Mean Building Height Research Articles

The impact of urban morphology on land surface temperature across urban-rural gradients in the Pearl River Delta, China

Optimizing urban morphology effectively mitigates urban heat island effects. However, previous research on urban morphology and land surface temperature (LST) has often concentrated on entire cities or specific local areas, neglecting the heterogeneity within urban regions. This study investigates the impact of two-dimensional (2D) and three-dimensional (3D) urban morphology on LST based on urban-rural gradients. Firstly, we calculated nine 2D and 3D indicators to comprehensively depict the urban morphology. Then, we employed a multi-iterative quantile method based on nighttime light data to delineate three city subclasses: urban core, suburb, and rural area (USR). Finally, we quantified the impact of these indicators on LST thorough correlation analysis and a random forest (RF) model. Results indicate that, overall and post-USR classification, the sum of building area (SBA) is the primary factor influencing LST, contributing up to 31.6% in suburb. The main 3D factors affecting LST differ across subclasses: in rural area, it is the sum of building surface (SBS, 12.9%); in suburb, it is the spatial congestion degree (SCD, 9.7%); and in urban cores, it is the mean building height (MBH, 12.0%). Notably, the influence of 3D indicators increases from 68.1% in rural area to 72.3% in urban core. In urban core, MBH and standard deviation of height (SDH) are negatively correlated with LST. Case studies in Tianhe District (Guangzhou) and Futian District (Shenzhen) confirmed these indicators do have a certain effect on reducing the temperature. This study provides valuable insights for improving the urban thermal environment and promoting sustainable urban development.

Urban 3D building morphology and energy consumption: empirical evidence from 53 cities in China

The impact of building morphology on building energy consumption has been extensively studied. However, research on how 3D building morphology affects energy consumption at a macroscopic scale is lacking. In this study, we measured the mean building height (BH), mean building volume (BV), and mean European nearest neighbor distance (MENN) of the city to quantify the 3D building morphology. We then used a spatial regression model to analyze the quantitative impact of urban 3D building morphology on per capita electricity consumption (PCEC). Results indicate that at the macroscopic scale of the city, the BH and the MENN have a significant positive impact on the PCEC, while the BV has a significant negative impact on the PCEC. Moreover, the inclusion of the 3D building morphology greatly improves the model’s ability to explain building energy efficiency, surpassing the impact of traditional economic factors. Considering the 3D building morphology indicators together, buildings with a lower height, a larger volume, and a more compact 3D morphology have greater potential for energy savings and are more conducive to electricity conservation. This study offers valuable insights for the energy-efficient arrangement of buildings.

Spatial coupling relationship between architectural landscape characteristics and urban heat island in different urban functional zones

The process of urbanization exacerbates the urban heat island phenomenon. In order to reasonably identify urban functional zones, and reveal the influence of buildings on land surface temperature (LST). Guiyang, a multi-mountainous city in China, was taken as the research area, Open Street Map (OSM) and land use type data were used to identify urban functional zones. Subsequently, the correlation between urban building morphology metrics and LST was analyzed using Pearson correlation and boosted regression tree (BRT) model. The results showed that: (1) There were significant differences in LST among different urban functional zones. (2) The relative contribution value of building density (BD) to land surface temperature was high, and it significantly exacerbated the urban heat island effect across all urban functional zones, with the maximum impact on LST reaching 2.5 °C in Utility zone. (3) Mean building height (MBH), Southerly wind speed (S_Wind speed) and Landscape shape index of building (LSI_Building) had the effect of alleviating thermal environment, and the mitigation degree of land surface temperature was the highest in Residential zone and Ecological zone, reaching 0.6 °C, 0.8 °C and 2.0 °C, respectively. This study enhances our understanding of the relationship between urban functions and LST, providing valuable insights for mitigating the urban thermal environment and promoting sustainable development.

