Urban Morphological Characteristics Research Articles

Digital preservation of heritage urban facades: An integrated approach using historic building information modeling and shape grammar analysis

Traditional heritage architecture is renowned for its unique characteristics, which are shaped by local climatic, social, and construction requirements, as well as specific architectural styles that contribute to a sense of identity and place. Preserving the aesthetic and cognitive values inherent in traditional styles poses a significant challenge compared to other tangible assets. However, the digitization of built heritage offers a proactive approach to conservation. In this regard, the management of heritage building information through Building Information Modeling (BIM) plays a crucial role in preventive conservation. The utilization of multi-dimensional digital information models, known as Historic Building Information Modeling (HBIM), is one such approach. This study aims to explore the application of HBIM in representing built heritage, focusing specifically on the research gap pertaining to the need for an effective and integrated methodology for modeling information related to heritage urban facades. The objective is to generate parametric models within the framework of HBIM information management, thereby contributing to preventive preservation mechanisms. The methodology employed in this study consists of two main parts. Firstly, information is derived by analyzing the structural characteristics of heritage facades using Shape Grammar. Secondly, the acquired information is utilized to develop a comprehensive library of HBIM for heritage architectural elements. The results of this study demonstrate the proposed methodology's effectiveness in determining heritage urban facades' morphological, mathematical, relational, and Euclidean characteristics.

Investigating urban morphological drivers of household water use in developing economies: A structural equation model approach

Urban populations are snowballing, increasing household (HH) water consumption. The growing housing density, the expanding infrastructure, and altered land use patterns place increasing pressure on the water supply and usage. This makes it harder for urban HHs to obtain clean water and worsens water scarcity, with infrastructural and environmental implications for highly populated metropolitan areas. This study aims to quantitatively evaluate the impact of urban morphology on household water use in a developing economy using a Structural Equation Model (SEM), specifically within the growing urban centres surrounding Kolkata in South Bengal. The primary hypothesis projects an increase in urban morphological indicators and HH characteristics that significantly affect water use. The findings reveal that increases in urban morphological indicators are associated with a 34.2% decrease in HH water use, highlighting the importance of urban planning strategies that emphasize compact and diverse urban forms. This work highlights new ways of building cities that consider morphological aspects and allow cities to expand in a way that conserves HH water while being resilient to rapid urban change.

Synergistic assessment of multi-scenario urban waterlogging through data-driven decoupling analysis in high-density urban areas: A case study in Shenzhen, China

Extreme meteorological events and rapid urbanization have led to serious urban flooding problems. Characterizing spatial variations in flooding susceptibility and elucidating its driving factors are essential for preventing damages from urban pluvial flooding. However, conventional methods, limited by spatial heterogeneity and the intricate mechanisms of urban flooding, frequently demonstrated a deficiency in precision when assessing flooding susceptibility in dense urban areas. Therefore, this study proposed a novel framework for an integrated assessment of urban flood susceptibility, based on a comprehensive cascade modeling chain consisting of XGBoost, SHapley Additive exPlanations (SHAP), and Partial Dependence Plots (PDP) in combination with K-means. It aimed to recognize the specific influence of urban morphology and the spatial patterns of flooding risk agglomeration under different rainfall scenarios in high-density urban areas. The XGBoost model demonstrated enhanced accuracy and robustness relative to other three benchmark models: RF, SVR, and BPDNN. This superiority was effectively validated during both training and independent testing in Shenzhen. The results indicated that urban 3D morphology characteristics were the dominant factors for waterlogging magnitude, which occupied 46.02 % of relative contribution. Through PDP analysis, multi-staged trends highlighted critical thresholds and interactions between significant indicators like building congestion degree (BCD) and floor area ratio (FAR). Specifically, optimal intervals like BCD between 0 and 0.075 coupled with FAR values between 0.5 and 1 have the potential to substantially mitigate flooding risks. These findings emphasize the need for strategic building configuration within urban planning frameworks. In terms of the spatial-temporal assessment, a significant aggregation effect of high-risk areas that prone to prolonged duration or high-intensity rainfall scenarios emerged in the old urban districts. The approach in the present study provides quantitative insights into waterlogging adaptation strategies for sustainable urban planning and design.

