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利用社交媒体数据模拟城市空气质量趋势面

王艳东1 荆彤1 姜伟1 王腾1 付小康1

(1.武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079)

【摘要】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。

【关键词】 社交媒体;新浪微博;城市空气质量;趋势面;

【DOI】

【基金资助】 国家自然科学基金(41271399); 测绘地理信息公益性行业科研专项经费(201512015); 高等学校博士学科点专项科研基金(20120141110036); 国家科技支撑计划(2012BAH35B03);

Modeling Urban Air Quality Trend Surface Using Social Media Data

WANG Yandong1 JING Tong1 JIANG Wei1 WANG Teng1 FU Xiaokang1

(1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China 430079)

【Abstract】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 different 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.

【Keywords】 social media; Sina Weibo; urban air quality; trend surface;

【DOI】

【Funds】 National Natural Science Foundation of China, No. 41271399; China Special Fund for Surveying, Mapping and Geoinformation Research in the Public Interest, No. 201512015; Specialized Research Fund for the Doctoral Program of Higher Education, No. 20120141110036; National Key Technology Research and Development Program of China, No. 2012BAH35B03;

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This Article

ISSN:1671-8860

CN: 42-1676/TN

Vol 42, No. 01, Pages 14-20

January 2017

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Article Outline

Abstract

  • 1 Establishment method of air quality trend surface
  • 2 Extraction of regions with relatively severe air pollution
  • 3 Conclusions
  • References