Building Coverage Ratio Research Articles

Modeling urban canopy air temperature at city-block scale based on urban 3D morphology parameters– A study in Tianjin, North China

Urban 3D morphology significantly influences the outdoor thermal environment. Understanding the influence of urban expansion in both horizontal and vertical landscapes helps the canopy urban heat island (CUHI) effect mitigation. However, the microscale numerical CUHI models are difficult to be applied for large-area CUHI effect studies. On the other hand, the mesoscale CUHI models make a wider study area workable but lost some of the model accuracies. To perform a large-area study on the CUHI effect with relatively light computing costs and fine accuracy, this paper builds a canopy air temperature predicting model at city-block scale with urban 3D morphology parameters including building coverage ratio (BCR), grass coverage ratio (GCR) and the mean value of building height (BH) to obtain the citywide block-mean 2-m temperature (T2M). The model accuracy was validated through RMSEs and comparison with the mereological station data. The proposed model shows an RMSE of 0.286 straight °C and an R-square of 0.83. Using the validated model, Tianjin with an area of 647 km 2 was performed to investigate the effects of vertical landscape on the canopy air temperature under different scenarios between 2010 and 2016, including the changes in landcover and building heights. It finds that a 40% increase in BCR may lead to the highest canopy air temperature, and the increase of BH may lead to an increase in the canopy air temperature in low-rise and high-rise building areas, but there is an opposite trend in multi-story and mid-rise building areas.

Measuring the Non-Linear Relationship between Three-Dimensional Built Environment and Urban Vitality Based on a Random Forest Model.

Urban vitality is a major indicator used for evaluating the sustainability and attractiveness of an urban environment. Global experience indicates that urban vitality can be stimulated through a reasonable urban design. However, it remains incompletely understood in the literature which building-related indicators can substantially affect urban vitality in Asian countries. To give an insight into this question, our study took a step forward by focusing specifically on the influence of the three-dimensional built environment on urban vitality, based on which decision makers could enhance urban vitality from the perspective of vertical building design. A machine-learning-based framework was developed in this study. First, we utilized several building-related indicators to thoroughly measure the spatial characteristics of buildings at the township level. Second, the relationship between a three-dimensional built environment and urban vitality was revealed based on a combined use of the correlation method, scatter charts, and a random forest. In the random forest, both a benchmark and a new model were constructed to evaluate the importance of those building-related indicators. The results suggested that urban vitality was closely related to the three-dimensional built environment, which played an even more important role than common benchmark factors in stimulating urban vitality. The building coverage ratio, density of tall buildings, and floor area ratio were essential spatial drivers behind urban vitality. Therefore, urban designers and decision makers should not only take traditional factors into account but also carefully consider the potential influence of high-rise buildings and the outdoor thermal environment so that urban vitality can be enhanced. Our study's results can offer practical recommendations for improving urban vitality from the perspective of vertical building design. The proposed framework can also be used for measuring the potential influence of the three-dimensional built environment in other areas.

Seasonal effects of urban morphology on land surface temperature in a three-dimensional perspective: A case study in Hangzhou, China

Architecture influences the design of urban landscapes and hence plays an important role in the regulation of urban thermal environment. Studies on the correlation between multi-dimensional building morphology and land surface temperature (LST) have been conducted in many cities around the world. However, general conclusions on their relationship have not been drawn, owing to the differences in climatic, social, and economic conditions between cities. Moreover, the multivariate and nonlinearity of the three-dimensional (3D) indicators also require more robust and specific statistical models. In this study, the random forest (RF) regression model was used to quantitatively analyse the seasonal differences in the responses of nine multi-dimensional building indicators to LST. The results show that the building coverage ratio (BCR), high building ratio (HBR), and architecture height standard deviation (AHSD) always had the most substantial influence. Across seasons, the average relative contribution ratios of BCR, HBR, and AHSD were 13.23–33.50%, 11.37–20.06%, and 9.02–24.12%, respectively. In general, 2D indicators tended to have a higher impact in warmer seasons while 3D indicators showed more impact on LST in cooler seasons. According to the marginal effect analysis, the BCR, cubic index (CI), and aggregation index (AI) were the main heating indicators, whereas AHSD and HBR were the main cooling indicators. Overall, our findings contribute to the understanding of the urban heat island phenomenon, which remains an important issue in the context of sustainable landscape and urban planning.

