Nonlinear effects of built environment perception and objective features on street vitality in historic districts

Amid urban expansion, historic districts face challenges such as declining vitality and deteriorating spatial quality. Using the streets of Xi’an’s historical and cultural district as examples, this research utilizes multisource data, including points of interest (POIs), street view images, and Baidu heatmaps, alongside analytical techniques such as machine learning. This study explores the determinants of street vitality from the dual perspectives of its external manifestation and spatial carriers. A quantitative framework for measuring street vitality in historic districts is established, thoroughly examining the driving factors behind street vitality. Additionally, the relationship between built environment indicators and street vitality is elucidated through statistical analysis methods. The findings reveal significant, time-varying influences of these spatial carriers on human vitality, with distinct spatial distribution patterns of human activity across different times, and the significance of the influence of external representations of human vitality and various types of spatial carriers varies over time. Based on these insights, this paper proposes strategies for enhancing the vitality of historic streets, aiming to rejuvenate and sustain the diverse and dynamic energy of these districts. It provides a foundation for revitalizing the vigor of cultural heritage zones and offers strategies applicable to similar urban contexts.

Analyzing the distribution characteristics of the vitality of traditional streets and its influencing factors is of great significance to the sustainable development of historic streets. Taking the Wenming Street Historic District in Kunming City as an example, combining field research and online open-source data to construct a street vitality evaluation index system for historic districts, measuring the distribution characteristics of its street vitality, and analyzing the impact of built environmental indicators on street vitality through stepwise multiple linear regression. Studies have shown that: (1) The street vitality of Wenming Street Historic District presents a spatial distribution characteristic of high in the southeast and low in the northwest, and the distribution of vitality is obviously uneven; (2) accessibility and functional density are significantly positively correlated with street vitality, and street length is significantly negatively correlated. The order of influence is accessibility> functional density> street length. On this basis, the strategy of optimizing the vitality of the streets in the historic districts is proposed in order to provide a reference for the revitalization of the historic districts.

The loss of traditional features and place memory, and ultimately vibrancy in historic districts, has attracted substantial attention in today’s urban design. Most conventional theories are of the consensus that diversity and accessibility characteristics play important roles in creating street vitality, whereas how these characteristics influence street vitality in historic districts has not been thoroughly explored. Furthermore, it is less clear as to which characteristics exert greater influence. Taking the Drum Tower Muslim District, a historical neighborhood in Xi’an, China, as a case study, this paper employs geospatial data to examine how diversity and accessibility influence street vitality. This study identifies seven factors of diversity and accessibility, and incorporates them into a spatial multivariate regression model for analysis. The results indicate that accessibility makes a stronger impact on the street vitality than diversity does. Furthermore, the closeness of streets, the functional density, the intersection density, the location of public transportation and the density of public infrastructure are the top five factors influencing street vitality. The outcome of this study will shed light on what constitutes a vibrant historic district and will help to inform us as to where and how we can improve street vitality.

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.

As an important part of urban vitality, street vitality is an external manifestation of street economic prosperity and is affected by the built environment and the surrounding street vitality. However, existing research on the formation mechanism of street vitality focuses only on the built environment itself, ignoring the spatial spillover effect on street vitality. This study uses 5290 street segments in Shenzhen as examples. Utilizing geospatial and other multisource big data, this study creates spatial weight matrices at varying distances based on different living circle ranges. By combining the panel threshold model (PTM) and the spatial panel Durbin model (SPDM), this study constructs a spatial autoregressive threshold model to explore the spatial nonlinear effects of street vitality, considering various spatial weight matrices and thresholds of construction intensity and functional diversity. Our results show the following: (1) Street vitality exhibits significant spatial spillover effects, which gradually weaken as the living circle range expands (Moran indices are 0.178***, 0.160***, and 0.145*** for the 500 m, 1000 m, and 1500 m spatial weight matrices, respectively). (2) Construction intensity has a threshold, which is 0.1466 under spatial matrices of different distances. Functional diversity has two thresholds: 0.6832 and 2.2065 for the 500 m spatial weight matrix, and 0.6832 and 1.4325 for the 1000 m matrices, and 0.6832 and 1.2724 for 1500 m matrices. (3) As an international metropolis, street accessibility in Shenzhen has a significant and strong positive impact on its street vitality. This conclusion provides stakeholders with spatial patterns that influence street vitality, offering a theoretical foundation to further break down barriers to street vitality.

