Objective  Urban parks provide a wide range of ecosystem services to support residents’ health and leisure life. Continuously attracting people is beneficial to the improvement of the use efficiency of urban parks. Meanwhile, the internet of things (IOT) can help identify and manage the coupling relationship between the flow of people and space, so as to realize the dynamic monitoring of the interaction between population distribution and green space. As population distribution has been greatly affected by objective and subjective scenarios such as epidemic control, it is necessary to focus on urban parks for further research. In addition, the connotation of space vitality has been extended to people gathering and attraction maintaining. However, the relationship between vitality fluctuation and intensity of urban parks is rarely analyzed from the dynamic perspective, which makes it difficult to identify park units with similar intensity but different fluctuation changes and further adopt differentiated renewal measures. Therefore, under the perception of population distribution, this research analyzes the relationship between vitality intensity and fluctuation, and uses this index to divide the dynamic patterns of parks, so as to identify the temporal vitality characteristics of various urban parks under different scenarios. What’s more, the research analyzes the influence of dynamic population scale rather than static scale factor, on the use of urban parks, and then proposes the strategy for improving park vitality to meet the behavioral needs of residents.

Methods  Taking some administrative areas of Tianjin as an example, the research obtains dynamic population distribution data based on sensors, and sets two experimental scenarios respectively targeting objective time and subjective control. Specifically, the research analyzes the vitality intensity and fluctuation of parks, and then divides the dynamic vitality modes involved and spatial layout characteristics thereof. In addition, from the perspective of demand, the research conducts the regression analysis of dynamic population scale and park vitality within the service radius to explore the influence of dynamic population on the service efficiency of parks classified by grade and mode.

Results  From the perspective of dynamic distribution of population, the research firstly identifies the characteristics of parks in different vitality modes, and then explores the key points of park optimization in a demand-oriented manner and specifically draws the following conclusions. 1) As a whole, in terms of daytime on weekdays, the vitality characteristics of urban parks are mainly “high intensity and low fluctuation” and “low intensity and high fluctuation”, which indicates that parks’ attractiveness to a certain population is often inversely proportional to the variation degree of population scale. It is found that parks characterized by high vitality intensity and low vitality fluctuation have higher service efficiency, and are able to attract a large number of people steadily, while those characterized by low vitality intensity and high vitality fluctuation have the lowest service efficiency, and can attract only a few people to stay intermittently, thus entailing further optimization. 2) The vitality difference between various modes of comprehensive parks is greater than that of community parks, and the vitality intensity of the latter is higher than that of the former in the daytime, endowing the latter with higher service efficiency. In addition, compared with community parks, comprehensive parks rely more on location such as geographical center. 3) As an important demand factor, the population scale within the service radius has a great influence on the vitality intensity and fluctuation of parks at all levels. To be more specific, the vitality of a community park largely depends on the dynamic scale characteristics of the population within a fifteen-minute life circle in which it is located, and the service efficiency thereof is demand-oriented. However, the internal composition and external environment are more essential to comprehensive parks.

Conclusion  Based on the dynamic characteristics of urban parks, the research concludes that at the macroscopic system adjustment level, increasing the number of community parks can improve the overall service ability rather than building new comprehensive parks or expanding the existing park area. At the mesoscopic site selection optimization level, community parks emphasize uniform spatial coverage, while comprehensive parks focus more on the site selection of geographical centers or highly accessible traffic locations. At the microscopic vitality enhancement level, it is beneficial to optimize the overall layout of community parks based on the spatial distribution of residents. The internal structure and external environment at the enhanced supply side have a greater influence on comprehensive parks. Finally, in the future, it is urgent to actively perceive people to realize the technically integrated process of “monitor-feedback-adjustment”, so as to facilitate the intelligent site selection and real-time adjustment of internal and external components of urban parks.