Nonlinear effects of built environment on living street vitality considering spatial heterogeneity: Evidence from Xiamen Island, China

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.

Previous studies substantiate built environment influences street vitality. However, most of them focus on whether the built environmental elements have an influence on the street vitality, and ignore the spatiotemporal heterogeneity of the influences at the district scale. Using multisource big data, we comprehensively measure the street vitalities of different periods and the built environment in different dimensions on Xiamen Island. Geographically and temporally weighted regression (GTWR) models are constructed to systematically analyze the spatiotemporal heterogeneity of the influences of the built environment on the street vitality. Results show that the influence of function remains constant over time. Transit has the strongest effect on the improvement of street vitality during peak hours. The impact of design is strongest in the evening. The effect of accessibility gradually strengthened over time, reaching the highest in the evening. In terms of the spatial dimension, the heterogeneity brought about by the new and old urban areas is significant. The spatial heterogeneity of design’s influences is prominently brought about by large green lands and landscape streets. Density, bus station and spatial scale have strong temporal and spatial stability. Length is the most unstable during the weekdays. In order to maintain street vitality and form sustainable traffic, differentiated strategies of vitality enhancement should be formulated according to the locations and attributes of the streets.

Amid rapid global urbanization, folk cultural spaces are facing a pronounced “resilience crisis.” Existing studies primarily emphasize material preservation while lacking a holistic assessment of cultural spaces. Using Xiamen Island as a case study, this research integrates GIS-based spatial analysis, questionnaire surveys, and statistical modeling to develop a resilience assessment framework for folk cultural spaces, encompassing four key dimensions: connectivity, modularity, diversity, and adaptability. The study systematically identifies spatial differentiation, formation mechanisms, and typological patterns of these spaces. The main findings are as follows: First, the resilience of folk cultural spaces on Xiamen Island exhibits a spatial pattern characterized by “dual-core leadership, corridor transition, and marginal vulnerability.” High-resilience areas are mainly concentrated in Siming Old Town and the Wuyuanwan district, representing two typical development trajectories—“organic evolution” and “planned intervention.” Second, the influencing mechanisms of each resilience dimension show pronounced spatial heterogeneity, reflecting the coupled effects of structural characteristics, social processes, and governance logics across different urban contexts. Third, three resilience zones are identified through K-means clustering, providing a typological basis for developing differentiated strategies for protection and renewal. This study provides theoretical insights and methodological references for the “living” preservation and adaptive governance of folk cultural spaces.

With the unprecedented urbanization processes around the world, cities have become the main areas of political, cultural, and economic creation, but these regions have also caused environmental degradation and even affected public health. Ecological landscape is considered as an important way to mitigate the impact of environmental exposure on urban residents. Therefore, quantifying the quality of urban road landscape and exploring its spatial heterogeneity to obtain basic data on the urban environment and provide ideas for urban residents to improve the environment will be a meaningful preparation for further urban planning. In this study, we proposed a framework to achieve automatic quantifying urban street quality by integrating a mass of street view images based on deep learning and landscape ecology. We conducted a case study in Xiamen Island and mapped a series of spatial distribution for ecological indicators including PLAND, LPI, AI, DIVISION, FRAC_MN, LSI and SHDI. Additionally, we quantified street quality by the entropy weight method. Our results showed the streetscape quality of the roundabout in Xiamen was relatively lower, while the central urban area presented a belt-shaped area with excellent landscape quality. We suggested that managers could build vertical greening on some streets around the Xiamen Island to improve the street quality in order to provide greater well-being for urban residents. In this study, it was found that there were still large uncertainties in the mechanism of environmental impact on human beings. We proposed to strengthen the in-depth understanding of the mechanism of environmental impact on human beings in the process of interaction between environment and human beings, and continue to form general models to enhance the ability of insight into the urban ecosystem.

