Characteristics Of Urban Environment Research Articles

The Development of the Berliner as a Political Person under the Influence of the Urban Cultural Environment

 Introduction. The article explores the reasons for the emergence of ethnic diversity in the   city of Berlin, encompassing political, historical, geographical, anthropological, and sociological aspects of the urban environment. It also outlines the current issues in German   society that shape the political culture of its people. The relevance of the topic is driven by   the political radicalization of migrants in Germany, particularly in Berlin, resulting from   cultural globalization that impacts traditional political institutions and behavioral models of   the city's residents. The crisis of the liberal-democratic model and the increase in the   number of Eurosceptics also play a significant role in the transformation of the political   culture of contemporary Berliners. Solving the problem of political radicalization of migrants   is equally important for the Russian Federation, especially in St. Petersburg, combining   characteristics of the European urban environment and the polyethnic Russian state, whose   residents consider themselves Russians despite national and cultural differences.   Methodology and sources. The research is grounded in Ernest Burgess's model of   concentric zones. The study relies on research methods such as synthesis, comparison,   analysis, and sociological approaches. Data from the Federal Statistical Office of Germany,   the results of the 2016 elections to the Berlin House of Deputies, the 2021 elections, and the   subsequent 2023 elections, historical data, and findings from other researchers on the topic   served as sources for the study.  Results and discussion. The study identifies features of urban culture shaping the   polyethnic Berlin society, including the ethnic diversity of residents, unique self-identification   of Berliners, the presence of distinct urban districts, squatter movements, and the influence   of architectural and spatial characteristics on the formation of human social behavior. Some   city problems were also highlighted, such as the crisis of formation of a distorted perception   of right and left currents among Berliners, the failure of multiculturalism policies, the   migration crisis, and housing shortages.   Conclusion. The development of a Berliner as a political being is influenced by the   characteristics of the urban environment and certain issues facing the city, where the   residential district, properties of the city's political culture, and ethnic background impact   the political views of its inhabitants 

Environmental health, economy, and amenities interactively drive migration patterns among China's older people.

The increasing number of older adult migrants is rapidly changing regional demographic and social structures in China. There is an urgent need to understand the spatial patterns and factors that influence older adults to migrate, especially the role of environmental health. However, this issue has been under-studied. This study focused on intra-provincial and inter-provincial older adult migrants as research subjects, estimated their spatial concentration index based on the iterative proportional fitting approach, and explored the factors influencing their migration using the GeoDetector Model. The results showed the following: (1) In 2015, more than 76% of inter-provincial older adult migrants were distributed in Eastern China, and most intra-provincial older adult migrants were scattered in sub-provincial cities. (2) Compared to factors relating to economy and amenities, environmental health by itself played a relatively weak role in the migration of older adults, but the interaction among environmental health, economy, and amenities was a key driving force of older adult migration. (3) There were significant differences in the dominant environmental health factors between inter-provincial migration and intra-provincial migration, which were temperature and altitude, respectively. Our findings can help policymakers focus on the composition of older adult migrants based on urban environmental health characteristics and rationally optimize older adult care facilities to promote supply-demand matching.

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.

Using Remote Sensing Data in Urban Crime Analysis: A Systematic Review of English-Language Literature from 2003 to 2023

Drawing from environmental criminology principles, this article explores the existing literature to assess the utility of remote sensing data in detecting and analysing features in the urban environment that are associated with crime occurrence. A systematic review of the literature in the English language from 2003 until the first half of 2023 from two major databases, Scopus and Science Direct, is carried out. As many as 910 publications were selected, from which 36 publications satisfied the selection criteria. Findings show that neighborhood's design has a quantifiable imprint that is possible to be observed with very high spatial-resolution imagery. Given its high spatial and temporal resolution, remote sensing data can to different degrees support the identification of criminogenic features in urban environments (streets and roads, property boundaries, housing density, characteristics and density of vegetation as well as luminosity levels), but when it is used for the detection of potentially illegal activities, infringement of people's privacy and methods lacking validation still present serious concerns. The article concludes with a discussion of the opportunities and challenges of using remote sensing data in crime analysis.

