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 mapping 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 Beijing 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 Beijing 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.
4. Results
4.1. Characteristics of the Independent Variables
Table 3 shows the main characteristics of the independent variables. The spatial distributions of the eight visual–spatial indicators are displayed in
Figure 4. The average greenness of street space in Beijing is 12.8%, with a street openness of 29.5%, street enclosure of 28.8% and street walkability of 17%, while the four values of imageability, traffic flow, blueness and naturalness are low. In terms of spatial distribution, streets with good greening conditions are usually located near the northern part of central Beijing, while the southwest side, due south side and southeast side of the greening situation need to be improved. Streets with higher openness values are usually located near the boundary of the Five Rings district. In contrast, streets containing higher values of enclosure and traffic flow are distributed in the inner district. The spatial distribution patterns of both are similar, especially the street distribution with higher traffic flow values. Both the north side of Chaoyang District and Haidian District have a good street enclosure, and the streets with high motorization are concentrated on the main roads of the city. Streets with high imageability are located around the Four Rings area, while high values of walkability are sparsely located in the central area. Regarding the blueness and natural indicators, streets with low values of both are sparsely distributed across the Fifth Ring district.
The spatial distributions of the six urban psychological perceptual indicators are displayed in
Figure 5. For urban psychological perceptual indicators, the mean value of beautiful is 40.086. Streets with higher beautiful values are usually located near the Beijing district’s boundary. The mean boring value is 61.352. The streets with higher boring values are located north and south of Beijing. The mean depression value is 55.081. Streets containing higher values of depression are distributed in the inner district. The mean scores of lively and safe are 50.765 and 44.391, respectively. Lively and safe exhibit a consistent spatial distribution, and downtown areas are more lively and safer than the surrounding suburbs. The mean value of wealthy is 52.777, and high wealthy values are more evenly distributed.
4.2. Spatial Distribution of Urban Vibrancy
The spatial distribution of urban vibrancy in Beijing calculated by two types of urban sensing data is shown in
Figure 6. For the neighborhood scale, we also used social check-in data and POIs as proxy indicators to quantify urban vibrancy, and used these indicators divided by the neighborhood area to obtain the vibrancy distribution, as shown in
Figure 6A,B. For details, see reference [
10]; the formulae are as follows:
where
denotes the quantified indicator of urban vibrancy in the neighborhood
;
and
indicate the total
POIs and geographical area of
.
where
denotes the quantified indicator of urban vibrancy in the neighborhood
;
and
indicate the total check-ins and geographical area of
.
In
Figure 6A,B, the vibrancy indicators calculated using the POIs and social media check-in data show overall similarity. The deeper the color, the higher the vibrancy of the city, and vice versa. High-vibrancy areas are mainly located within the Second Ring Road and the northern belt of Beijing, while low-vibrancy areas are mainly located in the area between the fourth and Fifth Ring roads. However, some differences exist between
Figure 6A and B. In
Figure 6A, localized areas in the west and south are significantly more vibrant than in
Figure 6B, whereas localized vibrancy in the northwest of
Figure 6B is higher than in
Figure 6A. To reduce biases in portraying urban vibrancy using a single data source, the comprehensive urban vibrancy index is quantified using Equation (7). POIs data and social media check-in data were weighted as 0.98 and 0.02, respectively. The distribution of comprehensive vibrancy at the street level was obtained by spatially connecting the comprehensive vibrancy index to the corresponding streets, as shown in
Figure 6C. It can be seen that the spatial distribution of comprehensive vibrancy is similar to the spatial distribution of vibrancy obtained from the two types of urban sensing data. Street vibrancy values are higher in areas within the Second Ring Road, and gradually decrease in areas extending from the center of the Fifth Ring Road to the periphery. Interestingly, we find that short streets tend to be more vibrant in dense street networks (e.g., Nanluoguxiang, Xidan, and others). This result may align with those of Jacobs [
19] and Zhang et al. [
19,
56]. Hence, optimizing short urban street space and increasing its vibrancy can help enhance the vibrancy of other areas of the city.
Notably, street vibrancy can vary significantly, even among adjacent neighborhoods. This is difficult to intuitively derive from previous neighborhood-based quantifications of vibrancy. In
Section 5.2, we further analyze the details and differences between neighborhood-based and street-based vibrancy measurements to highlight the contributions of this study.