Street vitality is crucial for sustainable urban development, yet current understanding of how built environment perceptions influence vitality remains limited by global statistical approaches and the lack of interpretable frameworks for analyzing spatial heterogeneity. This study proposes a novel multi-level interpretative framework combining Multiscale Geographically Weighted Regression (MGWR) with SHAP values to examine spatial variations in perception-vitality relationships. Using multi-source data from Hohhot, China, including mobile phone signals, POI data, and street view imagery, we analyzed how four dimensions of environmental perception influence street vitality across different urban contexts. The analysis reveals significant spatial heterogeneity in perception-vitality relationships, with varying effects across urban locations. Pleasure perception shows the strongest positive influence (SHAP values 0.072-0.103), while convenience perception exhibits an unexpected inverse U-shaped relationship with vitality. The machine learning approach (R² = 0.421) outperforms traditional methods in capturing nonlinear effects and complex interactions. The findings demonstrate the importance of considering both spatial heterogeneity and nonlinear relationships in understanding street vitality, suggesting the need for context-sensitive approaches to urban design and planning interventions.

Encouraging cycling through the improvement of streetscape perception: A bottom-up investigation into the relationship between street greening and bicycling volume

Rapid city expansion and intensive human activities have remarkably affected nitrogen flow, leading to increasingly intricate spatial heterogeneity of nitrogen flow. Focused on the temporal characteristics of nitrogen flow at certain city scales, the existing research has missed comprehensive grid-scale spatial models for nitrogen flow. To address this gap, this study develops a comprehensive spatial model for nitrogen flow by incorporating both natural and anthropic processes. Taking Xiamen as its research case, this study utilizes grid technology and spatial analysis to build a detailed spatial model for nitrogen flow at the grid scale. The results of spatial characteristics of Xiamen in 2015 revealed that hotspots of nitrogen input were primarily located in the surrounding areas outside and east of Xiamen, with the maximum nitrogen input reaching 20.07 × 104 kg/ha. However, the hotspots of nitrogen load in the atmosphere were concentrated in the urban center (i.e., Xiamen Island) and the nearby sea areas. The maximum nitrogen outputs can reach 18.32 × 104 kg/ha, which is 18 times the total nitrogen output to the water environment. Additionally, it was found that a significant gradient correlation exists between nitrogen flow and population density. These findings provide support for low-nitrogen spatial planning and emission reduction policymaking.

Building cycling-friendly street environments is crucial for promoting sustainable urban mobility. However, existing studies exploring the influence of the built environment on cycling have paid limited attention to the three-dimensional characteristics of street landscapes and have mostly relied on linear assumptions. To address these gaps, this study employs street view imagery and interpretable machine learning methods to investigate the nonlinear and interaction effects of street landscape elements on residents’ cycling preferences in Xiamen Island, China. The results reveal that the visual indices of buildings, sky, vegetation, and roads are the most influential variables affecting cycling preferences. These factors exhibit pronounced nonlinear relationships with cycling preference. For instance, buildings exhibit a threshold effect, with positive influences on cycling preference when the building index is below 0.12 and negative effects when it exceeds 0.12. A low sky index significantly suppresses cycling preference, whereas higher values offer only limited additional benefits, with an optimal range of 0.1–0.25. Vegetation contributes positively only at relatively high levels, suggesting that its index should ideally exceed 0.3. The road index shows a V-shaped relationship: values between 0.15 and 0.25 reduce cycling preference, whereas values below 0.15 or above 0.25 enhance it. Moreover, clear interaction effects among these variables are observed, suggesting that the combined visual composition of the streetscape plays an important role in shaping cycling preferences. These findings deepen the understanding of how street landscape characteristics influence cycling behavior and provide nuanced, practical insights for designing cycling-friendly streets and promoting sustainable travel in urban environments.

Urban street vitality has been a perennial focus within the domain of urban planning. This study examined spatial patterns of street vitality in the old city of Nanjing during working days and weekends using real-time user datasets (RTUDs). A spatial autoregressive model (SAM) and a multiscale geographically weighted regression (MGWR) model were employed to quantitatively assess the impact of various factors on street vitality and their spatial heterogeneity. This study revealed the following: (1) the distribution of street vitality in the old city of Nanjing exhibited a structure centered around Xinjiekou, with greater regularity and predictability in street vitality on working days than on weekends; (2) eight variables, such as traffic location, road density, and functional density, are positively associated with street vitality, whereas the green view index is negatively associated with street vitality, and commercial location benefits street vitality at weekends but detracts from street vitality on working days; and (3) the influence of variables such as traffic location and functional density on street vitality is contingent on their spatial position. Based on these results, this study provides new strategies to enhance the street vitality of old cities.

