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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]

China has made remarkable achievements in reducing carbon emissions in recent years. However, there is still much reduction room before achieving carbon neutrality. In Beijing, the capital of China, it is a strategic choice to respond to global climate change by promoting green and low-carbon development. This paper calculates the carbon dioxide emissions of key industries in Beijing and analyzes the temporal evolution trend of carbon emissions. Carbon dioxide emissions in Beijing before 2030 are predicted based on the grey prediction GM (1,1) and BP neural network model. The effects of factors of carbon dioxide emissions are discussed using the threshold regression model under different economic conditions. The results show that energy consumption intensity, GDP per capita, and the ownership of civil cars have a positive impact on carbon dioxide emissions, while the number of permanent residents and urban green space areas have a negative impact on carbon dioxide emissions. These findings of carbon emission prediction and influencing factors contribute to carbon reduction path design. Related policy implications on carbon emission reduction are put forward from the aspects of promoting industrial upgrading, accelerating the construction of advanced economic structures, optimizing transportation structures, and strengthening green building development. [ABSTRACT FROM AUTHOR]

Assessing park accessibility plays an essential role in providing rational recreational services for residents in a city. The perceptions and comments of residents are also important nonspatial factors for accessibility. However, there are few accessibility studies that are combined with public perceptions. Addressing this deficit, this study proposes a perception-based, multi-travel mode, two-step floating catchment area (PM2SFCA) method to calculate park accessibility. First, we quantified the selection probability of residents to parks by integrating the Huff model and the people's perceptions towards parks. Next, under four travel modes (walking, biking, driving and public transport), we combined the Huff model and the two-step floating catchment area method to compute park accessibility. Furthermore, the Gini coefficient and the Pearson correlation coefficient were used to illustrate the proposed method compared with the traditional E2SFCA method. Based on the above, taking the area of Beijing within the Fifth Ring Road as a study area, this paper facilitated the accessibility computation. The results indicated that the spatial distribution patterns of accessibility differed greatly under the four travel modes. Even under the same travel mode, there was an uneven accessibility distribution. Areas with high accessibility were mainly concentrated in the north, and some marginal areas also presented higher accessibility to parks. The comparative analysis results suggest that our proposed method for accessibility measurements alleviates the underestimation and overestimation of accessibility values obtained by a traditional method such as the center and edge of the study area. The research explores a new research perspective for measuring park accessibility. Furthermore, this study offers better guidance for policymakers trying to optimize park spatial distribution issues. [ABSTRACT FROM AUTHOR]

Simple Summary: This study addresses the concern of managing and coexisting with community cats in urban areas. The study was conducted in urban China through a survey involving 5382 participants. It explores the residents' perceptions of the general health of community cats and the relationships between humans and these feline inhabitants. The findings show that about 70% of participants considered all or most of the community cats to be in good health, and about 60% reported harmonious or non-conflict coexistence. It was found that residents' relationships with community cats were associated with socio-demographic factors such as education, marital status, and income level. The study highlights the importance of customized approaches to cat management in various cities and communities. This provides useful information for stakeholders to create efficient policies and interventions for the welfare of the community cats and the people who live alongside them. The management and coexistence of community cats in urban areas is a growing concern amid global urbanization. Through a survey-based investigation, we examine the residents' perceptions of the general health of community cats and human-cat relationships in urban China. The data from 5382 participants revealed that approximately 70% of participants perceived community cats as being in good health, and 60% reported harmonious or non-conflict coexistence between residents and these cats. Around 45% of the participants rescued or helped community cats, 38% expressed their intention to adopt, and 18% complained about the issues of community cats to management staff. Linear, logistic, and multilevel-logistic regressions were employed to examine the associations between the types of cities and communities or the participants' socio-demographics and the perceived well-being of community cats or human-cat relationships. The results show that the cats in fourth-tier cities (e.g., county-level cities) had poorer living conditions than in first-tier cities (e.g., Beijing), while the cats in urban village communities (e.g., villages in the city) were less likely to exhibit good health than in ordinary commercial housing communities. The results also show that socio-demographic variables, such as educational attainment, marital status, and income level, predicted participants' relationships with community cats. This study is the first of its kind. It provides valuable insights for stakeholders to develop effective policies and interventions on cat management, emphasizing the need for tailored strategies in diverse urban settings and populations. [ABSTRACT FROM AUTHOR]

