The Impact of Street Space Perception Factors on Elderly Health in High-Density Cities in Macau—Analysis Based on Street View Images and Deep Learning Technology

The communication of artificial intelligence and deep learning in computer tomography image recognition of epidemic pulmonary infectious diseases

The objectives are to improve the diagnostic efficiency and accuracy of epidemic pulmonary infectious diseases and to study the application of artificial intelligence (AI) in pulmonary infectious disease diagnosis and public health management. The computer tomography (CT) images of 200 patients with pulmonary infectious disease are collected and input into the AI-assisted diagnosis software based on the deep learning (DL) model, "UAI, pulmonary infectious disease intelligent auxiliary analysis system", for lesion detection. By analyzing the principles of convolutional neural networks (CNN) in deep learning (DL), the study selects the AlexNet model for the recognition and classification of pulmonary infection CT images. The software automatically detects the pneumonia lesions, marks them in batches, and calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes and density, the most common shadow is the ground-glass opacity. The detection rate of the manual method is 95.30%, the misdetection rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of the DL-based AI-assisted lesion method is 99.76%, the misdetection rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, the proposed model can effectively identify pulmonary infectious disease lesions and provide relevant data information to objectively diagnose pulmonary infectious disease and manage public health.

Current methods for evaluating people's exposure to green space: A scoping review

People's exposure to green space is a critical link between urban green space and urban residents' health. Since green space may affect human health through multiple pathways regarding diverse human health outcomes, the measurement of people's exposure to green space must be tailored to concrete study contexts and research questions. In this scoping review, we systematically categorized the available green space representations and metrics in the last two decades that can be used to derive people's exposure to green space regarding different research topics. A three-phase systematic review was conducted after a generalized search of relevant research articles from the three most-used publication databases, namely Scopus, the Web of Science, and PubMed. We identified 260 research articles that particularly discuss green space representations and metrics. We further developed a multi-pathway framework to articulate the complicated context issues in green space studies. We categorized the most relevant green space representations and metrics into five groups, including green space indices, the delineation, inventory, and usage of green space, the spatiotemporal evolution of green space, the attributes and components of green space, and the green space landscape and fragmentation. Finally, we discussed the inter-conversion between different green space representations and metrics, the "mobility-turn" in green space studies and how it may affect the derivation of people's exposure to green space, and other potential methodological issues in measuring people's exposure to green space. Our scoping review provides the most comprehensive framework and categories for deriving people's exposure to green space to date, which may strongly support a broad range of studies that concern green space's health effects.

Effect of built environment on BMI of older adults in regions of different socio-economic statuses

Background

Numerous studies have ignored the influence of underdeveloped urban surroundings on the physical health of China's ageing population. Lanzhou is a typical representative of a less developed city in China.

Methods

This study investigated the relationship between body mass index (BMI) and built environment amongst older adults in regions of different socio-economic statuses (SES) using data from medical examinations of older adults in Lanzhou, as well as calculating community built environment indicators for regions of different SES based on multiple linear regression models.

Results

Results showed that age and underlying disease were negatively associated with overall older adult BMI in the study buffer zone. Land use mix, number of parks and streetscape greenery were positively associated with older adult BMI. Street design and distance to bus stops were negatively connected in low SES regions, but population density and street design were negatively correlated in high SES areas.

Conclusion

These findings indicate that the built environment of SES regions has varying impacts on the BMI of older persons and that planners may establish strategies to lower the incidence of obesity amongst older adults in different SES locations.

Street View Imagery (SVI) in the Built Environment: A Theoretical and Systematic Review

Street view imagery (SVI) provides efficient access to data that can be used to research spatial quality at the human scale. The previous reviews have mainly focused on specific health findings and neighbourhood environments. There has not been a comprehensive review of this topic. In this paper, we systematically review the literature on the application of SVI in the built environment, following a formal innovation–decision framework. The main findings are as follows: (I) SVI remains an effective tool for automated research assessments. This offers a new research avenue to expand the built environment-measurement methods to include perceptions in addition to physical features. (II) Currently, SVI is functional and valuable for quantifying the built environment, spatial sentiment perception, and spatial semantic speculation. (III) The significant dilemmas concerning the adoption of this technology are related to image acquisition, the image quality, spatial and temporal distribution, and accuracy. (IV) This research provides a rapid assessment and provides researchers with guidance for the adoption and implementation of SVI. Data integration and management, proper image service provider selection, and spatial metrics measurements are the critical success factors. A notable trend is the application of SVI towards a focus on the perceptions of the built environment, which provides a more refined and effective way to depict urban forms in terms of physical and social spaces.