Analyzing the scale dependent effect of urban building morphology on land surface temperature using random forest algorithm

With continuous urban densification, revealing impacts of urban structures on thermal environment is necessary for climate adaptive design. In this study, random forest and partial difference plots were employed to depict the relative importance and interdependent effects of complex building morphology to land surface temperature (LST) variability. The six spatial factors of building density (BD), mean building height (MBH), building height difference (BHD), floor area ratio (FAR), building volume density (BVD) and mean compactness factor (MCF) were calculated at grids of 90, 300, 600 and 900 m. The results showed that BD, MCF and MBH exerted stable and significant impacts on LST with the highest prediction accuracy at 600 m neighborhood scale, and FAR and BVD were the least correlated to LST changes. Meanwhile, the influencing factors presented different correlation patterns with LST. Among them, the increase of BD had a positive linear effect on LST. MCF and MBH were nonlinearly correlated with the LST variation, and their threshold values of cooling effect were also identified. In addition to controlling BD, it also suggested that comprehensively arranging more small-volume buildings as well as increasing building height to enlarge shadow coverage were more conducive to ground heat mitigation.

Assessment of Mutual Variation of Near-Surface Air Temperature, Land Surface Temperature and Driving Urban Parameters at Urban Microscale

The Urban Heat Island (UHI) effect is of critical concern for cities’ adaptation to climate change. The UHI effect shows substantial intra-urban variation at the city microscale, causing disparities in thermal comfort and energy consumption. Therefore, air temperature assessment should be prioritized for effective heat mitigation and climate adaptation. However, meteorological stations’ spatial distribution is far from meeting the scale that the UHI and its driving parameters operate. This limitation hampers demonstrating the intra-city variability of UHI and its origin of sources; for example, most studies employ Land Surface Temperature (LST), usually without demonstrating the relationship between UHI and LST. The current body of knowledge on urban climate implies a much better understanding and more detailed information on the spatial pattern of UHI and the driving factors to provide decision-makers with tools to develop effective UHI mitigation and adaptation strategies. In an attempt to address the adequacy of the use of LST and UPs in describing the intra-city variability of UHI, this study investigates the relationship between LST daytime and nighttime, and air temperature (Ta) daytime and nighttime, and driving urban parameters (UPs) of UHI together. Although it is well recognized that the intensity of the UHI is characterized by Ta, particularly at night, so-called nocturnal UHI, the use of remotely sensed LST is common, owing to the lack of spatially detailed Ta data in cities. Our findings showed that nocturnal UHI is weakly correlated with nighttime LST with a Pearson correlation (r) of 0.335 at p > 0.05 and that it is not correlated with daytime LST for the case study, highlighting the need for Ta observations for representing the intra-urban variation of nocturnal UHI. Among UPs, Sky View Factor (SVF), Building Volume Density (BVD), and Road Network Density (RND) explained 69% of the variability of Ta nighttime that characterizes nocturnal UHI. Therefore, UPs that performed well in estimating nocturnal UHI may be used in the absence of densely distributed Ta measurements. In a further investigation of the urban cooling phenomenon based on UHI diurnal changes, a particular region with high nighttime temperatures spoiled the Ta daytime and nighttime coherence. This region is characterized by high Mean Building Height (MBH), BFD, and BVD that re-emits heat, low SVF that prevents urban cooling, and high RND that releases extra heat at night. These particular UPs can be of prior interest for urban cooling. The present study, exploring the relationships of LST and Ta in a diurnal context, offers a further understanding of the preference of LST, Ta, or UPs to characterize UHI. Ta, in relation to major causative factors (UPs), provides insights into addressing the localities most vulnerable to the UHI effect and possible strategies targeting heat mitigation for sustainability and climate change resilience.

