A Systematic Measurement of Street Quality through Multi-Sourced Urban Data: A Human-Oriented Analysis

Real-Time Tracking Data and Machine Learning Approaches for Mapping Pedestrian Walking Behavior: A Case Study at the University of Moratuwa

The growing urban population and traffic congestion underline the importance of building pedestrian-friendly environments to encourage walking as a preferred mode of transportation. However, a major challenge remains, which is the absence of such pedestrian-friendly walking environments. Identifying locations and routes with high pedestrian concentration is critical for improving pedestrian-friendly walking environments. This paper presents a quantitative method to map pedestrian walking behavior by utilizing real-time data from mobile phone sensors, focusing on the University of Moratuwa, Sri Lanka, as a case study. This holistic method integrates new urban data, such as location-based service (LBS) positioning data, and data clustering with unsupervised machine learning techniques. This study focused on the following three criteria for quantifying walking behavior: walking speed, walking time, and walking direction inside the experimental research context. A novel signal processing method has been used to evaluate speed signals, resulting in the identification of 622 speed clusters using K-means clustering techniques during specific morning and evening hours. This project uses mobile GPS signals and machine learning algorithms to track and classify pedestrian walking activity in crucial sites and routes, potentially improving urban walking through mapping.

Determinants affecting the blood mercury levels of preschool children in Shanghai, China: A cross-sectional study

Infants and children are vulnerable to mercury (Hg)-induced toxicity, which has detrimental effects on their neurological development. This study measured blood Hg levels (BMLs) and identified potential factors influencing BMLs, including demographic and socioeconomic factors, lifestyle, and daily dietary habits, among 0 to 7-year-old children in Shanghai. Our study recruited 1474 participants, comprising 784 boys and 690 girls. Basic demographic and lifestyle information were obtained and blood Hg were analyzed using the Direct Mercury Analyzer 80. The blood Hg concentrations of children in Shanghai ranged from 0.01 to 17.20 μg/L, with a median concentration of 1.34 μg/L. Older age, higher familial socioeconomic status, higher residential floors, and a higher frequency of consuming aquatic products, rice, vegetables, and formula milk were identified as risk factors. Other potential influencing factors including the mother's reproductive history (gravidity and parity), smoking (passive smoking), supplementation of fish oil and calcium need to be further investigated. These findings can be useful in establishing appropriate interventions to prevent children's high blood Hg concentrations in Shanghai and other similar metropolitan cities.

Exploring the Relationship between Urban Street Spatial Patterns and Street Vitality: A Case Study of Guiyang, China

Understanding how street spatial patterns are related to street vitality is conducive to enhancing effective urban and street design. Such analysis is facilitated by big data technology as it enables more accurate methods. This study cites data from street view imagery (SVI) and points of interest (POI) to assess street vitality strength after the classification of street spatial and vitality types to explore the relationship between street spatial patterns and street vitality with a further discussion on the layout features of street vitality and its strength in various street spatial patterns. First, street spatial patterns are quantified based on SVI, which are further classified using principal component analysis and cluster analysis; POI data are then introduced to identify street vitality patterns and layout, and the strength of street vitality is evaluated using spatial overlay analysis. Finally, relevance analysis is explored to cast light on the relationship between street vitality layout and street spatial patterns by overlaying street spatial pattern, street vitality types, and street vitality strength in the grid cells. This paper takes the urban area of Guiyang, China, as an example and the analysis shows that a pattern is discovered in Guiyang regarding the layout of street vitality types and vitality strengths across different street spatial patterns; compact street spaces should be prioritized in designing street space renovation; and cultural leisure vitality is most adaptive to street spatial patterns. Based on big data and using grids to understand the intrinsic relationship between street spatial patterns and the type and strength of street vitality, this paper brings more options to urban street studies in terms of perspective and methodology.