STICAP : Spatio-temporal Interactive Attention for Citywide Crowd Activity Prediction

Accurate citywide crowd activity prediction (CAP) can enable proactive crowd mobility management and timely responses to urban events, which has become increasingly important for a myriad of smart city planning and management purposes. However, complex correlations across the crowd activities, spatial and temporal urban environment features and their interactive dependencies, and relevant external factors (e.g., weather conditions) make it highly challenging to predict crowd activities accurately in terms of different venue categories (for instance, venues related to dining, services, and residence) and varying degrees (e.g., daytime and nighttime). To address the above concerns, we propose STICAP , a citywide spatio-temporal interactive crowd activity prediction approach. In particular, STICAP takes in the location-based social network check-in data (e.g., from Foursquare/Gowalla) as the model inputs and forecasts the crowd activity within each time step for each venue category. Furthermore, we have integrated multiple levels of temporal discretization to interactively capture the relations with historical data. Then, three parallel Residual Spatial Attention Networks ( RSAN ) in the Spatial Attention Component exploit the hourly, daily, and weekly spatial features of crowd activities, which are further fused and processed by the Temporal Attention Component for interactive CAP . Along with other external factors such as weather conditions and holidays, STICAP adaptively and accurately forecasts the final crowd activities per venue category, enabling potential activity recommendation and other smart city applications. Extensive experimental studies based on three different real-world crowd activity datasets have demonstrated that our proposed STICAP outperforms the baseline and state-of-the-art algorithms in CAP accuracy, with an average error reduction of 35.02%.

Assessing the Soundscape Characteristics of Historical Urban Environments: An Analysis of the Historical Erbil Citadel and Its Environments

This research addresses the neglect of sensory features, specifically the soundscape, in studies focused on preserving historic areas. The aim was to contribute to soundscape research by examining the effect of sound on the perception of urban historical places and the impact of the “renewal and transformation” process on audio-visual experiences. This study focused on the historical Erbil Citadel as a case study area, known for its extensive cultural history. Sound sources in the region were identified, recorded, and analyzed using software to calculate the Sound Pressure Level (SPL). The sound sources assessed as a result of the analysis were examined using Schafer’s sound characteristics. In this study, data was collected through a soundwalk study in the historical Erbil Citadel, and its surroundings were identified. The characteristics of the sounds were determined from the sound recordings, enabling the distinction of sounds contributing to the city’s identity. A route was established in its current state, and it was identified which regional precautions should be taken to preserve the sound identity of the city.

How Did Urban Environmental Characteristics Influence Land Surface Temperature in Hong Kong from 2017 to 2022? Evidence from Remote Sensing and Land Use Data

Urbanization has led to environmental challenges, with the urban heat island effect being a prominent concern. Understanding the influence of urban environmental characteristics (UECs) on land surface temperature (LST) is essential for addressing this issue and promoting sustainable urban development. The spatiotemporal characteristics and influencing factors of LST have been investigated in past studies, but research that explicitly investigates the key factors and long-term spatial relationships affecting LST in city-scale urban areas is limited. Remote sensing techniques provide valuable insights into LST patterns and the relationship between urban environment and temperature dynamics. We utilized Landsat 8 images to derive the LST and six spectral indices from 2017 to 2022 in Hong Kong, a city characterized by high population density and rapid urban growth. We also acquired land use data to reflect Hong Kong’s dynamic urban landscape. The complex interactions between urban environment and LST were analyzed using various analytical techniques, including slope trend analysis, land use change detection, and correlation analysis. Finally, we constructed a random forest model to assess the importance of each environmental characteristic. Our findings provide three key insights for regions experiencing rapid urbanization. First, the LST showed an increasing trend in Hong Kong from 2017 to 2022, with the annual LST rising from 21.13 °C to 23.46 °C. Second, we identify negative relationships between LST and vegetation (−0.49)/water bodies (−0.49) and a positive correlation between LST and built-up areas (0.56) utilizing Pearson’s correlation. Third, the dominant influence of built-up areas was underscored, contributing as much as 53.4% to elevated LST levels, with specific attention to the substantial reclamation activities in Hong Kong. The insights from this study provide valuable guidance for policymakers, urban planners, and environmental researchers to formulate evidence-based strategies to achieve a resilient, livable urban future.

