Urban Landscape Characteristics Research Articles

Estimating the impact of 2D urban landscape patterns on extreme precipitation based on non-stationary models in the Guangdong-Hong Kong-Macao Greater Bay Area, China.

Rapid urbanization has significantly altered surface landscape configurations, leading to complex urban climates. While much attention has been focused on impervious surfaces' impact on extreme precipitation, a critical gap remains in understanding how various 2D urban landscape components influence extreme precipitation across different durations. Through an analysis of the non-stationarity and spatiotemporal variations in extreme precipitation across the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 1990 to 2020, we constructed the non-stationary Generalized Additive Models for Location Scale and Shape (GAMLSS) model by introducing six urban landscape structural metrics as explanatory variables for each of the 27 meteorological stations in the GBA. Additionally. we assessed the frequency of these metrics in the best-fitting models and predicted design values across different interannual periods. Our findings reveal that aggregation metrics (patch density: PD) and diversity metrics (Shannon's Diversity Index: SHDI) appeared more frequently in the best-fitting models than other metrics within all extreme precipitation indices. For short-duration extreme precipitation indices (≤ 3h), the area matrix (Impervious Surface Percentage: ISP), PD, and SHDI were selected more often than other metrics, whereas for long-duration (> 3h), PD and SHDI had a higher relative frequency as ISP's impact decreased. Design values peaked in the 2010s across all return periods (100, 50, and 20years), highlighting the importance of integrating urban landscape features into non-stationary models of extreme precipitation. This research provides valuable insights for improving the management of urbanization-induced heavy precipitation and flood risks.

Modeling of the process of spreading fine dust in the atmosphere of residential areas

The features of modeling the dispersion process of fine dust in the atmosphere of residential areas were examined, which not only enhanced understanding of cement dust dispersion processes but also allowed for the development of practical recommendations to mitigate its negative impact on the environment and public health. It is well known that cement production is a primary source of air pollutants, particularly cement dust, which degrades atmospheric quality and poses a serious health risk to nearby populations. The research employed a combination of mathematical modeling and data analysis to predict the dispersion of cement dust in residential areas adjacent to industrial facilities. The model simulated the behavior of particulate matter in the atmosphere, accounting for variables such as wind direction, emission rates, and particle size. Consequently, the study provided an accurate forecast of dust concentrations at varying distances from the emission source, offering valuable insights into the scope of the pollution. Moreover, the analysis revealed that cement dust disperses over wide areas, leading to elevated levels of particulate matter in residential zones, which can significantly affect human health, especially respiratory conditions. In addition to modeling, the study assessed the efficiency of current dust collection systems in cement production plants. It was found that improvements in dust collection technologies could result in a 10-15 % reduction in dust emissions. As a result, the study advocates for the modernization of filtration and capture equipment as a practical solution for mitigating the environmental footprint of cement production. The benefits of this research extend beyond immediate air quality improvements, as the reduction in emissions could lead to enhanced public health outcomes and a decrease in long-term environmental damage. The findings underscore the importance of continuous air quality monitoring and regulatory compliance to control industrial pollution. The study's conclusions suggest that further research is needed to refine the dust dispersion model by incorporating additional environmental factors such as humidity, temperature, and urban landscape features. Future studies could also explore the long-term effectiveness of improved dust collection technologies in diverse climatic and geographic conditions.

Elite poems and mass proverbs: exploring the historic landscape in Nanchang City, China

The rapid process of urbanisation has led to the erosion of the preservation of features of urban landscape. Research on historical landscapes in ancient urban planning has predominantly concentrated on the elite class, neglecting the broader population. Nanchang, a city with a history spanning over two millennia of urban development, is the focus of this study. The study integrates elite poems and mass proverbs through GIS spatial matching and KH Coder word frequency analysis to research the historic landscape in Nanchang City. The research uncovers that the elite class focuses on the historical, literary, and aesthetic aspects of Nanchang, while the masses emphasise urban structure, functionality, and aspirations. These dual perspectives underscore Nanchang’s evolution from inception to formalisation, highlighting its ability to cater to diverse societal needs. The efforts on the distribution of landscape spots and strict modulus reflect the adaptability and sustainability of Nanchang City.