A Machine Learning and Computer Vision Study of the Environmental Characteristics of Streetscapes That Affect Pedestrian Satisfaction

Pedestrian-friendly cities are a recent global trend due to the various urbanization problems. Since humans are greatly influenced by sight while walking, this study identified the physical and visual characteristics of the street environment that affect pedestrian satisfaction. In this study, vast amounts of visual data were collected and analyzed using computer vision techniques. Furthermore, these data were analyzed through a machine learning prediction model and SHAP algorithm. As a result, every visual feature of the streetscape, for example, the visible area and urban design quality, had a greater effect on pedestrian satisfaction than any physical features. Therefore, to build a street with high pedestrian satisfaction, the perspective of pedestrians must be considered, and wide sidewalks, fewer lanes, and the proper arrangement of street furniture are required. In conclusion, visually, low enclosure, adequate complexity, and large green areas combine to create a highly satisfying pedestrian walkway. Through this study, we could suggest an approach from a visual perspective for the pedestrian environment of the street and see the possibility of using computer vision techniques.

Predicting the effect of street environment on residents' mood states in large urban areas using machine learning and street view images

BACKGROUND

Researchers have demonstrated that the built environment is associated with mental health outcomes. However, evidence concerning the effects of street environments on mood in fast-growing Asian cities is scarce. Traditional questionnaires and interview methods are labor intensive and time consuming and pose challenges for accurately and efficiently evaluating the impact of urban-scale street environments on mood.

OBJECTIVE

This study aims to use street view images and machine learning methods to model the impact of street environments on mood states in a large urban area in Guangzhou, China, and to assess the effect of different street view elements on mood.

METHODS

A total of 199,754 street view images of Guangzhou were captured from Tencent Street View, and street elements were extracted by pyramid scene parsing network. Data on six mood state indicators (motivated, happy, positive-social emotion, focused, relaxed, and depressed) were collected from 1590 participants via an online platform called Assessing the Effects of Street Views on Mood. A machine learning approach was proposed to predict the effects of street environment on mood in large urban areas in Guangzhou. A series of statistical analyses including stepwise regression, ridge regression, and lasso regression were conducted to assess the effects of street view elements on mood.

RESULTS

Streets in urban fringe areas were more likely to produce motivated, happy, relaxed, and focused feelings in residents than those in city center areas. Conversely, areas in the city center, a high-density built environment, were more likely to produce depressive feelings. Street view elements have different effects on the six mood states. "Road" is a robust indicator positively correlated with the "motivated" indicator and negatively correlated with the "depressed" indicator. "Sky" is negatively associated with "positive-social emotion" and "depressed" but positively associated with "motivated". "Building" is a negative predictor for the "focused" and "happy" indicator but is positively related to the "depressed" indicator, while "vegetation" and "terrain" are the variables most robustly and positively correlated with all positive moods.

CONCLUSION

Our findings can help urban designers identify crucial areas of the city for optimization, and they have practical implications for urban planners seeking to build urban environments that foster better mental health.

Evaluation of Street Space Quality Using Streetscape Data: Perspective from Recreational Physical Activity of the Elderly