Revealing the Environmental Characteristics of Towns in the Middle Himalayas Using a Geographic Information System and Self-Organizing Map

Through long-term interactions with the natural environment, the ethnic groups in the mid-Himalayas have formed unique urban environmental characteristics. Effectively identifying urban environmental characteristics is a prerequisite for implementing sustainable urban management strategies. This study took 194 towns in the mid-Himalayan as the research objects. GIS was used to statistically analyze the terrain, climate, soil, and other environmental characteristics of the towns. The SOM (Self-organizing map) method was used to classify the comprehensive environmental characteristics of the towns. The results show that the main urban environmental characteristics in this area are low-altitude towns account for a large proportion, gentle-slope towns account for a small proportion, rainfall is mainly 125–265 mm, vegetation cover is dominated by high-coverage towns, mainly distributed in central and southern parts, the soil is dominated by embryonic soil and alluvial soil. The SOM method overcomes the subjectivity and low degree of automation in traditional research on urban environmental characteristics using threshold indicator methods or feature interpretation methods. Based on environmental characteristics, the towns were divided into six categories, and the classification results showed a distinct north–south zonal distribution pattern. There were significant differences in the environmental characteristics of towns in different clusters, such as the towns in cluster 5 had high altitudes, low rainfall, and low vegetation coverage, while the towns in cluster 2 had low altitudes, high rainfall, and high vegetation coverage. Finally, based on the SOM clustering results, governance strategies were proposed for towns in different clusters to cope with climate and environmental changes and promote sustainable development in the mid-Himalayan.

Characteristics of urban neighbourhood environments and cognitive age in mid-age and older adults.

In this cross-sectional study, we examined the extent to which features of the neighbourhood natural, built, and socio-economic environments were related to cognitive age in adults (N=3418, Mage=61 years) in Australia. Machine learning estimated an individual's cognitive age from assessments of processing speed, verbal memory, premorbid intelligence. A 'cognitive age gap' was calculated by subtracting chronological age from predicted cognitive age and was used as a marker of cognitive age. Greater parkland availability and higher neighbourhood socio-economic status were associated with a lower cognitive age gap score in confounder- and mediator-adjusted regression models. Cross-sectional design is a limitation. Living in affluent neighbourhoods with access to parks maybe beneficial for cognitive health, although selection mechanisms may contribute to the findings.

Reductions in community noise levels in vancouver, Canada, during pandemic lockdown and association with land cover type

BackgroundUrban transportation noise is a major public concern because of its adverse effects on health. The determinants of urban noise exposure have not been widely explored but the “natural experiment” presented by the COVID-19 lockdowns presented a unique opportunity.This study examined the relationship between environmental characteristics and urban noise pollution during the COVID-19 related lockdown in Metro Vancouver, Canada, from March 21st to May 18th, 2020. MethodsWe used noise exposure data from the Vancouver International Airport (YVR) noise management program, comparing the noise levels during “Phase One” of the COVID-19 lockdown in 2020 to the corresponding time period in 2019 from 21 Noise Monitoring Terminals (NMTs) located throughout Metro Vancouver. We modelled the relationship between the change in noise level and the physical NMT environments, including land cover, and total length of roads at four different time periods (24Hr, daytime, evening and nighttime) and within three different buffer zones (100 m, 250 m, and 500 m). ResultsOf 59,472 hourly measurements of community noise, the 24-h noise level was reduced by an average of 2.20 dBA between 2019 and 2020. Higher proportions of greenspace, barren areas, and soil-cover around NMTs resulted in stronger noise reductions and higher density of building, pavement, and water weakened the amount of noise reduction. Proximity of high-volume traffic roads (highways) were associated with weaker noise reduction. ConclusionThe COVID-19 related lockdown was associated with reduced noise in Metro Vancouver, and the relative reduction depended on the types of the environment surrounding the NMT. Future research on the effects of urban environmental characteristics on geographic inequality in noise levels and health consequences of the COVID-19 related lockdown is merited.

