| 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 |
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| 引用本文： | 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. |
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| 作者姓名： | 王波 甄峰 张姗琪 黄学锋 周亮 |
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| 作者单位： | 1.中山大学地理科学与规划学院,广州 5102752.南方海洋科学与工程广东省实验室（珠海）,珠海 5190003.广东省城市化与地理环境空间模拟重点实验室,广州 5102754.南京大学建筑与城市规划学院,南京 2100935.江苏省智慧城市设计仿真与可视化技术工程实验室,南京 2100936.南京大学地理与海洋科学学院,南京 2100937.兰州交通大学测绘与地理信息学院,兰州 730070 |
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| 基金项目： | 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) |
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| 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。
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| 关 键 词： | 居民活动 建成环境 空间关联性 空气质量 广州 |
| 收稿时间： | 2020-07-20 |
The impact of air pollution on urban vibrancy and its built environment heterogeneity:An empirical analysis based on big data |
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| 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. |
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| Authors: | WANG Bo ZHEN Feng ZHANG Shanqi HUANG Xuefeng ZHOU Liang |
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| 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. |
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| Keywords: | human behaviour built environment spatial dependence air quality Guangzhou |
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