A Study on the Safety Index Service Model by Disaster Sector using Big Data Analysis

doi:10.15683/kosdi.2020.12.31.682

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2020 Vol.16, Issue 4 Preview Page Next Page

Original Article

A Study on the Safety Index Service Model by Disaster Sector using Big Data Analysis 빅데이터 분석을 활용한 재해 분야별 안전지수 서비스 모델 연구: 정명균¹ㆍ이석형²ㆍ김창수³*
Myoung Gyun Jeong¹, Seok Hyung Lee², Chang Soo Kim²*; ¹Lecturer, Information & Computer Center, Pukyong National University, Busan, Republic of Korea
²Doctorate Completion, Department of Information Systems, Pukyong National University, Busan, Republic of Korea
³Full Professor, Department of IT Convergence and Application Engineering, Pukyong National University, Busan, Republic of Korea

31 December 2020. pp. 682-690

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Abstract

Purpose: This study builds a database by collecting and refining disaster occurrence data and real-time weather and atmospheric data. In conjunction with the public data provided by the API, we propose a service model for the Big Data-based Urban Safety Index. Method: The plan is to provide a way to collect various information related to disaster occurrence by utilizing public data and SNS, and to identify and cope with disaster situations in areas of interest by real-time dashboards. Result: Compared with the prediction model by extracting the characteristics of the local safety index and weather and air relationship by area, the regional safety index in the area of traffic accidents confirmed that there is a significant correlation with weather and atmospheric data. Conclusion: It proposed a system that generates a prediction model for safety index based on machine learning algorithm and displays safety index by sector on a map in areas of interest to users.

Keywords

Regional Safety Index

Big Data Analysis

Machine Learning Algorithm

Urban Safety Index

Public Data Analysis

연구목적: 본 연구는 재난 발생 데이터와 실시간 기상·대기 관련 데이터를 수집하고 정제과정을 통하여 데이터베이스를 구축하고, API로 제공되는 공공 데이터와 연계하여 빅 데이터 기반의 도시안전지수의 서비스 모델을 제안하고자 한다. 연구방법: 재난 발생과 관련한 다양한 정보를 공공 데이터와 SNS를 활용하여 수집하고, 기계학습 알고리즘으로 분석한 결과를 중심으로 이용자 관심지역의 재난상황을 실시간 대시보드로 확인하고 대처하는 방법을 제공하고자 한다. 연구결과: 분야별 지역안전지수와 기상·대기의 상관관계가 높은 속성을 추출하여 예측모델과 비교하면 교통사고 분야의 지역안전지수는 기상·대기 데이터와 상당한 상관관계가 있음을 확인하였다. 결론: 기계학습 알고리즘 기반의 안전지수 예측모델을 생성하여 이용자 관심 지역에 분야별 안전지수를 지도에 표시하는 시스템을 제안하였다.

키워드

지역안전지수

빅 데이터 분석

기계학습 알고리즘

도시안전지수

공공데이터 분석

References

Bae, B.G., Choi, S.H. (2013). "The study of disaster management using social bigdata." Journal of the Korean Institute of Information Scientists and Engineers, Vol. 40, pp. 32-34.
Choi, Y.C., Seo, G.D. (2018). "Establish priority of kind of disaster for city safety." Journal of the Architectural Institute of Korea Structure & Construction, Vol. 34, No. 7, pp. 11-18.
Hwang, H., Seo, Y., Jeon, T., Kim, C. (2018). "Design and implementation of an urban safety service system using realtime weather and atmosphere data." Journal of Korea Multimedia Society, Vol. 21, No. 5, pp. 599-608.
Jang, H.J. (2018). "Big data application algorithm for safe community implementation." Journal of the Urban Design Institute of Korea , Vol. 19, No. 1, pp. 37-50. 10.38195/judik.2018.02.19.1.37
Kang, M.M., Kim, S.R., Park, S.M. (2012). "Analyze and leverage big data." Journal of the Korean Institute of Information Scientists and Engineers, Vol. 30, No. 6, pp. 25-32.
Kim, E., Ra, I., Rhee, K., Kim, C.S. (2014), "Estimation of real-time flood risk on roads based on rainfall calculated by the revised method of missing rainfall." pp. 6418-6431. 10.3390/su6096418
Kim, J.G., Park, C.S. (2019). "A comparative study on the importance of the components of the community disaster resilience on disaster response." Journal of the Korea Society of Disaster Information, Vol. 15, No. 3, pp.339-346.
Koo, W.H., Back, M.H. (2018). "A study on the consciousness survey for the establishment of safety village in disaster" Journal of the Korea Society of Disaster Information, Vol. 14, No. 3, pp.238-246.
Lee, J.H., Song, K.S., Kang, J.A., Hwang, J.R. (2015). "A study on the efficient extraction method of SNS data related to crime risk factor." Journal of the Korea Society of Computer and Information, Vol. 20, No. 1, pp. 255-263. 10.9708/jksci.2015.20.1.255
Min, G.Y., Jeong, D.H. (2013). "Research on assessment of impact of big data attributes to disaster response decision-making process." Journal of the Society for e-Business Studies, Vol. 18, No. 3,1, pp. 17-43. 10.7838/jsebs.2013.18.3.017
Min, G.Y., Jeong, D.H. (2014). "A study on the big data properties for rapid disaster response activities." Journal of the Korea Intelligent Information Systems Society, pp. 259-263.
Ministry of the Interior and Safety (2010). Manual of Safe City Operation.
Ministry of the Interior and Safety (2013) BPR/ISP Project Results Report for the Establishment and Utilization of Common Foundation for Big Data.
Yun, S.O. (2013). "A study on the classification of risks caused by big data." Journal of Korean Association for Regional Information Society, Vol. 16, No. 2, pp. 93-122.

