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Fan PY, He Q, Tao YZ. Identifying research progress, focuses, and prospects of local climate zone (LCZ) using bibliometrics and critical reviews. Heliyon 2023; 9:e14067. [PMID: 36915474 PMCID: PMC10006492 DOI: 10.1016/j.heliyon.2023.e14067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 02/27/2023] Open
Abstract
The local climate zone (LCZ) has been an important land surface classification used to differentiate urban climate between localities. The general knowledge maps of LCZ studies are needed when LCZ-related research has attracted great attention. This study integrated bibliometrics and critical review to understand the status quo and suggest future research directions. Bibliometrics provided a statistical technique to explore large volumes of article data from the Web of Science, ScienceDirect, and Scopus databases, based on the Co-Occurrence 13.4 (COOC) software. The bibliometric results indicated a rapid increase in LCZ publications and identified the high-frequency keywords which can be clustered into two groups, including a human thermal comfort-related group and the other urban climatology-related one. From 2011 to 2020, the effects of land use and urban morphology on urban climate and heat island effects predominated the LCZ-related research. Since 2021, the research focuses had shifted to the fields of thermal environment and heatwave, due to the growing demand for human thermal comfort and heat risk reduction. Moreover, this study identified 'Land Surface Temperature' and 'Heatwave' as two focuses of LCZ-related research during the last decade. Their critical reviews demonstrated the need for additional in-depth LCZ-heatwave studies that consider the risk of human exposure. This study also recommended incorporating hydrological concerns and social issues into the LCZ plan for a more integrated LCZ research outlook. Overall, this study provides not only a comprehensive understanding of LCZ knowledge networks, but also critical details on research focuses and potential research prospects.
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Affiliation(s)
- Ping Yu Fan
- Department of Geography, Hong Kong Baptist University, Hong Kong S. A. R., China
| | - Qing He
- MOE Key Laboratory of Fundamental Physical Quantities Measurement & Hubei Key Laboratory of Gravitation and Quantum Physics, PGMF and School of Physics, Huazhong University of Science and Technology, Wuhan, China
- Corresponding author.
| | - Yi Zhou Tao
- College of Landscape Architecture, Zhejiang Agriculture & Forestry University, Hangzhou, China
- Corresponding author.
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Tang L, Kasimu A, Ma H, Eziz M. Monitoring Multi-Scale Ecological Change and Its Potential Drivers in the Economic Zone of the Tianshan Mountains' Northern Slopes, Xinjiang, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2844. [PMID: 36833543 PMCID: PMC9957405 DOI: 10.3390/ijerph20042844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/02/2023] [Accepted: 02/03/2023] [Indexed: 06/18/2023]
Abstract
Accurately capturing the changing patterns of ecological quality in the urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM) and researching its significant impacts responds to the requirements of high-quality sustainable urban development. In this study, the spatial and temporal distribution patterns of remote sensing ecological index (RSEI) were obtained by normalization and PCA transformation of four basic indicators based on Landsat images. It then employed geographic detectors to analyze the factors that influence ecological change. The result demonstrates that: (1) In the distribution of land use conversions and degrees of human disturbance, built-up land, principally urban land, and agricultural land, represented by dry land, are rising, while the shrinkage of grassland is the most substantial. The degree of human disturbance is increasing overall for glaciers. (2) The overall ecological environment of the northern slopes of Tianshan is relatively poor. Temporally, the ecological quality changes and fluctuates, with an overall rising trend. Spatially, ecological quality is low in the north and south and high in the center, with high values concentrated in the mountains and agriculture and low values in the Gobi and desert. However, on a large scale, the ecological quality of the Urumqi-Changji-Shihezi metropolitan area has worsened dramatically compared to other regions. (3) Driving factor detection showed that LST and NDVI were the most critical influencing factors, with an upward trend in the influence of WET. Typically, LST has the biggest influence on RSEI when interacting with NDVI. In terms of the broader region, the influence of social factors is smaller, but the role of human interference in the built-up area of the oasis city can be found to be more significant at large scales. The study shows that it is necessary to strengthen ecological conservation efforts in the UANSTM region, focusing on the impact of urban and agricultural land expansion on surface temperature and vegetation.
