Street Quality Research Articles

Measuring Street Quality: A Human-Centered Exploration Based on Multi-Sourced Data and Classical Urban Design Theories

Advancements in analytical tools have facilitated numerous studies on perceived street quality. However, most have focused on limited aspects of street quality, failing to capture a comprehensive perception. This study introduces a quantitative approach to holistically measure street quality by integrating three key dimensions: visual perception, network accessibility, and functional diversity. Using Beijing and Shanghai as case studies, we employed artificial neural networks to analyze street view images and quantify the visual characteristics of streets. Additionally, street network accessibility was assessed through spatial design network analysis, and functional diversity was evaluated using the entropy of points of interest (POIs) data. The evaluation results were combined using the analytic hierarchy process. The reliability and accuracy of this method were validated through further testing. Our approach offers a human-centered, large-scale measurement framework, providing valuable insights for urban street renewal and design.

Combining human perception and street accessibility to provide information for better street construction: a case study of Chengdu City, China

ABSTRACT High-quality street spaces have a positive impact on residents’ lives. Revealing the differences in human perception and walkability across street spaces can highlight priority areas for street quality enhancement and provide valuable insights that help urban managers make informed decisions. This study first utilised machine learning models (SegNet) to extract visual elements from street view images and collected volunteers’ ratings of six perceptual dimensions of street scenes. Subsequently, it employed random forest algorithms and spatial autocorrelation models to predict and analyse overall street perception. Furthermore, principal component analysis was used to consolidate the six perceptual scores, coupled with an analysis of street walkability, to identify streets that most required quality enhancement. Finally, correlation and regression analyses were conducted between human perception ratings and street visual elements. The results indicated that buildings positively influence perceptions of wealth, safety, liveliness, and beauty and are the most significant positive perception elements. Conversely, walls were the most significant negative perception elements. Additionally, trees positively impacted perceptions of liveliness and beauty. Lastly, streets categorised as “high walkable-low perception” exhibited a radial dispersion from the city centre. In conclusion, the findings of this study provide valuable insights for enhancing street-space quality in Chengdu and other cities.

Research on the Factors Influencing the Spatial Quality of High-Density Urban Streets: A Framework Using Deep Learning, Street Scene Images, and Principal Component Analysis

Research on the Factors Influencing the Spatial Quality of High-Density Urban Streets: A Framework Using Deep Learning, Street Scene Images, and Principal Component Analysis

Exploring the Spatially Heterogeneous Effects of Street-Level Perceived Qualities on Listed Real Estate Prices Using Geographically Weighted Regression (GWR) Modeling

The listed price of real estate is a subjective reflection of its value by sellers, usually related to structural, neighborhood, and environmental attributes. Although previous studies have proposed the hedonic pricing model, factors related to perception are rarely seen in explanatory variables. This study aims to explore the impact of street-level perceived qualities on the listed price per square meter of plot set by the seller of the real estate using the Geographically Weighted Regression (GWR)-based hedonic pricing model and analyzes the spatially heterogeneous effects of the coefficients. In the city of Eindhoven, the Netherlands, Google Street View photos collected at 200 m intervals were employed to calculate representative variables of perceptual quality via a validated convolutional neural network, alongside structural and neighborhood attributes. The final model includes eight explanatory variables, and the results indicate that, apart from the plot area and the number of rooms, the influencing mechanisms of other factors are different. The impact of perceived beautiful quality on listed real estate prices demonstrates obvious distinctions between the north and the south. Perceived livability (positive) and depressing (negative) qualities show similar heterogeneous characteristics. This study offers a comprehensive approach to promote diverse strategies for real estate development across urban areas and recommends a heightened emphasis on the design quality of residential streets.

Assessing the Degree of Publicness in Streets

Streets have always been a vital source of public life in Indian cities. The advent of virtual technologies and changingurban realities, have impacted its publicness and activation. Additionally, increasing degree of publicness in privatelyowned public spaces poses significant reduction in street life. Hence, it is important to assess the street qualities whichcontribute to public life. The physical, non-physical and metaphysical qualities of street assessments will bring out theessential qualities of streets that contribute to public life in contemporary streets. This paper enquires socio-culturaland sensory aspects of street life. It would assess parameters that contribute or contradict the degree of publicness.The study would discuss and build assessments from Vikas Mehta, William H. Whyte, Henry Lefebvre and Jan Gehl’stheories and perspectives. This research would build a critical enquiry about temporality and everyday urbanism andcontribute to the discussions on placemaking and tactical urbanism strategies.