Information

Publisher :The Korean Society of Disaster Information
Publisher(Ko) :한국재난정보학회
Journal Title :Journal of the Society of Disaster Information
Journal Title(Ko) :한국재난정보학회논문집
Volume : 16
No :4
Pages :682-690
DOI :https://doi.org/10.15683/kosdi.2020.12.31.682

[1] Bae, B.G., Choi, S.H. (2013). "The study of disaster management using social bigdata." Journal of the Korean Institute of Information Scientists and Engineers, Vol. 40, pp. 32-34.

[2] Choi, Y.C., Seo, G.D. (2018). "Establish priority of kind of disaster for city safety." Journal of the Architectural Institute of Korea Structure & Construction, Vol. 34, No. 7, pp. 11-18.

[3] Hwang, H., Seo, Y., Jeon, T., Kim, C. (2018). "Design and implementation of an urban safety service system using realtime weather and atmosphere data." Journal of Korea Multimedia Society, Vol. 21, No. 5, pp. 599-608.

[4] Jang, H.J. (2018). "Big data application algorithm for safe community implementation." Journal of the Urban Design Institute of Korea , Vol. 19, No. 1, pp. 37-50. 10.38195/judik.2018.02.19.1.37

[5] Kang, M.M., Kim, S.R., Park, S.M. (2012). "Analyze and leverage big data." Journal of the Korean Institute of Information Scientists and Engineers, Vol. 30, No. 6, pp. 25-32.

[6] Kim, E., Ra, I., Rhee, K., Kim, C.S. (2014), "Estimation of real-time flood risk on roads based on rainfall calculated by the revised method of missing rainfall." pp. 6418-6431. 10.3390/su6096418

[7] Kim, J.G., Park, C.S. (2019). "A comparative study on the importance of the components of the community disaster resilience on disaster response." Journal of the Korea Society of Disaster Information, Vol. 15, No. 3, pp.339-346.

[8] Koo, W.H., Back, M.H. (2018). "A study on the consciousness survey for the establishment of safety village in disaster" Journal of the Korea Society of Disaster Information, Vol. 14, No. 3, pp.238-246.

[9] Lee, J.H., Song, K.S., Kang, J.A., Hwang, J.R. (2015). "A study on the efficient extraction method of SNS data related to crime risk factor." Journal of the Korea Society of Computer and Information, Vol. 20, No. 1, pp. 255-263. 10.9708/jksci.2015.20.1.255

[10] Min, G.Y., Jeong, D.H. (2013). "Research on assessment of impact of big data attributes to disaster response decision-making process." Journal of the Society for e-Business Studies, Vol. 18, No. 3,1, pp. 17-43. 10.7838/jsebs.2013.18.3.017

[11] Min, G.Y., Jeong, D.H. (2014). "A study on the big data properties for rapid disaster response activities." Journal of the Korea Intelligent Information Systems Society, pp. 259-263.

[12] Ministry of the Interior and Safety (2010). Manual of Safe City Operation.

[13] Ministry of the Interior and Safety (2013) BPR/ISP Project Results Report for the Establishment and Utilization of Common Foundation for Big Data.

[14] Yun, S.O. (2013). "A study on the classification of risks caused by big data." Journal of Korean Association for Regional Information Society, Vol. 16, No. 2, pp. 93-122.

Journal of the Society of Disaster Information ISSN:1976-2208(Print) 2671-5287(Online) 한국재난정보학회논문집

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