Examining temporally varying nonlinear effects of urban form on urban heat island using explainable machine learning: A case of Seoul

Many empirical studies have examined the relationship between urban form and canopy layer urban heat island (CUHI). However, these studies mostly have used small sample sizes and statistical methods that assume CUHI responds linearly to urban form features. Additionally, the differences in the effect of urban form on CUHI at different time instances during the diurnal cycle have not received sufficient attention. To address these issues, this study employs a relatively large sample of 1,058 microclimate sensors in Seoul, Korea, and analyzes CUHI at 9 a.m., 3 p.m., and 9 p.m. on a typical summer day. Gradient boosting decision tree (GBDT) based regression model and linear regression model and their variants with spatial information were constructed and compared for each time instance. Results show that spatially explicit GBDT models had the highest accuracy. Feature importance analysis shows that built form factors such as mean building height were more important during the afternoon, while surface fractions such as road surface fraction had greater influences during the morning and nighttime. Partial dependence plots (PDPs) show that urban form features influence CUHI in a complex nonlinear manner that varies for each time instance. PDPs were further scrutinized based on their activation and threshold effects. GBDT model findings directionally aligned with linear regression, but they provided nuanced insights into the form-CUHI relationship. These findings help planners to better understand the complexity of urban climate and intervention required to reduce CUHI magnitude across the diurnal cycle.

Study on the Response of the Summer Land Surface Temperature to Urban Morphology in Urumqi, China

Increases in urban temperature affect the urban ecological environment and human health and well-being. In urban morphology, building characteristics are important factors affecting the land surface temperature (LST). Contemporary research focuses mainly on the effects of land use, urban tissue configuration, and street networks on the LST, and the effects of building characteristics on the LST need to be further understood. The mean LST and the urban morphology indicators of a single grid were calculated via a remote sensing inversion and a spatial analysis, and a geographically weighted regression (GWR) model was established to explore the influence of the building coverage ratio (BCR), mean building height (BH_mean), floor area ratio (FAR), and mean sky view factor (SVF_mean) on the LST. The results show that the correlations between the urban morphology indicators and the LST at a scale of 100~500 m are of different degrees, and the correlations are more significant at a scale of 200 m. Therefore, the optimal spatial scale for studying the influence of urban morphology indicators on the LST is 200 m. The fitting effect of the GWR model is significantly better than that of the ordinary least squares (OLS) method, and the effects of each indicator on the thermal environment have spatial non-stationarity. The BCR, BH_mean, FAR, and SVF_mean differ in their ability to raise and lower the temperature in different spatial zones, and the order of influence is as follows: BCR > SVF_mean > FAR > BH_mean. This study will provide a reference for the urban planning of Urumqi.

Representing the effects of building height variability on urban canopy flow

AbstractWe conducted large‐eddy simulations over 98 urban arrays with varying building densities and height distributions. Compared with uniform‐height urban arrays, the influence of height variability on urban flow is pronounced and acts differently in two idealized urban configurations: the low buildings induce higher wind speed and stronger turbulence over staggered arrays but act inversely over aligned building configurations. The flow motions around tall buildings generate strong dispersive fluxes, which are sometimes of similar magnitude to the turbulent momentum flux and responsible for a persistent isolated roughness flow pattern in the upper canopy regardless of the urban density. Tall buildings further contribute disproportionately to the form drag of the urban surface, reaching up to 3.9 times the form drag induced by buildings of height equal to the average building height, in dense layouts. The flow inflection points—that is, the largest wind‐speed gradient that defines the aerodynamic interface between the urban canopy flow and the surface layer flow above—are found to be displaced to the maximum building height if less than 25% of buildings are below the mean building height. These findings provide critical insight for the development of urban canopy models, where the impacts of height variability on flow are often linked to the vertical variation in urban density alone. To address this deficiency, we provide a case study that considers the drag amplification due to the impact of vertical urban structures in the urban canopy model, enabling high‐resolution regional climate models to reproduce urban air flows better.

Regulation on air temperature by residential area morphology: A case study in Xuzhou City, China.

