Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
권호 표지
DOI QR코드

DOI QR Code

Visualization of women's safety facility index based on public data analysis: Focusing on Seoul

공공데이터 분석 기반 여성안전 시설지수 시각화: 서울시 중심으로

  • 김형균 (국민대학교 소프트웨어학부)
  • Received : 2021.01.08
  • Accepted : 2021.04.20
  • Published : 2021.04.28
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, an index of women's safety facilities was created and visualized using public data related to Seoul. CPTED, the women's safety facilities index was created by collecting and analyzing eight data related to the local women's safety index and five major crime victims of women. As a result of the correlation analysis between the factors of the female safety facility index and the number of female crime victims, three data were selected as the main factors, "CCTV," "street lamps," and "female security guardians", which were found to be meaningful at the 95% level of reliability. The distinction women's safety facility index was calculated by weighting the correlation coefficient between the main factors for calculating the women's safety facility index, and visualized using Python's Follium library.

본 논문에서는 서울시와 관련한 공공데이터를 이용해 여성 안전시설지수를 작성하고 시각화하였다. CPTED, 지역여성 안전지수 관련 8가지 데이터와 여성 5대 범죄피해자 데이터를 수집·분석하여 여성 안전시설지수를 작성하였다. 여성 안전시설지수 요소와 여성 범죄 피해자 수 간의 상관분석 결과 신뢰도 95% 수준에서 유의미하다고 결과가 나온 'CCTV', '가로등', '여성안심지킴이집' 3가지 데이터를 주요 요소로 선정하였다. 여성 안전시설 지수 산출을 위한 주요 요소간의 상관계수를 이용해 가중치를 부여해서 구별 여성 안전시설 지수를 산출하고 파이썬의 Follium 라이브러리를 이용해 시각화하였다.

Keywords

References

  1. W. S. Park. (2019). Gender statistics in Seoul in 2019. https://opengov.seoul.go.kr
  2. S. Y. Oh. (2017). Study of Causes and Solutions of Violent Crimes against Second-class Citizens - Focus on Women's Violent Crimes -. Korean Police Studies Review, 16(3), 225-250. https://doi.org/10.38084/2017.16.3.10
  3. J. H. Ki. (2015). A Study of Impact of Urban Population Characteristics on Violent Crimes. Journal of the Korean Regional Development Association, 27(1), 107-124.
  4. J. H. Ryu. (2020). CPTED and Fear of Crime. Journal of Korean Public Police and Security Studies, 17(1), 15-32.
  5. J. H. Jang. (2018). Study on Effectiveness of CPTED for preventing crimes : Based on panel data analysis of 25 districts in Seoul. Korean Police Studies Review, 17(4), 267-292. https://doi.org/10.38084/2018.17.4.11
  6. M. H. Cho. (2013). "A plan to secure school safety by applying CPTED". Master's Thesis. Yongin University Graduate School.
  7. M. Lee. (2018). Map Visualization-Folium. https://ericnjennifer.github.io/python_visualization/2018/01/21/PythonVisualization_Chapt6.html
  8. M. G. Kim, M. H. Lee. (2016). A Visualization Method of Spatial Information based on Web Map Service. Journal of Digital Convergence, 14(2), 209-216. https://doi.org/10.14400/JDC.2016.14.2.209
  9. Psychoria. (2019). JSON data format handling in Python. https://psychoria.tistory.com/703
  10. S. W. Park. (2015). A Study on the Visualization of Images in the Mind through Texts and Maps - Based on My Works. Journal of Korean Society of Media & Arts, 13(1), 23-34.
  11. J. A. Kim, M. G Kim. (2019). Effect of data visualization education with using Python on computational thinking of six grade in elementary school. Journal of The Korean Association of Information Education, 23(3), 197-206. https://doi.org/10.14352/jkaie.2019.23.3.197
  12. D. H. Lee. (2015). An Alternative Approach for Implementing Interactive Media Contents - a Case Study of Teaching Computer Game Programming using Python. Journal of Korean Society of Media & Arts, 13(1), 145-156.
  13. Y. S. Lee. (2018). Python-based Software Education Model for Non-Computer Majors. Journal of the Korea Convergence Society, 9(3), 73-78. https://doi.org/10.15207/JKCS.2018.9.3.073
  14. J. B. Lee. (2019). Analysis of Tensor Processing Unit and Simulation Using Python. The Journal of the Institute of Internet, Broadcasting and Communication, 19(3), 165-171. https://doi.org/10.7236/JIIBC.2019.19.3.165
  15. K. M. Kim, H. S. Kim. (2014). A Case Study on Necessity of Computer Programming for Interdisciplinary Education. Journal of Digital Convergence, 12(11), 339-348. https://doi.org/10.14400/JDC.2014.12.11.339
  16. W. S. Moon. (2018). Analysis of error data generated by prospective teachers in programming learning. Journal of The Korean Association of Information Education, 22(2), 205-212. https://doi.org/10.14352/jkaie.2018.22.2.205