Transit-oriented development (TOD) has become a dominant form of spatial planning and land use in large cities internationally. As the intersections of urban space and rail transportation, metro station areas play a key public service function in the lives of city residents. Based on the "5D" index and Node-Place theory in the metro station area, current research on the built environment in metro station areas focuses on improving the economic and transportation efficiency while neglecting public perception of the construction of station space. Sentiments, as an important part of the individual's perception, are closely related to the built environment. Therefore, this study takes 187 metro stations within the fifth ring road of Beijing, China, as an example and extracts public sentiment information from social media data using a wide range of natural language processing techniques to quantitatively analyze the distribution of the public's sentiment characteristics (including intensity, polarity, and category) in the metro station area and deeply explores the spatial correlation with the distribution of the objective built environment elements. The study shows that influenced by the spatial design of the metro station, density, land use functions, etc., the sentiment intensity of the station area within the Fifth Ring Road of Beijing is "strong in the east and weak in the west, strong in the north and weak in the south", and the sentiment polarity has the characteristic of gradually negative from inside to outside in a circular pattern. Synthesizing the sentiment perception in the metro station area, our study further divided the Beijing metro station area into four major categories and eight specific subtypes. [ABSTRACT FROM AUTHOR]

Due to the massive congestion in ground transportation in Beijing, underground rail transit has gradually become the main mode of travel for residents of large urban areas. Because the average daily traffic of the Beijing subway is over 12 million passengers, ensuring the safety of underground rail transit is particularly important. Big data shows that more than 4000 passengers participate in Long-term Stay in the Subway every day. However, the behaviors of these passengers have not been characterized. This paper proposes a method for identifying the Long-term Staying in Subway System (LSSS) in the subway based on the shortest path and analyze its travel mode. In combination with the past research of scholars, we try to quantify the suspected behavior with a database of assumed suspected behavior records. Finally, we extract the spatial-temporal travel characteristics of passengers and we propose a SAE-DNN algorithm to identify suspected anomalies; the accuracy of the training set can reach 95.7%, and the accuracy of the test set can also reach 93.5%, which provides a reference for the subway operators and the public security system. [ABSTRACT FROM AUTHOR]

Background: Residential green spaces and the co-inhabiting bird communities provide critical cultural ecosystem services for urban dwellers, which is critical for citizens' psychological well-being but has largely been overlooked, especially in China. This study attempted to delineate the relationship between human-nature interactions occurring at different scenarios in the health deriving procedure, and identify the key factors influencing urban residents' psychological well-being and the potential critical human-nature connections within their living spaces. Methods: Bird, plant, and social surveys were sequentially conducted. A model selection procedure based on AICc information was used to find the key factors that are responsible for residents' psychological well-being, and logistic regression analysis was conducted to figure out the key drivers for the occurrence of critical within-living-space human-nature connections. Results: Most of the respondents are aware of the neighborhood biodiversity and most of them are fond of it, and respondents who prefer fauna over flora have a considerably lower depression degree. Watching birds is the only critical within-living-space nature-related activity that is responsible for residents' psychological well-being. Both residents' psychological well-being and bird watching activity were simultaneously affected by nature-related activities within and outside the living spaces, while their preference for different aspects of biodiversity and their capability in perceiving them were also important, despite the difference in exact parameters. In addition, personal attributes such as age, financial income, and physical or mental health status also contribute to psychological well-being. Conclusions: This study successfully bridged citizens' minds with health products, and the major contribution was the demonstration of a spatial-scale-oriented circuit for people-nature connections, which contributes to the practical application of nature-based therapy. [ABSTRACT FROM AUTHOR]

As urbanization continues to accelerate, the urban heat island effects have become one of the most important issues affecting the urban environment and people's living experience. Numerous studies have shown that urban parks and green spaces can effectively alleviate the problem of the urban heat island effect and provide cooling and humidifying effects. Vegetation communities are a fundamental part of urban parklands, and multi-layered vegetation communities are considered to have better cooling and humidifying effects. Previous studies have focused on comparative analyses between different cover types of vegetation communities but have not explored the differences in the cooling and humidifying effects of multi-layered vegetation communities of the same cover type. Therefore, the Olympic Forest Park in Beijing was selected as the subject of this study, and multi-layered vegetation-covered (tree-shrub-grass) with different degrees of densities and uncovered squares were selected for the control and comparison. The cooling and humidifying effects of multi-layered vegetation communities with different canopy densities at different times of the day through field measurements were studied, and the influencing factors for this were analyzed. The results show that the tree cover is the core factor affecting temperature; the degree of the canopy density of multi-layered vegetation communities is significantly and positively correlated with the intensity of cooling and humidification, and the cooling and humidifying effect of multi-layered vegetation communities increases as the degree of canopy density increases. The results of this study can provide some references for the planning and design of urban parks and green spaces. [ABSTRACT FROM AUTHOR]