Due to woodlands and farmlands being replaced by residential areas in cities, continuous urbanization resulting in frequent urban heat island effects, especially in summer when high temperature seriously threaten health and lives of citizens. Although scientists realized that reasonable residential area morphology could effectively regulate air temperature and improve microclimate, it is lack of air temperature regulation-oriented specifications and requirements on morphology of residential areas. In this study, we used three types of morphological parameters of 15 residential areas in Xuzhou City and air temperature data via field investigation to analyze air temperature regulation caused by residential area morphology. The results showed that key morphological parameters of residential areas were different in morning and afternoon. In morning, independent effects of mean building height, street aspect ratio, and complete aspect ratio contributed 15.4%, 7.3%, and 6.8%, while those of building density, sky view factor, and the ratio of building surface area to floor area were 21.1%, 23.1%, and 6.9% in afternoon, respectively. Threshold values of efficiency of morphological parameters of residential areas were different between morning and afternoon. There were significant correlations between some morphological parameters of residential area. The results could provide data support and methodological reference for residential areas design in Xuzhou and surrounding cities.

Urban spontaneous plant richness in response to the 2D/3D building and green space patterns in a highly urbanized area

The impact of landscape patterns on urban plant diversity has received significant attention; however, previous studies have primarily focused on two-dimensional (2D) patterns. Limited investigations have been conducted to explore the effects of three-dimensional (3D) landscape patterns on urban plant diversity. Spontaneous plants are ideal objects for studying the response of plant diversity to urban environments due to their independence from intentional human inputs. Buildings, as a crucial symbol of urbanization, exhibit strong 3D characteristics in urban landscapes. Green spaces, the most important source of plant propagules in cities, are closely related to spontaneous plant diversity in urban areas. Therefore, we examined how 2D/3D building and green space patterns influence urban spontaneous plant species richness and compared the responses between different categories (native and non-native). We surveyed spontaneous plants in the built-up areas of Shenzhen, a highly urbanized region situated along China’s southern coast. Our survey recorded 278 species belonging to 208 genera and 77 families. Results from boosted regression tree models (BRT) indicated significant correlations between spontaneous plant species richness and various metrics, including building coverage ratio (BCR), mean building height (MAH), green space coverage ratio (GCR), edge density of green spaces (ED_G), spatial congestion degree (SCD), and floor area ratio (FAR). These metrics exhibited complex nonlinear correlations with the species richness of spontaneous plants. Furthermore, native and non-native species richness responded differently to building and green space patterns; while native species richness was equally affected by green space and building patterns, non-native species richness was more strongly impacted by building patterns than green space ones. Our study quantified the nonlinear relationships between urban spontaneous plant species richness and 2D/3D green space and building patterns, demonstrating that optimizing green space and building patterns can be a practical approach for managing and promoting spontaneous plant diversity in highly urbanized areas.

Deep learning-based building height mapping using Sentinel-1 and Sentinel-2 data

Accurately mapping building height at a fine scale is crucial for comprehending urban systems. However, existing methods suffer from limitations such as coarse resolutions, long delays, and limited applicability for large-scale mapping. This challenge is particularly significant in China, where rapid urbanization has led to complex urban scenario. To address this issue, we propose a novel approach that capitalizes on publicly available Sentinel-1/-2 and crowdsourced data. Our method employs a dual-branch structure building height estimation network (BHE-NET) and an improved multi-modal Selective-Kernel (MSK) module to fuse optical and SAR features. The validation results, derived from building height data across 63 cities, demonstrate strong performance with a root mean square error (RMSE) of 4.65 m. We further test the scalability of our approach by mapping three most developed urban agglomerations in China. In comparison with four recent studies, our method captures finer details of building height while mitigating the overestimation in urban high-density building clusters. Moreover, we further investigate the relationship between population and mean building height as well as the building volume at city level. Our work opens up new possibilities for producing fine-scale building height map of China at a 10-m resolution using remote sensing data.