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Affiliation(s)
- Lina Tang
- School of Geography and Tourism, Xinjiang Normal University, Urumqi 830054, China
| | - Alimujiang Kasimu
- School of Geography and Tourism, Xinjiang Normal University, Urumqi 830054, China
- Research Centre for Urban Development of Silk Road Economic Belt, Xinjiang Normal University, Urumqi 830054, China
- Xinjiang Key Laboratory of Lake Environment and Resources in Arid Zone, Urumqi 830054, China
| | - Haitao Ma
- Key Laboratory of Regional Sustainable Development Modelling, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Mamattursun Eziz
- School of Geography and Tourism, Xinjiang Normal University, Urumqi 830054, China
- Xinjiang Key Laboratory of Lake Environment and Resources in Arid Zone, Urumqi 830054, China
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Liu B, Deng Y, Li X, Li M, Jing W, Yang J, Chen Z, Liu T. Sub-Block Urban Function Recognition with the Integration of Multi-Source Data. SENSORS (BASEL, SWITZERLAND) 2022; 22:7862. [PMID: 36298215 PMCID: PMC9609143 DOI: 10.3390/s22207862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
The recognition of urban functional areas (UFAs) is of great significance for the understanding of urban structures and urban planning. Due to the limitation of data sources, early research was characterized by problems such as singular data, incomplete results, and inadequate consideration of the socioeconomic environment. The development of multi-source big data brings new opportunities for dynamic recognition of UFAs. In this study, a sub-block function recognition framework that integrates multi-feature information from building footprints, point-of-interest (POI) data, and Landsat images is proposed to classify UFAs at the sub-block level using a random forest model. The recognition accuracies of single- and mixed-function areas in the core urban area of Guangzhou, China, obtained by this framework are found to be significantly higher than those of other methods. The overall accuracy (OA) of single-function areas is 82%, which is 8-36% higher than that of other models. The research conclusions show that the introduction of the three-dimensional (3D) features of buildings and finer land cover features can improve the recognition accuracy of UFAs. The proposed method that uses open access data and achieves comprehensive results provides a more practical solution for the recognition of UFAs.
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Affiliation(s)
- Baihua Liu
- College of Geographical Science, Harbin Normal University, Harbin 150025, China
- Guangdong Open Laboratory of Geospatial Information Technology and Application, Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Yingbin Deng
- Guangdong Open Laboratory of Geospatial Information Technology and Application, Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511485, China
| | - Xin Li
- College of Geographical Science, Harbin Normal University, Harbin 150025, China
- Guangdong Open Laboratory of Geospatial Information Technology and Application, Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Miao Li
- College of Geographical Science, Harbin Normal University, Harbin 150025, China
| | - Wenlong Jing
- Guangdong Open Laboratory of Geospatial Information Technology and Application, Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511485, China
| | - Ji Yang
- Guangdong Open Laboratory of Geospatial Information Technology and Application, Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511485, China
| | - Zhehua Chen
- Guangdong Provincial Institute of Land Surveying & Planning, Guangzhou 510075, China
| | - Tao Liu
- College of Geographical Science, Harbin Normal University, Harbin 150025, China
- Guangdong Open Laboratory of Geospatial Information Technology and Application, Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
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Analysis on Seasonal Variation and Influencing Mechanism of Land Surface Thermal Environment: A Case Study of Chongqing. REMOTE SENSING 2022. [DOI: 10.3390/rs14092022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Rapid urbanization has brought about many problems in urban environments, including the deterioration of the thermal environment, which greatly affects the sustainable development of cities and the health of urban residents. Therefore, a comprehensive and thorough study of the thermal environment is necessary. In this study, quantitative and qualitative analyses of the interannual and seasonal variation of the thermal environment in the urban area of Chongqing were conducted using a combination of equal sector analysis, mathematical statistics, and principal component analysis. In addition, the mechanism by which multiple integrated human and natural factors affect the urban thermal environment in different seasons was explored. The thermal environment of the land surface has a clear spatial correlation with built-up areas. From 2010 to 2020, the Liangjiang New Area and the western high-tech zone were the main areas of thermal environment area expansion. From 2010 to 2020, a decrease in the area of low-temperature zones and an increase in the area of high-temperature zones were the main trends. In particular, during the summer of 2020, the low-temperature zones almost disappeared, and the area of high-temperature zones was nearly twice as large as in the other seasons. The combined influence of multiple factors on surface temperature has certain seasonal characteristics. The slope, elevation, normalized water body index, fractional vegetation cover, normalized imperviousness index, and nighttime light intensity were the main factors affecting the surface temperature, whereas landscape pattern indicators, as well as the population and points of interest (POI) density, had a low influence. The strength of the combined influence of these multiple factors of the different seasons exhibited the following order: winter > spring > summer > autumn.