Uncertainty and sensitivity analyses for the reduction factor of sheltering for radiation exposures

Sheltering is one of the countermeasures used to mitigate radiation exposure during nuclear power plant accidents. The effectiveness of sheltering for inhalation exposure is often expressed by the reduction factor, which is defined as the ratio of the indoor to the outdoor cumulative radioactivity concentrations or doses. The indoor concentration is mainly controlled by the air exchange rate, penetration factor, and indoor deposition rate. Meanwhile, the air exchange rate depends on surrounding environmental conditions: the wind speed, leakage area normalised by the floor area of the house, and gross building coverage ratio. In this study, the ranges of the uncertainty of the reduction factors for particles and iodine in reactive gas form were investigated under various environmental conditions, and sensitivity analyses were conducted to understand the parameter with the most influence on the uncertainty of the reduction factor. From the results of the uncertainty analyses, the calculated reduction factor was highly variable depending on the environmental condition and the airtightness of the houses. The median and 95th percentile of the reduction factors for the older houses were 0.5 and 0.9 for particles and 0.07 and 0.4 for iodine in reactive gas form, respectively and these ranges were smaller for newer houses. From the results of the sensitivity analyses, the wind speed was the most influential parameter determining the reduction factor. Additionally, the wind speed was less influential for the reduction factor in newer houses.

The effect of building plan form on thermal comfort in the traditional residential patterns of the hot and dry climate of Qom

Buildings should be constructed such that their energy efficiency is high in different climatic conditions while providing acceptable thermal comfort at the same time. Climatic and architectural incompatibilities will increase energy consumption to provide thermal comfort but have adverse effects, both economically and environmentally. Climatic design is the best and most effective way to maximize the use of renewable energy sources. Traditional Iranian houses have always exhibited this type of architecture and can provide suitable examples for design for contemporary architects. This study aimed to investigate the effect of building plan forms on the thermal comfort temperature in residential buildings with the help of traditional architectural patterns. The method of this research is descriptive-analytical and has been conducted through case studies and simulation tests. Data collection has been undertaken through literature review and field observations. The houses in this study were classified based on their building plan form. Ecotect and EnergyPlus software were used for modeling and Climate Consultant software was used for climate analysis. Findings reveal that the Building Coverage Ratio (BCR) and the introverted plan forms are two significant factors in climatic design to provide thermal comfort in the hot and dry climate of Iran. Among the investigated models, results show that the one-sided form with northeast-southwest orientation and northeast placement has the most hours (2609 h per year) in the range of thermal comfort and the best plan form among the studied forms.

Deep Learning Based Urban Building Coverage Ratio Estimation Focusing on Rapid Urbanization Areas

Urban parameters, such as building density and the building coverage ratio (BCR), play a crucial role in urban analysis and measurement. Although several approaches have been proposed for BCR estimations, a quick and effective tool is still required due to the limitations of statistical-based and manual mapping methods. Since a building footprint is crucial for the BCR calculation, we hypothesize that Deep Learning (DL) models can aid in the BCR computation, due to their proven automatic building footprint extraction capability. Thus, this study applies the DL framework in the ArcGIS software to the BCR calculation task and evaluates its efficiency for a new industrial district in South Korea. Although the accuracy achieved was limited due to poor-quality input data and issues with the training process, the result indicated that the DL-based approach is applicable for BCR measuring, which is a step toward suggesting an implication of this method. Overall, the potential utility of this proposed approach for the BCR measurement promises to be considerable.