Exploring the Impact of Built Environment Attributes on Social Followings Using Social Media Data and Deep Learning

Streets are an important component of urban landscapes and reflect the image, quality of life, and vitality of public spaces. With the help of the Google Cityscapes urban dataset and the DeepLab-v3 deep learning model, we segmented panoramic images to obtain visual statistics, and analyzed the impact of built environment attributes on a restaurant’s popularity. The results show that restaurant reviews are affected by the density of traffic signs, flow of pedestrians, the bicycle slow-moving index, and variations in the terrain, among which the density of traffic signs has a significant negative correlation with the number of reviews. The most critical factor that affects ratings on restaurants’ food, indoor environment and service is pedestrian flow, followed by road walkability and bicycle slow-moving index, and then natural elements (sky openness, greening rate, and terrain), traffic-related factors (road network density and motor vehicle interference index), and artificial environment (such as the building rate), while people’s willingness to stay has a significant negative effect on ratings. The qualities of the built environment that affect per capita consumption include density of traffic signs, pedestrian flow, and degree of non-motorized design, where the density of traffic signs has the most significant effect.

Measuring Perceived Psychological Stress in Urban Built Environments Using Google Street View and Deep Learning

An urban built environment is an important part of the daily lives of urban residents. Correspondingly, a poor design can lead to psychological stress, which can be harmful to their psychological and physical well-being. The relationship between the urban built environment and the perceived psychological stress of residents is a significant in many disciplines. Further research is needed to determine the stress level experienced by residents in the built environment on a large scale and identify the relationship between the visual components of the built environment and perceived psychological stress. Recent developments in big data and deep learning technology mean that the technical support required to measure the perceived psychological stress of residents has now become available. In this context, this study explored a method for a rapid and large-scale determination of the perceived psychological stress among urban residents through a deep learning approach. An empirical study was conducted in Gangnam District, Seoul, South Korea, and the SegNet deep learning algorithm was used to segment and classify the visual elements of street views. In addition, a human-machine adversarial model using random forest as a framework was employed to score the perception of the perceived psychological stress in the built environment. Consequently, we found a strong spatial autocorrelation in the perceived psychological stress in space, with more low-low clusters in the urban traffic arteries and riverine areas in Gangnam district and more high-high clusters in the commercial and residential areas. We also analyzed the street view images for three types of stress perception (i.e., low, medium and high) and obtained the percentage of each street view element combination under different stresses. Using multiple linear regression, we found that walls and buildings cause psychological stress, whereas sky, trees and roads relieve it. Our analytical study integrates street view big data with deep learning and proposes an innovative method for measuring the perceived psychological stress of residents in the built environment. The research methodology and results can be a reference for urban planning and design from a human centered perspective.

Environmental Justice in Greater Los Angeles: Impacts of Spatial and Ethnic Factors on Residents' Socioeconomic and Health Status

Environmental justice advocates that all people are protected from disproportionate impacts of environmental hazards. Despite this ideal aspiration, social and environmental inequalities exist throughout greater Los Angeles. Previous research has identified and mapped pollutant levels, demographic information, and the population's socioeconomic status and health issues. Nevertheless, the complex interrelationships between these factors remain unclear. To close this knowledge gap, we first measured the spatial centrality using sDNA software. These data were then integrated with other socioeconomic and health data collected from CalEnvironScreen, with census tract as the unit of analysis. Finally, structural equation modeling (SEM) was executed to explore direct, indirect, and total effects among variables. The results show that the White population tends to reside in the more segregated areas and lives closer to green space, contributing to higher housing stability, financial security, and more education attainment. In contrast, people of color, especially Latinx, experience the opposite of the environmental benefits. Spatial centrality exhibits a significant indirect effect on environmental justice by influencing ethnicity composition and pollution levels. Moreover, green space accessibility significantly influences environmental justice via pollution. These findings can assist decision-makers to create a more inclusive society and curtail social segregation for all individuals.

