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

Wan, Tianyue; Lu, Wei; Sun, Peijin

doi:10.3389/fpubh.2022.816317

Cited by 8 publications

(9 citation statements)

References 70 publications

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“…As pointed out in the study by [ 18 , 22 ], which investigated several accelerometer placements, including at the hip (belt), wrist, upper arm, ankle, and thigh of the test person, using numerous accelerometers aids in activity identification. Thus, as illustrated in Figure 2 , in this study, participants were advised to secure their mobile phones to their waistbands with designated holders.…”

Section: Methodsmentioning

confidence: 99%

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

Sawandi,

Jayasinghe,

Retscher

2024

Sensors

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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.

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Section: Methodsmentioning

confidence: 99%

“…Current research on evaluating human walking behavior primarily concentrates on the construction of index systems, as well as data collecting and processing. However, it does not include any verification of the reliability of measurement findings or the validity of measurement models [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

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

Sawandi,

Jayasinghe,

Retscher

2024

Sensors

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“…Liu [54] highlighted the social characteristics of streets by integrating them into a single vitality index. In order to test the validity of the proposed model, Wan [55] conducted a comparative analysis of the results of public assessment, expert scoring, and model measurements to verify whether the measurements were objective and convincing.…”

Section: Spatial Sensing Of Streets From a Big Data Perspectivementioning

confidence: 99%

A Spatial Analysis of Urban Streets under Deep Learning Based on Street View Imagery: Quantifying Perceptual and Elemental Perceptual Relationships

Sun,

Xu,

et al. 2023

Sustainability

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Measuring the human perception of urban street space and exploring the street space elements that influence this perception have always interested geographic information and urban planning fields. However, most traditional efforts to investigate urban street perception are based on manual, usually time-consuming, inefficient, and subjective judgments. This shortcoming has a crucial impact on large-scale street spatial analyses. Fortunately, in recent years, deep learning models have gained robust element extraction capabilities for images and achieved very competitive results in semantic segmentation. In this paper, we propose a Street View imagery (SVI)-driven deep learning approach to automatically measure six perceptions of large-scale urban areas, including “safety”, “lively”, “beautiful”, “wealthy”, “depressing”, and “boring”. The model was trained on millions of people’s ratings of SVIs with a high accuracy. First, this paper maps the distribution of the six human perceptions of urban street spaces within the third ring road of Wuhan (appearing as Wuhan later). Secondly, we constructed a multiple linear regression model of “street constituents–human perception” by segmenting the common urban constituents from the SVIs. Finally, we analyzed various objects positively or negatively correlated with the six perceptual indicators based on the multiple linear regression model. The experiments elucidated the subtle weighting relationships between elements in different street spaces and the perceptual dimensions they affect, helping to identify the visual factors that may cause perceptions of an area to be involved. The findings suggested that motorized vehicles such as “cars” and “trucks” can negatively affect people’s perceptions of “safety”, which is different from previous studies. We also examined the influence of the relationships between perceptions, such as “safety” and “wealthy”. Finally, we discussed the “perceptual bias” issue in cities. The findings enhance the understanding of researchers and city managers of the psychological and cognitive processes behind human–street interactions.

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“…To construct an evaluation system that more accurately reflects the characteristics of a living street, the study selected a variety of dimensional indicators based on the perspective of the residents. The evaluation system was developed after consulting Wan et al (2022) and other studies on spatial quality measurement [34].…”

Section: Construction Of the Evaluation Systemmentioning

confidence: 99%

“…Significant differences in the measurements of the indicators can skew the results. Due to the different tendencies and magnitudes of the evaluation indicators, the sample data must be normalized and dimensionless in order to facilitate indicator comparison and data calculation [34]. The method of calculation is as follows:…”

Section: Spatial Quality Evaluation Of Living Streetsmentioning

confidence: 99%

Evaluating the Spatial Quality of Urban Living Streets: A Case Study of Hengyang City in Central South China

Duan,

Liao,

Liu

et al. 2023

Sustainability

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Evaluating the spatial quality of a living street entails identifying and assessing the outdoor space that influences residents’ leisure and recreation, which may contribute to urban renewal. The application of multi-source data and deep learning technology enables an objective evaluation of large-scale spatial quality as opposed to the traditional questionnaire survey or experts’ subjective evaluation. Based on street view images, points of interest, and road network data, this study developed subjective and objective evaluation indicators for the central city of Hengyang using semantic segmentation and ArcGIS spatial analysis. This study then assigned weights to each indicator and calculated the spatial quality score for living streets. In addition, the subjective evaluations of the street view images were compared to test and verify the validation of the objective evaluation model. Finally, the study analyzed the accessibility within 500 m of the study area using Spatial Syntax and ArcGIS to overlay the low spatial quality score with the highest accessibility to identify the streets with the highest priority in the subsequent urban plan. The results indicate that the spatial quality of living in the west of Hengyang is higher than that in its northeast region. In addition, Xiao Xia Street, Guanghui Street, and Hengqi Road comprised the majority of the areas that required a priority update. Correspondingly, our research is expected to be a useful management tool for identifying urban street space issues and guiding urban renewal.

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

Cited by 8 publications

References 70 publications

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

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

A Spatial Analysis of Urban Streets under Deep Learning Based on Street View Imagery: Quantifying Perceptual and Elemental Perceptual Relationships

Evaluating the Spatial Quality of Urban Living Streets: A Case Study of Hengyang City in Central South China

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