Efforts to Reduce River Water Discharge through Land Use Control (Case Study: Upstream Ciliwung Watershed, Indonesia)

Land-use change is the main cause of high runoff compared to other factors. The type of land use also plays a role in determining the amount of runoff that occurs. The Upstream Ciliwung watershed is blamed as the main causes of the flood disaster in Jakarta, the capital city. The land use in the upper stream has changed and causes increasing of run off to be higher than the capacity of river to drain it. This study aims to explore the land use changes of the Upstream Ciliwung Watershed from 1996 to 2016 and their consequences on the stream run off. Furthermore, the study also recommends the efforts needed to reduce river water discharge. The land use changes are identified by using spatial analysis method. The calculation of run off uses the rational method. Average rainfall data for every ten years is calculated by use of Gumbell distribution formula. The study shows that in the period 1996 – 2016, there was a change in land use in the upstream Ciliwung watershed. The area that continues to decline includes primary forest, open land, paddy fields, shrubs, and mixed gardens, all of which are categorized as undeveloped areas. The runoff discharge from 1996 – 2016 has increased from 278.77 to 465.57 mm/second that indicate runoff discharge is already in the Standby I position. Efforts that need to be made to reduce runoff water flow in the Ciliwung watershed are: 1) consistently develop and control land use change in the area based on the Jabodetabekpunjur Spatial Planning 2) consistently apply the building coverage ratio (BCR) regulation; 3) construct engineering and technology approach such as build infiltration wells in settlement, 4) forest rehabilitation and 5) conducting law enforcement.

Assessing the impacts of urban morphology factors on the energy performance for building stocks based on a novel automatic generation framework

The building sector accounts for an increasing amount of energy and emissions with continued urbanization, and improving the energy performance of building stocks at the early planning stage is cost-efficient and easy to operate. This study presents an automatic generation framework to quantify the impacts of urban morphology factors on the energy performance of neighborhood-scale building stocks. The typical office building shape is firstly determined by a field survey and clustering analysis. Then the building modular cells-based method is introduced to establish a comprehensive energy performance database for building stocks. The impacts of urban morphology factors on energy performance are further prioritized via global sensitivity analysis. A case study in Shanghai, China demonstrated the proposed automatic generation framework which can act as a generalized paradigm to model building stocks and be widely applicable to other climate zones. The urban morphology factors representing buildings’ geometric characteristics tend to have greater impacts on energy performance, among which the top influencing factors are relative compactness, building coverage ratio, and building height distribution. The proposed framework and the findings are expected to generate valuable insights into energy-efficient urban design.

Evaluation of the Street Canyon Level Air Pollution Distribution Pattern in a Typical City Block in Baoding, China.

Urban traffic pollution, which is strongly influenced by the complex urban morphology, has posed a great threat to human health. In this study, we performed a high-resolution simulation of traffic pollution in a typical city block in Baoding, China, based on the Parallelized Large-eddy simulation Model (PALM), to examine the distribution patterns of traffic-related pollutants and explore their relationship with urban morphology. Based on the model results, we conducted a multi-linear regression (MLR) analysis and found that the distribution of air pollutants inside the city block was dominated by both traffic emissions and urban morphology, which explained about 70% of the total variance in spatial distribution of air pollutants. Excluding the contribution of emissions, over 50% of the total variance can still be explained by the urban morphology. Among these urban morphological factors, the key factors determining the spatial distribution of air pollution are “Distance from the road” (DR), “Building Coverage Ratio” (BCR) and “Aspect Ratio” (H/W) of the street canyon. Specifically, urban areas with lower Aspect Ratio, lower BCR and larger DR are less affected by traffic pollution. Compiling these individual factors, we developed a complex Urban Morphology Pollution Index (UMPI). Each unit increase in UMPI is associated with a one percent increase of nearby traffic pollution contribution. This index can help urban planners to semi-quantitatively evaluate building groups which tend to trap or ventilate traffic pollution and thus help to reduce human exposure to street canyon level pollution through either traffic emission control or urban morphology amelioration.

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.

Neighborhood, built environment and resilience in transportation during the COVID-19 pandemic.