Spatial quality measurement of commercial streets in old cities of medium-sized cities in China

ABSTRACT Chinese urban development has entered the stage of stock planning, and as an important part of urban public space, the construction of street space has a direct impact on urban development. High-quality street space contributes to sustainable urban development.High-quality street spaces are highly important for constructing vibrant and sustainable cities. Taking Jiaozuo city, a medium-sized city, as an example, this article establishes an evaluation index system for commercial street space quality from the aspects of external characterization and objective material space and uses the Pearson correlation coefficient to explore the influencing factors of urban commercial street space quality. This study finds that (1) crowd activities on commercial streets have different temporal and spatial patterns on weekdays and days off; (2) the external characterization of urban street space quality has a significant positive correlation with objective material spatial elements such as the density of public transportation stops, functional density, functional mixing, interface continuity, and spatial green visibility rate; (3) the enhancement of street accessibility and fine-tuning of functions are key to improving the spatial quality of commercial streets.This study provides theoretical basis and ideas for improving the spatial quality of commercial streets in medium-sized cities in China and promoting sustainable urban development.

Evaluating and Comparing Human Perceptions of Streets in Two Megacities by Integrating Street-View Images, Deep Learning, and Space Syntax

Street quality plays a crucial role in promoting urban development. There is still no consensus on how to quantify human street quality perception on a large scale or explore the relationship between street quality and street composition elements. This study investigates a new approach for evaluating and comparing street quality perception and accessibility in Shanghai and Chengdu, two megacities with distinct geographic characteristics, using street-view images, deep learning, and space syntax. The result indicates significant differences in street quality perception between Shanghai and Chengdu. In Chengdu, there is a curvilinear distribution of the highest positive perceptions along the riverfront space and a radioactive spatial distribution of the highest negative perceptions along the ring road and main roads. Shanghai displays a fragmented cross-aggregation and polycentric distribution of the streets with the highest positive and negative perceptions. Thus, it is reasonable to hypothesize that street quality perception closely correlates with the urban planning and construction process of streets. Moreover, we used multiple linear regression to explain the relationship between street quality perception and street elements. The results show that buildings in Shanghai and trees, pavement, and grass in Chengdu were positively associated with positive perceptions. Walls in both Shanghai and Chengdu show a consistent positive correlation with negative perceptions and a consistent negative correlation with other positive perceptions, and are most likely to contribute to the perception of low street quality. Ceilings were positively associated with negative perceptions in Shanghai but are not the major street elements in Chengdu, while the grass is the opposite of the above results. Our research can provide a cost-effective and rapid solution for large-scale, highly detailed urban street quality perception assessments to inform human-scale urban planning.

Perceptual Evaluation of Street Quality in Underdeveloped Ethnic Areas: A Random Forest Method Combined with Human–Machine Confrontation Framework Provides Insights for Improved Urban Planning—A Case Study of Lhasa City

Perceptual Evaluation of Street Quality in Underdeveloped Ethnic Areas: A Random Forest Method Combined with Human–Machine Confrontation Framework Provides Insights for Improved Urban Planning—A Case Study of Lhasa City

Enhancing the Sense of Place in Indian Religious Streets: A Comparative Study

Indian religious cities are renowned for their spiritual significance and cultural vibrancy, with streets serving as vital arteries that connect devotees to sacred sites and cultural landmarks. This research paper investigates the intricate dynamics of public open spaces within Indian religious cities, focusing on two iconic streets: Vishram Bazaar Street in Mathura and Bhakti Vedanta Marg in Vrindavan. Through a comprehensive analysis of physical, perceptual, socio-cultural, and functional aspects, the study aims to understand the nuances shaping the sense of place in these revered urban settings. Findings reveal a juxtaposition of moderately negative and positive perceptions, indicating the need for targeted interventions to enhance the overall quality of these streetscapes and preserve their cultural and spiritual significance. Recommendations for physical improvements, cultural preservation initiatives, and functional enhancements are proposed to enrich the urban experience and promote sustainable development in Indian religious cities. Key Words: Indian religious cities, Streetscape dynamics, Sense of place analysis, Vishram Bazaar Street, Bhakti Vedanta Marg, Cultural vibrancy, Sacred sites, Infrastructure challenges, Community perceptions, Socio-cultural analysis, Street quality, Cultural preservation, Place-making, Collaborative planning

Measuring human perception of residential built environment through street view image and deep learning