Constructing the Quality Measurement Model of Street Space and Its Application in the Old Town in Wuhan

The quality of street space is the comprehensive suitability evaluation from the objective physical environments and the subjective pedestrian perception. Since the existing quality measurement models of street space do not consider both subjective and objective aspects, it is difficult for planners to accurately locate the low-quality streets that need to be regenerated. To solve this problem, this study proposes a new 5D+3S measurement model for street space quality evaluation. This model incorporates the widely acknowledged 5D dimensions of the physical environments (Design, Destination accessibility, Distance to transit, Density, and Diversity), and the 3S dimensions (Sociality, Safety, and Status) of walking perception derived from the keywords clustering on relevant literature. To test the validity of the proposed model, this study makes a comparative analysis on the results of the public assessment, expert scoring, and model measurement to verify whether the measurement results are objective and convincing. The results show that the quality grade obtained by the proposed measurement model is highly consistent with the subjective evaluation outcomes of the public and experts. Thus, the proposed measurement model is effective in quality measurement of the street space, which provides a new idea for future large-scale diagnosis of city public space quality.

Evaluating the Effects of Built Environment on Street Vitality at the City Level: An Empirical Research Based on Spatial Panel Durbin Model

There is evidence that the built environment has an influence on street vitality. However, previous studies seldom assess the direct, indirect, and total effect of multiple environmental elements at the city level. In this study, the features of the street vitality on Xiamen Island are described based on the location-based service Big Data. Xiamen Island is the central urban area of Xiamen, one of the national central cities in China. With the help of multi-source data such as street view images, the condition of design that is difficult to effectively measure with traditional data can be better explored in detail on a macro scale. The built environment is measured through a 5D system at the city level, including Density, Diversity, Design, Destination accessibility, and Distance to transit. Spatial panel Durbin models are constructed to analyze the influence of the built environment on the street vitality on weekdays and weekends, and the direct, indirect, and total effects are evaluated. Results indicate that at the city level, the built environment plays a significant role in promoting street vitality. Functional density is not statistically significant. Most of the elements have spatial effects, except for several indicators in the condition of the design. Compared with the conclusions of previous studies, some indicators have different effects on different spatial scales. For instance, on the micro scale, greening can enhance the attractiveness of streets. However, on the macro scale, too much greening brings fewer functions along the street, which inhibits the street vitality. The condition of design has the greatest effect, followed by destination accessibility. The differences in the influences of weekdays and weekends are mainly caused by commuting behaviors. Most of the built environment elements have stronger effects on weekends, indicating that people interact with the environment more easily during this period.

Neighbourhood physical environment, intrinsic capacity, and 4-year late-life functional ability trajectories of low-income Chinese older population: A longitudinal study with the parallel process of latent growth curve modelling

BACKGROUND

Knowledge of how intrinsic capacity (IC) and neighbourhood physical environment shape functional ability (FA) trajectories in later life remains understudied. We investigated four-year trajectories of IC and their impact on FA trajectories and the association between neighbourhood physical environment and FA trajectories among community-dwelling older adults in Hong Kong, China.

METHODS

We conducted a four-wave longitudinal study from 2014 to 2017 in Hong Kong with 2,081 adults aged 65 and above. FA was assessed by The Chinese Lawton Instrumental Activities of Daily Living Scale. We used cognition, affect, locomotion, sensory capacity, and vitality to capture the multiple domains of IC. Neighbourhood physical environment attributes included green space, land use diversity, and availability of facilities, assessed within 200- and 500-meter buffers of respondents' homes. We used the parallel process of latent growth curve model.

FINDINGS

IC (Unstandardized coefficient, β = -0.02, p<0.001) and FA (β = -0.20, p<0.001) each decreased significantly over time. Individuals with declines in IC experienced a faster decline in FA over time. Green space within a 200-meter buffer (β = 1.15, p = 0.023), the number of leisure (β = 0.03, p = .0.043) and public transport (β = 0.08, p = .0.003) facilities within a 500-meter buffer slowed the rate of FA decline.

INTERPRETATION

The level of FA decreased over time in later life. Changes in IC shaped FA trajectories. Increased residential green space and the number of leisure and public transport facilities in the neighbourhood may help slow FA decline over time.

FUNDING

The Hong Kong Housing Society.

Neighborhood built environment and late-life depression: A multilevel path analysis in a Chinese society

OBJECTIVE

Neighborhood built environments (BEs) are increasingly recognized as being associated with late-life depression. However, their pathways are still understudied. This study investigates the mediating effects of physical, social activities (PA & SA) and functional ability (FA) in the relationships between BEs and late-life depression.

