Browse > Article
http://dx.doi.org/10.7848/ksgpc.2019.37.6.417

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)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.37, no.6, 2019 , pp. 417-426 More about this Journal
Abstract
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.
Keywords
Spatio-temporal Visualization; Flow Visualization; PM10 Flow;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 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.
2 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.   DOI
3 KOTI (2019), ViewT, The Korea Transport Institute, https://viewt.ktdb.go.kr (last date accessed: 15 October 2019).
4 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.   DOI
5 MOLIT (2019), ITS, Ministry of Land, Infrastructure and Transport, http://nodelink.its.go.kr (last date accessed: 15 October 2019).
6 Nullschool (2019), Earth, Nullschool, http://earth.nullschool.net (last date accessed: 15 October 2019).
7 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.   DOI
8 Tufte E.R. (1991), Envisioning information, Optometry and Vision Science, Vol. 68, No. 4, pp. 322-324.   DOI
9 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.
10 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.
11 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.   DOI
12 Windy (2019), Windy map and weather forecast, Windy, http://www.windy.com (last date accessed: 15 October 2019).
13 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.   DOI
14 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.   DOI
15 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)
16 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.   DOI
17 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.
18 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.
19 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.   DOI
20 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.
21 IQAir (2019), Airvisual Earth, IQAir, http://www.airvisual.com/earth (last date accessed: 15 October 2019).
22 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.
23 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)
24 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)   DOI
25 KECO (2019), AirKorea, Korea Environment Corporation, https://www.airkorea.or.kr (last date accessed: 15 October 2019).
26 Keim, D., Kohlhammer, J., Ellis, G., and Mansmann, F. (2010), Mastering the information age: Solving problems with visual analytics, Eurographics Association, Goslar, Germany.
27 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.
28 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.   DOI
29 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.   DOI
30 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.
31 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.   DOI
32 Lewer, J.J. and Van den Berg, H. (2008), A gravity model of immigration, Economics Letters, Vol. 99, No. 1, pp. 164-167.   DOI
33 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.   DOI
34 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.   DOI
35 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.