Origin-destination (OD) flow pattern mining is an important research method of urban dynamics, in which OD flow clustering analysis discovers the activity patterns of urban residents and mine the coupling relationship of urban subspace and dynamic causes. The existing flow clustering methods are limited by the spatial constraints of OD points, rely on the spatial similarity of geographical points, and lack in-depth analysis of high-dimensional flow characteristics, and therefore it is difficult to find irregular flow clusters. In this paper, we propose an OD flow clustering method based on vector constraints (ODFCVC), which defines OD flow event point and OD flow vector to express the spatial location relationship and geometric flow behavior characteristics of OD flow. First, the OD flow vector coordinate system is normalized by the Euclidean distance-based OD flow event point spatial clustering, and then the OD flow clusters with similar flow patterns are mined using adjusted cosine similarity-based OD flow vector feature clustering. The transformation of OD data from point set space to vector space is realized by constraining the vector coordinate system and vector similarity through two-step clustering, which simplifies the calculation of high-dimensional similarity of OD flow and helps mining representative OD flow clusters in flow space. Due to the OD flow cluster property, the k-means algorithm is selected as the basic clustering logic in the two-step clustering method, and a sum of squared error perceptually important points algorithm considering silhouette coefficients (SSEPIP) is adopted to automatically extract the optimal cluster number without defining any parameters. Tested by origin-destination flow data in Beijing, China, new traffic flow communities based on traffic hubs are obtained by using the ODFCVC method, and irregular traffic flow clusters (including cluster mode, divergence mode, and convergence mode) with representative travel trends are found. [ABSTRACT FROM AUTHOR]

As a product of the development of the Internet and the sharing economy, shared bicycles are beneficial in solving the last mile problem of public transportation for urban residents and expanding the service area of urban public transportation to a certain extent. This paper analyses the spatial-temporal characteristics of shared bicycles in the city of Beijing by using kernel density estimation and statistical analysis methods. The maximum coverage location problem model is used to quantify the effects of shared bicycles on the subway and bus urban public transportation systems. The analysis results are of great importance to identify the effects of shared bicycle usage on an urban bus public transportation system from the view of space-time. The results show that (1) shared bicycles are largely located within 1.5 km from the subway station and 500 m from the bus stations; (2) the number of the shared bicycle usage changes along with the distance from the subway and bus stations; (3) subway and bus stations are divided into six types based on the sink-source characteristics of shared bicycles; (4) the service areas of subway and bus stations are expanded by approximately 2.34 and 1.33 times, respectively, after the implementation of shared bicycles. [ABSTRACT FROM AUTHOR]

In order to study the impact of food safety concerns and government regulation on Chinese urban residents' organic food consumption willingness and behavior, an "online + offline" survey of 799 urban residents in Beijing has been conducted. Based on the theory of planned behavior, a structural equation model (SEM) was established and the government's food production support regulation (GP) and sales guarantee regulation (GC) were incorporated separately into the SEM as moderator variables. The path influence coefficients of respondents' food safety concerns, subjective norms, and perceived behavioral control on organic food consumption willingness were 0.065 (p < 0.05), 0.174 (p < 0.01) and 0.574 (p < 0.01), respectively. The influence of GP on organic food consumption willingness was 0.243 (p < 0.01), but its moderating effect on the promotion effect of food safety concerns and attitude to organic food consumption willingness was −0.001 (p < 0.01). The moderating effect of GC on the transformation from consumption willingness to behavior was 0.083 (p < 0.05). The results show that respondents attach the most importance to the comparison of costs and the benefits of organic food. Although the government's food production regulation will weaken the driving effect of food safety concern and benefit perception of organic food consumption willingness, it will still promote organic food consumption willingness on the whole. The government's supervision of food processing and sales is conducive to the occurrence of organic food consumption behavior. [ABSTRACT FROM AUTHOR]