The quality of street space has attracted attention. It is important to understand the needs of different population groups for street space quality, especially the rapidly growing elderly group. Improving the quality of street space is conducive to promoting the physical leisure activities of the elderly to benefit to their health. Therefore, it is important to evaluate street space quality for the elderly. The existing studies, on the one hand, are limited by the sample size of traditional survey data, which is hard to apply on a large scale; on the other hand, there is a lack of consideration for factors that reveal the quality of street space from the perspective of the elderly. This paper takes Guangzhou as an example to evaluate the quality of street space. First, the sample street images were scored by the elderly on a small scale; then the regression analysis was used to extract the street elements that the elderly care about. Last, the street elements were put into the random forest model to assess street space quality io a large scale. It was found that the green view rate and sidewalks are positively correlated with satisfaction, and the positive effect increases in that order. Roads, buildings, sky, vehicles, walls, ceilings, glass windows, runways, railings, and rocks are negatively correlated with satisfaction, and the negative effect increases in that order. The mean satisfaction score of the quality of street space for the elderly’s recreational physical activities in three central districts of Guangzhou (Yuexiu, Liwan, and Haizhu) is 2.6, among which Xingang street gets the highest quality score (2.92), and Hailong street has the lowest quality score (2.32). These findings are useful for providing suggestions to governors and city designers for street space optimization.

Quantitative Evaluation of Urban Style at Street Level: A Case Study of Hengyang County, China

Urban style is the comprehensive expression of the material environment, the associated cultural connotation and social life. Under the influence of globalization and rapid urban expansion, many cities around the world show a global convergence in style, which poses a challenge in terms of satisfying both function and local identity. However, the current insufficiency of research on the quantitative evaluation of urban style makes it hard to have a full grasp on how urban style can instruct land use and landscape planning strategies. In this paper, we propose Suitability, Aesthetics and Vitality as three core dimensions of urban style, and construct a quantitative evaluation framework for urban style evaluation at the street level. Taking a street in Hengyang County, China as an example, the method’s operability is demonstrated, and the results show that urban style performance is closely related to building construction periods, trends of urban expansion, and the natural environment. Improvement strategies include harmonizing urban spatial form, increasing the diversity of land use, and moderately improving the quality of building facades. This method can be applied at a greater scale to effectively reflect local characteristics and relevant problems. It can also provide an objective basis for future planning and construction.

Creating a Healthy Environment for Elderly People in Urban Public Activity Space

According to statistics, the global, population aging problem is severe and growing rapidly. The aging problem is most obvious in some European countries, and most of them are developed countries, such as Japan, Italy, Germany, France, etc. The current internal and external environments of parks in China are complex. The inefficient utilization of space in urban parks is a prominent problem. The design of public spaces that only considers the visual experience is incomplete. Based on the optimization of urban park space planning principle, this study examined a new measure of the acoustic environment in elderly public activity space and designed a new elderly healthy urban park environment. Methods: Using the main parks in Shenyang (Zhongshan Park, Nanhu Park, Youth Park, and Labor park) as the study sites, this study analyzed problems in the acoustic environmental data through on-site inspection, questionnaire survey, and physical data collection. By using general linear regression and multiple regression methods, this study analyzed the impacts of plant density, site elevation, structure enclosure, functional mixing degree on the acoustic environment, and elderly population activities. Based on the acoustic environment, we propose improvements and construction ideas, as well as technical methods, for urban elderly public activity space planning. The utility of the “elderly public activity space planning principle” was also considered. Results: Elderly activity space in urban parks was affected by three main factors—plant density, degree of structural enclosure, and function mixing degree. These factors should be optimized to construct healthy acoustic environments and attract different types of people. Discussion: Compared to past studies, the new influencing factors of the planning principle for elderly public activity space found in this study, would benefit the urban park environment for the elderly and support sustainable development of cities. Conclusions: This study proposes three optimizations to the elderly urban park space planning principle and builds four healthy models of elderly urban space activity.

Research on Constructing a Healing Environment for the Street Spaces of a High-Density City: Using Street Spaces in Macao's Old City Area

It is commonly recognized that street spaces in high-density cities are able to cause negative impacts in terms of residents’ physical and mental health. This research intends to investigate and analyze how residents use street spaces in a high-density city in order to construct a healing environment for these street spaces. The research was conducted in Macao’s old town by using spatial syntax methods to define the research areas, and implemented on-site observations that evaluated the age of the residents in the space and the conditions of their usage of the space. The study collected data through expert grading and employed the Analytic Hierarchy Process to calculate the weight of each indicator in order to attain accurate and objective research outcomes. The evaluation results indicate that the current Macao street spaces are poor healing environments. By analyzing the effective factors for constructing a healing environment in these street spaces, so that residents can get more space for healing when they use it, the paper aims to provide a model example for those who are involved with city governance, planning and design.