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

Eunmi Lee,Van Huy Pham,Jinbae Sul,Kyeong Soo Kim,Sanghyuk Lee

doi:10.3390/su11216140

Eunmi Lee, Van Huy Pham + Show 3 more

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https://doi.org/10.3390/su11216140

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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.

Highlights

With the advent of the Fourth Industrial Revolution, interest in smart cities and big data has been increasing
This study demonstrated that using computational tools such as text mining for analyzing large volumes of text data allows city governments to uncover latent citizens’ policy demand enabling a richer analysis
This research involved the analysis of big data on citizen complaints, which reflect what urban residents desire from their local governments in terms of a wide range of issues [43]

Summary

Introduction

With the advent of the Fourth Industrial Revolution, interest in smart cities and big data (big data generally refers to large and complex sets of data that represent digital traces of human activities and can be defined in terms of scale or volume, analytical methods [1], or organizational effects [2]) has been increasing. Cities around the world collect massive amounts of data related to urban life, from objects (e.g., energy infrastructure) and people (e.g., energy use from household residents). This information, if analyzed properly, can become useful to various stakeholders, including citizens, local and national governments, and companies [5,6]. The Seoul government collects data on public health, transportation, and residences to design sustainable solutions that improve the life of urban residents In this case data was utilized to identify the use and demand for night buses, and was subsequently used to improve these public bus services [17]. Other similar cases include local governments in Santander (Spain) and Cosenza (Italy) [20,21]

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