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Analysis of Public Complaints to Identify Priority Policy Areas: Evidence from a Satellite City around Seoul

Author

Listed:
  • Eunmi Lee

    (Social Science Research Institute, Yonsei University, Seoul 03722, Korea)

  • Sanghyuk Lee

    (Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
    Centre for Smart Grid and Information Convergence, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China)

  • Kyeong Soo Kim

    (Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
    Centre for Smart Grid and Information Convergence, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China)

  • Van Huy Pham

    (Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam)

  • Jinbae Sul

    (Institute of Public Affairs, Yonsei University, Seoul 03722, Korea)

Abstract

Conventional studies on policy demand identification that are anchored in big data on urban residents are limited in that they mostly involve the top-down and government-oriented use of such data. It restricts treatment to specific issues (e.g., public safety and disaster management), even from the beginning of data collection. Scant research has emphasized the general use of data on civil complaints—which are independent of areas of application—in the examination of sustainable cities. In this work, we hypothesized that the analyses of civil complaint data and big data effectively identify what urban residents want from local governments with respect to a broad range of issues. We investigated policy demand using big data analytics in examining unstructured civil complaint data on safety and disaster management. We extracted major keywords associated with safety and disaster management via text mining to inquire into the relevant matters raised in the civil complaints. We also conducted a panel analysis to explore the effects exerted by the characteristics of 16 locally governed towns on residents’ policy demands regarding safety and disaster management-related complaints. The results suggest that policy needs vary according to local sociocultural characteristics such as the age, gender, and economic status of residents as well as the proportion of migrants in these localities, so that, city governments need to provide customized services. This research contributes to extend with more advanced big data analysis techniques such as text mining, and data fusion and integration. The technique allows the government to identify more specifically citizens’ policy needs.

Suggested Citation

  • Eunmi Lee & Sanghyuk Lee & Kyeong Soo Kim & Van Huy Pham & Jinbae Sul, 2019. "Analysis of Public Complaints to Identify Priority Policy Areas: Evidence from a Satellite City around Seoul," Sustainability, MDPI, vol. 11(21), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:21:p:6140-:d:283255
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    References listed on IDEAS

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    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    4. Chirag Rabari & Michael Storper, 2015. "Editor's choice The digital skin of cities: urban theory and research in the age of the sensored and metered city, ubiquitous computing and big data," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 8(1), pages 27-42.
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    1. Young Bae & Byung-Deuk Woo & Sungwon Jung & Eunchae Lee & Jiin Lee & Mingu Lee & Haegyun Park, 2023. "The Relationship Between Government Response Speed and Sentiments of Public Complaints: Empirical Evidence From Big Data on Public Complaints in South Korea," SAGE Open, , vol. 13(2), pages 21582440231, April.

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