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author:

Yang, H. (Yang, H..) [1] | Xu, W. (Xu, W..) [2] (Scholars:徐伟铭) | Shao, E. (Shao, E..) [3] | Liao, Y. (Liao, Y..) [4] | Lin, X. (Lin, X..) [5]

Indexed by:

Scopus PKU CSCD

Abstract:

The POI data and Place2vec model within the third ring of Fuzhou City were used to identify the functional zones in the city, on the basis of which we were able to analyze the characteristics of the spatial differentiation of the thermal field levels in each functional zone. We subsequently constructed the functional zone warming sensitivity index using a boosting regression tree algorithm to quantitatively measure the sensitivity difference of different functional zones in the process of surface warming and then explored the spatial differentiation mechanism of the urban thermal environment on a regional scale. The results showed the following. First, the identification framework of urban functional zones was constructed based on POI data and the Place2vec model; and the identification of the five types of functional zones—the living service and residential zone, public management and service zone, commercial zone, industrial zone, green space and square zone—was highly accuratecy. Second, the internal thermal field grades of the green space and square zone are mainly low-temperature and sub-low-temperature, while the rest of the four types of internal surface heat field grades of functional zones are dominated by medium-temperature, sub-high-temperature, high-temperature, and very-high-temperature zones. Additionally, the distribution of sub-low-temperature, low-temperature, and very-low-temperature zones is not significant. Except for the green space and square zone, all four types of functional zones exhibit high-temperature phenomena, of which the extremely high-temperature zone accounts for the most in the industrial zone (as high as 20.68%) and the least in the green space and square zone(1.90%). Third, the overall sensitivity to temperature increase of each type of functional zone, in descending order, is as follows: industrial zone, living service and residential zone, commercial zone, public management and service zone, green space and square zone. The sensitivity to temperature increase of each functional zone in different warming stages differs significantly under the high-temperature gradient. The sensitivity to temperature increase of the ground cover of different functional zones has the distinctive characteristics of the functional zones. Specifically, the higher the degree of development of the zone, the higher the sensitivity to temperature increase of the built land. The higher the degree of regional construction development, the greater the difference between the sensitivity of construction land and the sensitivity of vegetation and water, and the sensitivity of construction land is always higher than the sensitivity of vegetation and water in each functional zone. The results of this study will help overcome the limitations of existing urban thermal environmental research and provide a scientific decision-making basis for promoting the rational layout of urban functional areas, alleviating the urban heat island effect, preventing the risk of high temperatures under extreme weather conditions, and realizing sustainable urbanization. © 2024 Editorial Committee of Tropical Geography. All rights reserved.

Keyword:

BRT Fuzhou Place2vec sensitivity to temperature increase surface warming urban functional areas urban thermal environment

Community:

  • [ 1 ] [Yang H.]The Academy of Digital China, Fuzhou University, Fuzhou, 350108, China
  • [ 2 ] [Yang H.]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou University, Fuzhou, 350002, China
  • [ 3 ] [Yang H.]National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou, 350002, China
  • [ 4 ] [Xu W.]The Academy of Digital China, Fuzhou University, Fuzhou, 350108, China
  • [ 5 ] [Xu W.]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou University, Fuzhou, 350002, China
  • [ 6 ] [Xu W.]National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou, 350002, China
  • [ 7 ] [Shao E.]The Academy of Digital China, Fuzhou University, Fuzhou, 350108, China
  • [ 8 ] [Shao E.]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou University, Fuzhou, 350002, China
  • [ 9 ] [Shao E.]National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou, 350002, China
  • [ 10 ] [Liao Y.]The Academy of Digital China, Fuzhou University, Fuzhou, 350108, China
  • [ 11 ] [Liao Y.]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou University, Fuzhou, 350002, China
  • [ 12 ] [Liao Y.]National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou, 350002, China
  • [ 13 ] [Lin X.]The Academy of Digital China, Fuzhou University, Fuzhou, 350108, China
  • [ 14 ] [Lin X.]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou University, Fuzhou, 350002, China
  • [ 15 ] [Lin X.]National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou, 350002, China

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Source :

Tropical Geography

ISSN: 1001-5221

CN: 44-1209/N

Year: 2024

Issue: 3

Volume: 44

Page: 557-568

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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