• 정보보호뉴스
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• no.9 s.132
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• pp.13-13
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• 2008

• 정보보호뉴스
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• pp.13-13
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• 2008

• 정보보호뉴스
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• s.133
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• pp.13-13
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• 2008

• 정보보호뉴스
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• pp.13-13
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• 2008

• 정보보호뉴스
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• 2008

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• 2008

• Informatization Policy
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• v.27 no.2
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• pp.66-83
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• 2020

Local issues that occur in cities typically garner great attention from the public. While local governments strive to resolve these issues, it is often difficult to effectively eliminate them all, which leads to complaints. In tackling these issues, it is imperative for local governments to use big data to identify the nature of complaints, and proactively provide solutions. This study applies the LDA topic modeling technique to research and analyze trends and patterns in complaints filed online. To this end, 9,625 cases of online complaints submitted to the city of Busan from 2015 to 2017 were analyzed, and 20 topics were identified. From these topics, key topics were singled out, and through analysis of quarterly weighting trends, four "hot" topics(Bus stops, Taxi drivers, Praises, and Administrative handling) and four "cold" topics(CCTV installation, Bus routes, Park facilities including parking, and Festivities issues) were highlighted. The study conducted big data analysis for the identification of trends and patterns in civil affairs and makes an academic impact by encouraging follow-up research. Moreover, the text mining technique used for complaint analysis can be used for other projects requiring big data processing.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.3
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• pp.13-24
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• 2018

For government and public institutions, civil complaints containing direct requirements of citizens can be utilized as important data in developing policies. However, it is difficult to draw accurate requirements using text mining methods since the nature of the complaint text is unstructured. In this study, a new method is proposed that draws the exact requirements of citizens, improving the previous text mining in analyzing the data of civil complaints. The new text-mining method is based on the principle of Co-Occurrences Structure Map, and it is structured by two-step association analysis, so that it consists of the first-order related word and a second-order related word based on the core subject word. For the analysis, 3,004 cases posted on the electronic bulletin board of Busan City for the year 2016 are used. This study's academic contribution suggests a method deriving the requirements of citizens from the civil affairs data. As a practical contribution, it also enables policy development using civil service data.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.79-98
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• 2018

The purpose of this study is to suggest a model analysing spatio-temporal characteristics of the civil complaints for the officially assessed land price based on big data mining. Specifically, in this study, the underlying reasons for the civil complaints were found from the spatio-temporal perspectives, rather than the institutional factors, and a model was suggested monitoring a trend of the occurrence of such complaints. The official documents of 6,481 civil complaints for the officially assessed land price in the district of Jung-gu of Incheon Metropolitan City over the period from 2006 to 2015 along with their temporal and spatial poperties were collected and used for the analysis. Frequencies of major key words were examined by using a text mining method. Correlations among mafor key words were studied through the social network analysis. By calculating term frequency(TF) and term frequency-inverse document frequency(TF-IDF), which correspond to the weighted value of key words, I identified the major key words for the occurrence of the civil complaint for the officially assessed land price. Then the spatio-temporal characteristics of the civil complaints were examined by analysing hot spot based on the statistics of Getis-Ord $Gi^*$. It was found that the characteristic of civil complaints for the officially assessed land price were changing, forming a cluster that is linked spatio-temporally. Using text mining and social network analysis method, we could find out that the occurrence reason of civil complaints for the officially assessed land price could be identified quantitatively based on natural language. TF and TF-IDF, the weighted averages of key words, can be used as main explanatory variables to analyze spatio-temporal characteristics of civil complaints for the officially assessed land price since these statistics are different over time across different regions.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.785-788
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• 2013

본 연구는 가로등 고장 처리를 수행하면서 가로등의 고장상태 신고가 잘 이루어지지 않아 고장 난 가로등에 대한 조치가 늦어지고 있다는 점을 착안한 것이다. 가로등에 고장이 발생하면 이를 발견한 주민들이 관리기관(기초지자체 등)에 민원 신고를 하거나 가로등 운영 관리자가 고장을 발견하게 된다. 여기에서 주민 신고는 어떤 가로등이 고장 났는지 가로등 ID를 알지 못하기 때문에 정확한 위치를 파악하는데 어려움이 있다. 둘째, 주민 신고가 대부분 전화로 민원을 요구하고, 신고 당시 정확한 가로등 위치를 알지 못하는 상태에서 상호간에 언성이 높아지고 결국 주민의 원망으로 남는다는 점이다. 따라서 가로등 고장 처리 중 민원을 발생시키는 문제를 해결하는 고장 신고 시스템을 개발하고자 한다. 우리는 가로등의 조도를 주기적으로 센싱하고 전송하여 고장 상태와 동작여부를 모니터링 함으로써 해결할 수 있다.