Research on the influence mechanism of park landscape characteristics on psychological perception around expressway based on soundscape theory: a case study of Chengdu Bailuwan Wetland Park

With the continuous development of China's transportation infrastructure strategies and urbanization, urban road networks have seen ongoing improvement. Yet noise pollution along expressways is gradually becoming a prominent issue within the broader challenge of urban noise. Noise control has recently emerged as a bottleneck for building livable cities, while well-designed landscapes can improve the quality of psychological perception. By precisely quantifying the multi-sensory composite perception characteristics of urban green space landscapes, this research explores the mechanism through which such landscapes influence psychological perception. The study site was selected in Bailuwan Wetland Park, Chengdu. Objective and subjective environmental data and questionnaire feedback were collected through sound walks and big data analysis based on the geographic information system, after which quantitative analysis was conducted on how landscape environmental characteristics influence environmental experiential perception. The research explores the mechanism of the influence of the regulation of landscape environment characteristic elements on psychological perception and constructs a visual prediction model to provide a universal theoretical basis and methodological support for the planning and regulation of urban green space landscape along expressways, which is of positive significance in guiding the regulation of landscape space.

Reshaping landscape factorization through 3D landscape clustering for urban temperature studies

As urban populations grow and cities expand, the challenge of managing urban heat and its environmental impacts becomes increasingly critical. Traditional methods for analyzing urban temperature dynamics often fall short in precisely capturing the complexity of urban landscapes. This paper introduces the 3D Landscape Clustering (3LC) framework, a new tool designed to analyze urban temperature dynamics by factoring in landscape variables. It clusters landscapes into homogeneous groups using high-resolution 3D land cover maps. The 3LC adopts a clustering mechanism to enhance flexibility and objectivity in landscape categorization, thereby enhancing the depth and accuracy of urban climate studies and moving beyond traditional classification frameworks such as the Local Climate Zone (LCZ). Case studies demonstrate its capability to provide detailed insights into the relationships between urban landscape features and temperature variations. Additionally, the paper details how the framework can excel in multi-city analyses and outlines advanced analytical techniques. Promising research opportunities and limitations are identified. This research reshapes our approach to landscape categorization, advancing our understanding of the interactions between landscape and climate dynamics, and contributing to more sustainable, climate-resilient cities.

Urban landscape affects scaling of transportation carbon emissions across geographic scales

Understanding the carbon dynamics of the transportation sector is necessary to mitigate global climate change. While urban scaling laws have been used to understand the impact of urban population size on carbon efficiency, the instability of these scaling relationships raises additional questions. Here, we examined the scaling of on-road transportation carbon emissions across 378 US metropolitan statistical areas (MSAs) using diverse urban landscape patterns and spatial units, from the MSA level down to 1 km grid cells. Beginning with a baseline scaling model that uses only population size, we expanded the model to include landscape metrics at each spatial scale based on correlation results. We found that: (1) urban landscape characteristics provide insights into carbon mechanisms not fully captured by population size alone, (2) the impact of population size on on-road carbon emissions transitions from linear to sub-linear scaling relationships as the geographic scale of analysis decreases, and (3) clustered urban developments can form carbon-efficient landscapes, while fragmented urban areas tend to be carbon-inefficient. Based on empirical evidence, this research advocates for hierarchical spatial planning and supports the implementation of policy measures aligned with smart growth principles to mitigate carbon pollution.

Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites

BackgroundThe spread of mosquito-transmitted diseases such as dengue is a major public health issue worldwide. The Aedes aegypti mosquito, a primary vector for dengue, thrives in urban environments and breeds mainly in artificial or natural water containers. While the relationship between urban landscapes and potential breeding sites remains poorly understood, such a knowledge could help mitigate the risks associated with these diseases. This study aimed to analyze the relationships between urban landscape characteristics and potential breeding site abundance and type in cities of French Guiana (South America), and to evaluate the potential of such variables to be used in predictive models.MethodsWe use Multifactorial Analysis to explore the relationship between urban landscape characteristics derived from very high resolution satellite imagery, and potential breeding sites recorded from in-situ surveys. We then applied Random Forest models with different sets of urban variables to predict the number of potential breeding sites where entomological data are not available.ResultsLandscape analyses applied to satellite images showed that urban types can be clearly identified using texture indices. The Multiple Factor Analysis helped identify variables related to the distribution of potential breeding sites, such as buildings class area, landscape shape index, building number, and the first component of texture indices. Models predicting the number of potential breeding sites using the entire dataset provided an R² of 0.90, possibly influenced by overfitting, but allowing the prediction over all the study sites. Predictions of potential breeding sites varied highly depending on their type, with better results on breeding sites types commonly found in urban landscapes, such as containers of less than 200 L, large volumes and barrels. The study also outlined the limitation offered by the entomological data, whose sampling was not specifically designed for this study. Model outputs could be used as input to a mosquito dynamics model when no accurate field data are available.ConclusionThis study offers a first use of routinely collected data on potential breeding sites in a research study. It highlights the potential benefits of including satellite-based characterizations of the urban environment to improve vector control strategies.

