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Community outdoor public spaces are indispensable to urban residents' daily lives. Analyzing community outdoor public spaces from a behavioral perspective is crucial and an effective way to support human-centered development in urban areas. Traditional behavioral analysis often relies on manually collected behavioral data, which is time-consuming, labor-intensive, and lacks data breadth. With the use of sensors, the breadth of behavioral data has greatly increased, but its accuracy is still insufficient, especially in the fine-grained differentiation of populations and behaviors. Computer vision is more efficient in distinguishing populations and recognizing behaviors. However, most existing computer vision applications face some challenges. For example, behavior recognition is limited to pedestrian trajectory recognition, and there are few that recognize the diverse behaviors of crowds. In view of these gaps, this paper proposes a more efficient approach that employs computer vision tools to examine different populations and different behaviors, obtain important statistical measures of spatial behavior, taking the Bajiao Cultural Square in Beijing as a test bed. This population and behavior recognition model presents several improvement strategies: Firstly, by leveraging an attention mechanism, which emulates the human selective cognitive mechanism, it is capable of accentuating pertinent information while disregarding extraneous data, and the ResNet backbone network can be refined by integrating channel attention. This enables the amplification of critical feature channels or the suppression of irrelevant feature channels, thereby enhancing the efficacy of population and behavior recognition. Secondly, it uses public datasets and self-made data to construct the dataset required by this model to improve the robustness of the detection model in specific scenarios. This model can distinguish five types of people and six kinds of behaviors, with an identification accuracy of 83%, achieving fine-grained behavior detection for different populations. To a certain extent, it solves the problem that traditional data face of large-scale behavioral data being difficult to refine. The population and behavior recognition model was adapted and applied in conjunction with spatial typology analysis, and we can conclude that 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. This provides more comprehensive and reliable decision support for fine-grained planning and design. [ABSTRACT FROM AUTHOR]

People-oriented landscape experiences have become the focus of park design with the increasing demand of outdoor activities from urban residents, whereas thermal-comfort-guided landscape design has attracted more attention in academic circles. Based on the investigation of the microclimate of typical subregions in riverside parks, this paper uses AI recognition to analyze the spatiotemporal distribution of camping crowds, and considers the correlation between landscape morphological parameters, microclimates, and crowd behavior. Finally, we built a model to raise the number of landscape optimization strategies for landscape design. The results show that landscape morphological parameters, such as tree height, crown canopy, and sky visibility factor (SVF), can significantly affect the air temperature (Ta), relative humidity (RH), and physiological temperature (PET) in an environment, while hardly affecting wind speed (WS). For microclimate parameters, Ta has a moderate correlation with camping behavior, with a correlation coefficient of −0.145 and a p-value of 0.040, while the corresponding correlation with PET was non-significant with a p-value of 0.622. The temporal distribution of the number of campers per day show a linear upward trend with a reasonable goodness-of-fit, with an adjusted R2 above 0.789 for all subregions. The model based on landscape morphological parameters has a good fit, with coefficients of tree height and crown canopy of −0.195 and 1.316, respectively. This study provides theoretical support and design suggestions for the design of riverside parks based on crowd behavior patterns. [ABSTRACT FROM AUTHOR]

Smart and proactive surveillance of crowds has turned up into greater importance in recent years due to the increase in urban population and immutable crowd disasters. Although the behaviours that emerged in a crowd are often unpredictable, researches in computer vision try to figure out this emergent behaviour based on the psychological and cognitive aspects of the crowd. This review is intended to analyse the insights shared from these aspects for analysing crowd behaviour. We also try to uncover the crowd psychological theories present in the literature that largely helps in determining the non-adaptive crowd behaviours. Further, this paper discusses an outlook of state-of-the-art psychological, cognitive computing and cognitive psychological approaches of crowd behaviour analysis. This paper also provides a discussion on the benchmark datasets available for vision-based crowd analysis. The comprehensive discussion and insights based on crowd psychology and cognition will provide a deep understanding of the fundamental prospects of smart crowd behaviour analysis. [ABSTRACT FROM AUTHOR]

Leisure and popular culture have long played an important role in all aspects of life throughout the African continent. It has been recognised that the analysis of leisure and popular culture can put social practices, as well as political issues into relief. For instance, in colonial Brazzaville, Gold Coast and urban Zanzibar, dress and fashion not only symbolised change, but they were also tools for affirming and 'crystallising' new generational, gender, status and ethnic identities (Martin 1991, 1995; Akyeampong 1998; Fair 1998, 2001). This paper argues that this was also the case in urban colonial Tanganyika. For instance, young city dwellers - both men and women, Christians and Muslims, elite and non-elite - creatively 'appropriated' Western clothes in order to express new identities and aspirations, foster social changes, and sometimes bring about political transformations. In order to perceive such broader dynamics, this paper makes use of local debates on the changing fashions which took place in the Swahili-language press. This paper demonstrates that educated Africans used letters to the editor and mashairi (poems) - as a forum mainly to make comments and complain about 'new' clothing styles. Chiefly based on evidence found in the monthly magazine Mambo Leo, and partly in archival documents and oral testimonies (as well as photographic support) collected during my Ph.D. fieldwork carried out in Tanzania between 2004 and 2005, this paper aims to illustrate the ways in which new personal and collective identities were constructed (and contested) through clothing practices in everyday life. To a lesser extent, this paper also focuses on the interactions between local fashions and global elements. The focus on dress contributes to grasp local strategies of resiliency and broader processes through which the so-called 'common' people forged their notions of the 'modern' and produced 'translocal' connections. [ABSTRACT FROM AUTHOR]

Understanding city residents' collective geotagged behaviors (CGTBs) in response to hazards and emergency events is important in disaster mitigation and emergency response. It is a challenge, if not impossible, to directly observe CGTBs during a real-time matter. This study used the number of location requests (NLR) data generated by smartphone users for a variety of purposes such as map navigation, car hailing, and food delivery to infer the dynamics of CGTBs in response to rainstorms in eight Chinese cities. We examined rainstorms, flooding, and NLR anomalies, as well as the associations among them, in eight selected cities across mainland China. The time series NLR clearly reflects cities' general diurnal rhythm, and the total NLR is moderately correlated with the total city population. Anomalies of the NLR were identified at both the city and grid scale using the Seasonal Hybrid Extreme Studentized Deviate (S-H-ESD) method. Analysis results demonstrated that the NLR anomalies at the city and grid levels are well associated with rainstorms, indicating that city residents request more location-based services (e.g., map navigation, car hailing, food delivery, etc.) when there is a rainstorm. However, the sensitivity of the city residents' collective geotagged behaviors in response to rainstorms varies in different cities as shown by different peak rainfall intensity thresholds. Significant high peak rainfall intensity tends to trigger city flooding, which leads to increased location-based requests as shown by positive anomalies in the time series NLR. [ABSTRACT FROM AUTHOR]