How Do Urban Walking Environments Impact Pedestrians’ Experience and Psychological Health? A Systematic Review

Daily walks are recommended for health gains, and walkable urban environments are recommended as one strategy to combat climate change. Evidence of the relationship between physical environments and psychological health is increasing. The aim of this study was to systematically review and compile evidence regarding micro-scale characteristics in urban outdoor environments that impacted pedestrian short-term experience and/or long-term psychological health. The databases ScienceDirect, Scopus, PubMed, PsychInfo, and Google Scholar were used. To explore the area, a large heterogeneity in publications was allowed; therefore, it was not possible to conduct a meta-analysis. From 63 publications, data items were extracted from full text and categorized according to the main study characteristics. Environmental characteristics impacting pedestrians psychologically were identified and categorized into themes: grey, green, blue, and white areas, and weather, temporalities, topography, person factors, and safety. Environmental factors were analyzed from the perspective of the circumplex model of human affect (negative/positive dimensions and activation/deactivation). The findings included the fact that urban pedestrians need both positively activating and deactivating (restorative) areas during walkabouts. Perceived safety is essential for experiencing the positive aspects of urban environments. Some characteristics interact differently or have different importance for health in different groups. To further develop research on pedestrian environments, psychological experiences should be included.

Automatic bike sharing system planning from urban environment features

ABSTRACT The planning process for bike sharing systems is often complex, involving multiple stakeholders and several considerations: finding hotspots in the potential demand, and dimensioning the system, requires an intimate knowledge of urban mobility patterns and specific local features of the city. The significant costs associated with dynamic rebalancing of bike sharing systems, i.e. with moving bikes across the city to correct the demand imbalance and ensure that they are available where and when they are needed, make correct planning even more critical for the economic viability of the system. In this work, we consider urban environment data from multiple sources and different cities in Europe and the United States to design an automated planning pipeline to place stations in an area with no direct knowledge of the demand. The first step in the planning is to build models of activity patterns and correlate them with features of the urban environment such as land use and mass transit availability; these statistical models can then be used to expand an existing network or even create an entirely new one in a different city. A use case in New York City shows that our system can effectively plan a bike sharing system expansion, providing a valuable first step for the planning process and allowing system designers to identify gaps in existing systems and the locations of potential demand hotspots.

Tourism Morphometrics in Venice: Constructing a Tourism Services Index (TSI) to unmask the spatial interplay between tourism and urban form

Understanding how the built environment affects tourism saturation in cities is of central importance to the efficacy of tourism management strategies that rely on spatial datasets to inform space-specific policies. By employing the historic city of Venice (Italy) as a case study, this article provides a nuanced methodology defined as “tourism morphometrics” to inform tourism management plans and to detect urban form configurations prone to “overtourism.” The research contribution is twofold: first, it harnesses spatial datasets on lodging (6642 Airbnb listings, InsideAirbnb), dining (997 restaurants, Tripadvisor), and shopping (226 stores, Google Points of Interest) to construct a highly-granular ‘Tourism Services Index’ (TSI) that reveals the spatial distribution of tourism service capacity in Venice; second, it empirically reveals the spatial interplay between urban form parameters—landmarks, bridges, and street network—and TSI values. More specifically, statistical analyses found that TSI scores were positively associated with the number of street intersections, street widths values, and proximity to landmarks and bridges in Venice. The results offer insights into the relationship between tourism services and the spatial features of urban environments in which visitors move and behave. By leveraging by-product geospatial datasets, this research argues for a renewed management of overtourism through space-specific planning models.

