利用社交媒体数据模拟城市空气质量趋势面

Modeling Urban Air Quality Trend Surface Using Social Media Data

近年来,随着城市的发展,空气污染日益严重。目前,我国城市空气质量监测主要依靠空气质量监测站,但监测站数量有限,并且空气质量在一个城市的不同区域会出现较大起伏,单一利用监测站不易发现城市所有位置的空气质量起伏变化。对此,利用带有地理位置信息的新浪微博数据,分析空气污染相关主题微博与空气质量监测站点空气质量指数(air quality index,AQI)数据的相关性,建立两者间的函数关联,提出了一种建立城市空气质量趋势面的方法。实验结果表明,该方法不仅能定性地表现出城市不同区域的相对空气质量,也可定量、细粒度地展示城市空气质量情况。

Air pollution is getting worse with the development of cities in recent years.Urban air quality is mainly monitored by air quality monitoring stations at present.However,the number of stations is limited and the air quality fluctuates in different urban areas.So it is unefficient to detect air quality＇s distribution in a city by air quality monitoring stations only.Based on Sina Weibo data with location information,we propose an urban air quality trend surface modeling method by analysing the correlation between air pollution related topic microblogs and air quality monitoring station AQI data.The study reveals that our method not only qualitatively shows the relative air quality in diffferent regions of the city,but also demonstrations the urban air quality in a quantitative and fine-grained way.The findings of this study evaluate the feasibility of using a new type of large-scale data source for research on air quality estimation of any location in a city,and are of great significance when reflecting air quality distribution and finding areas where are relatively air polluted.