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- An analysis of public topics and sentiments based on social media during
the COVID-19 Omicron Variant outbreak in Shanghai 2022
Abstract: Abstract The outbreak of the COVID-19 Omicron variant in Shanghai in 2022 elicited complex emotions among Shanghainese during the two-month quarantine period. This paper aims to identify prevailing public themes and sentiments by analyzing social media posts from Weibo. Initially, we conducted research based on a dataset of 90,000 Weibo posts during the 2022 COVID-19 outbreak in Shanghai. By examining social media data that mirrors residents' emotional shifts and areas of focus during unforeseen circumstances, we have developed an analytical framework combining hotspot analysis and public sentiment assessment. Subsequently, we employed the Latent Dirichlet Allocation (LDA) method to conduct topic modeling on the Weibo text data. The SnowNLP sentiment classification method was then utilized to quantify sentiment values. Ultimately, we performed spatial visualization of sentiment and concern data, categorizing them into distinct time periods based on Shanghai's infection curve. This approach allowed us to investigate concern focal points, sentiment trends, and their spatiotemporal evolution characteristics. Our findings indicate that variations in public sentiment primarily hinge on the severity of the epidemic's spread, emerging events, the availability of essential resources, and the government's ability to respond promptly and accurately. It is evident that, while residents' concerns shift over time, their primary objective on social media remains expressing demands and releasing emotions. This research offers an avenue for leveraging public opinion analysis to enhance governance capacity during crises, fortify urban resilience, and promote public involvement in governmental decision-making processes.
PubDate: 2024-08-02
DOI: 10.1007/s43762-024-00128-y
- Computational urban science needs to go beyond computational
Abstract: Abstract In this paper, I advocate for a radical expansion of computational urban science to encompass a multidisciplinary, human-centered approach, addressing the inadequacies of traditional methodologies in capturing the complexities of urban life. Building on insights from Jane M. Jacobs and others, I argue that integrating computational tools with disciplines like sociology, anthropology, and urban planning can significantly enhance our understanding and management of urban environments. This integration facilitates a deeper analysis of cultural phenomena, improves urban policy design, and promotes more sustainable, inclusive urban development. By embracing qualitative research methods—such as ethnography and participatory observations—alongside computational analysis, I highlight the importance of capturing the nuanced social fabrics and subjective experiences that define urban areas. I also stress the necessity of including community stakeholders in the research process to ensure that urban science not only analyzes but also improves the lived experiences of urban populations. Furthermore, I underscore the need for ethical governance and the mitigation of biases inherent in computational tools, proposing rigorous model auditing and the inclusion of diverse perspectives in model development. Overall, this work champions a holistic approach to urban science, aiming to make cities smarter, more equitable, and responsive to their residents’ needs.
PubDate: 2024-07-19
DOI: 10.1007/s43762-024-00130-4
- Global Trends in Housing Research over the Past three Decades
Abstract: Abstract This paper reviews a large number of scholarly articles in the housing field spanning the last thirty years, from 1993 to 2022 by implementing bibliometric analysis method. We examine scientific outputs, identify influential articles, journals, international collaboration and evolution of research trends. Keywords such as “Housing price,” “Housing policy,” “Affordable housing,” “Homeownership,” “Housing market,” “Urban planning,” and “Neighborhood” have been identified as the most prevalently cited terms during this period. Furthermore, the prominence of terms such as “China,” “Gentrification,” “Public housing,” “Social housing,” “Homelessness,” “Migration,” “Urbanization,” “Energy,” “Inequality,” “Land use,” “Gender,” and “Foreclosure” have grown in importance, pointing to future research trends. The analysis also reveals that articles pertaining to the COVID-19 pandemic predominantly address the comprehensive effects of the virus on aspects of mental and physical health, consumer behavior, and economic and societal challenges.
