Resources Science ›› 2020, Vol. 42 ›› Issue (12): 2463-2474.doi: 10.18402/resci.2020.12.16

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Spatial variability of causative factors of heat islands from the perspective of metropolitan functional blocks

WU Rongrong1(), XIE Miaomiao1,2(), LIU Qi1, LI Hanting1, GUO Qiang1, LI Xinyu3   

  1. 1. School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
    2. Key Laboratory of Land Consolidation, Ministry of Natural Resources, Beijing 100035, China
    3. Beijing Institute of Landscape Architecture, Beijing 100102, China
  • Received:2019-10-16 Revised:2020-01-03 Online:2020-12-25 Published:2021-02-25
  • Contact: XIE Miaomiao E-mail:krystal_rong@163.com;xiemiaomiao@cugb.edu.cn

Abstract:

In previous urban heat island (UHI) studies, factor interaction and spatial heterogeneity analysis are generally lacking in the exploration of causative factors, which results in difficulties and ineffectiveness in the implementation of UHI effect mitigation strategy. To address this problem, our study used the functional blocks of human activity as study unit based on point-of-interest data, and applied Geodetector to analyze the relationship between the interaction of various causative factors and surface temperature. The metropolitan area of Beijing was taken as the study area. The results show that the influencing degree of individual factors on land surface temperature as well as that of interacting factors varies significantly among the 14 types of functional blocks. Individual factor detection shows that the contribution of vegetation cover is as high as 72.3% in the whole area, while the degree of influence in different functional blocks differs. Population and economic development level are more prominent in functional blocks with frequent human activities. Factor interaction detection shows that the effect of interactive factors can explain much greater amount of the variance of temperature than that of individual factors. The interaction of vegetation and population has the most significant influence on the variation of temperature in the study area. In the commercial activity-related blocks and the public administration-related blocks, the dominant interactive influence is from vegetation and population. But in the industrial-related blocks, the dominant interactive influence is from impervious surface and economic development level, and impervious surface and vegetation. There is a significant nonlinear enhancement effect in 50 % of all blocks. It is also identified that the administration and public services and commercial mixed blocks and commercial blocks are the areas with the highest risk of high temperature in the city. The Geodetector model using functional blocks performed better than the global model at the municipal level, which can better quantify the influence of various factors on the surface temperature in different spatial locations. This study can provide some reference for alleviating the urban heat island effect for different areas within a metropolitan.

Key words: urban heat island, functional blocks, Geodetector, interaction, spatial heterogeneity, Beijing