Research on the Spatial and Temporal Dynamics of Crowd Activities in Commercial Streets and Their Relationship with Formats—A Case Study of Lao Men Dong Commercial Street in Nanjing
Abstract
:1. Introduction
2. Literature Review
2.1. Related Research on Pedestrian Activity Trajectories
2.2. Research on the Correlation between Population Activity and Formats
3. Data and Methods
3.1. Study Area
3.2. Data
3.3. Method
3.3.1. Acquisition of Crowd Trajectory Data
3.3.2. Crowd Spatial Characteristics Analysis Method
3.3.3. Variable Selection and Statistical Analysis
4. Results
4.1. Characteristics of Crowd Activities
4.1.1. Spatiotemporal Characteristics of Crowd Stay Activities
4.1.2. The Characteristics of the Crowd Spatial Trajectories
- (1)
- Crowd trajectory cluster 1 (Figure 12a), accounting for 40.49% of the total number of active people, is mainly distributed in the central and eastern areas of the commercial street, which is dominated by catering and retail formats. This feature has strong spatial mobility, and people mainly carry out activities such as sightseeing and consumption and have short stays in spaces such as retail and recreational facilities.
- (2)
- Crowd-tracking cluster 2 (Figure 12b) is mainly concentrated in the middle of the commercial street, where the formats are mainly retail and leisure. The spatial mobility of the crowd under this characteristic is weak; crowds often stop and stay for a long time in leisure, entertainment, and other formats. The stay activities of the crowd are relatively concentrated, accounting for 18.48% of the total number of active people.
- (3)
- Trajectory cluster 3 (Figure 12c) is mainly concentrated in the central and southern parts of the commercial street, which is dominated by catering and entertainment formats. Under these conditions, the spatial mobility of a crowd is strong. Although the crowd stays more frequently, they mainly conduct short-stay activities such as watching and resting, and the number of active people accounts for 13.54% of the total number of active people.
- (4)
- In contrast, the number of people in trajectory cluster 4 (Figure 12d) is relatively small at only 3.8%. They are mainly active in the northwest and southeast places of the commercial street, which is dominated by cultural and leisure industries. The spatial flow of crowds is weak, and people mainly carry out short-stay activities such as sightseeing and eating.
4.2. The Relationship between Commercial Street Formats and Crowd Activity Characteristics
5. Discussion
5.1. Research Findings
- (1)
- A new framework for crowd activity analysis was proposed. Compared with traditional research methods, the framework can improve the efficiency and accuracy of data analysis, and it uses machine learning algorithms such as supervised learning algorithms and convolutional autoencoders to extract crowd-dynamic spatial activity information. Although research on machine learning algorithms for studying crowd activities has made some progress, S. Williams et al. used image processing technology to conduct quantitative statistics of location information in time and space dimensions and qualitative discussions of human activities [41]. However, it is not comprehensive to only rely on location information to measure crowd activities. Y. Li et al. used deep learning methods to explore the relationship between a street’s built environment and the street vitality but ignored the details of crowd activities [33]. Therefore, we used stay points to identify people’s stay or movement behavior in space to obtain the activity status of the crowd, and we explore the influencing factors of crowd activities in combination with the space environment they live in. Furthermore, this method can help us understand the flow trend of people in space by studying the crowd’s activity trajectory clusters, which can provide guidance for the spatial layout of commercial streets. This method is not only limited to commercial streets; it is also helpful for us to conduct correlational analysis of crowd activities in different spatial environments, such as parks, central areas, cities, and other spatial scales, making it easier to obtain the characteristics of crowd activities at different spatial scales.
- (2)
- We extracted the visited formats in a commercial street according to the stay areas under different trajectory clusters and analyzed the correlation between the characteristics of crowd activities and formats under different spatial activity ranges. Based on a subjective perspective of people’s preferences for formats, we explored the reasons why people chose commercial formats. Previous studies tended to discuss all commercial formats within the scope of the research [89,90], that is, they tended to include all business formats within the scope of research into the scope of discussion and used the business formats in the research area as part of the spatial environment to study the connection between people and space. However, we focused on commercial formats visited on a commercial street, which is more targeted and reflects the real needs and behavioral preferences of people on the street.
- (3)
- The proposed crowd analysis framework was applied to a commercial street. The results show that our method can accurately characterize the spatiotemporal characteristics of crowd activity on commercial streets, and understanding the stay rules of crowds can inspire retailers and provide certain suggestions for the spatial layout of business formats. Therefore, this method is effective and contributes significantly to enhancing the competitiveness of commercial streets. In addition, Lao Men Dong is a typical representative of small-scale, low-rise commercial streets with rich format types, diverse functions, and frequent crowd activities. Since this type of commercial street emphasizes pedestrian orientation, in order to ensure the safety and comfort of crowd activities, any destination in most commercial streets should be limited to 800 m, and the total walking distance should not exceed 1500 m [91]. The area of Lao Men Dong Commercial Street is 700,000 square meters. Its spatial scope exceeds the spatial scale of most low-rise commercial streets, and it has strong universal applicability. Therefore, revealing the degree of correlation between format space and crowd activities in Lao Men Dong Commercial Street can also provide insights into the sustainable development of commercial streets of the same scale.