Characterising the vertical structure of buildings in cities for use in atmospheric models

Urban schemes for numerical weather prediction (NWP) often assume an infinite street canyon with constant height and width, impacting turbulent and radiative fluxes. We develop parameterisations for urban morphology profiles, with five complexity levels, using data from six cities at 2 km × 2 km resolution. Comparisons of parameterised building plan area to these ‘true’ data show that 90% of building fraction profiles have bias errors (BE) at any height of <0.03. An effective building diameter (D) is used to characterise the proportionality between building plan area and building normalised perimeter length. The six-city mean D is 21 m. Relations for D have normalised BE (nBE) < 16%, increasing to 26% when total wall area is assumed to be unknown. Impacts from using these new morphology relations are tested with SPARTACUS-Urban radiative transfer simulations. The effective shortwave albedo has a nBE 2–10% (cf. ‘true’). Within-canyon absorption have larger nBEs, suggesting the bulk albedo hides within-canopy errors. Overall, nBE increase as less morphology data are provided, notably when omitting total wall area. We conclude that urban vertical variability using the proposed relations are acceptable for NWP, requiring only: surface building plan area, mean building height, and effective building diameter.

Associations between summer wind environment and urban physical indicators in commercial blocks based on field measurement and simulation in Xi'an

As a major driving force to improve economic output, commercial blocks also play a significant role in enhancing urban vitality. Due to its characteristics of high building density and large building volume, many wind environment problems are likely to appear in commercial blocks. In this research, we simulated the wind environment of four theoretical block models and real commercial blocks in STREAM software. The results suggest that under the same other conditions, the wind speed inside the block is the lowest when its building footprint ratio reaches about 56%. When the building footprint ratio is greater than 56.3% and gradually increasing, or less than 39.1% and gradually decreasing, the wind speed both inside and around the block will be steadily increased. In addition, we also found the quantitative correlations between the wind environment and other urban physical indicators, such as mean building height, stagger ratio of building height and enclosure degree, according to which we proposed the adjustment strategies of the spatial form of commercial blocks. Finally, we used four cases to verify the effectiveness of these strategies in optimizing the wind environment of commercial blocks.

On the Calculation of Urban Morphological Parameters Using GIS: An Application to Italian Cities

The identification of parameters that can quantitatively describe the different characteristics of urban morphology is fundamental to studying urban ventilation and microclimate at the local level and developing parameterizations of the dynamic effect of an urban area in mesoscale models. This paper proposes a methodology to calculate four morphological parameters, namely mean height, aspect ratio, sky view factor, and plan area ratio, of five cities located in southern (Bari and Lecce), central (Naples and Rome), and northern (Milan) Italy. The calculation is performed using the Geographical Information System (GIS), starting from morphological and land use data collected and analyzed in shapefiles. The proposed methodology, which can be replicated in other cities, also presents in detail the procedure followed to properly build input data to calculate the sky view factor using the UMEP GIS tool. The results show a gradual increase in the plan area index, λp, and mean building height, H¯, moving from the south to the north of Italy. Maximum values of λp and H¯ are obtained in the regions of Milan, Rome, and Naples, where the highest spatially-averaged values are also found, i.e., λp = 0.22, H¯ = 10.9 m in Milan; λp = 0.19, H¯ = 12.7 m in Rome; λp = 0.20, H¯ = 12 m in Naples. Furthermore, for all the cities investigated, areas characterized by the Corine Land Cover class as “continuous urban fabric” are those with medium sky view factor SVF values (around 0.6–0.7) and λp values (around 0.3) typical of intermediate/compact cities. The methodology employed here for calculating morphological parameters using GIS proves to be replicable in different urban contexts. This opens to a better classification of cities in local climate zones (LCZ), as shown for the Lecce region, useful for urban heat island (UHI) studies and to the development of parameterizations of the urban effects in global and regional climate models.