As an important part of the urban built environment, streets exploring the influence mechanism between the built environment and human perception. It is one of the issues in building healthy cities. In this study, the residential streets of Zhongshan Distict, Dalian were selected as the study site, including Mountain Low-rise Neighborhood, Old Mid-rise Neighborhood, and Modern High-rise Neighborhood. Meanwhile, spatial measurement and human perception perception evaluation of the street environment were based on Deep learning and street view image (SVI). The study used human perceptions as dependent variables, and physical features as the independent variables. Finally, two regression models of positive and negative perceptions were established to analyze the relationship between them. The results showed that in the three types of neighborhood, positive perception was mainly focused on Mountain Low-rise Neighborhood; Negative perception was mainly focused on Old Mid-rise Neighborhood. Greenness, Openness, Natural Landscape, Natural to artificial ratio of the horizontal interface, and Natural to artificial ratio of the vertical interface had a positive influence on positive perception. Pedestrian occurrence rate, Enclosure, and Vehicle Occurrence rate had a negative influence on negative emotive. Greenness was the physical feature that most affected human perception. This study provided a method for objectively evaluating the quality of the street built environment. It is important for promoting the quality of residential streets and public mental health.

3D Point Cloud and GIS Approach to Assess Street Physical Attributes

The present research explores an innovative approach to objectively assessing urban streets attributes using 3D point clouds and Geographic Information Systems (GIS). Urban streets are vital components of cities, playing a significant role in the lives of their residents. Usually, the evaluation of some of their physical attributes has been subjective, but this study leverages 3D point clouds and digital terrain models (DTM) to provide a more objective perspective. This article undertakes a micro-urban analysis of basic physical characteristics (slope, width, and human scale) of a representative street in the historic centre of Valencia (Spain), utilizing 3D laser-scanned point clouds and GIS tools. Applying the proposed methodology, thematic maps were generated, facilitating the objective identification of areas with physical attributes more conducive to suitable pedestrian dynamics. This approach provides a comprehensive understanding of urban street attributes, emphasizing the importance of addressing their assessment through advanced digital technologies. Moreover, this versatile methodology has diverse applications, contributing to social sustainability by enhancing the quality of urban streets and open spaces.

Pedestrian-oriented development in Beirut: A framework for estimating urban design impacts on pedestrian flows through modeling, participatory design, and scenario analysis

As municipalities are setting ambitious targets to increase non-motorized transportation mode shares, analytic frameworks for examining how such targets will be met become increasingly important. In this article, we update the Urban Network Analysis pedestrian modeling framework designed to link land use and urban development changes with pedestrian mobility outcomes by introducing new methods for capturing the effects of street properties on pedestrian travel demand and adjusting pedestrian trip generation rates with k-nearest accessibility scores based on destination availability in each context. This framework was used in conjunction with a participatory design process in Beirut, Lebanon to estimate pedestrian mobility impacts of three urban design scenarios. We illustrate how each scenario affects pedestrian trip generation across various trip types and trip distribution on individual street segments. Our approach demonstrates how urban design interventions–both land use changes and street quality improvements–can influence pedestrian travel demand. Estimates of these changes can provide planners and policymakers with valuable benefit-cost analyses of public space improvements, and a framework for understanding how site-specific planning and development decisions can impact progress towards (or away from) non-motorized mobility goals.

Data-driven framework for analyze the visual quality of streets: A systematic measurement of street quality through street view images utilizing deep learning algorithms

The study developed a framework that utilizes deep learning algorithms to analyse the visual quality of streets (VQOS) through street view images (SVIs), while overcoming the constraints observed in existing practices. The study consisted of four main stages, including literature reviews and expert discussions, development of deep learning algorithms, testing of developed algorithms, and validation. The study developed both convolutional neural network (CNN) and feed forward neural network (FFNN) algorithms, using 2684 street view images extracted from Google Street View images and Cityscape datasets, rated by an expert panel and the general public. The proposed framework comprises stages such as the collection and rating of street view images, image processing, developing and training the CNN, and testing and validating stages. The proposed framework achieved 90.51% internal validation accuracy using the accuracy metric in Keras and 86.7% external validation accuracy, with an accepted level of kappa accuracy of 80%. Urban planners, designers, architects, and landscape architects can use this framework as a tool for quantitatively measuring and mapping the visual quality of streets and assessing the impact of proposed developments and guidelines on the visual quality of streets. This proposed framework will enhance the effectiveness of their new proposals and designs.

