한국측량학회지 (Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)

  • 제37권6호
  • /
  • Pages.417-426
  • /
  • 2019
  • /
  • 1598-4850(pISSN)
  • /
  • 2288-260X(eISSN)
한국측량학회 (Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)
권호 표지
DOI QR코드

DOI QR Code

중력모델을 적용한 미세먼지 흐름 패턴 시공간 시각화

Spatio-temporal Visualization of PM10 Flow Pattern Using Gravity Model

  • Lee, Geon-Woo (Dept. of Geoinformation Engineering, Sejong University) ;
  • Yom, Jae-Hong (Dept. of Environment, Energy & Geoinformatics, Sejong University)
  • 투고 : 2019.09.25
  • 심사 : 2019.11.28
  • 발행 : 2019.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

이 연구에서는 미세먼지 시공간 변화 표현의 단점을 개선하고자 미세먼지를 흐름으로 시각화하였다. 일반적으로 미세먼지 흐름 시각화는 농도 분포와 바람장을 중첩해 표현하지만 도시 단위 이하 국지적 이동의 경우 바람과 미세먼지 이동이 다를 수 있으므로 바람장을 사용하는 것이 적합하지 않을 수 있다. 제시하는 시각화 방법론은 미세먼지 자료에서 직접 흐름 정보를 추출한다는 점에서 기존 연구와 차별성을 갖는다. 공간 상호작용을 설명하는 중력모델을 확장한 흐름 추출 방법을 미세먼지 자료에 적용하여 미세먼지 분포 변화에서 흐름 정보를 추출하였다. 이를 위해 공간보간법을 이용하여 미세먼지 분포도를 작성하였으며 추출된 미세먼지 흐름 정보를 물방울 모양의 움직이는 입자를 이용해 동적으로 시각화하였다. 산업 및 교통 활동이 시작하는 오전 5~7시 시간대를 대상으로 서울시 미세먼지 평균 흐름을 시각화하였으며 미세먼지 요인 중 하나인 교통정보와 연계하여 시각적으로 관련성을 분석하였다.

Conventional visualization of PM (Particulate Matter)10 flows applies superimposition of concentration distribution maps and wind field maps. This method is efficient for small scale maps where only macro flow trends are of interest. However, in the case of urban areas, local flows are difficult to model at micro level using wind fields, and therefore different methods of flow extraction is deemed necessary. In this study, flow information is extracted and visualized directly from the PM10 density data by using the gravity model. This method has the advantage that additional information such as wind field is not necessary for estimating the intensity and direction of PM10 flow. The extracted spatio-temporal flow patterns of PM10 are analyzed with relation to traffic information.

