大都市功能区块视角下的热岛影响因素空间分异

doi:10.18402/resci.2020.12.16

摘要/Abstract

摘要：

针对以往热岛影响因素研究中缺乏因子综合作用及作用程度空间异质性分析的现状,本文以大都市北京为研究区,利用兴趣点（POI）数据划分承载不同人类活动的用地功能区块,结合地理探测器探究在市域整体及不同用地功能区块中植被、水体、不透水面及社会经济要素等多维因素对地表温度的影响（单因子作用与因子交互作用）。结果表明：①各单因子、因子交互作用对于地表温度的影响程度在北京市14类用地功能区块中存在空间分异。单因子探测表明整体上植被对温度的影响力高达72.3%,其作用程度与社会经济因素作用程度的差距在人类活动较为频繁的区块显著缩小;②因子交互作用探测显示两因子交互作用对温度的影响远大于单因子作用效果,整体水平上植被与人口交互作用是控制温度分异的主导因子,解释力达到78.9%,而在包含商业服务或公共管理服务的区块植被与人口交互作用均为影响温度分异的主导因素,包含工业的区块则为不透水与经济水平、植被交互作用为主导,且50 %区块内因子交互作用存在非线性增强现象。③根据风险探测识别出不同功能区块高温风险区特征,商业服务区块、公共管理与商业服务混合（公-商）区块为城市高温风险较高的区域。基于用地功能区块的地理探测器模型对地表温度空间分异的解释优于市域水平的全局模型,可以较好地量化各因素在不同空间位置上对地表温度的影响,为制定分区域有针对性的城市热岛效应缓解策略提供参考。

关键词: 城市热岛, 功能区块, 地理探测器, 交互作用, 空间分异, 北京

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

武蓉蓉, 谢苗苗, 刘琦, 李汉廷, 郭强, 李新宇. 大都市功能区块视角下的热岛影响因素空间分异[J]. 资源科学, 2020, 42(12): 2463-2474.

WU Rongrong, XIE Miaomiao, LIU Qi, LI Hanting, GUO Qiang, LI Xinyu. Spatial variability of causative factors of heat islands from the perspective of metropolitan functional blocks[J]. Resources Science, 2020, 42(12): 2463-2474.

图/表 10

图1

图2

表1

因子交互作用类型"

判据	交互作用
$q (X 1 ? X 2) < Min (q (X 1), q (X 2))$	非线性减弱
$Min (q (X 1), q (X 2)) < q (X 1 ? X 2) < Max (q (X 1), q (X 2))$	单因子非线性减弱
$q (X 1 ? X 2) > Max (q (X 1), q (X 2))$	双因子增强
$q (X 1 ? X 2) = q (X 1) + q (X 2)$	独立
$q (X 1 ? X 2) > q (X 1) + q (X 2)$	非线性增强

表1

表2

高温风险区等级划分"

风险区等级	划分原则
一级风险区	$? X iB (i = 1 、 2 、 3 、 4 、 5)$
二级风险区	$? X iB (i = 1 、 2 、 3)$
三级风险区	$? X iB (i = 1 、 2 、 3)$

表2

图3

图4

图5

图6

表3

图7

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区块	人口密度/（千人/km²）	GDP/（万元/km²）	ISA	NDVI	MNDWI
商服	18.541~32.797	50.558~77.997	0.250~0.586	0.106~0.236	0.000~0.082
商-交	10.835~18.540	19.777~50.557	0.250~0.586	0.106~0.236	—
居-商	2.101~6.595	0.011~2.603	0.080~0.149	0.106~0.236	—
公-商	18.541~32.797	50.558~77.997	0.250~0.586	0.000~0.105	—
工业	0.045~2.100	0.011~2.603	0.000~0.027	0.237~0.360	—
公-工	6.596~10.834	2.604~9.083	0.000~0.027	0.106~0.236	—
交-工	0.045~2.100	0.011~2.603	0.000~0.027	0.237~0.360	—
居住	2.101~6.595	2.604~9.083	0.080~0.149	0.237~0.360	—
居-交	2.101~6.595	0.011~2.603	0.000~0.027	0.106~0.236	—
公-居	6.596~10.834	2.604~9.083	0.028~0.079	0.106~0.236	—
公-交	10.835~18.540	2.604~9.083	0.150~0.249	0.106~0.236	0.000~0.082
公-管	18.541~32.797	50.558~77.997	0.250~0.586	0.106~0.236	—
交通	2.101~6.595	2.604~9.083	0.150~0.249	0.106~0.236	—
其他	6.596~10.834	2.604~9.083	0.250~0.586	0.237~0.360	0.391~0.535