COVID-19 has swept the world, and the unprecedented decline in transit ridership has been noticed. However, little attention has been paid to the resilience of the transportation system, particularly in medium-sized cities. Drawing upon a light rail ridership dataset in Salt Lake County from 2017 to 2021, we develop a novel method to measure the vulnerability and resilience of transit ridership using a Bayesian structure time series model. The results show that government policies have a more significant impact than the number of COVID-19 cases on transit ridership. Regarding the built environment, a highly compact urban design might reduce the building coverage ratio and makes transit stations more vulnerable and less resilient. Furthermore, the high rate of minorities is the primary reason for the drops in transit ridership. The findings are valuable for understanding the vulnerability and resilience of transit ridership to pandemics for better coping strategies in the future.

How to harmonise variations in streetscape skeletons under zoning regulations: Considering their external diseconomies

The arrangement of buildings along roads creates one of the most fundamental patterns of three-dimensional streetscape skeletons, primarily defined as a set of building heights and setbacks in a district. Under zoning regulations, building heights and setbacks are indirectly controlled by the building coverage ratio (BCR) and the floor area ratio (FAR). Variations in the BCR result in variations in streetscape skeletons. Moderate complexity of streetscape skeletons is a necessary condition for aesthetic streetscape. Understanding the relationships between variations in the BCR, building heights and setbacks is thus important in order to harmonise streetscape skeletons, smaller variations in building heights, and setbacks, however, this relationship has yet to be theoretically investigated due to the complex relationship between buildings. The objective of this paper is therefore to formulate the relationship between variations in building heights and setbacks as a function of the standard deviation of BCR, and, based on this formulation, to discuss how to indirectly harmonise variations in streetscape skeletons under zoning regulations. This formulation enables us to analytically investigate the relationship between these two functions and the standard deviation of a BCR. An indirect scheme for harmonising variations in streetscape skeletons under zoning regulations is proposed on the basis of this formulation. The external diseconomies of inharmonious streetscape skeletons are quantitatively defined in order to incentivise plot owners to harmonise streetscape skeletons. The optimal building height and setback criteria are computed, which minimises the social cost of inharmonious building heights and building setbacks in a district. This scheme for incentivising plot owners to reduce their social costs is expected to contribute to indirectly harmonising streetscape skeletons.

Urban performance based on biometeorology index in high-density, hot, and humid cities

• The correlation between high density and urban performance based on biometeorology index for eight metropolitan cities in Indonesia is introduced. • The need to re-evaluate spatial planning in Indonesia, especially cities with high BCR is the urgency to achieve a preferred urban biometeorology. • Reducing BCR especially in dense area to promote 30% open space by increase the value of FAR. • The results show potential improving urban biometeorology by promote FAR to increase urban ventilation. This article aims to discuss urban performance based on the biometeorology of eight dense cities in Indonesia (i.e., Jakarta, Bandung, Medan, Padang, Pontianak, Banjarmasin, Makassar, and Denpasar). The interconnection between urban morphology and human biometeorology provides an understanding of meteorological vulnerability and thermo–physiological aspects, which is expressed as the physiologically equivalent temperature (PET). The hourly meteorological data from eight cities’ weather stations was collected from 2009 to 2019. The building density is later defined as the building coverage ratio (BCR) and is divided by a low BCR, which is less than 70%, and a high BCR, which is more than 70%. The BCR is then correlated with the meteorology variables, Ta max, RH, and v, to calculate its impact on the PET. The increase of Ta max in cities, both for low BCRs ( r = 0.76) and high BCRs ( r = 0.54), is found to have a significant correlation with an increase in PET. Meanwhile, increasing the value of v for both low BCRs ( r = -0.67) and high BCRs ( r = -0.65) decreases PET . However, modifying the value of the PET for cities with a high BCR is more complicated. The microclimate within the building layout must be manipulated to increase the value of v . A humid condition for a high BCR is beneficial to reduce the PET.