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Koç A, Caf A, Koç C, Kejanli DT. Examining the temporal and spatial distribution of potential urban heat island formations. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:11455-11468. [PMID: 34536226 DOI: 10.1007/s11356-021-16422-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 09/05/2021] [Indexed: 06/13/2023]
Abstract
Due to urbanization worldwide, gradual increase in construction and use of irregular urban topography affect urban climate negatively, triggering urban heat island (UHI) formations in cities and thereby causing them to become uninhabitable places for human comfort. This study, which covers the province of Diyarbakır in Turkey, aims to determine the spatial and temporal distribution of areas with potential urban heat island (UHI) by using remote sensing methods and satellite/terrain data available between 2001 and 2019. According to the Landsat 7 satellite, an area with a potential of 27.4 km2 in 2001, 20.8 km2 in 2006, 27.4 km2 in 2008, 16.7 km2 in 2010, and 12.2 km2 in 2012 was determined. According to the Landsat 8 satellite, it was measured as 14.49 km2 in 2017 and 15.67 km2 in 2018. According to Landsat 8 satellite data, areas with UHI potential increased by 14.6% over a 3-year period. According to Landsat 7 data, there has been a continuous fluctuation over the years. One of the important results of this study is that between 2001 and 2019, the higher the rate of change according to the surface temperature, the larger the area with the potential of the heat island. At the same time, it has been determined that spatially potential UHIs have a great potential not in the city center, but in the surrounding areas close to the center and in the topographically hollow areas.
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Affiliation(s)
- Ahmet Koç
- Diyarbakir Vocational School of Technical Sciences, Department of Park and Garden Plants, Dicle University, Diyarbakır, Turkey.
| | - Ahmet Caf
- Vocational School of Technical Sciences, Department of Park and Garden Plants, Bingöl University, Bingöl, Turkey
| | - Canan Koç
- Faculty of Architecture, Department of Urban Planning, Dicle University, Diyarbakır, Turkey
| | - Devrim Türkan Kejanli
- Faculty of Architecture, Department of Urban Planning, Dicle University, Diyarbakır, Turkey
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Guerri G, Crisci A, Congedo L, Munafò M, Morabito M. A functional seasonal thermal hot-spot classification: Focus on industrial sites. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 806:151383. [PMID: 34742796 DOI: 10.1016/j.scitotenv.2021.151383] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 10/29/2021] [Accepted: 10/29/2021] [Indexed: 06/13/2023]
Abstract
This study was focused on the metropolitan area of Florence in Tuscany (Italy) with the aim to provide a functional spatial thermal anomaly indicator obtained throughout a thermal summer and winter hot-spot detection. The hot-spot analysis was performed by applying Getis-Ord Gi* spatial statistics to Land Surface Temperature (LST) layers, obtained from Landsat 8 remote sensing data during the 2015-2019 daytime summer and winter period, to delimitate summer hot- and cool-spots, and winter warm- and cold-spots. Further, these ones were spatially combined thus obtaining a comprehensive summer-winter Thermal Hot-Spot (THSSW) spatial indicator. Winter and summer mean daily thermal comfort profiles were provided for the study area assessing the Universal Thermal Climate Index (UTCI) by using meteorological data available from seven local weather stations, located at a maximum distance of 350 m from industrial sites. A specific focus on industrial sites was carried out by analyzing the industrial buildings characteristics and their surrounding areas (50 m buffer), through the following layers: industrial building area (BA), surface albedo of buildings (ALB), impervious area (IA), tree cover (TC), and grassland area (GA). The novel THSSW classification applied to industrial buildings has shown that about 50% of the buildings were located in areas characterized by summer hot-spots. Increases in BA and IA revealed warming effects on industrial buildings, whereas increases in ALB, TC, and GA disclosed cooling effects. A decrease of about 10% of IA replaced by TC and GA was associated with about 2 °C decrease of LST. Very strong outdoor heat stress conditions were observed during summer daytime, whereas moderate winter outdoor cold stress conditions were recorded during nighttime until the early morning. The thermal spatial hot-spot classification in industrial areas provides a very useful source of information for thermal mitigation strategies aimed to reduce the heat-related health risk for workers.