Community-based assessment of the dynamics of urban landscape characteristics and ecosystem services in the rainforest and guinea savanna ecoregions of Nigeria

Understanding the dynamics of urban landscapes and their impacts on ecological well-being is crucial for developing sustainable urban management strategies in times of rapid urbanisation. This study assesses the nature and drivers of the changing urban landscape and ecosystem services in cities located in the rainforest (Akure and Owerri) and guinea savannah (Makurdi and Minna) of Nigeria using a combination of remote sensing and socioeconomic techniques. Landsat 8 datasets provided spatial patterns of the normalised difference vegetation index (NDVI) and normalised difference built-up index (NDBI). A household survey involving the administration of a semi-structured questionnaire to 1552 participants was conducted. Diminishing NDVI and increasing NDBI were observed due to the rising trend of urban expansion, corroborating the perception of over 54% of the respondents who noted a decline in landscape ecological health. Residential expansion, agricultural practices, transport and infrastructural development, and fuelwood production were recognised as the principal drivers of landscape changes. Climate variability/change reportedly makes a 28.5%–34.4% (Negelkerke R2) contribution to the changing status of natural landscapes in Akure and Makurdi as modelled by multinomial logistic regression, while population growth/in-migration and economic activities reportedly account for 19.9%–36.3% in Owerri and Minna. Consequently, ecosystem services were perceived to have declined in their potential to regulate air and water pollution, reduce soil erosion and flooding, and mitigate urban heat stress, with a corresponding reduction in access to social services. We recommend that urban residents be integrated into management policies geared towards effectively developing and enforcing urban planning regulations, promoting urban afforestation, and establishing sustainable waste management systems.

Effects of Urban Greenway Environmental Types and Landscape Characteristics on Physical and Mental Health Restoration

As important linear public spaces, urban greenways are highly important for improving public health. Many studies have proven the benefits of urban greenways for human well-being, but fewer studies have focused on the impact of their specific environmental types and characteristics on physical and mental health. In this study, 100 subjects participated in a comparative experiment on three types of urban greenways (urban roads, urban parks, and urban rivers), and corresponding physiological indicator (systolic blood pressure [SBP], diastolic blood pressure [DBP], and pulse) and psychological indicator (perceived restorativeness scale [PRS] and positive and negative affect schedule [PANAS]) data were collected. The results indicated that (1) different greenway environment types lead to different physiological and psychological states; (2) urban park-type greenways (SBP [t = 2.37, p = 0.020], DBP [t = 2.06, p = 0.042], PANAS = 2.80, PRS = 5.39) have the greatest physical and mental recovery benefits, followed by urban river-type greenways (SBP [t = 2.84, p = 0.006], DBP [t = 1.29, p = 0.200], PANAS = 2.30, PRS= 5.02) and urban road-type greenways (SBP [t = 0.78, p = 0.440], DBP [t = 0.37, p = 0.716], PANAS = 2.00, PRS = 4.15); (3) plant color and layer diversity have a significant positive impact on the mental health benefits of the three greenway types; (4) the aesthetics of waterscapes and ornaments can significantly improve the perceived restoration ability of urban river-type greenways; and (5) the comfort of pathways and facilities can effectively promote the psychological recovery potential of urban road-type greenways. These findings systematically demonstrate for the first time the differences in restoration potential among urban greenways of different environmental types and summarize the key landscape characteristic predictors influencing the restoration potential of various types of urban greenways. Our research provides new ideas for proactive greenway interventions for physical and mental health and for enriching the restorative environmental science system.

