KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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An Analysis of Effects of Emergency Fine Dust Reduction Measures and National Petition Using Regression Analysis and Text Mining

회귀분석과 텍스트마이닝을 활용한 미세먼지 비상저감조치의 실효성 및 국민청원 분석

  • 김애니 (동덕여자대학교 정보통계학과) ;
  • 정소희 (동덕여자대학교 정보통계학과) ;
  • 최현빈 (동덕여자대학교 정보통계학과) ;
  • 김현희 (동덕여자대학교 정보통계학과)
  • Received : 2018.07.06
  • Accepted : 2018.08.03
  • Published : 2018.11.30
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Abstract

Recently, the Seoul government implemented 'Free Public Transportation' policy and 'Citizen Participatory Alternative-Day-No-Driving' system as 'Emergency Fine Dust Reduction Measures'. In this paper, after identifying the effectiveness of the two traffic policies, suggestions for direction of future fine dust policy were made. The effect of traffic on the fine dust was analyzed by regression analysis and the responses to the two traffic policies and petitions were analyzed using text mining. Our experimental results show that the responses to the policy were mostly negative, and the influence of the domestic factors was considerable unlike expectation of citizens. Moreover, the result made us possible to know people's specific needs on fine dust reduction policy. Finally, based on the result, the suggestions for fine dust reduction policy direction were provided.

최근 서울시에서는 '미세먼지 비상저감조치'로 '대중교통 무료' 정책과 '시민 참여형 차량 2부제'를 시행하였다. 본 논문에서는 두 교통정책에 대한 실효성을 파악한 뒤, 향후 미세먼지 정책의 방향을 제시하였다. 교통이 미세먼지에 미치는 영향은 회귀분석으로, 두 정책에 대한 시민들의 반응과 향후 정책에 대한 시민들의 의견은 텍스트마이닝 기법을 통해 알아보았다. 분석 결과, 정책에 대한 시민들의 의견은 대부분 부정적이었고 국외 요인이 미세먼지의 주된 원인이라는 시민들의 생각과 달리 국내 요인의 영향도 상당하였다. 또 국민청원을 통해 시민들이 원하는 구체적인 정책의 내용을 알 수 있었다. 위 결과를 토대로 향후 미세먼지 정책이 나아갈 방향을 제시하였다.

Keywords

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Fig. 1. Number of Likes/Dislikes and Comment for Each Policy

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Fig. 3. Coefficients of Stepwise Regression Analysis

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Fig. 4. Clusters of Topic

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Fig. 2(B). Word Cloud of Alternative-Day-No-Driving System

Table 1. Air Pollution Source Categories

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Table 2. Reclassification of KSIC industry

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Table 3. Properties of Each Topics in Topic Modeling

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