LCZ-based city-wide solar radiation potential analysis by coupling physical modeling, machine learning, and 3D buildings

Addressing climate change and urban energy problems is a great challenge. Building Integrated Photovoltaics (BIPV) plays a pivotal role in energy conservation and carbon emission reduction. However, traditional approaches to assessing solar radiation on buildings with physical models are computing-intensive and time-consuming. This study presents a hybrid approach by integrating physical model-based solar radiation calculation and machine learning (ML) for city-wide building solar radiation potential (SRP) analysis. By considering urban morphology, land cover, and meteorological characteristics, local climate zones (LCZs) are classified. The SRP of representative LCZs is precisely evaluated using computing-intensive physical models integrated with 3D building models. A ML model is then developed to effectively predict the SRP of building roofs and facades throughout the city. An experiment was conducted in Shenzhen, China to validate the presented approach. The results demonstrate that Shenzhen has a total annual building solar radiation of 3.28∗1011kwh. Luohu District exhibits the highest SRP density. The LCZ-based analysis highlights that compact low-rise LCZs offer greater SRP for roofs, while compact high-rise LCZs do so for facades. Moreover, BIPV could cut CO2 emission by up to 41.85 million tons annually. Notably, solar PV installation only on rooftops in Shenzhen could meet 87.81% of the city's electricity department's carbon reduction goal. This study provides an alternative for city-wide SRP estimation by combining physical modeling and ML and offers valuable insights for data-driven and model-driven urban planning and management in low-carbon cities.

Research on the historical spatial characteristics and conservation strategies of colonial cities in Asia and Africa: a case study of 21 typical cities

Colonial cities in Asia and Africa, developed under the dual influence of native and sovereign cultures during the colonial era, exhibit unique urban morphological characteristics and reflect the planning ideologies of diverse cultures. Thus, the morphological study of colonial cities in Asia and Africa is crucial for understanding and identifying the historical and cultural value of these cities, which in turn enables the formulation of precise conservation strategies. In light of this, the study develops an urban morphological analysis methodology comprising "Colonial Background Analysis—Morphological Characteristics Analysis—Driving Factor Extraction" based on the Conzenian Approach, which is used to examine the morphology of 21 typical colonial cities in Asia and Africa across various colonial periods. As a result, "Dual-City" emerges as the core spatial characteristic of colonial cities in Asia and Africa, reflecting the morphological differences in road layouts, urban fabric, and urban cores between native and European zones. Additionally, as native settlement sizes, social structures, and cultural identities differ between cities, three major spatial patterns of the "Dual-City" model can be identified: separated, mediated, and integrated spatial patterns, resulting from the varied planning approaches applied by colonizers. Furthermore, suggestions for the conservation of colonial heritage are proposed based on the driving factors analysis from the morphological study.

How do the 3D urban morphological characteristics spatiotemporally affect the urban thermal environment? A case study of San Antonio

Urban landscapes, characterized by intricate interactions within a three-dimensional (3D) framework, give rise to complex mechanisms that underlie the Surface Urban Heat Island phenomenon. While global case studies have emphasized the impact of 3D urban morphological components on urban thermal environments, conducting quantitative assessments to identify spatial and seasonal variations in these relationships poses significant challenges. Based on Lidar and land cover dataset, this study first investigated the 3D urban morphology characteristics across nine components, and then assessed their spatial and seasonal impact on the urban thermal environment through Geographically Weighted Regression (GWR). We found that Impervious Surface Fraction (ISF) has the greatest warming effect, whereas the Vegetation Coverage Fraction (VCF) exerts the strongest cooling influence on the thermal environment across all seasons. Building Height (BH) and Street Aspect Ratio (SAR) exhibit spatially varying cooling effects, while Building Number (BN) and Standard Deviation of Building Height (BH_SD) have minimal warming impacts. Additionally, the influence of BH on the thermal environment is significant primarily in areas characterized by higher building coverage. VCF has a stronger cooling effect in central regions with limited vegetation during spring and winter, but this effect diminishes compared to suburban ones in summer and autumn. These findings provide valuable insights into developing effective heat mitigation strategies at both city and community levels, ultimately fostering sustainable urban development.