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Affiliation(s)
- Giulia Guerri
- Institute of Bioeconomy (IBE), National Research Council, 50019 Florence, Italy.
| | - Alfonso Crisci
- Institute of Bioeconomy (IBE), National Research Council, 50019 Florence, Italy
| | - Luca Congedo
- Italian National Institute for Environmental Protection and Research (ISPRA), 00144 Rome, Italy
| | - Michele Munafò
- Italian National Institute for Environmental Protection and Research (ISPRA), 00144 Rome, Italy
| | - Marco Morabito
- Institute of Bioeconomy (IBE), National Research Council, 50019 Florence, Italy; Centre of Bioclimatology (CIBIC), University of Florence, Florence, Italy
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Zhou Y, Feng L, Zhang X, Wang Y, Wang S, Wu T. Spatiotemporal patterns of the COVID-19 control measures impact on industrial production in Wuhan using time-series earth observation data. SUSTAINABLE CITIES AND SOCIETY 2021; 75:103388. [PMID: 34608429 PMCID: PMC8482229 DOI: 10.1016/j.scs.2021.103388] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/20/2021] [Accepted: 09/20/2021] [Indexed: 05/16/2023]
Abstract
Understanding the spatiotemporal patterns of the COVID-19 impact on industrial production could improve the estimation of the economic loss and sustainable work resumption policies in cities. In this study, assuming and checking a correlation between the land surface temperature (LST) and industrial production, we applied the BFAST algorithm and linear regression models on multi-temporal MODIS data to derive monthly time-series deviation of LST with a spatial resolution of 1 × 1 km, to quantificationally explore the fine-scale spatiotemporal patterns of the COVID-19 control measures impact on industrial production, within Wuhan city. The results demonstrate that (1) the trend of time-series LST could partly reflect the impact of the COVID-19 pandemic on industrial production, and the year-around industrial production was less than expectations, with a fall of 14.30%; (2) the most serious COVID-19 impact on industrial production appeared in Mar. and Apr., then, after the lifting of lockdown, some regions (approximate 4.90%) firstly returned to expected levels in Jun, and almost all regions (98.49%) have completed the resumption of work and production before Nov.; (3) the southwest and south-central had more serious impact of the COVID-19 pandemic, approximate twice as much as that in the north and suburban, in Wuhan. The results and findings elaborated the spatiotemporal distribution and their changes during 2020 within Wuhan, which could provide a beneficial support for assessment of the COVID-19 pandemic and implementation of resumption plans for sustainable development.
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Affiliation(s)
- Ya'nan Zhou
- College of Hydrology and Water Resources, Hohai University, Address: No. 1, Xikang Road, Nanjing 210010, China
| | - Li Feng
- College of Hydrology and Water Resources, Hohai University, Address: No. 1, Xikang Road, Nanjing 210010, China
| | - Xin Zhang
- Aerospace information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yan Wang
- College of Hydrology and Water Resources, Hohai University, Address: No. 1, Xikang Road, Nanjing 210010, China
| | - Shunying Wang
- College of Hydrology and Water Resources, Hohai University, Address: No. 1, Xikang Road, Nanjing 210010, China
| | - Tianjun Wu
- School of Science, Chang'an University, Xi'an 710064, China
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Yin J, Zhan Q, Tayyab M, Zahra A. The Ventilation Efficiency of Urban Built Intensity and Ventilation Path Identification: A Case Study of Wuhan. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182111684. [PMID: 34770197 PMCID: PMC8582679 DOI: 10.3390/ijerph182111684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/29/2021] [Accepted: 11/03/2021] [Indexed: 11/16/2022]
Abstract
Urban ventilation is being hampered by rough surfaces in dense urban areas, and the microclimate and air quality of the urban built environment are not ideal. Identifying urban ventilation paths is helpful to save energy, reduce emissions, and improve the urban ecological environment. Wuhan is the capital city of Hubei, and it has a high urban built intensity and hot summers. Taking Wuhan city, with a size of 35 km ×50 km, as an example, the built environment was divided into grids of 100 m × 100 m and included the building density, floor area ratio, and average building height. The ventilation mechanism of the urban built intensity index has previously been explained. The decrease in building density is not the sole factor causing an increase in wind speed; the enclosure and width of the ventilation path and the height of the front building are also influential. Twelve urban built units were selected for CFD numerical simulation. The ventilation efficiency of each grid was evaluated by calculating the wind speed ratio, maximum wind speed, average wind speed, and area ratio of strong wind. The relationship between the urban built intensity index and ventilation efficiency index was established using the factor analysis method and the Pearson correlation coefficient; building density and average building height are the most critical indexes of ventilation potential. In addition, the layout of the building also has an important impact on ventilation. A suitable built environment is that in which the building density is less than 30%, the average building height is greater than 15 m, and the floor area ratio is greater than 1.5. The urban built intensity map was weighted to identify urban ventilation paths. The paper provides a quantitative reference for scientific planning and design of the urban spatial form to improve ventilation.