Analysis of spatial variation of street landscape greening and influencing factors: an example from Fuzhou city, China

Urban street greening is an important part of urban green infrastructure, and Green View Index (GVI) is widely used to assess urban street quality and ecosystem service value as an important indicator to quantify the perception of green street landscape from a pedestrian perspective. However, the distribution of street greenery is imbalanced. Therefore, to explore the differences in street greening levels within urban cities, we crawled streetscape data using the Internet to assess the spatial distribution patterns of urban street GVI using deep learning and spatial autocorrelation, and combined 11 surrounding environmental features with multi-source geographic data to further analyze the key factors influencing the spatial variation of block GVI using ordinary least squares, geographically weighted regression (GWR) models, and multi-scale geographically weighted regression (MGWR) models. The results show that the mean value of GVI in Fuzhou city is low (23.08%), with large differences among neighborhoods and a significant spatial autocorrelation. Among the regression models, MGWR has the best fit with an R2 of 0.702, where the variables of NDVI, house price, accessibility of water bodies and parks, and the proportion of built-up land have a greater impact on GVI, and the factors do not have the same spatial effect size. The results can provide a scientific basis for promoting green visual equity in different blocks.

Quantifying the spatial quality of urban streets with open street view images: A case study of the main urban area of Fuzhou

With the advancement of global urbanization, urban streetscapes have become a critical part of urban public spaces. As one of the important ways to enhance the living environment, relevant government departments have accelerated the construction of urban streetscapes; however, certain issues, such as emphasis on surface and light protection, still persist. The visual effects of street landscapes hardly meet people's daily and spiritual needs. Therefore, assessing the quality of urban streetscapes has scientific and practical significance for improving urban planning and construction and creating a new intelligent and green city. In this study, the spatial distribution of vehicle interference index, spatial feasibility index, road area index, green visual index, sky visibility index, spatial enclosure index, color richness index, and visual entropy evaluation indices were drawn from the framework of combining deep learning and machine learning to quantify the spatial quality of urban streets by integrating a large number of streetscape images. The spatial quality of street landscape was evaluated according to the ELO scoring mechanism and random forest method, taking the main urban area of Fuzhou City as an example. The results showed that the overall street landscape quality of Fuzhou City was high, and the spatial distribution showed a pattern of concentrated high values in the center and dispersed high values in the periphery. Therefore, based on quantifying the spatial quality of urban streets, strategies, such as the pavement layout of pedestrian and vehicular paths, control of the continuity and permeability of visual landscape elements, and plant color matching, should be regulated. The method proposed in this study can provide novel insights for urban builders to improve the construction of cities.

Spatial Patterns and Multi-Dimensional Impact Analysis of Urban Street Quality Perception under Multi-Source Data: A Case Study of Wuchang District in Wuhan, China

The human spatial perception of urban streets has a high complexity and traditional research methods often focus on access surveys of human perception. Urban streets serve as both a direct conduit for pedestrians’ impressions of a city and a reflection of the spatial quality of that city. Street-view images can provide a large amount of primary data for the image semantic segmentation technique. Deep learning techniques were used in this study to collect the boring, beautiful, depressing, lively, safe, and wealthy perception scores of street spaces based on these images. Then, the spatial pattern of urban street-space quality perception was analyzed by global Moran’s I and GIS hotspot analyses. The findings demonstrate that various urban facilities affect street quality perception in different ways and that the strength of an influencing factor’s influence varies depending on its geographical location. The results of the influencing factors reveal the difference in the degree of influence of positive and negative influencing factors on various perceptions of the visual dimension of pedestrians. The primary contribution of this study is that it reduces the potential bias of a single data source by using multi-dimensional impact analysis to explain the relationship between urban street perception and urban facilities and visual elements. The study’s findings offer direction for high-quality urban development as well as advice for urban planning and enhanced design.

