空气污染对城市活力的影响及其建成环境异质性——基于大数据的分析

doi:10.11821/dlyj020200685

地理研究 ›› 2021, Vol. 40 ›› Issue (7): 1935-1948.doi: 10.11821/dlyj020200685

空气污染对城市活力的影响及其建成环境异质性——基于大数据的分析

王波¹^,²^,³(), 甄峰⁴^,⁵(), 张姗琪⁴^,⁵, 黄学锋⁶, 周亮⁷

1.中山大学地理科学与规划学院,广州 510275
2.南方海洋科学与工程广东省实验室（珠海）,珠海 519000
3.广东省城市化与地理环境空间模拟重点实验室,广州 510275
4.南京大学建筑与城市规划学院,南京 210093
5.江苏省智慧城市设计仿真与可视化技术工程实验室,南京 210093
6.南京大学地理与海洋科学学院,南京 210093
7.兰州交通大学测绘与地理信息学院,兰州 730070

收稿日期:2020-07-20 接受日期:2020-11-09 出版日期:2021-07-10 发布日期:2021-09-10
通讯作者: 甄峰
作者简介:王波（1987-）,男,湖南衡阳人,博士,副教授,硕士生导师,研究方向为城市地理与区域规划、智慧城市研究。E-mail: wangbo68@mail.sysu.edu.cn
基金资助:
国家自然科学基金项目(41901191);国家自然科学基金项目(41930646);国家社会科学重点基金(20AZD040);广州市基础与应用基础研究项目(202102020795);中央高校基本科研业务费项目(19lgpy42)

The impact of air pollution on urban vibrancy and its built environment heterogeneity: An empirical analysis based on big data

WANG Bo¹^,²^,³(), ZHEN Feng⁴^,⁵(), ZHANG Shanqi⁴^,⁵, HUANG Xuefeng⁶, ZHOU Liang⁷

1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, Guangdong China
3. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Guangzhou 510275, China
4. School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China
5. Jiangsu Provincial Engineering Laboratory of Smart City Design Simulation & Visualization, Nanjing University, Nanjing 210093, China
6. School of Geography and Ocean Science, Nanjing University, Nanjing 210093, China
7. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China

Received:2020-07-20 Accepted:2020-11-09 Online:2021-07-10 Published:2021-09-10
Contact: ZHEN Feng

摘要/Abstract

摘要：

建设充满活力的城市空间得到地理和城乡规划学者的广泛关注。随着空气污染问题的加剧,空气质量影响居民在城市空间中的活动,但鲜有研究考察空气污染与城市活力的定量关系。基于广州市2019年新浪微博签到记录、日气象和空气质量数据、以及建成环境数据,本研究构建以街道为空间单元、以天为时间单元的面板数据,通过标准差椭圆（SDE）以及面板回归模型测度空气污染对城市活力的抑制效应以及该抑制效应在不同建成环境上的异质性。研究得到以下结论：① 城市活力SDE面积随空气质量指数（AQI）上升而收缩,轻度污染和中度污染的城市活力SDE面积仅为空气质量优的约80%和30%。② 运用空间面板回归模型控制街道的空间关联性后,空气质量指数（AQI）对城市活力具有明显负向影响,AQI每增加1个单位,日活动强度减少约0.10次/10 km²;当空气质量恶化到中等污染后,AQI每增加1个单位,日活动强度减少约0.14次/10 km²。③ 空气污染对城市活力的抑制效应在不同建成环境上存在异质性,POI密度、离城市中心距离强化空气污染对城市活力的抑制效应,而地铁站密度、道路交叉口密度、土地利用混合度则弱化空气污染对城市活力的抑制效应。本研究有助于更好厘清空气污染、建成环境与城市活力的关系,并为优化建成环境以缓减空气污染对城市活力抑制效应提供分析支撑。

关键词: 居民活动, 建成环境, 空间关联性, 空气质量, 广州

Abstract:

Urban vibrancy describes people’s interactions with urban space. And enhancing urban vibrancy is important for urban sustainable development, and thereby attracts the interest of both geography and urban researchers and policy makers. Although evidence suggests that air pollution may influence people’s out-of-home activity, few studies have quantitatively measured how air pollution depresses urban vibrancy. On the basis of Sina Weibo check-in data and daily weather and air quality data in Guangzhou in 2019 and the built environment of this city, this study compiles samples of city vibrancy in 150 neighbourhoods and 365 days, forming a strongly balanced panel dataset. By Standard Deviational Ellipse (SDE) analysis and both general and spatial panel regression models, this study examines how air pollution negatively influences urban vibrancy and the heterogeneity role of the built environment in this depression effect. Our findings demonstrate that urban vibrancy space varies across different levels of air quality index (AQI). Specifically, the size of SDEs of urban vibrancy when AQI is between 50-150 and 150-200 is about 80% and 30% of that when AQI is no more than 50. After we control the spatial dependence (i.e., spatial autocorrelation), spatial panel regression results reveal that air pollution significantly lowers urban vibrancy. The daily activity intensity decreases 0.10 times per ten kilometers with a one-unit increment in AQI. More seriously, once AQI is above 150, this depression effect grows to 0.14 times per ten kilometers with a one-unit increment in AQI. We also test the heterogeneity role of the built environment in this depression effect. The results indicate that while POI density and distance to city center increase the depression effect, the density of metro stations and interactions and land-use diversity decrease the depression effect. It is evident that the depression effect of air pollution on urban vibrancy is not evenly distributed, varying across neibourhoods with different built environment characteristics. Compared to the city center, the outskirts bear a larger depression effect. Therefore, urban vibrancy space may be more polarized when air quality deteriorates and thus, challenging urban spatial restructuring development. Our spatial panel data analysis at the neighourhood scale improves our understanding of the mechanism among air pollution, built environment, and urban vibrancy, which provides evidence-based support for built environment planning and management at the neighbourhood scale to decrease the depression effect of air pollution on urban vibrancy.