River valley urban network and morphology: A study on the urban morphology evolution of Lanzhou.

The present study investigates the dynamic evolution characteristics of urban spatial morphology by analyzing real road network data from 2000, 2010, and 2020, along with nighttime lighting data employing spatial analysis methods and spatial syntax models. Accordingly, two separate dimensions of urban morphology: internal and external, are covered. First, the integration and synergy of interior morphology features are analyzed using spatial syntactic modeling. Subsequently, the spatial compactness, fractal dimension, and level of center of gravity shift of the city are assessed by combining the nighttime lighting data with the earlier dataset. This analysis facilitated the deep exploration of the spatiotemporal evolution of the city's external morphology. Building upon this foundation, the interaction between the "internal and external" domains was analyzed further. The main findings of the study reveal a synchronous pattern of urban expansion throughout the evolution of urban spatial morphology. Furthermore, the urban form was observed to undergo a progressive transformation, transitioning from a "single core" morphology to a "primary and secondary double core" morphology. Over time, this development progressed and evolved into a "belt-like multi-core" structure. Additionally, the coupling characteristics further validate the relationship between the structure of the road network and the urban morphology in river valley-type cities. In particular, accessibility of dense and horizontally distributed transportation network was found to significantly influence the spatial development of these cities. As observed, the findings provides valuable insights into understanding the characteristics of internal and external associations regarding urban spatial patterns.

Response of summer Land surface temperature of small and medium-sized cities to their neighboring urban spatial morphology

Buildings, hard surfaces, vegetation, and water bodies are the four main urban surface types in the study region that are categorized in this study. It uses summer land surface temperature (LST)1 data obtained with a spatial resolution of 1 × 1 m using thermal imaging mounted on a drone, with a case study centering on a small to medium-sized Chinese town. To assess urban spatial morphology indicators within different ranges around the sample sites, buffer zones at 30 m, 50 m, 100 m, and 150 m were developed. The study determined the effective response range, identified the important urban spatial morphology indicators influencing LST, and examined the link between summer LST and various urban spatial morphologies for the four surface types. The results show that, close to the sample locations, Natural Environment Indicators (NEI) often have a cooling effect whereas Building Indicators (BI) typically have a warming effect. The influence of spatial morphologies on summer LST was primarily centered between 30 m and 50 m from the sample sites. The primary spatial morphological indicators determining LST for various underlying surface types within various spatial ranges are the building structure index (BSI) and water surface rate (WP). The response modes, response ranges, and response intensities of LST, however, varied significantly among various underlying surface types. This work provides new insights into how urban morphological characteristics affect the fine-scale spatial variation of LST within urban heat islands (UHI).

Spatiotemporal Distribution Characteristics and Multi-Factor Analysis of Near-Surface PM2.5 Concentration in Local-Scale Urban Areas

Near-surface PM2.5 concentrations have been greatly exacerbated by urban land expansion and dense urban traffic. This study aims to clarify the effects of multiple factors on near-surface PM2.5 concentrations from three perspectives of background climatic variables, urban morphology variables, and traffic-related emission intensity. First, two case areas covering multiple local blocks were selected to conduct mobile measurements under different climatic conditions. The observed meteorological parameters and PM2.5 concentration were obtained through GIS-based imaging. These interpolation results of air temperature and relative humidity reveal highly spatiotemporal diversity, which is greatly influenced by artificial heat emissions and spatial morphology characteristics in local areas. The PM2.5 concentration on measurement days also varies considerably from the lowest value of 44~56 μg/m3 in October to about 500 μg/m3 in December in Harbin winter and ranges between about 5 μg/m3 and 50 μg/m3 in Guangzhou summer. The correlation analysis reveals that both the climatic conditions and urban morphology characteristics are significantly correlated with local PM2.5 concentration. Especially for Guangzhou summer, the PM2.5 concentration was positively correlated with the street traffic emission source intensity with correlation coefficient reaching about 0.79. Multivariate nonlinear formulas were applied to fit the association between these factors and PM2.5 concentration with higher determined coefficients. And optimization strategies are thus suggested to improve the urban air quality in local-scale areas. This attribution analysis contributes to environmentally friendly urban construction.