Особенности теплового поля города Ростова-на-Дону по данным дистанционного зондирования Земли

Thermal fields of natural and anthropotechnogenic origin are an integral part of the urban habitat, which influences the functioning of its components, as well as determines the conditions of existence of living organisms including humans. Settlements act as areas of formation of extensive temperature anomalies, mainly positive ones, known as urban heat island. This phenomenon was discovered more than 200 years ago and is currently being investigated in detail. However, the study of smaller anomalies - neighbourhood-scaled and local - is also a subject of scientific and practical interest. These heterogeneities in the distribution of thermal energy are caused by the presence or functioning of individual objects, and therefore, on the one hand, have a direct impact on the state of the environment and human living conditions, and on the other hand, are a promising area for the development of environmental engineering and settlement rational arrangement. As part of the conducted research, the main patterns of spatial distribution of the Earth's surface temperature within the Rostov-on-Don city district were identified, as well as the areas of formation of anomalies of local and neighbourhood scale and objects and processes contributing to their formation were identified. The results and conclusions can be used for further study of ecological and geophysical features of urban landscapes, and also have an applied value in determining the direction and consequences of the impact of the advancement of specific types of urban development of the territory.

Differences in Emotional Preferences toward Urban Green Spaces among Various Cultural Groups in Macau and Their Influencing Factors

This study explores the diversity in emotional tendencies and needs toward urban green spaces (UGSs) among people from different cultural backgrounds in the wave of cultural integration. We utilized social media data as research tools, gathering a wide range of perspectives and voices. Utilizing geolocation data from 176 UGSs in Macau, we collected 139,162 social media comments to analyze the emotional perceptions of different cultural groups. Furthermore, we conducted regression analysis on the number of posts and emotional intensity values from four linguistic groups—Chinese, English, Southeast Asian languages, and Portuguese—in UGSs, correlating them with ten locally relevant landscape features. Our findings reveal diverse attitudes, emotional inclinations, and functional and design needs of different linguistic groups toward UGSs, as follows: (1) there were significant differences in emotional intensity and tweet counts across 176 UGSs; (2) Chinese and Portuguese speakers showed a more positive attitude toward plazas and natural ecological areas, whereas English- and Southeast-Asian-language speakers tended to favor recreational areas and suburban parks; (3) Chinese speakers exhibited a more positive emotional intensity toward sports facilities, while English speakers placed more emphasis on green space areas, architecture, sports infrastructure, and plant landscapes; (4) there was no specific landscape feature preference for Portuguese- and Southeast-Asian-language speakers. This research not only deepens our understanding of the emotional perceptions and preferences of UGSs among different cultural groups but also explores the association between these groups and various urban landscape features. This provides important theoretical and practical insights for future UGS planning, construction, and promoting multicultural coexistence for sustainable urban development.

Beyond just green: Explaining and predicting restorative potential of urban landscapes using panorama-based metrics

Amidst the growing concerns about mental health issues among urban dwellers, there has been increased attention to the restorative potential (RP) of urban landscapes. However, research into the relevant landscape features tends to lack the integration of quantifiable methodologies, which are essential for practical implementation in urban planning and management. To address this gap, our study employed panorama-based metrics relying on computer vision to quantify the features of urban landscapes. We established an explanatory (generalized linear mixed model, GLMM) and a predictive (Random Forest) model to comprehensively elucidate the relationship between these landscape features and RP. The RP outcome was derived from an online survey of respondents (n = 1500) with diverse socio-demographic characteristics, where each respondent was randomly assigned panoramic images from a pool of a hundred landscape scenes taken across the city of Singapore. The GLMM regression identified sky, tree, water, and depth as the primary influences of RP. Furthermore, the Random Forest model demonstrated commendable predictive performance and was used to visually showcase the scenes within the testing dataset with corresponding RP outcomes. To illustrate the potential application of this predictive tool, we also performed spatial mapping for RP prediction, taking the Singapore Botanic Gardens as an example. The subsequent discussion underscored the simultaneous effect of tree, sky, and depth on RP from a quantitative perspective using LOWESS curves. By employing metrics and models that explain and predict the RP, this study contributes evidence-based insights that underscore the pivotal role of trees and openness (sky and depth) in enhancing the psychological benefits of urban landscapes and provides an effective predictive tool that contributes to urban landscape planning and management.