METHOD

We conducted a cross-sectional analysis with data from 2,081 community-dwellers aged 65 years and above in Hong Kong in 2014. Two road-network-based service area buffers (200- and 500-meter buffers) adjusted by terrain and slope from participants' residences were created to define the scope of neighborhoods. BEs comprised population density in District Council Constituency Areas (DCCAs), urban greenness, land use diversity, and neighborhood facilities within 200- and 500-meter buffers. Multilevel path analysis models were used.

RESULTS

More urban greenness within both buffers and more commercial facilities within a 500-meter buffer were directly associated with fewer depressive symptoms. SA mediated the relationship between the number of community facilities and depressive symptoms within a 200-meter buffer. Neighborhood urban greenness and the number of commercial facilities had indirect associations on depressive symptoms within a 500-meter buffer, which were mediated by FA.

CONCLUSION

Our findings have implications for the ecological model of aging. The mediating effects of SA and FA underscore the importance of promoting active social lifestyles and maintaining FA for older adults' mental health in high-density cities. Policy implications on how to build age-friendly communities are discussed.

Evaluating Street Greenery by Multiple Indicators Using Street-Level Imagery and Satellite Images: A Case Study in Nanjing, China

Street greenery plays an essential role in improving the street environment and residents’ health. The evaluation of street greenery is of great value to establish environmentally friendly streets. The evaluation indicators of present studies evaluating street greenery were relatively single, either the Green View Index (GVI) or Normalized Difference Vegetation Index (NDVI), which cannot describe the greenery condition in its entirety. The objective of this study is to assess the street greenery using multiple indicators, including GVI, NDVI, and Vegetation Structural Diversity (VSD). We combined street view images with a semantic segmentation method to extract the GVI and VSD and used satellite images to calculate the NDVI in the urban area of Nanjing, China. We found correlations and discrepancies of these indicators using statistical analyses in different urban districts, functional areas, and road levels. The results indicate that: (1) the GVI and NDVI are strongly correlated in open spaces, whereas weakly correlated in residential and industrial lands, (2) the areas with higher VSD are mainly located in the new city, whereas the VSD in the old city is lower, and a weak negative correlation exists between the GVI and VSD in the research area, and (3) the old city has a higher GVI level compared to the new city on the main road, whereas the new city has a higher GVI level than the old city on the branch road. Compared with the GVI, the trend of VSD in the old city and the new city is relatively consistent. Our findings suggest that considering multiple indicators of street greenery evaluation can provide a comprehensive reference for building more human-friendly and diversified street green belts.

Measuring Human-Scale Living Convenience through Multi-Sourced Urban Data and a Geodesign Approach: Buildings as Analytical Units

Living convenience, as a perceptual quality of life, is gradually playing an increasingly important role in the context of seeking livable cities. A high degree of living convenience positively affects urban vitality, livability, and daily physical activities. However, it is hard to achieve a quantitative measurement of this intangible, subjective issue. This study presents a data-informed analytical approach to measuring the human-scale living convenience using multi-sourced urban data and geodesign techniques. Firstly, according to classical theories, living convenience is translated as the co-presentation of accessed number and diversity of urban facilities. Based on that, this study applies multi-sourced urban data, including points of interest (PoIs), buildings, and street networks, to compute the living convenience of each building in the 15 min community–life circle. Through the geoprocessing tools developed by ArcGIS API for Python (ArcPy), the living convenience of millions of buildings in an entire city can be computed efficiently. Kaifeng City from Henan Province, China, is selected as the case study, and the verification from local experts in urbanism shows high accuracy. The capacity to measure intangible perception exhibits the potential for this analytical approach in urban planning practices. Several explorations have been conducted in this direction, including analyzing the spatial heterogeneity in Kaifeng City and planning decision support for bus station arrangement. In short, this study contributes to the development of human-centered planning by providing continuous measurements of an ‘unmeasurable’ quality across large-scale areas. Insights into the perceptual-based quality and detailed mapping of living conveniences in buildings can assist in efficient planning strategies toward more livable and sustainable urbanism.