1. Introduction
Urban vibrancy is relevant to the sustainable development of cities [
1]. Creating and maintaining urban vibrancy not only attracts more human and economic capital to improve productivity and economic sustainability, but also promotes human activities to improve social sustainability [
2,
3]. Vibrant cities reveal greater prosperity and resilience in response to changes in society, the economy and the environment [
4,
5]. In addition, urban vibrancy improves people’s subjective perceptions of urban space and is critical to the rational deployment of local facilities and the improvement of residents’ quality of life [
6,
7,
8]. Therefore, revealing the factors associated with urban vibrancy and understanding the deeper causes of urban vibrancy are necessary for city managers and planners [
9,
10].
According to the scale of research, existing exploratory studies on the factors influencing urban vibrancy are mainly focused on both macro-scale and micro-scale [
8,
11,
12,
13]. In macro-scale studies, countries and cities as well as urban agglomerations are usually taken as study objects, studying the vibrancy of intra-city regions or between individual cities [
14]. This allows for a global understanding of the factors influencing urban vibrancy and provides a good portrayal of the overall spatial structure of urban vibrancy. Such methods mainly describe the spatial structure of urban vibrancy as a whole by constructing an evaluation system based on the magnitude of indicators within a neighborhood or mean raster [
8]. In micro-scale studies, traditional municipal organizational block units such as neighborhoods and traffic analysis zones are usually taken as the target. These studies focus on reporting the close relationship between human activity landscape, mixed-use, urban form and built environment, etc. [
10,
11,
15,
16]. Compared to macro-scale studies, these micro-scale studies are more closely related to planning and reality, which can improve the understanding of the complex relationship between human activities and the space of occurrence. However, analyzing the factors influencing urban vibrancy from a block perspective still inevitably overlooks the finer-grained spatial features within blocks that are effective for understanding urban vibrancy [
17]. Thus, exploring the factors influencing urban vibrancy at a more microscopic scale (e.g., the street) requires immediate attention.
As the basic unit of urban awareness and urban life, streets play a very important role in urban life. They are generally defined as the spaces formed by building facades on both sides of the street and the street itself, which are urban places for personal interaction and recreation [
18]. With the attributes of short length, high pedestrian density, mixed functions, and mixed building ages, streets are not only the main carriers of urban traffic, but also important visual spatial carriers for residents to intuitively perceive urban vibrancy [
19]. As Jacobs says, if the street is vibrant, the city is vibrant [
20]. The inherent properties of streets allow us to explore the deeper influences on urban vibrancy in the context of more microscopic and complex functional places.
The studies of street vibrancy have focused on two main aspects, namely, the measurement of vibrancy and the exploration of the influencing factors of vibrancy [
12,
13,
17]. Among the prerequisites for exploring the influencing factors of vibrancy is the need to find a suitable proxy to portray vibrancy itself. Existing studies on street vibrancy have been conducted from a qualitative perspective and lack strong data support [
4,
21,
22]. Some of the quantitative studies related to street vibrancy have been done by expert scoring and field research, which are costly and difficult to conduct on a large scale [
23,
24]. With the emergence of geographical big data and the proliferation of deep learning, there is an unprecedented opportunity for researchers to conduct large-scale quantitative studies of the factors that influence urban vibrancy. However, there are still some other shortcomings: Firstly, the influence of urban non-physical environmental features (e.g., urban psychological perception) that underpin urban planning and important functions in the public domain is neglected [
16,
25]. Most of them currently focus on social and physical environmental factors, for example, population, employment, income, land use, buildings, and transportation networks [
15,
22,
26,
27]. Second, the use of remote sensing images to construct visual indicators of vibrancy-influencing factors does not convey more detailed visual information about the urban microphysical environment. Third, relying on a single source of urban data (e.g., points of interest, social media check-in, house price data, land use, field surveys, etc.) to portray urban vibrancy lacks multifacetedness [
9].