Greenway is an important linear public space that meets the diverse needs of the public. With the increasing popularity of greenway construction, the study of different greenway usage behavior in different socio-economic areas is of great value to the detailed design and construction of greenway in the future. Using the theory of environment-behavior studies (EBS), this study selected representative urban greenways and suburban greenways in Beijing, China, and conducted a questionnaire survey. Descriptive statistics and the chi-squared test are used to quantitatively analyze and summarize the behavior of greenway users. It is found that user gender, educational level, and residence (i.e., permanent resident or visitor), as well as season of use, are highly similar for urban greenways and suburban greenways in Beijing. However, due to a close relationship with urban location, modified by temporal, spatial, and personal factors, different behavioral characteristics are evident as follows: (1) Urban greenways are most closely related to daily life, work and education of urban residents, with short travel distances, short single use time, high frequency of use, high social and cultural value, wide distribution of age groups and wide distribution of time periods of use. (2) Suburban greenways are an important choice for residents' outdoor activities on weekends and holidays. It is mainly used for ecological protection and sightseeing, supplemented by sports and fitness functions. It has the characteristics of low use frequency, high income level, wide distribution of time and distance, mainly used by young and middle-aged people, and used for a single time of more than 1 hour. Natural scenery along the trail is the most important attraction factor, and waterfront space and walking space are the main use behavior characteristics. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is a major challenge for society as a whole, and analyzing the impact of the spread of the epidemic and government control measures on the travel patterns of urban residents can provide powerful help for city managers to designate top-level epidemic prevention policies and specific epidemic prevention measures. This study investigates whether it is more appropriate to use groups of POIs with similar pedestrian flow patterns as the unit of study rather than functional categories of POIs. In this study, we analyzed the hour-by-hour pedestrian flow data of key locations in Beijing before, during, and after the strict epidemic prevention and control period, and we found that the pedestrian flow patterns differed greatly in different periods by using a composite clustering index; we interpreted the clustering results from two perspectives: groups of pedestrian flow patterns and functional categories. The results show that depending on the specific stage of epidemic prevention and control, the number of unique pedestrian flow patterns decreased from four before the epidemic to two during the strict control stage and then increased to six during the initial resumption of work. The restrictions on movement are correlated with most of the visitations, and the release of restrictions led to an increase in the variety of unique pedestrian flow patterns compared to that in the pre-restriction period, even though the overall number of visitations decreased, indicating that social restrictions led to differences in the flow patterns of POIs and increased social distance. [ABSTRACT FROM AUTHOR]

Urban rail transit (URT) systems are often regarded as the backbone of their respective city. The evolutionary features of URT systems have attracted much attention in recent years, but their evolution and their distinct function in contrast to other transit modes have seldom been investigated, especially quantitatively from the perspective of work–residence separation. Accordingly, we propose a framework for exploring the evolution of URT topological networks and demand-weighted networks, comparing the different impacts of all transit modes on work–residence separation. In this study, a URT passenger flow assignment model was formulated on the basis of travel cost function and an improved logit model was proposed that takes into account the heterogeneity of passengers. This model was used to generate a section load, which is regarded as a weight and able to reflect the residents' demand for travel by URT. Then, the fractal dimensions for a non-weighted network and demand-weighted network are proposed and their indications for transportation explained. Finally, the Beijing Subway System (BSS) is used as a case study by employing fifty years of network data and ten years of smart card data. Using fractal approaches, the different characteristics illustrated by the two networks were investigated and the reasons behind the observed patterns explained. In addition, the spatial features of the rail network, in terms of fractal indictors, were compared with population distribution and urban mobility for all modes, extracted from phone data as a proxy. Thus, the relationship between the residents' travel demand and traffic supply can be revealed to some extent. The main finding of this work is that demand must be taken into account when analyzing the fractal features of a transport network, lest the demand side be separated from the supply and important issues missed such as inconsistencies between demand and supply. Additionally, the role of rail transit in work–home imbalance can be investigated in the context of urban mobility for an entire city. [ABSTRACT FROM AUTHOR]

Where urban dwellers live at a fine scale is essential for the planning of services and response to city emergencies. Currently, most existing population mapping approaches considered census data as observational data for specifying models. However, census data usually have low spatial resolution and low frequency. Here, we presented a framework for mapping populations in residential neighborhoods with 30 m spatial resolution with little dependency upon census data. The framework integrated remote sensing and crowdsourcing data. The observational populations and number of households at residential neighborhood scale were obtained from real-time crowdsourcing data instead of census data. We tested our framework in Beijing. We found that (1) the number of households from a real estate trade platform could be a good proxy for accurate observational population. (2) The accuracy of the mapping population in residential neighborhoods was reasonable. The mean absolute percentage error was 47.26% and the R2 was 0.78. (3) Our framework shows great potential in mapping the population in real time. Our findings expand the knowledge in estimating urban population. In addition, the proposed framework and approach provide an effective means to quantify population distribution data for cities, which is particularly important for many of the cities worldwide lacking census data at the residential neighborhood scale. [ABSTRACT FROM AUTHOR]