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Affiliation(s)
- Jie Yin
- College of Civil Engineering and Architecture, China Three Gorges University, No. 8, University Road, Xiling District, Yichang 443002, China;
- School of Urban Design of Wuhan University, No. 8, Donghu South Road, Wuchang District, Wuhan 430072, China;
| | - Qingming Zhan
- School of Urban Design of Wuhan University, No. 8, Donghu South Road, Wuchang District, Wuhan 430072, China;
| | - Muhammad Tayyab
- College of Hydraulic and Environmental Engineering, China Three Gorges University, No. 8, University Road, Xiling District, Yichang 443002, China
- College of Economics and Management, China Three Gorges University, No. 8, University Road, Xiling District, Yichang 443002, China
- Correspondence: ; Tel.: +86-132-2728-0848; Fax: +86-717-6393309
| | - Aqeela Zahra
- School of Chemistry, Chemical Engineering and Life Sciences, Wuhan University of Technology, Wuhan 430070, China;
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An Application of the LCZ Approach in Surface Urban Heat Island Mapping in Sofia, Bulgaria. ATMOSPHERE 2021. [DOI: 10.3390/atmos12111370] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This article presents the results of the thermal survey of the capital of Bulgaria (Sofia) carried out in August 2019, with the application of an unmanned aerial system (UAS). The study is based on the concept of local climate zones (LCZs), taking into account the influence of the features of land use/land cover and urban morphology on the urban climate. The basic spatial units used in the study are presented in the form of a regular grid consisting of 3299 cells with sides of 250 × 250 m. A total of 13 types of LCZs were identified, of which LCZs 6, 5, 8, 4, D, and A form the largest share. In the thermal imaging of the surface, a stratified sampling scheme was applied, which allowed us to select 74 cells, which are interpreted as representative of all cells belonging to the corresponding LCZ in the urban space. The performed statistical analysis of the thermal data allowed us to identify both the most thermally loaded zones (LCZs 9, 4, and 5) and the cells forming Urban Cool Islands (mainly in LCZs D and C). The average surface temperature in Sofia during the study period (in the time interval between 8:00 p.m. and 10:00 p.m.) was estimated at 20.9 °C, and between the different zones it varied in the range 17.2–25.1 °C. The highest maximum values of LST (27.9–30.6 °C) were registered in LCZ 4 and LCZ 5. The relation between the spatial structure of the urban thermal patterns and urban surface characteristics was also analyzed. Regression analysis confirmed the hypothesis that as the proportion of green areas increases, surface temperatures decrease, and, vice versa, as the proportion of built-up and impermeable areas increases, surface temperatures increase. A heat load map (via applying a z-transformation to standardize the temperature values), a map of the average surface temperature, and a map of the average intensity of the heat island on the surface were generated in the GIS environment. The results of the study adequately reflect the complex spatial model of the studied phenomenon, which gives grounds to conclude that the research approach used is applicable to similar studies in other cities.
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Koumetio Tekouabou SC, Diop EB, Azmi R, Jaligot R, Chenal J. Reviewing the application of machine learning methods to model urban form indicators in planning decision support systems: Potential, issues and challenges. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2021. [DOI: 10.1016/j.jksuci.2021.08.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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