A GIS-based approach to assess the influence of the urban built environment on cardiac and respiratory outcomes in older adults

The impact of the urban built environment on cardiovascular and respiratory health has been studied extensively in children and adults. However, limited research exists on this topic in older adults. To fill this gap, we conducted a ten-year retrospective study of a cohort of elderly people living in Lausanne. We extracted cardiac and respiratory health outcomes of people living at the same address. A geographic information system (GIS) application was used to join spatial data between participant address and characteristics of the built environment (building age, building density, road density, proximity to vegetation and lake, elevation). To capture the impact of the urban landscape characteristics on the health of older people, the built environment descriptors, were considered alone and aggregated. Principal component analysis (PCA) was employed to resolve the multicollinearity among the built environment descriptors, while agglomerative hierarchical clustering was used to reveal underlying patterns and relationships within built environment variables. In addition, to better assess the effectiveness of ventilation in their homes, we characterized indoor air-associated mycobiomes in a representative sample of homes. This involved employing both culture-based methods and metabarcoding of the first internal transcribed spacer (ITS1) of the ribosomal DNA. The analysis revealed a significant association between exposure to the built environment as a whole and heart disease in the elderly. A higher prevalence was observed for the cluster of buildings near the lake/highway. The period of building construction showed an association with the prevalence of chronic lung disease and chronic cough, with a higher prevalence observed in houses built between 1975 and 2013. The contribution of indoor air pollution to worsening respiratory health was confirmed by higher Aspergillus spore loads and increased relative abundance of Gloeophyllum and Aureobasidium in indoor air, all of which were associated with respiratory outcomes. This research highlights the crucial influence of the built environment on the health status of the elderly population, with implications for targeted interventions and public health policy development.

Quantitative study on color characteristics of urban park landscapes based on K-means clustering and SD. method

Quantitative study on color characteristics of urban park landscapes based on K-means clustering and SD. method

Using butterfly survey data to model habitat associations in urban developments

Abstract One approach for measuring the potential biodiversity in new urban construction projects is through ecological models that predict how wildlife will respond. For the United Kingdom, such models have only been developed for birds, but to maximise the extent to which models represent overall biodiversity, species from different indicator groups must be considered. Here, we assess this possibility for butterflies by combining citizen science survey data with high‐resolution digital maps. We derive detailed characteristics of urban landscapes around survey sites using previously established methods and quantify their relationship to counts of adults of 18 butterfly species in urban and peri‐urban settings. Higher butterfly counts were found when traversing urban sites with larger areas of semi‐natural grassland, other managed greenspaces and adjacent arable land. Most of the butterfly community were found to have negative relationships with highly built‐up or fragmented landscapes. We found high species‐specificity for different details of urban form, particularly in habitat elements such as gardens, vegetation around railways and grass verges. Policy implications. Improving biodiversity is now part of legislation governing new construction projects from England and Wales. However, predicting quantitative changes from hypothetical land‐use modifications remains challenging. Our models provide the foundation through which butterfly abundance could be integrated into an urban biodiversity assessment tool, providing species‐ and community‐level statistics to non‐specialists from the urban planning and design sector. This would allow them to hone configurations for built surfaces, private gardens, greenspaces and wildlife areas and assess their capacity to provide residents with the intended access to nature.

Research on the Influencing Mechanism of Historic Urban Landscape Characteristics on Public Sentiments and the Spatio-temporal Differentiation Patterns—A Case Study of Shaoxing Ancient City in Zhejiang Province, China

Research on the Influencing Mechanism of Historic Urban Landscape Characteristics on Public Sentiments and the Spatio-temporal Differentiation Patterns—A Case Study of Shaoxing Ancient City in Zhejiang Province, China

How Do Urban Waterfront Landscape Characteristics Influence People’s Emotional Benefits? Mediating Effects of Water-Friendly Environmental Preferences

Landscapes in urban waterfront spaces are of high societal value as they can meet people’s physical, psychological, and social needs for health and well-being, but the relationship between waterfront landscape features and public health benefits has yet to be clarified. From the perspective of blue health, this paper takes blue spaces along the Min River of Fuzhou City as the research object. It constructs the mediation model of “Waterfront landscape characteristic (WLC)→Water-friendly environmental preference (WEP)→Emotional benefit (EB)”. The role of WEP in the mechanisms linking WLC and EB in different waterfront spaces was explored through the quantification of indicators, questionnaires, and physiological measurements. The main results showed that (1) the ability of different types of waterfront spaces to arouse positive emotions in the population is characterized by blue-dominated space > co-dominated space > green-dominated space, while the opposite is true in terms of inducing negative emotions; (2) green visibility, blue visibility, naturalness, water-friendliness, and openness are all significantly related to EBs and could affect positive emotions either directly or mediated by WEPs, but openness seems not to affect negative emotions through WEPs; (3) cleanliness affects the population’s WEP for the coherence dimension, but has no significant role in improving EBs. This study could expand new ideas for research in habitat health, provide a reference for embodied evidence-based waterfront landscape design, and help promote the construction and development of healthy cities.

