifies three temporal patterns in both macro- and micro-centralities: two “daytime-dominant” patterns linked to multifunctional activities and commuting, and a “nighttime-dominant” pattern in residential areas. The study concludes with several implications for urban planning, emphasizing the importance of incorporating multi-spatiotemporal scales.

量化城市中心性，定义为其在服务周边地区方面的相对重要性，提供了对城市结构的见解，优化了资源配置，并支持了战略性城市规划。中心性的形成有三个方面：服务量、空间范围和方向多样性。然而，现有的措施往往单独评估这些因素，很少将它们同时结合起来。此外，中心性分析通常会忽略局部视角和内部动态。为此，我们提出了一种新颖的基于起点-终点流量的中心性度量，即总扭矩功（TTW），它将这三个方面整合为一个值，概念为“流量的扭矩功”，其中流量、长度和方向对应于力的大小、杠杆臂和角位移。仿真实验验证了该方法的有效性。我们使用出租车和共享单车流量数据，将这一方法应用于分析北京的宏观和微观中心性。宏观中心性表现为单中心结构，在火车站、机场和商业中心附近值较高。微中心性表现为多中心性，其中地铁站的中心性更高。时间序列聚类确定了宏观和微观中心的三种时间模式：两种与多功能活动和通勤相关的“白天主导”模式，以及居住区的“夜间主导”模式。该研究总结了对城市规划的几点启示，强调了结合多时空尺度的重要性。

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引用次数: 0

Inclusive Digital Planning – Co-designing a collaborative mapping tool to support the planning of accessible public space for all 包容性数字规划——共同设计一种协作测绘工具，以支持所有人的无障碍公共空间规划

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-05-29 DOI: 10.1016/j.compenvurbsys.2025.102310

Johannes Flacke , Fenna Imara Hoefsloot , Karin Pfeffer

Digital planning is loaded with the expectation to make planning more inclusive. However, digital tools currently used in spatial planning processes to facilitate communication and participation of stakeholders often exclude people with disabilities through their design. Consequently, the research question of this study is how to design digital tools to support inclusive participation in the planning and design of public spaces to make them accessible for all. To answer this question, this research aimed to co-design an inclusive collaborative mapping tool with people with disabilities to enhance their participation in the planning and design of accessible public spaces. Developed in collaboration with eight people with various disabilities from the city of Zwolle in the Netherlands, the open-source mapping tool allows the in-situ registration of accessibility issues and supports collaborative decision-making workshops. The co-design process served to identify barriers and obstacles to the accessibility of public spaces in the city as well as user requirements for the inclusive design of the collaborative mapping tool. The tool was tested and evaluated in a collaborative mapping session with people with disabilities and municipal planners from the case study city. Our findings show that the design of inclusive digital planning tools is not limited to software features but also relates to hardware functionalities and the environment in which a tool is used. Taking the lessons learned from the co-design process, we argue that digital, physical, social and procedural accessibility are key to achieving inclusive digital planning.

数字规划承载着使规划更具包容性的期望。然而，目前空间规划过程中用于促进利益攸关方沟通和参与的数字工具往往将残疾人排除在设计之外。因此，本研究的研究问题是如何设计数字工具来支持包容性参与公共空间的规划和设计，使其对所有人开放。为了回答这个问题，本研究旨在与残疾人共同设计一个包容性的协作地图工具，以增强他们对无障碍公共空间规划和设计的参与。这个开源地图工具是与来自荷兰兹沃勒市的8名不同残疾人士合作开发的，它允许对无障碍问题进行现场登记，并支持协作决策研讨会。协同设计过程有助于识别城市公共空间可达性的障碍和障碍，以及用户对协作地图工具包容性设计的要求。在与来自案例研究城市的残疾人和市政规划者的协作绘图会议中，对该工具进行了测试和评估。我们的研究结果表明，包容性数字规划工具的设计不仅限于软件功能，还与硬件功能和工具使用环境有关。根据共同设计过程的经验教训，我们认为数字、物理、社会和程序可及性是实现包容性数字规划的关键。

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引用次数: 0

Convolutional neural networks for predicting the perceived density of large urban fabrics 用于预测大型城市结构感知密度的卷积神经网络

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-05-22 DOI: 10.1016/j.compenvurbsys.2025.102304

Guy Austern , Roei Yosifof , Tomer Michaeli , Shahar Yadin , Dafna Fisher-Gewirtzman