To address the above issues, this study proposes a new framework to study the factors influencing urban vibrancy at the street level based on multi-source data. The emergence of labeled geographic big data, especially street-view data, provides great advantages for map** the impact of physical environmental factors as well as non-physical environmental factors on urban vibrancy in street micro spaces. Firstly, a comprehensive urban vibrancy index is calculated using the adaptive weighting of two types of urban sensing data, namely, POI and check-in data, as a suitable proxy for accurately quantifying vibrancy. POI data can reflect the location of human activities and portray vibrancy in terms of physical space; check-in data can portray human activity patterns and portray vibrancy in terms of human activity space. Combining the two can portray vibrancy in a more comprehensive and effective way. Secondly, the factors influencing urban vibrancy are portrayed in terms of both urban visual–spatial and psychological perception. A full convolutional network (FCN-8s) is used to segment the streetscape images of Bei**g and use them as a basis to extract the above features that influence urban vibrancy. Thirdly, exploring the influencing factors of urban psychological perception reveals the deeper causes of the impact of psychological perception on urban vibrancy. Finally, an improved ridge regression model is proposed to model the relationship between features and vibrancy, reducing the covariance between features while avoiding the reduction of important features.
The feasibility of this research framework is verified using Bei**g as a case study. The findings of this study have the potential to provide important insights for people-oriented urban development and planning.
The main contributions of this study are as follows:
- (1)
Exploring the factors influencing urban vibrancy from the street scale with finer granularity;
- (2)
Portraying the factors influencing urban vibrancy from the two points of view of the objective physical environment (urban visual–spatial) and the non-physical environmental (psychological perception);
- (3)
Utilizing two types of urban sensing data to calculate the comprehensive vibrancy index as a proxy of urban vibrancy.
The remainder of this study is organized as follows.
Section 2 introduces the related work.
Section 3 introduces the study area and datasets used, describing the framework and corresponding methods, variables, and regression models in detail.
Section 4 reports and analyzes the results.
Section 5 discusses the results and implications. The
Section 6 concludes this study.
6. Conclusions
In this paper, we proposed a novel framework that systematically combines the impacts of visual–spatial features and urban psychological perceptions on urban vibrancy at the street scale. Based on the above analyses, the main finding that there is a positive linkage between influencing factors and urban vibrancy satisfies the aim of this study. Accordingly, this study makes several contributions to the literature.
This research integrates visual–spatial features and urban psychological perceptions to quantitatively investigate the influence of objective and subjective factors on urban vibrancy. We obtain satisfactory regression model performances, with R2 values of 0.706, 0.743 and 0.807. Compared to experiments, personal human perceptions have a more significant impact on urban vibrancy than visual–spatial features. This finding inspires us to recognize that urban psychological perceptions are essential for encouraging social activities and interactions in a street. A livelier and safer place will provide activity opportunities for urban residents. It provides a new research perspective that complements and refines previous quantitative urban vibrancy studies.
This study quantifies urban vibrancy in terms of both location and human activity. Utilizing POIs, reflected activity locations, and social media check-in data revealed activity patterns. Relying on a single source of data to characterize urban vibrancy may cause misunderstandings among urban researchers and urban planners; therefore, we calculated a comprehensive urban vibrancy index. In particular, the proposed methods describe the activity pattern of individuals and activity location spatial distinctions.
This research provides essential insights into constructing a vibrancy public space environment that meets the psychological and physical needs of the inhabitants, and enhances one’s perception of outdoor spaces. Further, this study provides a reference for city planners to build a people-centered, livable city. Lastly, this research enriches the systematic knowledge of urban managers and researchers regarding emotional characteristics and urban vibrancy, subsequently providing a basis for planning, managing, and designing responses, and for improving urban practices and management strategies.
On the above basis, several problems regarding the establishment of a general methodology for measuring urban vibrancy persist. Firstly, socioeconomic features are positively correlated with urban vibrancy. To some extent, although street view data can reflect the socioeconomic characteristics behind the spatiotemporal behavioral patterns of people, quantifying this remains a challenge. Second, we did not consider the impact of time lags in data collection. Within different time periods, the visuo-spatial features portrayed via street view data may be inconsistent, resulting in different psychological perceptions. In addition, as time changes, the locations of activities reflected by POIs change, and the human activity patterns portrayed by Weibo check-ins will also be different. We will further explore the characteristics of the spatial and temporal distribution of vibrancy and the spatial and temporal relationships between the factors in our future research work. Third, the analysis of the linkage between perception features and urban vibrancy merely focused on a single study area, namely, within the Fifth Ring Road district in Bei**g. Urban psychological perception is a relatively subjective characteristic, as different cities may appear to have distinct perception features due to their diverse cultural and social backgrounds. Accordingly, it may be possible that urban sensing in other cities may have different effects on urban vibrancy than the ones we identified in Bei**g. Based on this, we can conclude that the proposed framework can be applied to other cities; however, different findings regarding how perception features specifically affect urban vitality may be acquired. In future studies, we will further explore this issue within a larger research scope, such as China.