Resources Science ›› 2020, Vol. 42 ›› Issue (6): 1199-1209.doi: 10.18402/resci.2020.06.16

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Impact of air quality on public outings based on big data

AO Changlin(), WANG Jingxia, SUN Baosheng   

  1. Department of Management Science and Engineering, Northeast Agricultural University, Harbin 150030, China
  • Received:2019-07-12 Revised:2019-10-28 Online:2020-06-25 Published:2020-08-25

Abstract:

Exploring the impact of air quality on public outings based on big data not only can enrich the practical research on air quality issues, but also have important significance for strengthening air pollution control and promoting the development of tourism industry. Taking Harbin City as the research object, this study used the data of 16,554 Ctrip online reviews, the Ministry of Environmental Protection’s air quality index data, and historical weather data and constructed the number of public outings and satisfaction models based on negative binomial regression and ordered Probit regression to quantitatively assess the influences and degrees of air quality on public outings. The findings are as follows. (1) Controlling for other influencing factors such as temperature, wind speed, and holidays, air quality significantly affected the number of public outings and the satisfaction of outings. (2) The number of public outings decreased significantly with the increase of air pollution. Compared with excellent air quality, the number dropped by 40.1% when the air quality was severe, and by 15.1% when the air quality was good. (3) For the tourist attractions in Harbin City, severe air pollution significantly reduced satisfaction of public outings, while lower levels of air pollution did not significantly affect satisfaction of public outings. This research not only provides a new perspective with an accurate assessment of the influences of air quality on public outing behavior, but also provides references for relevant air pollution prevention and tourism policy formulation.

Key words: air quality, tourism big data, outings, negative binomial regression, ordered probit regression, Harbin City