As the main carrier of the evolution and development of historic districts, streets are the important foundation of street space, architectural texture and neighborhood vitality. However, in recent years, due to the inappropriate way of protecting the historic district and the rapid development of motorization, the spatial quality of the streets in the historic district has been declining and the vitality of the district has suffered a great impact. Located in Fengxiang District of Baoji City, Fengxiang Historic District has insufficient neighborhood vitality and poor spatial quality. In order to improve the vitality of the neighborhood and optimize the spatial quality of the neighborhood, we take "Wenchangxiang-Tongwenxiang-Feltersxiang Historic District" and "Xinzhuangxiang Historic District" in Fengxiang District as examples through a combination of qualitative description and quantitative analysis, and examine the causes of the lack of vitality of the neighborhood in terms of economy, culture and environment. The reasons for the lack of vitality of the neighborhoods are explored from three aspects. Finally, we propose the strategy to improve the vitality of the neighborhoods by upgrading the commercial space and creating diversified business modes, exploring the cultural connotation and forming regional linkage, and optimizing the neighborhood environment and strengthening the neighborhood management.

The complexity of urban street vitality is reflected in the interaction of multiple factors. A deep understanding of the multi-dimensional driving mechanisms behind it is crucial to enhancing urban street vitality. However, existing studies lack comprehensive interpretative analyses of urban multi-source data, making it difficult to uncover these drivers’ nonlinear relationships and interaction effects fully. This study introduces an interpretable machine learning framework, using Nanchang, China as a case study. It utilizes urban multi-source data to explore how these variables influence different dimensions of street vitality. This study’s innovation lies in employing an integrated measurement approach which reveals the complex nonlinearities and interaction effects between data, providing a more comprehensive explanation. The results not only demonstrate the strong explanatory power of the measurement approach but also reveal that (1) built environment indicators play a key role in influencing street vitality, showing significant spatial positive correlations; (2) different dimensions of street vitality exhibit nonlinear characteristics, with transit station density being the most influential one; and (3) cluster analysis revealed distinct built environment and socioeconomic characteristics across various street vitality types. This study provides urban planners with a data-driven quantitative tool to help formulate more effective strategies for enhancing street vitality.

With the progress of urban development, the spatial quality of urban streets has become one of the key concerns. In view of the actual new needs of urban micro-renewal, this study takes the street accessibility, regional accessibility and other aspects, takes Zhengzhou West Street Area as a case study, extracts the elements of street space based on the street view image data, combines the artificial intelligence technology with the sDNA spatial accessibility grid analysis technology, and synthesises the classification and scientific analysis of the cutting-edge urban data such as the urban street walkability index system, and based on this, proposes the street vitality optimisation strategy for historical blocks. Based on this, we propose the optimisation strategy of street vitality in historic districts, and provide technical support for urban micro-renewal in terms of analysis and theory.

Street vitality has become an important indicator for evaluating the attractiveness and potential for the sustainable development of urban neighborhoods. However, research on this topic may overestimate or underestimate the effects of different influencing factors, as most studies overlook the prevalent nonlinear and synergistic effects. This study takes the central urban districts of humid–hot cities in developing countries as an example, utilizing readily available big data sources such as Baidu Heat Map data, Baidu Map data, Baidu Building data, urban road network data, and Amap’s Point of Interest (POI) data to construct a Gradient-Boosting Decision Tree (GBDT) model. This model reveals the nonlinear and synergistic effects of different built environment factors on street vitality. The study finds that (1) construction intensity plays a crucial role in the early stages of urban street development (with a contribution value of 0.71), and as the city matures, the role of diversity gradually becomes apparent (with the contribution value increasing from 0.03 to 0.08); (2) the built environment factors have nonlinear impacts on street vitality; for example, POI density has different thresholds in the three cities (300, 200, and 500); (3) there are significant synergistic effects between different dimensions and indicators of the built environment, such as when the POI density is high and integration exceeds 1.5, a positive synergistic effect is notable, whereas a negative synergistic effect occurs when POI is low. This article further discusses the practical implications of the research findings, providing nuanced and targeted policy suggestions for humid–hot cities at different stages of development.

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.