Research note: Integrating big data to predict tree root blockages across sewer networks

Tree root blockages can occur when tree roots enter sewer pipes, resulting potentially in high economic and environmental costs. However, comprehensive understanding of which tree species and environmental factors (i.e., soil, hydrological and landscape properties) may most impact sewer blockages remains unclear. We integrated sewer pipe, tree and environmental data from 103 suburbs across seven local government areas to identify the most significant factors affecting root blockages along 902 km of sewer infrastructure in Sydney, Australia.Distributed Random Forest (DRF) models were used to predict 2,942 root blockages that occurred between 2010 and 2019 along 22,192 sewer pipe segments, and to relate these to 90,858 tree stems of 651 tree species, while accounting for other pipe and environmental variables. We found that tree root blockages were clustered across the urban landscape, creating “blockage hotspots”. Tree stem abundance was positively correlated with number of blockages.We found that urban morphology and pipe characteristics were more important than tree characteristics (for example stem size, useful life expectancy) in explaining root blockages. We also found that the number of tree stems and the abundance of the Chinese banyan (Ficus macrocarpa) were the most important variables predicting frequency of root blockages per unit pipe length. The most important landscape variables were slope, aspect, soil salinity and available water content, while most pipe-related variables also explained significant deviance in the DRF model. This study is among the first to mine large datasets representing urban infrastructure, tree and environmental datasets to predict their role on sewer blockages and toward the proactive management of sewerage infrastructure. Furthermore, our approach may be applied to improve urban greening efforts, thus minimising future conflicts with, and costly disruptions to, underground infrastructure.

Analyzing spatial and geometrical patterns of Tiruchirappalli and Tier-urban centers using Spatial Metrics

Abstract Understanding spatial and geometrical patterns of urbanization is crucial in tackling associated problems. As urbanization progresses through various stages of development, it reflects different forms, patterns, and interactions based on its physical and functional aspects. Spatial metrics is a well-acclaimed technique for quantifying urban morphological characteristics. The current study was conducted for Tiruchirappalli and six tier-urban centers located within a 40-kilometers radius to comprehend the comparative growth and spatial patterns. The urban centers are divided into eight zones based on direction for more precise quantification. The study employed Landsat 5 and 8 satellite images to classify land use/cover for the periods 1996, 2008, and 2020. FRAGSTAT is the software application used to analyze spatial metrics, at patch, class, and landscape levels. The study generated a significant amount of data about spatial and geometric patterns of growth. Area, edge, and aggregation metrics indicated that zones in Manachanallur, Manapparai, Musiri, Thiruverumbur, and Thuraiyur had protrusive urban growth during the study period. Transport networks have been the instrumental factor for such growth. Diversity metrics revealed Tiruchirappalli and Thiruverumbur have abundant patches of various classes in many of their zones since they retain patches like open, vegetation, and water bodies extensively. Shape metrics across all urban centers during the period 1996-2008 were more irregular in shape; it has become significantly smooth during 2008-2020 due to infill developments on the fringe areas. The period 1996-2008 recorded a huge transition of open areas into built-ups, attributing to infill development, especially inside the urban centers; similarly, during the period 2008-2020, edge expansion has been recorded widely across the urban centers. The current study is a significant contribution to urban research in understanding relative spatial and geometric patterns of urbanization.