Mapping fine-scale building heights in urban agglomeration with spaceborne lidar

The increasing availability of 3-D urban data yields new insights into urban developments and their implications for population density, energy consumption, and the carbon budget. However, existing products of urban building heights at a regional or global scale are mostly subject to coarse grid size or long-time lags. Fine-resolution building height maps remain unavailable for most regions to present recent vertical information. Also, the lack of sampled building heights with spatial and temporal consistency at larger scales makes it difficult to update building height maps. The newly available spaceborne lidar data from the Global Ecosystem Dynamics Investigation (GEDI) are expected to overcome these critical barriers. Here we propose a generalizable approach to mapping large-scale distributions of building heights by fusing GEDI-derived relative height metrics, optical data (i.e., Landsat-8 and Sentinel-2), and radar data (Sentinel-1). We applied our approach to the Yangtze River Delta region (YRD), one of China's largest urban agglomerations. We investigated the availability of building height samples generated from GEDI-based relative height metrics and the optimal grid size of building height mapping into 30-m (about footprint level) and 150-m (regional level) grid sizes. We finally developed a random forest model using active and passive sensors to extrapolate GEDI-derived samples discretely to the continuous building height map at the 150-m grid size in 2019. We revealed the spatial distribution patterns of building heights and the urbanization effect on the city's mean building heights in the YRD region. We found that: (1) GEDI-derived building height samples were highly consistent with the reference building height data at the footprint (i.e., Pearson's r = 0.86, RMSE = 9.25 m, n = 237) and regional (i.e., Pearson's r = 0.76, RMSE = 6.36 m, n = 69) levels. (2) The Pearson's correlation coefficient (r) and root mean square error (RMSE) of the final spatially continuous and up-to-date building height map were 0.85 and 5.03 m, respectively. (3) The resulting map showed a strong positive correlation between the city's mean building height and urbanization level. Our work makes it possible to use remotely sensed data to make global maps of building heights at a 150-m scale.

Comprehensive effect of the three-dimensional spatial distribution pattern of buildings on the urban thermal environment

The three-dimensional spatial distribution pattern of buildings is an important factor causing the spatial differences in the urban thermal environment. The study units were divided based on the urban road network, DEM, and building big data in Jinan. The Landsat 8 remote sensing images were used to invert the land surface temperature (LST), and Kriging interpolation was used to obtain the spatially continuous air temperature (AT). The integrated LST and AT represented the urban thermal environment. By constructing three-dimensional spatial distribution indicators of buildings and using analysis of variance and multiple correspondence analysis methods, the influence of the three-dimensional spatial distribution pattern of buildings on the urban thermal environment was comprehensively investigated. The results showed that all the indicators of the three-dimensional spatial distribution of buildings were factors that have prominent influence on the urban thermal environment. The interaction of mean DEM respectively with building density, volume ratio, mean building height, and mean building volume demonstrated significant effects on both LST and AT, and the analysis of variance results of these indicators with each temperature reached a significance level of ≤0.05. The types and levels of the main strongly correlated indicators varied with increasing temperature levels. The optimal combination of levels corresponding to the lower LST depended on the terrain, while the lower AT was affected by a combination of buildings and terrain. The height indicators contributed more to LST than to AT.

Combined impacts of buildings and urban remnant mountains on thermal environment in multi-mountainous city

The rapid development of urbanization has been making the pattern of urban thermal environment more complex and changeable. In order to reveal the influence of buildings and urban remnant mountains (URMs) on land surface temperature (LST). Guiyang, a multi-mountainous city in China, was taken as the research area, and the relationship between 12 landscape metrics and LST in different seasons was analyzed by Pearson correlation and boosted regression tree (BRT) model. The results showed that: (1) Mean building height (MBH) and building density (BD) are the dominant factors affecting LST, and MBH has a cooling effect on LST, with a maximum effect of 1.2°C in spring; while BD has a heating effect, with a maximum effect of 2.3°C in autumn. (2) Mountain vegetation coverage ratio (MVCR) is the most significant factor on LST among the landscape metrics of URMs, and negatively related to LST. (3) Two-dimensional (2D) metrics have a more obvious effect on LST than three-dimensional (3D) metrics in spring, summer and autumn, while 3D metrics have a stronger effect in winter. These findings could provide useful insights for urban planners to optimize urban spatial morphology and green space system layout to improve urban thermal comfort.