Environmental correlates of aquatic macroinvertebrate diversity in garden ponds: Implications for pond management

Abstract Garden ponds are a ubiquitous feature of urban landscapes and have the potential to be an important resource for biodiversity. However, the environmental and spatial factors influencing ecological communities within garden ponds have been poorly quantified to date, despite such evidence being critical to the development of effective management strategies that support biodiversity. This study aims to identify the environmental and spatial factors influencing macroinvertebrate richness, abundance and compositional variation among garden ponds and provide management recommendations at the local and landscape scale. In total, 99 macroinvertebrate taxa were recorded from 30 garden ponds. A negative association was recorded between ecological uniqueness (measured as the Local Contribution to Beta‐Diversity) and taxonomic richness among the garden ponds, and those ponds with high ecological uniqueness typically displayed high replacement (turnover) values. Surface area, total plant richness and non‐native plant richness were positively associated with macroinvertebrate richness. Taxonomic richness and abundance predominantly displayed a negative association with conductivity levels. Current management practices for garden ponds are highly variable, often focussed on individual ponds. Based on the findings of this study, we recommend that management should focus on making garden pond surface areas as large as possible, ensure that there is a wide variety of native aquatic plant species present and manage conductivity levels. Garden ponds likely comprise a significant component of the urban freshwater network, and considering their management at both local and landscape scales will ensure that biotic communities inhabiting urban landscapes can be more effectively supported.

Predictors of West African urban bird species richness and composition

AbstractUrbanisation and its associated habitat loss and fragmentation are considered a major threat to wildlife. In this study, we assessed the predictors of bird species abundance, richness and composition within 70 cells of 500 m by 500 m spread across the five urban areas constituting Ibadan metropolis. A total of 4167 individuals of 55 species belonging to 30 families were recorded. We report that Laughing dove Streptopelia senegalensis, Speckled pigeon Columba guinea and Yellow‐billed kite Milvus aegyptius contributed to a greater proportion of bird abundance across sites and Columbidae was the most abundant bird family. Bird species richness increased significantly with tree abundance but decreased significantly with the number of pedestrians. Similarly, foraging guild richness declined significantly with the number of pedestrians. Granivores and scavengers constituted the most abundant foraging guilds. Areas with high pedestrians' traffic were associated with fewer trees and were dominated by fewer numbers of species belonging to a few foraging guilds. This is likely because such areas offer limited foraging opportunities or cover from potential predators. We conclude that improving urban landscape characteristics through revegetation, establishment of green spaces and buffering from human disturbance will improve the composition and richness of avian species in the Ibadan metropolis.

The Influence of Visual Landscapes on Road Traffic Safety: An Assessment Using Remote Sensing and Deep Learning

Rapid global economic development, population growth, and increased motorization have resulted in significant issues in urban traffic safety. This study explores the intrinsic connections between road environments and driving safety by integrating multiple visual landscape elements. High-resolution remote sensing and street-view images were used as primary data sources to obtain the visual landscape features of an urban expressway. Deep learning semantic segmentation was employed to calculate visual landscape features, and a trend surface fitting model of road landscape features and driver fatigue was established based on experimental data from 30 drivers who completed driving tasks in random order. There were significant spatial variations in the visual landscape of the expressway from the city center to the urban periphery. Heart rate values fluctuated within a range of 0.2% with every 10% change in driving speed and landscape complexity. Specifically, as landscape complexity changed between 5.28 and 8.30, the heart rate fluctuated between 91 and 96. This suggests that a higher degree of landscape richness effectively mitigates increases in driver fatigue and exerts a positive impact on traffic safety. This study provides a reference for quantitative assessment research that combines urban road landscape features and traffic safety using multiple data sources. It may guide the implementation of traffic safety measures during road planning and construction.