Urban density, along with the associated urban morphology and topology, significantly influences human perception, emotions, and behavior, ultimately affecting our overall well-being. Over the past decades, experts have developed spatial analysis models and tools which evaluate how planning and design impact urban residents and the functionality of cities. One such spatial analyses model is the Urban Spatial Openness Index (USOI) which utilizes ray-casting to conduct 3D visibility analysis predicting the perceived density of entire cities on a macro-scale, represented as 2D heatmaps. In the urban scale, ray-casting analysis is computationally intense and requires significant resources, which hinders its effective application. In this paper, we use a Convolutional Neural Network (CNN) to train a model to predict perceived density in urban fabrics based on 2D heatmap images. The processes described in this paper include creating a dataset of corresponding USOI images and height images from several different cities, training a CNN model, and evaluating the model's performance. The model predicts USOI with a mean absolute error of 1.92 %, which is considered highly accurate for visual perception on the urban scale. This study showcases the capability of CNN models to predict perceived density as measured by the USOI. The use of a predictive model can significantly reduce the processing time of 3D visibility analysis on the urban scale.

城市密度，以及相关的城市形态和拓扑结构，显著影响着人类的感知、情感和行为，最终影响着我们的整体福祉。在过去的几十年里，专家们开发了空间分析模型和工具来评估规划和设计如何影响城市居民和城市功能。其中一个空间分析模型是城市空间开放指数（USOI），它利用光线投射进行三维可见性分析，预测宏观尺度上整个城市的感知密度，以2D热图表示。在城市尺度下，光线投射分析需要大量的计算量和资源，这阻碍了它的有效应用。在本文中，我们使用卷积神经网络（CNN）来训练一个基于二维热图图像的模型来预测城市织物的感知密度。本文描述的过程包括创建来自几个不同城市的相应USOI图像和高度图像的数据集，训练CNN模型，并评估模型的性能。该模型预测USOI的平均绝对误差为1.92%，对于城市尺度的视觉感知来说，这被认为是高度准确的。本研究展示了CNN模型预测由USOI测量的感知密度的能力。预测模型的使用可以显著缩短城市尺度三维能见度分析的处理时间。

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引用次数: 0

Deciphering Urban Soundscapes: A study of sensory experiences at Hong Kong Victoria harbour waterfronts using social media 解读城市声景：利用社会媒体在香港维多利亚港海滨进行感官体验研究

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-05-16 DOI: 10.1016/j.compenvurbsys.2025.102307

Haotian Wang, Zidong Yu, Xintao Liu

The impact of sensory experiences on physical and mental health in urban environments has gained significant attention, particularly the influence of soundscapes in waterfronts development. This study employed social media data from Twitter to quantitatively analyse the soundscape of Hong Kong Victoria Harbour waterfronts, offering a novel perspective in urban sensory research. Through comparative analysis between tourists and residents, it uncovered how different groups perceive soundscapes in these specific urban waterfronts setting. Utilizing a two-step analytical approach—initially applying rank-size distribution and mean difference index—this study mapped the spatial distribution of soundscapes and used global and local regression models to explore their correlations with key urban features such as building density, population density, and ethnic diversity. The findings revealed distinct spatial patterns in how soundscapes are experienced by tourists and residents at the Victoria Harbour waterfronts, influenced significantly by the built environment. For instance, while residents experience negative auditory sensory in high building density areas, tourists perceive these areas positively. Furthermore, this research underscored the differing correlations of population density on soundscape experience among these groups. Residents enjoy positive soundscape connections in bustling areas, whereas tourists prefer quieter environments. Moreover, the research also found the differences in how residents and tourists accept multicultural soundscapes. This study not only contributed theoretically by linking soundscapes to urban and socio-economic variables but also demonstrated the potential of social media data as a tool for studying urban sensory. The study findings could offer insights that are relevant to planning and design of urban waterfronts.

感官体验对城市环境中身心健康的影响已引起广泛关注，尤其是滨水开发中声景的影响。本研究利用Twitter上的社交媒体数据，定量分析香港维多利亚港海滨的声景，为城市感官研究提供了一个新的视角。通过对游客和居民的比较分析，揭示了不同群体在这些特定的城市滨水区环境中如何感知声景。本研究采用两步分析方法——首先应用秩-大小分布和平均差异指数——绘制了声景观的空间分布，并使用全球和局部回归模型来探索其与建筑密度、人口密度和种族多样性等关键城市特征的相关性。研究结果显示，在维多利亚港海滨，游客和居民对声景的体验有明显的空间模式，受建筑环境的显著影响。例如，在高建筑密度地区，居民的听觉感受是消极的，而游客对这些地区的感知是积极的。此外，本研究还强调了这些群体中人口密度与声景体验的不同相关性。居民喜欢热闹地区的正面音景连接，而游客更喜欢安静的环境。此外，研究还发现，居民和游客接受多元文化音景的方式存在差异。这项研究不仅通过将声景与城市和社会经济变量联系起来在理论上做出了贡献，而且还展示了社交媒体数据作为研究城市感官的工具的潜力。研究结果可以为城市滨水区的规划和设计提供相关的见解。