PubDate: 2024-07-18
DOI: 10.1007/s43762-024-00129-x
- Modelling the determinants for sustainable smart city through interpretive
structure modelling and analytic hierarchy process
Abstract: Abstract Rapid increasing urbanization and resource scarcity are global phenomena nowadays, leading to the urban transformation of cities into smart cities. This article explores sustainability by using the lens of the spirit of place (SOP) for smart city development by proposing a model for the transformation of the cities into smart cities and attainment of the sustainable development simultaneously based on Interpretive Structure Modelling (ISM) and Analytic Hierarchy Process (AHP). This study followed a systematic approach by utilizing an analytical framework that included an extensive literature review and urban experts' opinions for the identification of a pool of indicators and its evaluation for validity, pilot testing, and administration of a questionnaire to a population sample. The study utilizes a sample of 142 participants who have witnessed the transformation of their city over the years. The research showed that every place has its own identity known to be the ‘spirit of place’ that helps in assessing the sustainable characteristics and utilizing that in the path of planning and development for the attainment of sustainable development. It also showed that urban developers should consider local populations’ views and important aspects in designing and planning development projects to achieve sustainable development with resilient infrastructure. This study will help facilitate sustainability at a local level for urban developers, planners, and decision-makers while crafting strategic plans.
PubDate: 2024-06-12
DOI: 10.1007/s43762-024-00125-1
- Crowdsourcing the influence of physical features on the likely use of
public open spaces
Abstract: Abstract The configuration of public open spaces plays a crucial role in shaping how different people use them. Nevertheless, our understanding of how the physical features of public open spaces influence the activities conducted within them, and the extent to which this impact differs across various individuals and population groups, is currently limited. In this study, we explore how the physical characteristics of public open spaces influence the likelihood of use among individuals, spanning different age and gender groups. By employing crowdsourcing, street-level imagery, statistical comparisons, and reflexive thematic analysis we uncover significant variations in the suitability of public open spaces for distinct activities, such as socializing or exercising. Greenspaces emerge as the preferred choice for almost all activities, whereas streets are consistently rated as the least suitable. Additionally, we identified various characteristics that influence the activities people are likely to engage in. These include the size of the space, the presence of seating, natural elements such as vegetation or water bodies, and the proximity to transport infrastructure. Surprisingly, we do not observe statistically significant differences in preferences among most age and gender groups. Overall, our study underscores the need for providing a diverse range of public open spaces tailored to accommodate different individuals, population groups, and activities.
PubDate: 2024-06-11
DOI: 10.1007/s43762-024-00126-0
- A survey on applications of reinforcement learning in spatial resource
allocation
Abstract: Abstract The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Despite the progress, reinforcement learning still faces hurdles when it comes to spatial resource allocation. There remains a gap in its ability to fully grasp the diversity and intricacy of real-world resources. The environmental models used in reinforcement learning may not always capture the spatial dynamics accurately. Moreover, in situations laden with strict and numerous constraints, reinforcement learning can sometimes fall short in offering feasible strategies. Consequently, this paper is dedicated to summarizing and reviewing current theoretical approaches and practical research that utilize reinforcement learning to address issues pertaining to spatial resource allocation. In addition, the paper accentuates several unresolved challenges that urgently necessitate future focus and exploration within this realm and proposes viable approaches for these challenges. This research furnishes valuable insights that may assist scholars in gaining a more nuanced understanding of the problems, opportunities, and potential directions concerning the application of reinforcement learning in spatial resource allocation.
PubDate: 2024-06-07
DOI: 10.1007/s43762-024-00127-z
- Human-centric computational urban design: optimizing high-density urban
areas to enhance human subjective well-being
Abstract: Abstract Urban areas face increasing pressure due to densification, presenting numerous challenges involving various stakeholders. The impact of densification on human well-being in existing urban areas can be both positive and negative, which requires a comprehensive understanding of its consequences. Computational Urban Design (CUD) emerges as a valuable tool in this context, offering rapid generation and evaluation of design solutions, although it currently lacks consideration for human perception in urban areas. This research addresses the challenge of incorporating human perception into computational urban design in the context of urban densification, and therefore demonstrates a complete process. Using Place Pulse 2.0 data and multinomial logit models, the study first quantifies the relationship between volumetric built elements and human perception (beauty, liveliness, and safety). The findings are then integrated into a Grasshopper-based CUD tool, enabling the optimization of parametric designs based on human perception criteria. The results show the potential of this approach. Finally, future research and development ideas are suggested based on the experiences and insights derived from this study.