Exploring Adaptive UHI Mitigation Solutions by Spatial Heterogeneity of Land Surface Temperature and Its Relationship to Urban Morphology in Historical Downtown Blocks, Beijing

Heat stress brought on by the intensification of urban heat island (UHI) has caused many negative effects on human beings, which were found to be more severe in highly urbanized old towns. With the inconsistent findings on how urban spatial morphological characteristics influence land surface temperature (LST) and gaps between design practices being found, we chose Beijing Old Town (BOT) as the study area and took the basic planning implementation module “block” as a study to reveal the spatial heterogeneity of LST and its relationship to multiple urban morphological characteristics with higher spatial resolution calculated via WorldView3. Our results have shown that (1) UHI effect was significant and spatially heterogeneous in BOT, and significant hot areas with high LST value and small LST differences were found, as cold areas were the exact opposite. (2) The proportion of vegetated area, water, impervious surface, and urban spatial structure indicators i.e., building coverage ratio, mean height, highest building index, height fluctuation degree, space crowd degree and sky view factor were identified as significantly affecting the LST of blocks in BOT. (3) The effects of GBI components and configuration on LST varied within different block types; generally, blocks with GBI with larger patches that were more complex in shape, more aggregated, and less fragmented were associated with lower LST. Finally, in the context of integrating our study results with relevant planning and design guidelines, a strategy sample of adaptive GBI planning and vegetation design for blocks with different morphological features was provided for urban planners and managers to make a decision on UHI mitigation in the renewal process of BOT.

The influence of the landscape pattern on the urban land surface temperature varies with the ratio of land components: Insights from 2D/3D building/vegetation metrics

Recently, the impact of multidimensional building/vegetation landscape patterns on urban land surface temperature (LST) has received extensive attention. However, investigations on how two-dimensional (2D)/three-dimensional (3D) building and vegetation landscape patterns affect the urban thermal environment under different cover types are still limited. Thus, we use the boosted regression tree model to explore the separate and combined relative contributions and marginal effects of 2D/3D building and vegetation landscape patterns on the LST and to examine the impacts of differences in building and vegetation coverage. The results indicate that the mean architecture height, building coverage ratio, high building ratio, vegetation coverage ratio, and architecture height standard deviation are the main metrics affecting the LST, with relative contributions of 24.8%, 14.9%, 14.7%, 8.2%, and 7.6%, respectively. The building landscape roughness and fragmentation have the greatest influence on the LST, with contributions of 1.2 °C and 0.8 °C, respectively. Furthermore, changes in the building and vegetation coverage can significantly affect the LST. When building coverage dominates, building-based metrics largely affect the LST. When vegetation coverage dominates, the impact of vegetation landscape diversity, roughness, and fragmentation on the LST gradually increases. This study provides insights for improving urban thermal environment.

Exploring the relationship between the 2D/3D architectural morphology and urban land surface temperature based on a boosted regression tree: A case study of Beijing, China

With rapid urbanization, urban three-dimensional morphology and its ecological effects have received more attention. However, thorough investigations into the multiple scale impact of the 2D/3D architectural morphology on urban land surface temperature (LST) remain limited. Taking Beijing as a case study area, we quantified the contributions of the 2D/3D architectural morphology indicators and revealed their marginal effects on multiple scales using the boosted regression trees (BRT) method. The results showed that (1) the building coverage ratio and building height were the most significant factors influencing the LST across all spatial scales and seasons, (2) the 3D shape index, 3D fractal, and 3D adjacency were found to be influential factors, with sum contributions varying from 6.0% to 37.7%, and (3) in summer, the 3D shape index showed a stepwise negative correlation with the LST. The 3D fractal and 3D adjacency exhibited both positive and negative correlations with the LST. When the spatial scale was 240 m, the regulation amplitudes for the 3D shape index, 3D fractal, and 3D adjacency were 2.0°C, 1.0°C and 1.0°C, respectively. These findings provide quantitative insights that can be used to improve urban thermal environments and achieve sustainable urban development by adjusting architectural morphology.