The fragmented remodeling of historic districts undermines the spirit of place. Understanding the intricate relationship between the neighborhood environment and the spirit of place is essential for sustainable urban development. Current research predominantly relies on case studies and the concept of place, which are subjective and lack specific analysis of how the neighborhood environment shapes the spirit of place. In this study, we examine Chuancheng Street in Handan City as a case study. Utilizing the eXtreme Gradient Boosting (XGBoost) model and multi-source data, combined with SHapley Additive exPlanation (SHAP) and Partial Dependence Plots (PDP), we analyze the non-linear and synergistic effects of the street environment on the spirit of place in historic districts. The findings reveal that (1) the proportion of enduring sociability (PES) on the street significantly shapes the spirit of place, with cultural space elements being less prioritized in perception; (2) PES, green vision index (GVI), Integration_800 m, and mixed land use (MLU) have pronounced non-linear impacts on the spirit of place, with strong threshold effects, and these factors also demonstrate a synergistic effect; (3) There are notable spatial variations in the spirit of place across different blocks, particularly influenced by the authenticity of cultural heritage. This study provides fundamental insights into the spirit of place in historic neighborhoods, enabling a better understanding of complex urban dynamics and informing future street regeneration from a place perspective.

As a well-known urban landscape concept to describe urban space quality, urban street vitality is a subjective human perception of the urban environment but difficult to evaluate directly from the physical space. The study utilized a modern machine learning computer vision algorithm in the urban build environment to simulate the process, which starts with the visual perception of the urban street landscape and ends with the human reaction to street vitality. By analyzing the optimized trained model, we tried to identify urban street vitality’s visual features and evaluate their importance. A region around the Mochou Lake in Nanjing, China, was set as our study area. Seven investigators surveyed the area, recorded their evaluation score on each site’s vitality level with a corresponding picture taken on site. A total of 370 pictures and recorded score pairs from 231 valid survey sites were used to train a convolutional neural network. After optimization, a deep neural network model with 43 layers, including 11 convolutional ones, was created. Heat maps were then used to identify the features which lead to high vitality score outputs. The spatial distributions of different types of feature entities were also analyzed to help identify the spatial effects. The study found that visual features, including human, construction site, shop front, and roadside/walking pavement, are vital ones that correspond to the vitality of the urban street. The consistency of these critical features with traditional urban vitality features indicates the model had learned useful knowledge from the training process. Applying the trained model in urban planning practices can help to improve the city environment for better attraction of residents’ activities and communications.

Territorial tourism has brought new development opportunities for historic and cultural neighborhoods. However, an insufficient understanding of the spatial distribution and influencing mechanisms of neighborhood vitality continues to constrain effective revitalization strategies. This study takes the Shuitingmen Historical and Cultural Neighborhood in Quzhou, China, as a case study and develops a multi-dimensional vitality evaluation framework incorporating point-of-interest (POI) data, location-based service (LBS) heatmaps, street network data, historical resources, and environmental perception indicators. The Analytic Hierarchy Process (AHP) is applied to assign indicator weights and calculate composite vitality scores across 19 streets. The results reveal that (1) comprehensive evaluation corrects the bias of single indicators and highlights the value of integrated assessment; (2) vitality is higher on rest days than on weekdays, with clear temporal patterns and two types of daily fluctuation trends—similar and differential; and (3) vitality levels are spatially uneven, with higher vitality in central and western areas and lower performance in the southeast, often related to low accessibility and functional monotony. This study confirms a strong positive correlation between street vitality and objective spatial factors, offering strategic insights for the micro-scale renewal and sustainable revitalization of historic neighborhoods.

Adolescents' environmental perceptions mediate associations between streetscape environments and active school travel

The quality of life concerned with open spaces has more and more become an essential part of urban culture. The evaluation of environmental effects as perceived by people is primarily a subjective issue, rather than being simply based on objective parameters. This paper presents an approach called livingscape, used to assess the quality of urban spaces. It consists of analyzing and correlating psychometric tools to measure the perception of environmental quality with different aspects related to the urban blight (both in architectural and environmental terms) and objective investigation of environmental quality through the measurement of acoustic, visual, thermal, and IAQ physical parameters. Livingscape data were collected in 13 key-spaces of St. Salvario, an historical district in Turin (Italy), during summer 2010 and winter 2011, selected based on an historical analysis to characterize the past and present district soundscape and subdivided in nodes, paths, and edges. The subjective environmental perceptions are delineated through the analysis of the questionnaires submitted to the users of the area, outdoor. Objective measures (acoustical, lighting, and thermal parameters) were combined to subjective responses, thus providing a more complete key-spaces characterization. The investigation aims to describe the changes in the key-spaces characterization from 19th century to nowadays.