PubDate: 2024-05-28
DOI: 10.1007/s43762-024-00124-2
- Automated floodwater depth estimation using large multimodal model for
rapid flood mapping
Abstract: Abstract Information on the depth of floodwater is crucial for rapid mapping of areas affected by floods. However, previous approaches for estimating floodwater depth, including field surveys, remote sensing, and machine learning techniques, can be time-consuming and resource-intensive. This paper presents an automated and rapid approach for estimating floodwater depth from on-site flood photos. A pre-trained large multimodal model, Generative pre-trained transformers (GPT-4) Vision, was used specifically for estimating floodwater. The input data were flood photos that contained referenced objects, such as street signs, cars, people, and buildings. Using the heights of the common objects as references, the model returned the floodwater depth as the output. Results show that the proposed approach can rapidly provide a consistent and reliable estimation of floodwater depth from flood photos. Such rapid estimation is transformative in flood inundation mapping and assessing the severity of the flood in near-real time, which is essential for effective flood response strategies.
PubDate: 2024-05-27
DOI: 10.1007/s43762-024-00123-3
- An analysis of agglomeration structure for Beijing, Tianjin, and Hebei
based on spatial-temporal big data
Abstract: Abstract The Beijing-Tianjin-Hebei integration plan rose to the status of a national-level strategy in 2014. This paper provides a deep analysis of the Beijing-Tianjin-Hebei area’s inter-city commuter big data. This research analyzed the overview of spatial structure, polycentric structure, hierarchical structure and clustering characteristics of the BTH based on network analysis methods. It reveals that the inter-city commuter network exhibits clear polycentric characteristics, with Beijing acting as the central hub. The degree of network correlation between cities in Tianjin and Hebei is notably low, indicating that the flow of people primarily revolves around Beijing, while interactions between other cities remain limited. Therefore, it is necessary to further decentralize Beijing's non-capital core functions. The level of connectedness among the areas surrounding the Bohai Rim is not very high, and it has not developed the coastal advantage. The cooperation could be strengthed among the cities within Bohai Rim. The polycentric structure has initially taken shape, but it exhibits obvious polarization characteristics. It is necessary to strengthen the interaction of talents between cities to form secondary central units in BTH.
PubDate: 2024-04-18
DOI: 10.1007/s43762-024-00122-4
- Artificial intelligence of things for smart cities: advanced solutions for
enhancing transportation safety
Abstract: Abstract In the context of smart cities, ensuring road safety is crucial due to increasing urbanization and the interconnected nature of contemporary urban environments. Leveraging innovative technologies is essential to mitigate risks and create safer communities. Thus, there is a compelling imperative to develop advanced solutions to enhance road safety within smart city frameworks. In this article, we introduce a comprehensive vehicle safety framework tailored specifically for smart cities in the realm of Artificial Intelligence of Things (AIoT). This framework seamlessly integrates a variety of sensors, including eye blink, ultrasonic, and alcohol sensors, to bolster road safety. The utilization of eye blink sensor serves to promptly detect potential hazards, alerting drivers through audible cues and thereby enhancing safety on smart city roads. Moreover, ultrasonic sensors provide real time information about surrounding vehicle speeds, thereby facilitating smoother traffic flow. To address concerns related to alcohol consumption and its potential impact on road safety, our framework incorporates a specialized sensor that effectively monitors the driver’s alcohol levels. In instances of high alcohol content, the system utilizes GPS and GSM technology to automatically adjust the vehicle’s speed while simultaneously notifying pertinent authorities for prompt intervention. Additionally, our proposed system optimizes inter-vehicle communication in smart cities by leveraging Li-Fi technology, enabling faster and more efficient data transmission via visible light communication (VLC). The integration of Li-Fi enhances connectivity among connected vehicles, contributing to a more cohesive and intelligent urban transportation network. Through the structured integration of AIoT technologies, our framework lays a robust foundation for a safer, smarter, and more sustainable future in smart city transportation. It offers significant advancements in road safety and establishes the groundwork for further enhancement in intelligent urban transportation networks.
PubDate: 2024-04-17
DOI: 10.1007/s43762-024-00120-6
- Identifying influential climatic factors for urban risk studies in rapidly
urbanizing Region
Abstract: Abstract Severe weather events, such as heat waves, floods, pollution, and health threats, are becoming more common in metropolitan places across the world. Overcrowding, poor infrastructure, and fast, unsustainable urbanization are some of the problems that India faces, and the country is also susceptible to natural disasters. This research analyzes climatic variables affecting urban hazards in Bangalore (also known as Bengaluru) via a thorough review. Heat waves, urban floods, heat islands, and drought were identified in 156 qualifying publications using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method. Contributing variables were also considered. City development and urbanization were key to changing climate and increasing urban dangers. While long-term climatic variable distribution is uneven, warming is evident. The report promotes strong urban planning techniques, comprehensive policies, more green areas, and sustainable development beyond short-term heat response programs to boost urban climate resilience. This study shows how climate, land use, and urban dangers are interconnected. Future studies may benefit by categorizing urban risk studies and identifying climatic factors.