5.2. Limitations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Serial Number | Format Division | Quantity | Type |
---|---|---|---|
1 | Food | 82 | Restaurant, snack, sweets and dessert, coffee shop, teahouse, and bar |
2 | Shopping | 52 | Store, house building materials, hotels, home appliance, specialist shops, shop, and market |
3 | Life service | 36 | Graphics express printing, photo studio, and lottery sale |
4 | Leisure | 33 | Cinema, theater |
5 | Tourist attractions | 26 | Art museum, exhibition hall, museum, and scenic spot |
6 | Public service | 12 | Public toilet, government agencies |
7 | Others | 19 | Clinic, drugstore, other non-commercial functions |
Name | Length (m) | Starting Point Coordinates | Target Point Coordinates |
---|---|---|---|
f1 | 45.92 | 118.7827855, 32.0150326 | 118.782684, 32.0146936 |
f2 | 50.86 | 118.7817985. 32.0138379 | 118.7813574, 32.0139388 |
f3 | 26.18 | 118.7821165, 32.0129845 | 118.7823343, 32.0129092 |
Variables | Condition | Connection and Algorithm |
---|---|---|
Dependent variables | Number of tourists | Refers to the strength of crowd trajectory clusters and reflects the intensity of the trajectory characteristics of the population. Number of tourists is the number of people performing similar activity trajectories during the study time. |
Per capita number of stay | Refers to the intensity of spatial residence, and the intensity reflecting the characteristics of the population’s stay activity. Average number of tourists’ stay in the analysis cell. | |
Independent variables | Spatial distance of formats | Refers to the spatial location of the format and reflects the convenience of the format to the tourists. The spatial distance of the format refers to the average distance of the road network from each entrance and exit to the format. |
Quantity of each types of format | Refers to the intensity of format, reflects the number of visits to the format, and refers to the number of food formats, shopping formats, life service formats, leisure formats, tourist attraction formats, public service formats, and other formats. The number of formats is the number of each type of visited format in the analysis unit. | |
Diversity of formats | Refers to the degree of format and reflects the extent to which the population is inclined to the functional intensity of the format. Diversity is calculated using the entropy method [83]. |
Development Variables | Dependent Variables | |||
---|---|---|---|---|
Coefficients (Number of Tourists) | VIP Scores (Number of Tourists) | Coefficients (Per Capita Number of Stays) | VIP Scores (Per Capita Number of Stays) | |
Spatial location of formats | −0.03 | 0.253 | 0.01 | 0.39 |
Mixed degree of formats | −0.15 | 0.617 | 0.05 | 0.682 |
The number of food formats | 0.16 | 0.907 | 0.31 | 0.898 |
The number of shopping formats | 0.14 | 1.308 | 0.40 | 1.293 |
The number of life service formats | 0.18 | 1.28 | −0.07 | 1.263 |
The number of leisure formats | −0.01 | 0.885 | 0.26 | 0.873 |
The number of tourist attraction formats | 0.14 | 0.953 | 0.20 | 0.962 |
The number of public service formats | 0.21 | 1.27 | 0.22 | 1.252 |
The number of other formats | −0.20 | 1.037 | 0.09 | 1.027 |
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Hu, X.; Ren, Y.; Tan, Y.; Shi, Y. Research on the Spatial and Temporal Dynamics of Crowd Activities in Commercial Streets and Their Relationship with Formats—A Case Study of Lao Men Dong Commercial Street in Nanjing. Sustainability 2023, 15, 16838. https://doi.org/10.3390/su152416838
Hu X, Ren Y, Tan Y, Shi Y. Research on the Spatial and Temporal Dynamics of Crowd Activities in Commercial Streets and Their Relationship with Formats—A Case Study of Lao Men Dong Commercial Street in Nanjing. Sustainability. 2023; 15(24):16838. https://doi.org/10.3390/su152416838
Chicago/Turabian StyleHu, Xinyu, Yifan Ren, Ying Tan, and Yi Shi. 2023. "Research on the Spatial and Temporal Dynamics of Crowd Activities in Commercial Streets and Their Relationship with Formats—A Case Study of Lao Men Dong Commercial Street in Nanjing" Sustainability 15, no. 24: 16838. https://doi.org/10.3390/su152416838