키워드

참고문헌

  1. Ahn, J.Y. (2016), Micromap plots to visualize air pollution at national and local level in Korea, International Journal of Environmental Studies, Vol. 73, No. 2, pp. 277-285. https://doi.org/10.1080/00207233.2016.1148449
  2. Cabral, B. and Leedom, L.C. (1993), Imaging vector fields using line integral convolution, Proceedings of SIGGRAPH93, ACM, 2-6 August, Anaheim, CA, USA, pp. 263-270.
  3. Card, S.K., Mackinlay, J.D., and Shneiderman, B. (1999), Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann Publishers Inc., San Francisco, CA.
  4. Cho, H.L. and Jeong, J.C. (2009), The distribution analysis of PM10 in Seoul using spatial interpolation methods, Journal of Environmental Impact Assessment, Vol. 18, No. 1, pp. 61-69. (in Korean with English abstract)
  5. Deligiorgi, D. and Philippopoulos, K. (2011), Spatial interpolation methodologies in urban air pollution modeling: application for the greater area of metropolitan Athens, Greece, IntechOpen, pp. 341-362.
  6. Du, Y., Ma, C., Wu, C., Xu, X., Guo, Y., Zhou, Y., and Li, J. (2017), A visual analytics approach for station-based air quality data, Sensors, Vol. 17, No. 1, pp. 30-47. https://doi.org/10.3390/s17010030
  7. IQAir (2019), Airvisual Earth, IQAir, http://www.airvisual.com/earth (last date accessed: 15 October 2019).
  8. Javed, W., Ghani, S., and Elmqvist, N. (2012), GravNav: using a gravity model for multi-scale navigation, Proceedings of the International Working Conference on Advanced Visual Interfaces, AVI, 21-25 May, Capri Island, Itary, pp. 217-224.
  9. Jeollabuk-do (2017), The Causal Analysis of Particle Matter Using Big Data in Jeollabuk-do, Research Report, Jeollabukdo Research Institute of Health and Environment, Jeollabukdo, pp. 10-26. (in Korean)
  10. Jeong, J.C. (2014), A spatial distribution analysis and time series change of PM10 in Seoul city, Journal of the Korean Association of Geographic Information Studies, Vol. 17, No. 1, pp. 61-69. (in Korean with English abstract) https://doi.org/10.11108/kagis.2014.17.1.061
  11. KECO (2019), AirKorea, Korea Environment Corporation, https://www.airkorea.or.kr (last date accessed: 15 October 2019).
  12. Keim, D., Kohlhammer, J., Ellis, G., and Mansmann, F. (2010), Mastering the information age: Solving problems with visual analytics, Eurographics Association, Goslar, Germany.
  13. Keler, A. and Krisp, J.M. (2015), Spatio-temporal visualization of interpolated particulate matter (PM2.5) in Beijing, GI_Forum - Journal for Geographic Information Science, 7-10 July, Salzburg, pp. 464-474.
  14. Kim, S., Jeong, S., Woo, I., Jang, Y., Maciejewski, R., and Ebert, D.S. (2018), Data flow analysis and visualization for spatiotemporal statistical data without trajectory information, IEEE Transactions on Visualization and Computer Graphics, Vol. 24, No. 3, pp. 1287-1300. https://doi.org/10.1109/tvcg.2017.2666146
  15. Kincses, A. and Toth, G. (2014), The application of gravity model in the investigation of spatial structure, Acta Polytechnica Hungarica, Vol. 11, No. 2, pp. 5-19.
  16. KOTI (2019), ViewT, The Korea Transport Institute, https://viewt.ktdb.go.kr (last date accessed: 15 October 2019).
  17. Laidlaw, D.H., Kirby, R.M., Jackson, C.D., Davidson, J.S., Miller, T.S., Da Silva, M., and Tarr, M.J. (2005), Comparing 2D vector field visualization methods: a user study, IEEE Transactions on Visualization and Computer Graphics, Vol. 11, No. 1, pp.59-70. https://doi.org/10.1109/TVCG.2005.4
  18. Laramee, R.S., Hauser, H., Doleisch, H., Vrolijk, B., Post, F.H., and Weiskopf, D. (2004), The state of the art in flow visualization: dense and texture-based techniques, Computer Graphics Forum, Vol. 23, No. 2, pp. 203-221. https://doi.org/10.1111/j.1467-8659.2004.00753.x
  19. Lewer, J.J. and Van den Berg, H. (2008), A gravity model of immigration, Economics Letters, Vol. 99, No. 1, pp. 164-167. https://doi.org/10.1016/j.econlet.2007.06.019
  20. Li, H., Fan, H., and Mao, F. (2016), A visualization approach to air pollution data exploration-a case study of air quality index (PM2.5) in Beijing, China, Atmosphere, Vol. 7, No. 3, pp. 35-55. https://doi.org/10.3390/atmos7030035
  21. Li, X., Tian, H., Lai, D., and Zhang, Z. (2011), Validation of the gravity model in predicting the global spread of influenza, International Journal of Environmental Research and Public Health, Vol. 8, No. 8, pp. 3134-3143. https://doi.org/10.3390/ijerph8083134
  22. Liao, Z., Peng, Y., Li, Y., Liang, X., and Zhao, Y. (2014), A webbased visual analytics system for air quality monitoring data, 22nd International Conference on Geoinformatics, IEEE, 25-27 June, Kaohsiung, pp. 1-6.
  23. Liu, Y., Sui, Z., Kang, C., and Gao, Y. (2014), Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data, PloS One, Vol. 9, No. 1, pp. 1-11.
  24. Lu, W., Ai, T., Zhang, X., and He, Y. (2017), An interactive web mapping visualization of urban air quality monitoring data of China, Atmosphere, Vol. 8, No. 8, pp. 148-164. https://doi.org/10.3390/atmos8080148
  25. Martinez-Zarzoso, I. and Nowak-Lehmann, F. (2003), Augmented gravity model: an empirical application to Mercosur-European Union trade flows, Journal of Applied Economics, Vol. 6, No. 2, pp. 291-316. https://doi.org/10.1080/15140326.2003.12040596
  26. MOLIT (2019), ITS, Ministry of Land, Infrastructure and Transport, http://nodelink.its.go.kr (last date accessed: 15 October 2019).
  27. Nullschool (2019), Earth, Nullschool, http://earth.nullschool.net (last date accessed: 15 October 2019).
  28. Tian, G., Qiao, Z., and Xu, X. (2014), Characteristics of particulate matter (PM10) and its relationship with meteorological factors during 2001-2012 in Beijing, Environmental Pollution, Vol. 192, pp. 266-274. https://doi.org/10.1016/j.envpol.2014.04.036
  29. Tufte E.R. (1991), Envisioning information, Optometry and Vision Science, Vol. 68, No. 4, pp. 322-324. https://doi.org/10.1097/00006324-199104000-00013
  30. Vorapracha, P., Phonprasert, P., Khanaruksombat, S., and Pijarn, N. (2015), A comparison of spatial interpolation methods for predicting concentrations of particle pollution (PM10), International Journal of Chemical, Environmental and Biological Sciences, Vol. 3, No. 4, pp. 302-306.
  31. Wegenkittl, R., Groller, E., and Purgathofer, W. (1997), Animating flow fields: rendering of oriented line integral convolution, Proceedings of Computer Animation '97, IEEE, 2-3 September, Hungary, pp. 15-21.
  32. Windy (2019), Windy map and weather forecast, Windy, http://www.windy.com (last date accessed: 15 October 2019).
  33. Wong, D.W., Yuan, L., and Perlin, S.A. (2004), Comparison of spatial interpolation methods for the estimation of air quality data, Journal of Exposure Science and Environmental Epidemiology, Vol. 14, No. 5, pp. 404-415. https://doi.org/10.1038/sj.jea.7500338
  34. Xiao, K., Wang, Y., Wu, G., Fu, B., and Zhu, Y. (2018), Spatiotemporal characteristics of air pollutants (PM10, PM2. 5, SO2, NO2, O3, and CO) in the inland basin city of Chengdu, Southwest China, Atmosphere, Vol. 9, No. 2, pp. 74-90. https://doi.org/10.3390/atmos9020074
  35. Zhou, Z., Ye, Z., Liu, Y., Liu, F., Tao, Y., and Su, W. (2017), Visual analytics for spatial clusters of air-quality data, IEEE Computer Graphics and Applications, Vol. 37, No. 5, pp. 98-105. https://doi.org/10.1109/MCG.2017.3621228