PubDate: 2024-04-11
DOI: 10.1007/s43762-024-00121-5
- Digitizing cities for urban weather: representing realistic cities for
weather and climate simulations using computer graphics and artificial
intelligence
Abstract: Abstract Due to their importance in weather and climate assessments, there is significant interest to represent cities in numerical prediction models. However, getting high resolution multi-faceted data about a city has been a challenge. Further, even when the data were available the integration into a model is even more of a challenge due to the parametric needs, and the data volumes. Further, even if this is achieved, the cities themselves continually evolve rendering the data obsolete, thus necessitating a fast and repeatable data capture mechanism. We have shown that by using AI/graphics community advances we can create a seamless opportunity for high resolution models. Instead of assuming every physical and behavioral detail is sensed, a generative and procedural approach seeks to computationally infer a fully detailed 3D fit-for-purpose model of an urban space. We present a perspective building on recent success results of this generative approach applied to urban design and planning at different scales, for different components of the urban landscape, and related applications. The opportunities now possible with such a generative model for urban modeling open a wide range of opportunities as this becomes mainstream.
PubDate: 2024-03-12
DOI: 10.1007/s43762-023-00111-z
- Spatial heterogeneities of residents' sentiments and their associations
with urban functional areas during heat waves– a case study in Beijing
Abstract: Abstract The intensification of global heat wave events is seriously affecting residents' emotional health. Based on social media big data, our research explored the spatial pattern of residents' sentiments during heat waves (SDHW). Besides, their association with urban functional areas (UFAs) was analyzed using the Apriori algorithm of association rule mining. It was found that SDHW in Beijing were characterized by obvious spatial clustering, with hot spots predominately dispersed in urban areas and far suburbs, and cold spots mainly clustered in near suburbs. As for the associations with urban function areas, green space and park areas had significant effects on the positive sentiment in the study area, while a higher percentage of industrial areas had a greater impact on negative SDHW. When it comes to combined UFAs, our results revealed that the green space and park area combined with other functional areas was more closely related to positive SDHW, indicating the significance of promoting positive sentiment. Subdistricts with a lower percentage of residential and traffic areas may have a more negative sentiment. There were two main combined UFAs that have greater impacts on SDHW: the combination of residential and industrial areas, and the combination of residential and public areas. This study contributes to the understanding of improving community planning and governance when heat waves increase, building healthy cities, and enhancing urban emergency management.
PubDate: 2024-03-11
DOI: 10.1007/s43762-024-00119-z
- Geographical and temporal weighted regression: examining spatial
variations of COVID-19 mortality pattern using mobility and multi-source
data
Abstract: Abstract The COVID-19 pandemic has had profound adverse effects on public health and society, with increased mobility contributing to the spread of the virus and vulnerable populations, such as those with pre-existing health conditions, at a higher risk of COVID-19 mortality. However, the specific spatial and temporal impacts of health conditions and mobility on COVID-19 mortality have yet to be fully understood. In this study, we utilized the Geographical and Temporal Weighted Regression (GTWR) model to assess the influence of mobility and health-related factors on COVID-19 mortality in the United States. The model examined several significant factors, including demographic and health-related factors, and was compared with the Multiscale Geographically Weighted Regression (MGWR) model to evaluate its performance. Our findings from the GTWR model reveal that human mobility and health conditions have a significant spatial impact on COVID-19 mortality. Additionally, our study identified different patterns in the association between COVID-19 and the explanatory variables, providing insights to policymakers for effective decision-making.
