## 6 Conclusions and Prospects  
This paper took a community square in Beijing as the practice site and used a behavior detection algorithm of computer vision to effectively extract the population's attributes, behavior types, and time information, while establishing statistical charts. Through the analysis of the population types and spatial types, it was found that there was no obvious difference in the use of space by gender, while there was some difference in the use of space by age. Therefore, space has different use values for different age groups, and it is proposed that the needs of different age groups should be considered in spatial design. Through the analysis of crowds and behaviors, it was found that different crowds have different behavioral preferences, thus determining their behavioral needs. Through the analysis of behavior types and spatial types, it was found that there is inconsistency between some behaviors and spatial functions, reflecting the places where the current spatial functions are misplaced for behavioral needs, thus determining the focus points for spatial optimization. Through the analysis of behavior and time, it was concluded that different activity types have relatively concentrated time periods. Therefore, time factors are to be considered in meeting behavioral activity needs, and spatial time planning is proposed, that is, the same space should be transformed into different types of spaces at different time periods and different behavioral needs should be met at different time periods. In summary, our research makes contributions in the following aspects:  
1. We propose a method that can extract crowd attributes, behavior types, and time information from video images. Compared with traditional behavior research that mostly relies on the manual collection of behavior, this method realizes automation and efficiency. Compared with the current use of computer vision for behavior research, this method also realizes the extraction of fine-grained behavior. Our integrated framework can effectively extract more fine-grained behavioral information, providing a basis for precise spatial optimization to meet the behavioral needs of different crowds. 2. We introduce an attention mechanism to enhance important feature channels and suppress irrelevant feature channels, improving the efficiency of behavioral recognition. In model training, we integrated public datasets and self-made data, achieving higher robustness for specific scenarios. 3. Using the method proposed in this paper, we took a community outdoor public space as the practice site, effectively extracting crowd attributes, behavior types, and time information and combining the division of spatial types to propose an analysis framework for the correlation mechanism between behavior and space. We drew the following conclusions: different crowds have different behavioral preferences, there is inconsistency in the use of space by different crowds, there is inconsistency between behavioral and spatial function, and behavior is concentrated over time. The strong advantage of a computer vision behavior detection algorithm in automatically extracting video information provides conditions for fine-grained behavioral research at the macro scale in the future. Fine-grained behavioral research at the urban scale and with a timespan of more than one year will be of great value and of a broad scope. Conceptualization, methodology, review, and funding acquisition, L.W. ; investigation, software, writing--original draft, W.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the 2023 North China University of Technology Organized Scientific Research Project (Grant No. 110051360023 XN278-13) and the Beijing Municipal Science and Technology Commission Project (Grant No. Z181100009218001). Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data are not publicly available due to privacy reasons.