디지털 인터랙티브 전시콘텐츠에 적용된 인터페이스 제작방식이 관람객 만족도에 미치는 영향에 관한 연구

In recent years, complex cultural spaces have become prominent features of urban environments, encompassing a range of functions such as culture, arts, leisure, and commerce. These spaces have gained popularity among urban residents in search of cultural experiences and recreational activities, thereby creating opportunities for diverse cultural industries and fostering urban cultural diversity and creativity. This study classifies the interface elements of interactive exhibitions in complex cultural spaces. Additionally, it investigates and analyzes the enjoyment and preferences of visitors who encounter exhibition content associated with each interface technology element. The findings of this study provide valuable insights for the future development of exhibition content in complex cultural spaces.

Cross-Cultural Comparison of Urban Green Space through Crowdsourced Big Data: A Natural Language Processing and Image Recognition Approach

Understanding the relationship between environmental features and perceptions of urban green spaces (UGS) is crucial for UGS design and management. However, quantifying park perceptions on a large spatial and temporal scale is challenging, and it remains unclear which environmental features lead to different perceptions in cross-cultural comparisons. This study addressed this issue by collecting 11,782 valid social media comments and photos covering 36 UGSs from 2020 to 2022 using a Python 3.6-based crawler. Natural language processing and image recognition methods from Google were then utilized to quantify UGS perceptions. This study obtained 32 high-frequency feature words through sentiment analysis and quantified 17 environmental feature factors that emerged using object and scene recognition techniques for photos. The results show that users generally perceive Japanese UGSs as more positive than Chinese UGSs. Chinese UGS users prioritize plant green design and UGS user density, whereas Japanese UGS focuses on integrating specific cultural elements. Therefore, when designing and managing urban greenspace systems, local environmental and cultural characteristics must be considered to meet the needs of residents and visitors. This study offers a replicable and systematic approach for researchers investigating the utilization of UGS on a global scale.

Urban biotope classification incorporates urban forest and green infrastructure for improved environmental land-use planning in Mexico City

Urban forests are recognized worldwide as the most critical component of green infrastructure due to their capacity to provide various environmental goods and services. As cities continue to expand and their environmental problems intensify, there is a growing need for urban forests and green infrastructure to be better incorporated into strategic land-use planning, especially in developing cities. The first step in building an urban forest management plan is to capture characteristics of the urban forest and how these change across the built environment. Here, we used an urban biotope approach to classify urban forests and environmental characteristics in Mexico City. We sampled 500 fixed-area randomly stratified plots across the city to characterize urban forest structural and compositional variables. PCA and the broken-stick method were used to reduce the number of 25 urban forest variables down to five significant principal components that accounted for 78% of the data's cumulative variation. Ward's method helped classify biotopes into a hierarchical system with seven finer-level biotopes defined by urban forest characteristics (Dunn = 0.09, AC = 0.98), nested within two broader-level biotopes defined by forest canopy conditions (Silhouette = 0.59, AC = 0.99). A no-tree canopy biotope was extracted from sampling locations with no trees. The biotopes derived here can fundament biotope mapping, and support decision-making in urban forest planning, including the identification of available planting spaces, tree diversity targets, and canopy protection. Our work in Mexico City demonstrates how the biotope approach can be adapted and used to better incorporate urban forests and green infrastructure into future management planning for any city.

Urban religion beyond the city: theory and practice of a specific constellation of religious geography-making

ABSTRACT The concept of urban religion demands us to start operationally with analyzing characteristics of urban environments and their impact on religious forms of communication. Yet this notion was not necessarily designed to apply only to the city and related phenomena exclusively observed in city spaces. Practices, beliefs, even institutions developing as urban religion spread out beyond the city. Thus, the geography of lived urban religion and of agents of urbanity is different from what the same people imagine and geographically locate as city space. This article intends to develop the conceptual tools for analyzing this blurring of boundaries produced by religious semantics, discourses and practices interacting with implicit and explicit border-constructions linked to practices of ‘urbanity’. The highly debated ‘urban’ or ‘anti-urban’ character of ancient Christianities serves as our point of departure for developing comparative tools.