PubDate: 2024-03-01
DOI: 10.1007/s43762-024-00117-1
- Exploring the influence of transportation on urban spatial structure using
the spatial Durbin model: evidence from 265 prefecture-level cities in
China
Abstract: Abstract The interactive relation between transportation and urban spatial structure remains a significant yet challenging issue in transport engineering and urban planning. Most previous studies indicate that the coordination of transportation and urban structure is conducive to solve urban diseases and promote urban sustainable development. Grounded in the theory of city-region spatial structure, this study examines the spatiotemporal dynamics of urban spatial structure from 2006 to 2019 and investigates the impact of transportation on shaping urban spatial structure in prefecture-level cities in China using spatial Durbin model. Major findings include: first, the nighttime light remote sensing data is employed to characterize urban spatial structure with the mono-centricity index ranging from 0.26 to 0.48. The coastal cities tend to exhibit the polycentric structure, while the cities in western region often display the monocentric structure. Second, there is a gradual decline in mono-centricity structure in these cities. Spatial heterogeneity in urban spatial structure is evident in eastern, central, western and northeastern China. Third, transportation significantly and positively influences spatial structure, however, the impact varies across regions and city sizes. Finally, policy implications are proposed based on these findings, such as promoting the integrated land use-transportation development, implementing targeted regional policies, and enhancing land use spatial planning.
PubDate: 2024-02-28
DOI: 10.1007/s43762-024-00118-0
- Tropical cyclone warning and forecasting system in Bangladesh: challenges,
prospects, and future direction to adopt artificial intelligence
Abstract: Abstract Bangladesh is a disaster-prone area due to its geographic location, especially since it is affected by a tropical cyclone (TC) almost every year. TC causes severe damage to lives and livelihoods in this region of Bangladesh. TC prediction and monitoring are still based on the traditional statistical model. In general, the conventional statistical model has the limitation of not handling nonlinear datasets in a precious way. However, the country is gradually adopting modern technologies like artificial intelligence (AI), machine learning (ML), and Fourth Industrial Revolution (IR4) technology for disaster management. The purpose of this study is to identify the scope of adopting new technologies like machine learning and deep learning (DL) for cyclone prediction in countries like Bangladesh, which are cyclone-prone but have constraints on funds to invest in this field. To establish the idea, we examine the research work on the TC forecasting model used in the country from 2010 to 2022. This paper examines the TC forecasting model used to identify the scope of improvement in the current system based on AI and process a better cyclone prediction system using an AI-based model. This study intends to reveal the gaps in mainstream cyclone prediction methods and focus on cyclone prediction system improvement. Moreover, this work will summarize the current state of the TC prediction forecasting system in Bangladesh and how the incorporation of modern technology can increase its efficiency. Finally, as a final note, we conclude this paper with the answer of proximity to the proposal of including AI in cyclone detection and prediction systems. A workflow diagram to address cyclone prediction based on ML and DL has also been presented in this paper, which may augment the capacity of the Bangladesh Meteorological Department (BMD) in performing their responsibility. Moreover, some specific recommendations have been proposed to improve the cyclone prediction system in Bangladesh.
PubDate: 2024-01-30
DOI: 10.1007/s43762-023-00113-x
- Predicting Gross Domestic Product (GDP) using a PC-LSTM-RNN model in urban
profiling areas
Abstract: Abstract Gross Domestic Product (GDP) is significant for measuring the strength of national and global economies in urban profiling areas. GDP is significant because it provides information on the size and performance of an economy. The real GDP growth rate is frequently used to indicate the economy’s health. This paper proposes a new model called Pearson Correlation-Long Short-Term Memory-Recurrent Neural Network (PC-LSTM-RNN) for predicting GDP in urban profiling areas. Pearson correlation is used to select the important features strongly correlated with the target feature. This study employs two separate datasets, denoted as Dataset A and Dataset B. Dataset A comprises 227 instances and 20 features, with 70% utilized for training and 30% for testing purposes. On the other hand, Dataset B consists of 61 instances and 4 features, encompassing historical GDP growth data for India from 1961 to 2021. To enhance GDP prediction performance, we implement a parameter transfer approach, fine-tuning the parameters learned from Dataset A on Dataset B. Moreover, in this study, a preprocessing stage that includes median imputation and data normalization is performed. Mean Square Error, Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, Median Absolute Error, and determination coefficient (R2) evaluation metrics are utilized in this study to demonstrate the performance of the proposed model. The experimental results demonstrated that the proposed model gave better results than other regression models used in this study. Also, the results show that the proposed model achieved the highest results for R2, with 99.99%. This paper addresses a critical research gap in the domain of GDP prediction through artificial intelligence (AI) algorithms. While acknowledging the widespread application of such algorithms in forecasting GDP, the proposed model introduces distinctive advantages over existing approaches. Using PC-LSTM-RNN which achieves high R2 with minimum error rates.