Rapid urbanization fundamentally transforms how residents perceive and interact with built environments, yet the dynamic relationships between these evolving perceptions and street vitality remain inadequately understood. As cities undergo rapid transformation, traditional assumptions about fixed perception–vitality relationships may no longer hold, necessitating a deeper understanding of how these relationships evolve over time and space. This study aims to investigate how multiple dimensions of built environment perception influence street vitality and how these relationships evolve spatially and temporally in rapidly urbanizing contexts. We developed a multi-level interpretative framework combining Multi-scale Geographically Weighted Regression (MGWR) with machine-learning-based SHAP analysis to analyze multi-source data from Hohhot, China, spanning 2019–2023. Our approach examined four key perception dimensions—comfort, safety, convenience, and pleasure—and their impacts on street vitality patterns during a period of intensive urban development. The analysis reveals three major findings: first, perception–vitality relationships evolved from highly heterogeneous spatial patterns toward increasing homogenization over time, suggesting urban development standardization effects driven by rapid urbanization processes. Second, several perception dimensions underwent significant transformations, with safety perception shifting from negative to positive influence and convenience perception displaying complex nonlinear threshold effects as urban infrastructure matured. Third, the relative importance of perception dimensions changed over time, reflecting evolving urban priorities and resident expectations shaped by urbanization experiences. These findings demonstrate that perception–vitality relationships are dynamic rather than static, challenging assumptions about fixed environmental effects in urban planning. The study provides empirical evidence for implementing adaptive, context-sensitive urban interventions that acknowledge both spatial heterogeneity and temporal evolution, offering valuable insights for enhancing street vitality in rapidly urbanizing environments worldwide.

Spatial nonlinear effects of urban vitality under the constraints of development intensity and functional diversity

Simultaneous selection of variables and smoothing parameters in structured additive regression models

Taking the Tianhe District in Guangzhou, China, as a case and the urban street (road) as the basic research unit, this study analyzes the relationship between the built environment and street vitality to analyze the influencing factors of street vitality. The Tencent location service data are used to characterize street vitality, and the OLS and GWR models are used to construct the statistical relationship between the built environment and street vitality after establishing the urban built environment index. The results show that spatial heterogeneity is considered in the GWR model based on local geographic weighting, and its fitting effect is better than that of the OLS model, which can reveal the micro-local characteristics of the built environment’s effect on street vitality. Increasing the land-use mixing degree, new and old building mixing degrees, and land-use intensity (building density and volume ratio) can significantly increase street vitality, which proves to a certain extent that Jacobs’s relevant discussion is still highly practical for the Tianhe District.

Unraveling the association between the built environment and air pollution from a geospatial perspective

Street vitality is a key indicator of urban sustainability, and high-quality built environments can promote street vitality. However, existing research lacks refined measurement approaches for street vitality and sufficient understanding of the spatial relationship patterns between built environment and street vitality. This study employs street view images and image detection to achieve a fine-grained measurement of street vitality. Furthermore, it utilizes methods such as the Optimal Parameter Geographical Detector, Spatial Lag Model, and Multi-scale Geographically Weighted Regression to explore the relationships between built environment factors and both pedestrian and cycling vitality, and to identify their distribution patterns and spatial effects. The findings reveal that: (1) there are significant differences in the distribution of pedestrian and cycling vitality, with varying associations with built environment factors; (2) the relationships between built environment factors and the two types of vitality differ inside and outside scenic areas; (3) both forms of vitality exhibit clear spatial lag effects, with approximately one-third of the vitality values correlated with adjacent areas; and (4) the correlation between built environment factors exhibits spatial heterogeneity. This study provides refined understanding of street vitality, offering guidance for street renewal.