Exploring the impact of urban factors on land surface temperature and outdoor air temperature: A case study in Seoul, Korea

Land surface temperature (LST) and outdoor air temperature (OAT) have been widely used to investigate urban heat island (UHI) phenomena. Although LST and OAT were reported to show a substantial correlation, whether urban factors play a consistent role in both LST and OAT has not been well addressed in the existing literature. To fill this gap, this study examined the effects of urban factors on both LST derived from satellite image data and OAT measured at the pedestrian-level through a case study in Seoul, Korea. Two different temperature datasets were used for the same days in summer and winter, and corresponding urban spatial characteristics were calculated on the basis of geographic information system (GIS) data. The effects of urban variables were analyzed using the Genizi and partial correlation methods. The results from the Genizi method revealed that land use characteristics (i.e., building coverage and pervious ratios) were important factors influencing LST, whereas urban morphological characteristics (i.e., building height) had a greater influence on OAT. The partial correlation results confirmed these separate sets of urban factors above are key factors for LST and OAT. In addition, the partial correlation analysis demonstrated that the pervious ratio showed a negative correlation with LST and the building height consistently showed a significant influence on OAT in both seasons. In contrast, the canyon ratio showed a contrasting effect on OAT depending on the season, underscoring that urban factors can affect temperatures differently depending on the meteorological conditions. This study highlights the different effects of urban factors depending on temperatures.

The digital presentation of human-oriented urban design

With the process of modernization and industrialization, cities are increasingly becoming homogenous. The place value and vitality of cities are seriously lacking with a declining trend. Therefore, in recent years, China has proposed the core concept of human-oriented urban construction and development. However, the multi-scale characteristics of urban morphology are difficult to be understood and reconstructed through traditional top-down design processes due to limited individual perception and cognitive abilities. Today, digital technology has changed this situation, which provides both direct and indirect means to gain a deeper understanding of the city. Therefore, to achieve a greater depth with more precise sense of scale, and represent regional personality and cultural diversity, this article proposed a concept of experimental mastery of full-chain space of multi-scales in the city, which is promising to coordinate the global and local, groups and individuals. This research can: help overcome the various drawbacks during the development of traditional urban design; change the urban order and development model that is believed to be “one change fits all” under the influence of modern urban design paradigm and ideal utopia; improve the personalization and accuracy of public participation through digital presentation; promote human-oriented and sustainable urban design, as well as the growing needs of the people for a better life.

The Role of Subjective Perceptions and Objective Measurements of the Urban Environment in Explaining House Prices in Greater London: A Multi-Scale Urban Morphology Analysis

House prices have long been closely related to the built environment of cities, yet whether the subjective perception (SP) of these environments has a differing effect on prices at multiple urban scales is unclear. This study sheds light on the impact of people’s SP of the urban environment on house prices in a multi-scale urban morphology analysis. We trained a machine learning (ML) model to predict people’s SP of the urban environment around properties across Greater London with survey response data from an online survey evaluating people’s SP of street view image (SVI) and linked this to house price data. This information was used to construct a hedonic price model (HPM) and to evaluate the association between SP and house price data in a series of linear regression models controlling location information and urban morphological characteristics such as street network centralities at multiple urban scales, quantified using space syntax (SS) methods. The findings show that SP influences house prices, but this influence differs depending on the urban scale of analysis. Particularly, a sense of ‘enclosure’ and ‘comfort’ are important factors influencing house price variation. This study contributes by introducing SP of the urban environment as a new dimension into the traditional HPM and by exploring the economic impact of SP on the house price market at multiple urban scales.

