Capturing what human eyes perceive: A visual hierarchy generation approach to emulating saliency-based visual attention for grid-like urban street networks
Introduction
Visual hierarchy is an important notion in many areas of urban research that can greatly help understand people's cognition of urban morphologies (Jiang, 2012a; Lynch, 1960). As the skeletons of cities, urban streets attract considerable attention from urban residents and are regarded by Lynch (1960) as one of the five main city elements. Urban streets tend to have a hierarchical structure in the human mind due to their enormous imbalance (Brassel & Weibel, 1988; Jiang, 2008; Jiang, 2012b; Jiang, Liu, & Jia, 2013; Li, Fan, Luan, Yang, & Liu, 2014; Weibel & Jones, 1998). From the perspective of visual perception, those streets in a city that are distinctive and easy to attract people's visual attention are recognized first by the human mind (Tomko, Winter, & Claramunt, 2008). These prominent streets could be extracted at different levels to form a visual hierarchy, which represents the arrangement of streets in a way that implies visual importance or visual salience and influences the order in which the human eye perceives what it sees. Accordingly, the visual hierarchy of streets is an essential component for understanding and perceiving a city and could further shed light on human wayfinding and urban morphology (Car, Taylor, & Brunsdon, 2001; Huang, Schmidt, & Gartner, 2012).
Essentially, visual hierarchy is not only an external structural representation of the street network but also an intrinsic requirement of its structural organization, which is closely related to the characteristics of the street pattern. The patterns of urban streets are the result of many interacting phenomena over time, such as geography, natural setting, socioeconomic transformation, and so on (Feng & Sun, 2013). In this process, several typical street patterns are gradually formed: grid-like (planned) street networks, irregular (self-evolved) street networks, and hybrid street networks (planned and self-evolved structures coexist) (Serge, 2012). In the context of the sustainable development of transportation infrastructure, and driven by the dual requirements of street network formation and densification, the increasing grid-like characteristics of modern urban street networks are significant. Evidently, the grid-like street network becomes an indispensable part of a modern street network and will gradually be developed into the main style of future urban traffic environment (Fawaz & Newell, 1976; Hu, Lu, Wang, & Ye, 2015; Miyagawa, 2009; Tanner, 1966). Formally, a grid-like street network consists of a large number of straight streets intersecting at right angles, showing extremely regular characteristics (Serge, 2012).
However, due to the characteristic of over-regularization, it is difficult to differentiate visual salience of different streets on grid-like street networks, resulting in the cognitive confusion of the visual hierarchies in grid-like street networks. Existing methods usually evaluate the importance of streets by functional indices or visual indices (mainly length), also known as salience indices (Chaudhry & Mackaness, 2005; Huang, Zhu, Ye, Guo, & Wang, 2016). These salience indices are usually self-salience indicators that focus on the unique characteristics of streets themselves and neglect the visual competition relationship. They, therefore, lead to the low levels of visual discrimination on grid-like streets, and further cause the visual hierarchies of these grid-like streets being poorly captured and inconsistent with that perceived by human eyes. Taking two classical cities (London and San Francisco) as examples, as shown in Fig. 1(a) and (b), the hierarchies generated from these two cities' street networks are visualized using a spectral color legend with smooth transition from red to blue, where red and blue respectively represent the highest importance (Level 1) and lowest importance (Level 5) and using length as the evaluation indicator. Compared to the clear and vein-like hierarchical structure captured from the London street network (an irregular street network), the hierarchical structure of San Francisco (a grid-like street network) is not clearly captured. Owing to the special characteristics of grid-like street patterns, the limitations of the current salience indices when measuring the contribution of visual significance on grid-like street networks need to be addressed.
In this study, the human visual attention mechanism that can capture the focus of relatively significant elements at different levels of perception is brought in. A new salience measure model is proposed in this paper under the relative salience framework and by emulating the visual attention mechanism to increase visual discrimination of grid-like streets. The designed comprehensive visual salience (CVS) index combines the geometric competitive factors of natural streets at the local scale and psychological competitive factors of natural streets at the global scale to quantify the extent to which a street attracts human visual attention. The experimental results show that the performance of visual discrimination on the street hierarchy is greatly improved. Based on the CVS index, our hierarchy generation method could effectively detect visually prominent streets for grid-like street networks and generate the visual hierarchy of grid-like street networks that conform to the hierarchies perceived by human eyes.
The remainder of this paper is organized as follows. Section 2 provides a short overview of the hierarchy generation schemes. Section 3 elaborates on the hierarchy generation approach that is based on the human visual attention mechanism. Section 4 describes the study area and presents a comparative study between the proposed approach and two traditional approaches. Finally, conclusions are provided in Section 5.
Section snippets
Related work
The hierarchies of street networks have been widely studied in the fields of transportation, urban planning and geographic information science (Benz & Weibel, 2014; Carvalho & Penn, 2004; Huang et al., 2016; Jiang, 2008; Li & Dong, 2010). Hierarchy generation schemes in previous studies typically consist of two main components: basic structures and salience indices.
First, the Gestalt psychological principle which is the foundation of visual hierarchy is commonly employed to form the basic
Methodological framework
We present the basic framework of a hierarchy generation approach based on a new salience measure model which emulates the human visual attention mechanism to increase visual discrimination among street segments. The framework consists of three main elements as shown in Fig. 2:
(1) Generation of natural streets: The basic sensor unit first needs to be defined to quantify its features and generate visual hierarchy. In our study, an urban natural street is regarded as the basic sensor unit, which
Study area and data
The street network data sets of six cities were chosen in this study to evaluate the advantages of the proposed approach when compared to the conventional combination method. These cities are Manhattan in New York, Salt Lake City in Utah, San Francisco in California, Chicago in Illinois, Buffalo in New York and Santiago in Chile. All these street networks have typical grid-like characteristics and also are available in OpenStreetMap (OSM) databases (www.openstreetmap.org). In addition, to
Conclusion
In this paper, the proposed model simulates the human visual attention mechanism to evaluate the extent that a street attracts human visual attention. A novel visual salience indicator called comprehensive visual salience (CVS) is proposed and used as a basis for stratifying the hierarchy of urban street networks perceived by human eyes. For the calculation of the visual saliency indicator, the geometrical competitive factors at the local scale (the relative difference between each natural
Acknowledgements
This research was supported by the State Key Laboratory of Geo-information Engineering (SKLGIE2017-M-4-1) and the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources (KF-2018-03-038).
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