Key words: human behaviour, built environment, spatial dependence, air quality, Guangzhou

王波, 甄峰, 张姗琪, 黄学锋, 周亮. 空气污染对城市活力的影响及其建成环境异质性——基于大数据的分析[J]. 地理研究, 2021, 40(7): 1935-1948.

WANG Bo, ZHEN Feng, ZHANG Shanqi, HUANG Xuefeng, ZHOU Liang. The impact of air pollution on urban vibrancy and its built environment heterogeneity: An empirical analysis based on big data[J]. GEOGRAPHICAL RESEARCH, 2021, 40(7): 1935-1948.

图/表 6

图1

表1

变量定义与描述性统计

变量		描述	观测量	均值	方差	最小值	最大值
因变量	活动强度	$活动强度 = 街道日签到总数街道面积 (10 k m 2)$	54750	19.66	50.58	0.00	3014.00
控制变量（气象&日期属性）	风速	日平均风速 $(m / s)$	2190	1.83	0.80	0.45	7.69
	气温	日平均气温 $(℃)$	2190	22.81	5.84	6.94	34.06
	降水	日降水情况,虚拟变量：1=暴雨及以上,0=其他	2190	0.03	0.18	0	1.00
	日期	非工作日,虚拟变量：1=非工作日（包括双休日与节假日）,0=工作日	365	0.31	0.46	0	1.00
解释变量（空气污染&建成环境因素）	AQI	日空气质量指数	3650	68.82	28.58	20.08	167.92
	d	中度污染,虚拟变量：1=中度污染及以上,0=其他	3650	0.05	0.07	0	1.00
	POI密度	$POI 密度 = POI 总数 (个) 街道面积 (k m 2)$	150	520.76	557.14	1.98	2428.72
	地铁站密度	$地铁站密度 = 街道地铁站数 (个) 街道面积 (k m 2)$	150	0.70	1.21	0.00	6.79
	道路交叉口密度	$道路交叉口密 = 街道道路交叉口数 (个) 街道面积 (k m 2)$	150	13.80	15.26	0.00	70.89
	区位	$街道几何中心到市中心的直线距离 (km)$	150	16.70	16.39	0.49	84.04
	用地混合度	$Z i = 全域第 i 类土地用地面积 (k m 2) 全域 6 类土地用地总面积 (k m 2)$ $X i = 街道第 i 类土地用地面积 (k m 2) 街道 6 类土地用地总面积 (k m 2)$ $A i = X i Z i$ $P i = A i ∑ i = 1 n A i$ $街道用地混合度 = - 1 ∑ i = 1 n p i ln p i ln n, n = 6$	150	0.71	0.14	0.33	0.97

表1

图2

图3

图4

表2

基本面板及空间面板回归模型结果

变量	模型1		模型2		模型3		模型4		模型5		模型6
变量	Coef.	S.E.	Coef.	S.E.	Coef.	S.E.	Coef.	S.E.	Coef.	S.E.	Coef.	S.E.
AQI	-0.105***	0.008	-0.097***	0.008	-0.104***	0.008	-0.344***	0.047	-0.313***	0.047	-0.310***	0.047
AQId*	-0.035*	0.017	-0.033*	0.016	-0.035*	0.017	-0.048**	0.017	-0.045**	0.017	-0.048**	0.018
风速	-0.583***	0.126	-0.539***	0.126	-0.577***	0.136	-0.542***	0.126	-0.504***	0.126	-0.537***	0.134
气温	0.831***	0.110	0.765***	0.110	0.819***	0.119	0.765***	0.110	0.709***	0.110	0.757***	0.118
(气温)²	-0.012***	0.003	-0.011***	0.003	-0.012***	0.003	-0.011***	0.003	-0.010**	0.003	-0.011**	0.003
降水 (非暴雨=ref.)	-2.177***	0.334	-2.010***	0.334	-2.146***	0.356	-2.081***	0.335	-1.935***	0.334	-2.056***	0.355
日期 (非工作日=ref.)	0.999**	0.383	0.921*	0.382	0.994*	0.413	0.944*	0.382	0.877*	0.381	0.941*	0.409
AQI×POI密度	—	—	—	—	—	—	-0.0001**	0.000	-0.000**	0.000	-0.000**	0.000
AQI×地铁站密度	—	—	—	—	—	—	0.030***	0.006	0.030***	0.006	0.030***	0.006
AQI×交叉口密度	—	—	—	—	—	—	0.001*	0.000	0.001*	0.000	0.001*	0.000
AQI×区位	—	—	—	—	—	—	-0.002***	0.000	-0.002***	0.000	-0.002***	0.000
AQI×用地混合度	—	—	—	—	—	—	0.301***	0.053	0.274***	0.053	0.262***	0.053
$ρ$	—	—	0.076***	0.007	0.076***	0.007	—	—	0.070***	0.007	0.068***	0.007
街道固定效应	控制
季节固定效应	控制
天固定效应	控制
N	54750
	模型拟合度
Adjusted R²	0.086		0.090		0.086		0.256		0.265		0.264
AIC	562419.900		562294.100		562296.200		562241.700		562138.800		562144.600
Log-likelihood	-281199.000		-281135.100		-281136.100		-281104.900		-281052.400		-281055.300

表2

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空气污染对城市活力的影响及其建成环境异质性——基于大数据的分析

The impact of air pollution on urban vibrancy and its built environment heterogeneity: An empirical analysis based on big data

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