Outdoor Thermal Environment Regulation of Urban Green and Blue Infrastructure on Various Types of Pedestrian Walkways

Urban green and blue infrastructure (GBI) is effective in urban heat mitigation. However, the diurnal variations in the thermal regulation of GBI on different types of urban pedestrian walkways in the subtropical humid climate zone are unclear. Based on traverse measurements in January 2022, this study investigates how outdoor air temperature (Ta) and wet-bulb temperature (Tw) at the pedestrian level are affected by various urban morphology and GBI characteristics in the central urbanized area of Guangzhou (China). Six building-related, three vegetation-related, and three water-related parameters were applied in the analyses. Results show that green infrastructure (GI) has the strongest cooling effect (Pearson’s coefficient r = −0.33~−0.54) on Ta on cloudy evenings and the weakest cooling effect (r = −0.22~−0.32) on clear evenings. Blue infrastructure (BI) exhibits the highest correlations with the cooling effect for Ta on clear afternoons (r = −0.35~−0.51) and weaker, but more consistent, correlations throughout cloudy days (r = −0.23~−0.43). Strong correlations between GI and Tw cooling are observed in the morning and evening on clear days (r = −0.13~−0.48), while BI exhibits the strongest correlation with Tw cooling on clear afternoons (r = −0.13~−0.37). In addition, both GI and BI show a greater cooling effect on both Ta and Tw in low-rise areas compared to high-rise areas. Compared to the individual GI or BI, the integrated GBI has a higher cooling intensity for both Ta and Tw in high-rise areas. However, the cooling potential of integrated GBI is slightly lower for both Ta and Tw during noon and afternoon in low-rise areas. The findings of this study give insights into urban GBI planning and design of specific urban microclimate regulation strategies.

Impact of Biomass Ratio as a Synthetic Parameter in Soft Computing Approaches for a Decision-Making Tool for Biogas Plants in Urban Areas

The EU’s energy transition strategy highlights the significance of developing innovative energy models to encourage the utilization of renewable energy sources in urban areas. Utilizing local urban biomasses, including food waste, sewage, and green waste, can contribute to the establishment of energy systems that harness bio-waste for energy generation, thereby promoting circular economy principles and urban metabolisms. This paper proposes using a pre-design tool (based on soft computing approaches) that incorporates an initial analysis of the multidisciplinary feasibility of such systems as an effective strategy and valuable support for preliminary studies. It focuses on validating three “biomass ratio” parameters, integrating urban morphology and district characteristics with the amount of bio-waste in a peri-urban district comprising multifamily buildings. These parameters can be incorporated into a pre-design tool that facilitates multi-criteria decision analyses, aiding the design of innovative models that promote renewable energy sources in urban areas. The findings suggest that synthetic parameters can guide initial considerations, but they may overestimate the energy potential and should be further investigated. Hence, future research should explore complementary strategies for estimating biomass energy potential and extend the application of this methodology to other types of districts.

Spatial pattern assessment of dengue fever risk in subtropical urban environments: The case of Hong Kong

Dengue fever, a mosquito-borne fatal disease, brings a huge health burden in tropical regions. With global warming, rapid urbanization and the expansion of mosquitoes, dengue fever is expected to spread to many subtropical regions, leading to increased potential health risks on local populations. So far, limited studies assessed the dengue fever risk spatially for subtropical non-endemic regions hindering the development of related public health management. Therefore, we proposed a spatial hazard-exposure-vulnerability assessment framework for mapping the dengue fever risk in Hong Kong. Firstly, the spatial distribution of the habitat suitability for Aedes albopictus, the mosquito proxy for the dengue fever hazard, was predicted using a species distribution model (e.g., MaxEnt) relying on a list of variables related to local climate, urban morphology, and landscape metrics. Secondly, the spatial autocorrelation between high dengue hazard and high human population exposure in urban areas was measured. Finally, the dengue fever risk was assessed at community scale by integrating the results of vulnerability analysis basing on census data. This approach allowed the identification of 17 high-risk spots within Hong Kong. The landscape metrics about land utilities and vegetations, and urban morphological characteristics are the influential factors on the spatial distribution of dengue vector. In addition, the underlying factors behind each hot spot were investigated, and specific suggestions for dengue prevention were proposed accordingly. The findings provide a useful reference for developing local dengue fever risk prevention measures, with the proposed method easily exportable to other high-density cities within subtropical Asia and elsewhere.

