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

Eunmi Lee, Sanghyuk Lee, Kyeong Soo Kim, Pham Van Huy and Jinbae Sul
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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
Kyeong Soo Kim: Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
Pham Van Huy: 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

Sustainability, 2019, vol. 11, issue 21, 1-17

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.

Keywords: civil complaints; policy demand; safety and crisis management; text mining; panel analysis; sustainable urban; big data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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