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도시공간빅데이터를 활용한 CCTV 우선설치지수 개발 및 시범적용

Development and Application of CCTV Priority Installation Index using Urban Spatial Big Data

  • 김혜림 (경상국립대학교 도시공학과) ;
  • 문태헌 (경상국립대학교 도시공학과) ;
  • 허선영 (경상국립대학교 스마트공동체사업단 )
  • Hye-Lim KIM (Dept. of Urban Engineering, Gyeongsang National University) ;
  • Tae-Heon MOON (Dept. of Urban Engineering, ERI, Gyeongsang National University) ;
  • Sun-Young HEO (Gyeongsang National University)
  • 투고 : 2024.03.12
  • 심사 : 2024.04.17
  • 발행 : 2024.06.30

초록

방범용 CCTV는 지속적으로 증설되고 있으나 설치 위치 결정에 대한 가이드라인의 부재로 범죄 발생 다발지역과 무관한 위치에 CCTV가 설치되는 경우가 많다. 이에 본 연구에서는 도시공간빅데이터를 활용하여 CCTV 우선설치지수를 개발하고, 사례지역에 시범 적용하여 적용 가능성을 타진하였다. CCTV 우선설치지수는 범죄취약지수와 감시취약지수로 구성하였으며, 각각 머신러닝 알고리즘을 통해 예측한 그리드별 범죄발생건수, 가시권 분석을 통해 산출한 그리드별 감시불가면적의 비율을 활용하여 산출하였다. 지수를 시범지역에 적용한 결과 CCTV 가시권 분석에 Viewshed 기능을 활용함으로써 기존 버퍼 기능 활용 시 감시면적이 과대 추정되었던 문제를 해결할 수 있었다. 또한 해당 지수를 적용하여 CCTV 설치 위치를 결정할 경우, 감시면적을 효율적으로 개선 가능하다. 본 연구의 CCTV 위치 결정 프로세스에 따라 사례지역에 신규 CCTV를 추가 설치할 경우, 도로면적 대비 감시면적이 43.25%에서 83.73%로 증가하였다. 따라서, CCTV 우선설치지수는 스마트안전도시 조성을 위한 효과적인 의사결정 도구로 활용될 수 있을 것이다.

CCTV for crime prevention is expanding; however, due to the absence of guidelines for determining installation locations, CCTV is being installed in locations unrelated to areas with frequent crime occurrences. In this study, we developed a CCTV Priority Installation Index and applied it in a case study area. The index consists of crime vulnerability and surveillance vulnerability indexes, calculated using machine learning algorithms to predict crime incident counts per grid and the proportion of unmonitored area per grid. We tested the index in a pilot area and found that utilizing the Viewshed function in CCTV visibility analysis resolved the problem of overestimating surveillance area. Furthermore, applying the index to determine CCTV installation locations effectively improved surveillance coverage. Therefore, the CCTV Priority Installation Index can be utilized as an effective decision-making tool for establishing smart and safe cities.

키워드

과제정보

본 과제(결과물)는 2023년도 교육부의 재원으로 한국연구재단의 지원을 받아 수행된 지자체-대학 협력기반 지역혁신사업의 결과입니다.(2021RIS-003)

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