High-Resolution Greening Scenarios for Urban Climate Regulation Based on Physical and Socio-Economical Factors

Urban ecosystems represent the main providers of ecosystem services in cities and play a relevant role, among the many services, in the regulation of the urban microclimate and mitigation of the urban heat island effect. The amount, localization, and spatial configuration of vegetation (i.e., urban trees) are key elements for planners and designers aiming at maximizing the climate regulation potential and therefore extending the related benefits to a higher number of residents and city users. Different factors and constraints related to urban morphology and socio-economical characteristics of the urban environment influence the localization of new greening scenarios, therefore impacting the potential benefits that can be obtained by residents. This paper investigates these factors by identifying high-resolution greening scenarios that are able to maximize the cooling benefits for people and local residents. For the case study of metropolitan areas of Catania (Italy) with a hot Mediterranean climate, scenarios are derived by modelling physical and socio-economic factors as spatial constraints with the UMEP model and GIS spatial analysis. Results show that new greenery should be mostly located in public areas that are mostly used by residents. Built on the results obtained in the case study analyzed, the paper also proposes some general planning criteria for the localization of new urban greenery, which should be extended to other geographical urban contexts.

Urban Microclimate, Outdoor Thermal Comfort, and Socio-Economic Mapping: A Case Study of Philadelphia, PA

Urban areas are often warmer than rural areas due to the phenomenon known as the “urban heat island” (UHI) effect, which can cause discomfort for those engaging in outdoor activities and can have a disproportionate impact on low-income communities, people of color, and the elderly. The intensity of the UHI effect is influenced by a variety of factors, including urban morphology, which can vary from one area to another. To investigate the relationship between outdoor thermal comfort and urban morphology in different urban blocks with varying social vulnerability status, this study developed a geographic information system (GIS)-based workflow that combined the “local climate zone” (LCZ) classification system and an urban microclimate assessment tool called ENVI-met. To demonstrate the effectiveness of this methodology, the study selected two different urban blocks in Philadelphia, Pennsylvania–with high and low social vulnerability indices (SVI)–to compare their microclimate conditions in association with urban morphological characteristics such as green coverage area, sky view factor (SVF), albedo, and street height to width (H/W) ratio. The results of the study showed that there was a strong correlation between tree and grass coverage and outdoor air and mean radiant temperature during hot seasons and extremely hot days, which in turn affected simulated predicted mean vote (PMV). The effects of greenery were more significant in the block associated with a low SVI, where nearly 50% of the site was covered by trees and grass, compared to only 0.02% of the other block associated with a high SVI. Furthermore, the investigation discovered that reduced SVF, along with increased albedo and H/W ratio, had a beneficial impact on the microclimate at the pedestrian level within the two studied urban blocks. This study provided an effective and easy-to-implement method for tackling the inequity issue of outdoor thermal comfort and urban morphology at fine geographic scales.

Solar energy potential using GIS-based urban residential environmental data: A case study of Shenzhen, China

This study examines Shenzhen's potential for utilizing photovoltaics (PV) on buildings in terms of residential electricity consumption. Based on its geographic information system (GIS) data, typical local meteorological data, and electricity demand, the solar potential of Shenzhen has been thoroughly studied, critically analyzed, and compared with different urban morphology characteristics. Urban morphology influences the solar radiation received by individual buildings. First, four representative residential areas have been chosen. Using the 3D architectural modeling software Rhinoceros with its Grasshopper and Ladybug plug-ins, the solar potential of the areas was then evaluated. Following this, an economic analysis was carried out. Due to the high solar radiation of the rooftop areas, the results demonstrated that a combination of rooftops and high-performance facades (over 700 kWh/m2/year) produces a promising payback period. Therefore, this paper calls for action to start a large-scale program to promote and then mandate the use of renewable energy resources (RES), specifically solar, for the residential sector in the cities located in the southern region of China.