PubDate: 2024-01-29
DOI: 10.1007/s43762-024-00116-2
- Residential location choice: an investigation of transportation, public
facilities, and social factors
Abstract: Abstract Residential location choice is a crucial topic in transportation planning research since land use as well as residential land use can significantly affect a city's attractiveness for development and residence. Understanding the factors that influence households in their residential location choice is essential for policymakers to evaluate the effect of their decisions. In this study, the impact of transportation factors on the attractiveness of residential areas was investigated in Qazvin city, Iran, using the stated preference (SP) method and structural equation modeling (SEM). The results indicated that the type of housing and private house preference were significant factors influencing the residential location choice. Additionally, proximity to health centers, low pollution levels, and access to public transportation and taxi stations were the top priorities for residents when choosing a place to live. Notably, households with children in education had a greater emphasis on air pollution and the proximity to taxi stations, as these factors could affect their children's health and education. Overall, the findings suggested that transportation factors played a critical role in the residential location choice and that policymakers should prioritize public transportation and taxi services, as well as reduce pollution levels, to make residential areas more attractive and livable for Qazvin residents.
PubDate: 2024-01-17
DOI: 10.1007/s43762-024-00115-3
- Application note: evaluation of the Gini coefficient at the county level
in mainland China based on Luojia 1-01 nighttime light images
Abstract: The Luojia 1–01 (LJ1-01) night lighting satellite's superior spatial information capture capability provides conditions for accurate assessment of regional wealth distribution inequality (RWDI) at a small scale. This paper evaluated the wealth Gini coefficient (WGC) of 2,853 counties and 31 provinces in mainland China to establish a comprehensive picture of inequalities at county-level regions in China as a whole, using data from LJ1-01 and the Suomi National Polar Orbiter Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The WGC values (LJ-Gini) calculated by the LJ1-01 data are always higher than those (NPP-Gini) based on NPP-VIIRS, and the mean of the ratio between them is 1.7. Compared with NPP-Gini, LJ-Gini showed sensitivity to low RWDI areas. The average county and provincial LJ-Gini are statistically consistent, 0.77 and 0.78; County LJ-Gini’s volatility is significantly higher than that of the provincial LJ-Gini, with standard deviations (SD) 0.13 and 0.096. The differences of RWDI in the regions within some provinces are more significant than in other provinces. For example, the SD of Tibet is 0.31, while all provinces' average SD is 0.13. In addition, this paper establishes a grading criterion based on the normal distribution abstracted from provincial LJ-Gini to reflect the corresponding relationship between the LJ-Gini value and the five inequality ranks. Totally, RWDI demonstrates heterogeneity at various spatial scales and regions, and it correlates negatively with economic development. The superior performance of LJ1-01 data in evaluating county-level RWDI demonstrates its potential to evaluate RWDI on a smaller scale, such as communities and streets.
PubDate: 2024-01-09
DOI: 10.1007/s43762-023-00114-w
- Influence of urbanization on winter surface temperatures in a
topographically asymmetric Tropical City, Bhubaneswar, India
Abstract: Abstract Urban areas experience significant alterations in their local surface energy balance due to changes in the thermal properties of impervious surfaces, albedo, land use, and land cover. In addition, the embedded influence of urbanization and heat-trapping in the urban canopy cause city temperature warmer compared to its surroundings peri-urban regions. However, the influence of urbanization on winter surface temperatures remains unclear. In this study, the urbanization influence on winter surface temperature in Bhubaneswar, a tropical two-tier city in India, is assessed using a high-resolution (4 km × 4 km) urban canopy model coupled with the Weather Research and Forecasting model. Numerical experiments are conducted with no urban coupling (CTL) and with coupling of a single-layer urban canopy model (UCM) for the winters of 2004 and 2015. The study suggests that both model simulations exhibit a similar warm bias in mean surface temperature (~ 2.2 °C), but UCM’s surface temperature better agrees with the observations compared to CTL. The warm bias in both experiments is primarily contributed by a higher nighttime warm bias (~ 3.2 °C). The study reveals that urbanization contributes to ~ 0.4 °C increase in surface temperature in 2015, especially in the eastern lowland regions of the city, while the impact is minimal in 2004. In the western region, the influence is nullified, possibly due to lower surface specific humidity affecting longwave radiation in a higher terrain setting. This study underscores the significance of terrain and local microclimate conditions in shaping winter urban surface temperatures, shedding light on the complex interplay between urbanization and climate.
PubDate: 2023-12-20
DOI: 10.1007/s43762-023-00112-y
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