4.3. Analysis of the Linkage between the Influence Factors and Urban Vibrancy
We captured visual–spatial features and urban psychological perceptual features from the streetscape to analyze how these characteristics affect urban vibrancy. Specifically, 14 metrics (independent variables) and VIF values were calculated. After calculation, it was clear that the variables greenness, openness, enclosure, depression, lively, safety, and wealthy had a multicollinearity problem. The VIF values were all >10 (30.98, 52.48, 32.37, 90.12, 55.72, 31.65 and 60.87 for greenness, openness, enclosure, depression, lively, safety and wealthy, respectively). Thus, we used the improved ridge regression model to minimize the effect of the correlation of predictor variables.
We developed three models, the results of which are presented in
Table 4. The performances of the three models were evaluated using two indices: R
2 and k-fold value. Particularly, models 1, 2, and 3 have R
2 values of 0.706, 0.743, and 0.807, respectively. In addition, the k-fold values are 0.660, 0.700, and 0.75 for models 1–3, respectively. The R
2 and k-fold estimates increase from model 1 to model 3, and the fairly high values guarantee the performance of the regression model. From model 1 to model 2, the accuracy improved by 3.7 percentage points. The comparison result also reveals that urban psychological perceptual features have a more significant effect on urban vibrancy than visual–spatial features. From model 1 to model 3, the accuracy improved by about 10 percentage points. The results indicate that combining visual–spatial features and urban psychological perceptual features with regression approaches improves the R
2 significantly, and enhances model performance. Most importantly, the model that included all variables achieved the best performance. It is suggested that neither physical environmental nor non-physical environmental factors alone are sufficient to fully explain the factors influencing urban vibrancy, which confirms the conjecture proposed in this study. As can be seen in
Table 5, urban vibrancy also affects visual–spatial factors and psychological characteristics to varying degrees. The regression coefficients indirectly indicate which variables contribute more to the model’s goodness of fit, and they signify the importance of the variables. The ranks of the impacts of the 14 variables on urban vibrancy are shown in
Figure 7.
Visual–spatial indicators, enclosures, traffic flow, walkability, and imageability show positive effects on urban vibrancy. Specifically, the objects that show a stronger positive impact on urban vibrancy are enclosure and traffic flow. Intuitively, the urban vibrancy should be high near these objects, since there are many buildings, houses, and crowds gathering easily that are more willing to stop and be active there. In addition, the higher the vibrancy of the city, the higher the traffic flow, corresponding to more people in cars. Street walkability and urban imageability are the objects that show a medium positive impact on urban vibrancy. This suggests that good walkability, and differentiated and identifiable streets contribute to a vibrant urban space. Openness, blueness and nature show negative effects on urban vibrancy. Specifically, nature shows a strong negative impact on urban vibrancy and greenness has little impact on urban vibrancy. It indicates that more elements such as mountains, fields and open spaces reduce the urban vibrancy. On the other hand, excessively open city streets and numerous lakes and rivers are detrimental to the urban vibrancy.
Regarding urban psychological perceptual indicators, it was noted that the impacts of the lively, beautiful and wealthy components of the perception features ranked high among all eight perceptual variables. It can be deduced that the three aforementioned variables tend to contribute to urban vibrancy. More active places (e.g., a street with more shopping centers, available snacks, and entertainment facilities, among others) are more popular and have the most significant impact on urban vibrancy. Wealthy is positively correlated with urban vibrancy. This could mean that in more upscale places, consumer desires and spending power are also higher, indicating that urban vibrancy coincides with a faster pace of life.
In addition, depression, lively, wealthy, and safe show positive effects on urban vibrancy; however, both boring and beautiful show negative effects on urban vibrancy. The perception of safe in the street also has a strong influence on urban vibrancy. It suggests that the safer a place is perceived to be, the less likely it is that crime and accidents will occur, and the more likely it is that interactions between people and physical entities in that place will emerge. The results are consistent with those of previous research [
20]. In other words, boring, dull streets do not effectively promote urban vitality; streets that feel vibrant and have a higher safety value tend to attract more people.
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 Beijing. 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 Beijing. 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.