Urban tree composition is associated with breeding success of a passerine bird, but effects vary within and between years

Birds breeding in urban environments have lower reproductive output compared to rural conspecifics, most likely because of food limitation. However, which characteristics of urban environments may cause this deficiency is not clear. Here, we investigated how tree composition within urban territories of passerine birds is associated with breeding probability and reproductive success. We used 7 years of data of breeding occupancy for blue and great tits (Cyanistes caeruleus; Parus major) and several reproductive traits for great tits, from 400 urban nest boxes located in 5 parks within the city of Malmö, Sweden. We found that tits, overall, were less likely to breed in territories dominated by either non-native trees or beech trees. Great tit chicks reared in territories dominated by non-native trees weighed significantly less, compared to territories with fewer non-native trees. An earlier onset of breeding correlated with increased chick weight in great tits. Increasing number of common oak trees (Quercus robur) was associated with delayed onset of breeding in great tits. Notably, as offspring survival probability generally increased by breeding earlier, in particular in oak-dominated territories, our results suggest that delayed onset of breeding induced by oak trees may be maladaptive and indicate a mismatch to this food source. Our results demonstrate that tree composition may have important consequences on breeding success of urban birds, but some of these effects are not consistent between years, highlighting the need to account for temporal effects to understand determinants of breeding success and inform optimal management in urban green spaces.

Evaluation of planetary boundary layer schemes on the urban heat islands in the urban agglomeration over the greater bay area in South China

Urbanization of large cities exerts significant changes in surface air temperature, which subsequently lead to inadvertent local weather and climate changes. The exchanges of momentum, moisture and heat within the planetary boundary layer (PBL) impact the urban atmosphere representation. The applicability of PBL schemes in regional modeling varies with different climate regimes and underlying surface. As the first step to gaining a better forecast of urban climates, the performances of multiple PBL schemes (YSU, MRF, ACM2, BL, MYNN, UW and GBM) on reproducing the low-level urban atmosphere are evaluated over the Greater Bay Area, South China, during April–June 2018. With the aid of observations from in situ weather stations and radiosondes, the urban environmental characteristics, including surface air temperature and humidity, temperature profile, urban heat island (UHI), and PBL height, are assessed. The results show a cool/moist bias near the surface during nighttime and a warm/dry bias during daytime for all PBL schemes. The daytime bias is significant on weak-UHI days while the nocturnal bias appears to be significant on strong-UHI days. The so-called best scheme depends on the exact meteorological variables, diurnal cycles, and thermodynamic conditions that are of interest. Specifically, the MYNN and MRF schemes perform the best for the daytime and nighttime air temperature, respectively. The MRF scheme also presents the best performance in simulating the observed UHI, with a good agreement with the observed diurnal variation. The numerical exercises in this study may serve as a reference for an efficient operational way that is readily accessible to forecast air temperature in the urban agglomeration over South China.

Quantification through deep learning of sky view factor and greenery on urban streets during hot and cool seasons

The urban heat island effect has gained attention worldwide. Built environment characteristics such as sky view factor (SVF) and green view index (GVI) can affect urban thermal environments and pedestrians’ thermal comfort. With recent technological advances, Google Street View (GSV) can be used to rapidly obtain panoramic street-view images with high reliability, enabling convenient and low-cost environmental assessment of urban settings. In addition, deep learning technology for quantifying the characteristics of urban environments has advanced considerably. This study sought to (1) determine the consistency between deep learning and manual classification of urban environment characteristics and (2) investigate the effects of street-level SVF and GVI on thermal comfort, especially the differences in their effects during hot and cool seasons. The study was conducted in the West District of Taichung City, and GSV was used to capture images from which SVF and GVI were calculated. A total of 50 sample locations were selected for an onsite questionnaire and thermal comfort was measured to determine the effects of SVF and GVI. The results indicated deep learning and manual classifications of SVF and GVI to be highly correlated. With regard to effects, SVF had a significant positive effect on physiological equivalent temperature and thermal sensation votes. GVI also had a significant positive effect on physiological equivalent temperature, but no effect on thermal sensation votes. Thus, reducing SVF and implementing greening projects may improve thermal comfort of pedestrians on the streets. These results offer implications for future urban planning and large-scale urban thermal environment assessments.