Research on Street Color Environment Perception Based on CEP-KASS Framework

The color of urban streets plays a crucial role in shaping a city’s image, enhancing street appeal, and optimizing the experience of citizens. Nevertheless, the relationship between street color environment and residents’ perceptions has rarely been deeply discussed, and most of the existing studies adopt qualitative methods. To accurately and effectively assess the connection between street color environment and residents’ emotional perceptions, this paper introduces a quantitative research framework based on multi-source data called “Color Emotion Perception with K-Means, Adversarial Strategy, SegNet, and SVM (CEP-KASS)”. By combining K-Means unsupervised machine learning and SegNet computer vision techniques, it captures and analyzes visual elements and color data from Baidu Street View Images (BSVI). It then employs a human–machine adversarial scoring model to quantify residents’ perceptions of BSVI and uses the support vector machine regression model to predict the final perception scores. Based on these data, a Pearson correlation analysis and visual analysis were conducted on the elements and color in the urban environment. Subsequently, the streets were classified based on perception frequency and perception scores by integrating multi-source data, and areas within the third ring of Xuzhou City were selected for validating the research framework. The results demonstrate that utilizing street-view images and the CEP-KASS framework can quantitatively analyze urban color perception and establish a connection with residents’ emotions. In terms of color perception, red, orange, and blue all have a strong positive correlation with the interesting score, whereas black is positively correlated with a sense of safety. Regarding color attributes, low-saturation bright colors result in higher fun perception scores in urban spaces; too low saturation and brightness can affect their attractiveness to residents; brightness has an inverse relationship with the perception of safety, and adjusting brightness inversely can improve the perceived safety experience in certain urban external spaces. The street classification criteria based on perception frequency and perception scores proposed herein can provide references for planners to prioritize color transformation decisions, with a priority on emulating HSHF streets and transforming LSHF streets. When formulating color planning, suggestions for color adjustment can be given based on the correlation study of color with visual elements and perception scores, optimizing urban residents’ spatial perception and their emotional experiences. These findings provide robust theoretical support for further enhancing the visual quality of streets and refining urban color planning.

Research on the Spatial Quality of Urban Village Main Streets in Shenzhen from the Perspective of Nighttime Pedestrian Demands

Our findings shed light on existing theoretical investigations and the sustainable development of urban villages and the policy recommendations are proposed for the sustainable development of urban villages. It is widely acknowledged that public spaces in urban villages play an important role in residents’ everyday lives, and the nighttime activities of most pedestrians are confined to the main streets in urban villages. Main streets are an important public space in urban villages at night and are a focus for comprehensive renovation in China. There has not been adequate research on main streets at nighttime, and such research is necessary to improve the quality of urban spaces and achieve high-quality development. This study used methods such as literature analysis, on-site research, and semi-structured interviews to understand the spatial characteristics of the main streets of urban villages at night and summarize the demands of pedestrians. This study used the Kano model to adjust the structure of the analytic hierarchy process (AHP) model to form the AHP–Kano model and construct an evaluation system based on the evaluation dimension of pedestrian needs. We conducted a questionnaire-based survey of pedestrians on the main streets of Pingshan Village and Nantou Ancient City in Shenzhen, and obtained the dominant categories of pedestrian demands and the results of satisfaction evaluations from the main street spaces of the two urban villages at night. According to on-site research, we analyzed the problems in the main street spaces of the two urban villages at night, and propose targeted renovation suggestions, which have clear value for improving everyday public spaces in urban villages at nighttime.

Streets and Stress: A Pilot Study on How Quality and Design of Streets Impacts on Urban Stress.

To identify how quality and design of streets impacts urban stress. Few studies have comprehensively addressed environmental factors affecting stress in urban public spaces. However, a remarkable portion of our everyday life is spent in public spaces, particularly streets. This study seeks to evaluate the effect of three types of streets as major public spaces on stress. These include a street with the dominance of green spaces (A), a motorist-oriented street (B), and a pedestrian street (C). For this purpose, we selected a group of participants (n = 16) aged 20-30, with an equal number of men and women who were generally healthy and had normal stress levels. Participants were asked to wear an electroencephalogram (EEG) headset, walk different streets, and answer the Perceived Restorativeness Scale (PRS) and urban design qualities questionnaires. According to the results, participants experienced the highest stress in street type B and the lowest in type A. Green space and vegetation, a sense of security, privacy and coziness, climatic comfort, and safety of space had the most positive effect on stress reduction. Whereas noise pollution, vehicle traffic, and crowdedness were the most critical factors of stress. Finally, our findings suggest that the component of green space has a more significant effect on stress reduction compared with the elimination of vehicle traffic.

Walkability: a definition of quality from perception of users

This research considered walkability as an option or stage of urban displacement. The objective was to consider users’ perception of the quality of the environment for walking in urban roads, and the place chosen was the city of Canoinhas, in Santa Catarina. The method used was Quality Function Displacement (QFD), which used questionnaires applied to a sample of the population for analysis. At the end, five large groups of elements were identified that, according to the analysis, were considered more relevant for walking: 1) the existence of sidewalks; 2) the quality of the sidewalks; 3) the perception of security; 4) comfort and visual attractiveness and, 5) street quality. These groups were further divided into levels, generating 24 items that should be evaluated to improve the quality of walking.