Effects of Building Physics Form on Energy Consumption for Buildings

The building sector plays an important role in urban energy conservation and CO2 emissions reduction. Therefore, it is necessary to fundamentally explore the influencing factors and mechanisms of building energy consumption. This study aims to explore the impact of building physical form on energy consumption for buildings. Firstly, this study uses different types of buildings as research objects. And method of combining EnergyPlus and GIS is used to calculate building energy consumption. Then adopting regression analysis to evaluate the impact of six building form indicators on building energy use. The results show that building coverage ratio (BCR) has a positive relationship with point-type building energy consumption. And among mix-type buildings, sky view factor (SVF) has a positive impact on energy consumption, and building coverage ratio (BCR) has a negative impact on it. As for slab-type buildings, total wall surface area (WSA) has a positive effect on energy consumption, and building floor (BF) has a negative effect on energy consumption. However, the indicators are not enough to explain the building energy consumption, and further exploration is needed.

Urban greenery mitigates the negative effect of urban density on older adults' life satisfaction: Evidence from Shanghai, China

The influence of high-density environment on urban residents is controversial, and its effect varies with specific contexts. Meanwhile, urban planners and policy-makers are increasingly aware that urban greenery may mitigate the detrimental effects of crowded environments on quality of life in high-density cities. However, little empirical evidence is available in the context of China. This study aims to examine the complex relationship between urban density, urban greenery, and older people's life satisfaction, with survey data collected from 1,594 older adults in 129 neighborhoods in Shanghai, China. Urban density was assessed using floor area ratio and building coverage ratio respectively, and urban greenery was measured by street view greenery, greening rate, Normalized Differential Vegetation Index (NDVI), and accessibility to nearest parks. Results from structural equation modeling showed that higher urban density was related to lower life satisfaction, and a reduced sense of community was a significant pathway between higher urban density and lower life satisfaction. Furthermore, eye-level greenery cushioned the negative effect of urban density on life satisfaction. Our findings highlighted the necessity of optimizing high-density neighborhood environments and promoting eye-level greenery in high-density urban areas to create aging-friendly cities.

Investigation of typical residential block typologies and their impact on pedestrian-level microclimate in summers in Nanjing, China

The morphology of urban residential blocks in China, which is strictly affected by regulations, can potentially improve or deteriorate microclimates. This paper first proposes a framework for identifying typical residential block typologies using Nanjing as an example. A thorough investigation, consisting of 114 samples, was conducted, and 18 typical residential block typologies were summarized. Second, 3D non-isothermal numerical simulations were performed on the hottest days of summer using the ENVI-met. The effect of morphological indices on the microclimate at the pedestrian level was explored using statistical methods and a novel graphic method named m-SpaceMate. The results revealed a strong correlation between the urban heat island intensity (UHII), mean radiation temperature, and universal thermal climate index (UTCI) and floor area ratio (FAR), as well as between wind velocity (U) and building coverage ratio (BCR). A significant increase in FAR (by approximately 1.0) can result in a low UTCI that was driven by MRT and shading conditions. Six-floor blocks, with FAR between 1.8 and 2.1, had a better overall thermal environment (except for lower U) than that of 11-floor blocks. When considering a similar BCR, blocks with slab-type buildings tended to have a U that was higher by approximately 30%.

Exploring RAU-net for semantic segmentation of Philippines satellite images in identification of building density

ABSTRACT This study explores RAU-Net convolutional network architecture on satellite images by classifying geospatial objects on satellite images such as roofs to help in determining the building density of a specific location. This study developed a satellite image dataset in the Philippines and trained the dataset in an RAU-Net Convolutional Neural Network using Tensorflow Keras API. This study proves that U-Networks with a few datasets and provides acceptable performance scores. Furthermore, a new way to calculate Building Density in terms of Building Coverage Ratio (BCR) using satellite images was also presented in this study. The model and the analysis were integrated into an application that determines the BCR of a specific location through a satellite map.