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引用次数: 0

Digital twins and AI for healthy and sustainable cities 数字孪生和人工智能为健康和可持续发展的城市服务

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-05-12 DOI: 10.1016/j.compenvurbsys.2025.102305

Mark Birkin , Patrick Ballantyne , Seth Bullock , Alison Heppenstall , Heeseo Kwon , Nick Malleson , Jing Yao , Anna Zanchetta

The paper discusses the relevance of the latest advances in data science and artificial intelligence for urban systems research. It has a particular focus on the importance of recent innovations in the context of ‘wicked’ urban problems which continue to confront decision-makers within practical policy settings. It is argued that the latest advances in AI such as large language models offer the potential for transformative research, but only if properly specified within the unique and distinctive context of geographical space. The idea of a digital twin requires careful articulation to support the management of expectations and appropriate alignment within a social setting. At the end of the day, AI is not a panacea for the problems of cities, nor is it a substitute for imaginative policy design or interventions through consensus and good government. However in a world which is characterised by vast riches of data alongside enormous complexity of process, the investment in new tools and methods is a social and intellectual imperative in driving human understanding to new levels.

本文讨论了数据科学和人工智能的最新进展与城市系统研究的相关性。它特别关注最近创新在“邪恶”城市问题背景下的重要性，这些问题在实际政策设置中继续面临决策者。有人认为，人工智能的最新进展，如大型语言模型，为变革性研究提供了潜力，但前提是在地理空间的独特和独特背景下适当指定。数字孪生的概念需要仔细表述，以支持期望管理和社会环境中的适当协调。归根结底，人工智能不是解决城市问题的灵丹妙药，也不能替代富有想象力的政策设计或通过共识和良好政府进行干预。然而，在一个以海量数据和极其复杂的过程为特征的世界里，对新工具和新方法的投资是推动人类理解达到新水平的社会和智力要求。

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引用次数: 0

So close, yet so far: A new method for identification of high-impact missing links in pedestrian networks 如此接近，却又如此遥远：一种识别行人网络中高影响缺失环节的新方法

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-05-12 DOI: 10.1016/j.compenvurbsys.2025.102290

Matthew Wigginton Bhagat-Conway , Audrey Compiano , E. Irene Ivie

Post-war suburban development is often characterized by a disconnected pod-and-collector street pattern. This creates significant barriers to active travel, forcing even short trips to take roundabout routes on busy arterial roads. However, it also creates a network of low-stress neighborhood streets. We hypothesize that there are many opportunities to add short, low-cost pedestrian and bicycle links to these street networks to increase connectivity.

A key challenge is identifying these links. While planners have a good idea of where major infrastructure investments are beneficial, they are unlikely to be familiar with every neighborhood street and potential connections between them. We introduce an algorithm to automatically and efficiently identify potential new links based only on existing network topology, with no need to prespecify potential projects. We score these links based on their contribution to accessibility. We apply this algorithm to the pedestrian network of Charlotte, North Carolina, USA, and find opportunities to improve connectivity through new links and safe crossings of major roads.

战后郊区发展的特点往往是一个不连贯的豆荚和收集器的街道模式。这对主动出行造成了重大障碍，甚至迫使短途旅行在繁忙的主干道上绕道而行。然而，它也创造了一个低压力的社区街道网络。我们假设有很多机会在这些街道网络中增加短的、低成本的步行和自行车连接，以增加连通性。一个关键的挑战是确定这些联系。虽然规划者很清楚大型基础设施投资在哪些地方是有益的，但他们不太可能熟悉每一条社区街道以及它们之间的潜在联系。我们引入了一种算法，可以根据现有的网络拓扑自动有效地识别潜在的新链路，而无需预先指定潜在的项目。我们根据它们对可访问性的贡献对这些链接进行评分。我们将该算法应用于美国北卡罗来纳州夏洛特市的行人网络，并通过新的连接和主要道路的安全交叉来寻找改善连通性的机会。

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引用次数: 0

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