Urban streets profiling with coupled spatio-temporal characteristics and topological information from the biking perspective

IF 7.1 1区 地球科学 Q1 ENVIRONMENTAL STUDIES
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Abstract

Urban street profiling is the spatio-temporal pattern discovery of street-level urban areas, which plays a vital role in understanding urban structures and dynamics. Due to the natural topology and various geographic characteristics on the streets, it is necessary to combine multi-dimensional spatio-temporal information to understand different profiles of streets. This research aims to develop a street profiling framework according to the coupled characteristics of streets. At the start, a bidirected dual graph and a spatial weighted graph embedding method were used to solve the street representation. Then, the street profiles can be extracted by clustering embedding vectors of streets and feature importance analysis. As the case study, we employed the bike trajectories and street view images in Xiamen, China to depict the geographic attributes of streets. The results can reveal nine spatio-temporal street profiles from the biking perspective, including three spatial distribution patterns and two spatial semantic patterns. Urban streets in the study area show a significant hierarchical pattern because of locations and the spatial lags of the biking behaviors. Meanwhile, the spatio-temporal characteristics of biking behaviors are the main factors of street profiles, though the street environment attributes participate in over half the number of profile types. We further evaluated the profiling ability of the proposed framework and the importance of urban street profiles using coupled characteristics. Overall, this study explored the profiling method for coupling static and dynamic characteristics of urban streets. The profiling results also help understand street usage and experiences by bikers, which have a practical value on the human-oriented classification of streets and further urban development from a geographic view.

从骑自行车的角度,利用时空特征和拓扑信息耦合分析城市街道概况
城市街道剖面分析是对城市街道区域的时空模式发现,对了解城市结构和动态起着至关重要的作用。由于街道的自然拓扑结构和各种地理特征,有必要结合多维时空信息来了解街道的不同特征。本研究旨在根据街道的耦合特征开发一个街道剖析框架。首先,使用双向对偶图和空间加权图嵌入方法来解决街道表示问题。然后,通过聚类街道嵌入向量和特征重要性分析提取街道概况。作为案例研究,我们利用中国厦门的自行车轨迹和街景图像来描述街道的地理属性。研究结果从自行车运动的角度揭示了街道的九种时空特征,包括三种空间分布模式和两种空间语义模式。研究区域内的城市街道由于位置和骑车行为的空间滞后性,呈现出明显的层次模式。同时,自行车行为的时空特征是街道特征的主要因素,尽管街道环境属性参与了一半以上的特征类型。我们利用耦合特征进一步评估了所提框架的剖析能力和城市街道剖析的重要性。总之,本研究探索了城市街道静态和动态特征耦合的剖析方法。剖析结果还有助于了解街道的使用情况和骑车人的体验,这对于以人为本的街道分类和从地理角度进一步推动城市发展具有实用价值。
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来源期刊
CiteScore
13.30
自引率
7.40%
发文量
111
审稿时长
32 days
期刊介绍: Computers, Environment and Urban Systemsis an interdisciplinary journal publishing cutting-edge and innovative computer-based research on environmental and urban systems, that privileges the geospatial perspective. The journal welcomes original high quality scholarship of a theoretical, applied or technological nature, and provides a stimulating presentation of perspectives, research developments, overviews of important new technologies and uses of major computational, information-based, and visualization innovations. Applied and theoretical contributions demonstrate the scope of computer-based analysis fostering a better understanding of environmental and urban systems, their spatial scope and their dynamics.
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