Spatiotemporal reconstruction and drivers of tourism-oriented towns: A case study of Jinshitan

Reconstruction of settlement spaces is the process of optimizing and reorganizing the internal resources of a settlement, which is of great significance to settlement development. In this study, we used the land-use transfer matrix, land-use dynamics, building density, mean building height, and plot ratio in four time series to explore Jinshitan 2-dimensional and 3-dimensional space, production space reconstruction and its drivers. The conclusions were as follows: 1) 82.288% of cultivated land that was lost during the study period was transformed as construction land, which was concentrated in patches; 2) showed a high degree of consistency in terms of building density, mean building height, and floor area ratio; 3) domestic tourists accounted for the majority of tourism, and the population structure exhibited a non-agricultural transformation; and 4) the spatial reconstruction of Jinshitan has benefited from natural resource endowment, government policy guidance, market orientation, and participation of social subjects. This study suggests that Jinshitan should focus on protecting the natural environment and strengthening humanistic care in the future, and hopes to provide reference for the urbanization development of other tourism-oriented settlements.

Quantifying the Relationship between 2D/3D Building Patterns and Land Surface Temperature: Study on the Metropolitan Shanghai

In the context of urban warming associated with rapid urbanization, the relationship between urban landscape patterns and land surface temperature (LST) has been paid much attention. However, few studies have comprehensively explored the effects of two/three-dimensional (2D/3D) building patterns on LST, particularly by comparing their relative contribution to the spatial variety of LST. This study adopted the ordinary least squares regression, spatial autoregression and variance partitioning methods to investigate the relationship between 2D/3D building patterns and summertime LST across 2016–2017 in Shanghai. The 2D and 3D building patterns in this study were quantified by four 2D and six 3D metrics. The results showed that: (1) During the daytime, 2D/3D building metrics had significant correlation with LST. However, 3D building patterns played a significant role in predicting LST. They explained 51.0% and 10.2% of the variance in LST, respectively. (2) The building coverage ratio, building density, mean building projection area, the standard deviation of building height, and mean building height highly correlated with LST. Specifically, the building coverage ratio was the main predictor, which was obviously positively correlated with LST. The correlation of building density and average projected area with LST was positive and significant, while the correlation of building height standard deviation and average building height with LST was negative. The increase in average height and standard deviation of buildings and the decrease in building coverage ratio, average projected area, and density of buildings, can effectively improve the urban thermal environment at the census tract level. (3) Spatial autocorrelation analysis can elaborate the spatial relationship between building patterns and LST. The findings from our research will provide important insights for urban planners and decision makers to mitigate urban heat island problems through urban planning and building design.

Impact of Urban Park Design on Microclimate in Cold Regions using newly developped prediction method

• The temperature distribution in the park was analyzed in different periods. • The temperature distribution in park was correlated with various morphology parameters. • The vegetation coverage and the average building height are the key factors. • Environmental evaluation and prediction method was developed and verified. With the acceleration of urbanization, the urban heat island phenomenon has attracted increasing attention. Urban parks play an important role for urban heat island mitigation. However, few studies were focusing on the impact of urban parks design on the urban microclimate. This research is based on the largest comprehensive park in Xi'an - Xingqing Palace Park. Measurement points in 20 locations with different urban morphological characteristics were selected to obtain typical summer weather characters (air temperature, relative humidity, wind speed, and wind direction). The value of the urban morphology parameters within different radii from the measuring points are measured to establish the relationship between urban planning and urban meteorology, and construct the prediction model using correlation analyses and regression analyses. The results showing that green coverage rate is an important factor affecting the urban microclimate. Meanwhile, building density and mean building height are affecting microclimate changes during nights, with a wide radius around the buildings. This research established a practical and feasible urban planning evaluation method by analyzing and selecting the urban morphology parameters that affecting urban microclimate for providing insights for mitigating the urban heat island effects and improving the urban microclimate.