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A Construction of Geographical Distance-based Air Quality Dataset Using Hospital Location Information
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 Title & Authors
A Construction of Geographical Distance-based Air Quality Dataset Using Hospital Location Information
Kim, Hyeongsoo; Ryu, Keun Ho;
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 Abstract
As of late, air quality information has been actively gathered and investigated in order to find possible environmental risk factors that may affect the onset of cardiovascular disease. Nevertheless, existing studies are limited in the detailed analysis because they take advantage of the air quality information of the macro statistics divided into administrative districts. This paper proposes the construction of distance-based air quality dataset using a domestic hospital’s geographical location information as a reliable data gathering step for a more detailed analysis of environmental risk factors. For the construction of the dataset, air quality information was obtained by utilizing the geographical location of a hospital—in which a patient with cardiovascular disease had been admitted—and then matching the hospital with a meteorological and air pollution station in its vicinity. An air quality acquisition system based on GMap.net was devised for the purpose of data gathering and visualization. The reliability of the experiment was confirmed by evaluating the matching rate and error of air quality values between the acquired dataset with existing area-based air quality datasets from matched distances. Therefore, this dataset, which considers geographical information, can be utilized in multidisciplinary research for the discovery of environmental risk factors that can affect not only cardiovascular diseases but also potentially other epidemic diseases.
 Keywords
Geographical Information;Distance-based;Air Quality;Acquisition System;GMap.NET;Hospital Location;
 Language
Korean
 Cited by
 References
1.
Amiya, S., Nuruki, N., Tanaka, Y., Tofuku, K., Fukuoka, Y., Sata, N., Kashima, K., and Tsubouchi, H. (2009), Relationship between weather and onset of acute myocardial infarction: can days of frequent onset be predicted?, Journal of cardiology, Vol. 54, No. 2, pp. 231-237. crossref(new window)

2.
An, S.G. (2006), Recommendation for Application of Emergency Medical Information Center : Case by Patient Pre-hospital and Inter-hospital Transportation, Master's thesis, Public Health Yonsei University, Seoul, Korea, 56p. (in Korean with English abstract)

3.
Bang, J.S. (2012), Study on Analysis of Obstacles to EMT-paramedic's Pre-hospital Paramedic Emergency care for Cardioplegic Patients, Master's thesis, Dongshin University, Naju, Korea, 84p. (in Korean with English abstract)

4.
Cao, J., Cheng, Y., Zhao, N., Song, W., Jiang, C., Chen, R., and Kan, H. (2009), Diurnal temperature range is a risk factor for coronary heart disease death, Journal of Epidemiology, Vol. 19, No. 6, pp. 328-332. crossref(new window)

5.
GeoMidpoint (2015), Geographic midpoint calculation methods, GeoMidpoint, http://www.geomidpoint.com/calculation.html (last date accessed: 22 October 2015).

6.
GMap.NET (2015), Great maps for windows forms & presentation, CodePlex, http://greatmaps.codeplex.com (last date accessed: 13 October 2015).

7.
Goggins, W.B., Chan, E.Y., and Yang, C.Y. (2013), Weather, pollution, and acute myocardial infarction in Hong Kong and Taiwan, International Journal of Cardiology, Vol. 168, No. 1, pp. 243-249. crossref(new window)

8.
Joo, Y.K. (2014), Relationship among the Mortality of Cardiovascular, Respiratory and Nation Industrial Complex Air Pollution Using Meta Analysis, Master's thesis, Hanyang University, Seoul, Korea, 47p. (in Korean with English abstract)

9.
Lee, J.H., Chae, S.C., Yang, D.H., Park, H.S., Cho, Y., Jun, J.E., Park, W.H., Kam, S., Lee, W.K., Kim, Y.J., Kim, K.S., Hur, S.H., and Jeong, M.H. (2010), Influence of weather on daily hospital admissions for acute myocardial infarction (from the korea acute myocardial infarction registry), International Journal of Cardiology, Vol. 144, No. 1, pp. 16-21. crossref(new window)

10.
Lee, H.Y. and Park, M.Y. (2004), Analysis of the emergency medical service area using GIS: the case of Seoul, The Journal of GIS Association of Korea, Vol. 12, No. 2, pp. 193-209. (in Korean with English abstract)

11.
Park, H.J., Woo, K.S., Chung, E.K., Kang, T.S., Kim, G.B., Yu, S.D., and Son, B.S. (2015), A time-series study of ambient air pollution in relation to daily mortality count in Yeosu, Journal of Environmental Impact Assessment, Vol. 24, No. 1, pp. 66-77. (in Korean with English abstract) crossref(new window)

12.
Radišauskas, R., Bernotienė, G., Bacevičienė, M., Ustinavičienė, R., Kirvaitienė, J., and Krančiukaitė, D. (2014), Trends of myocardial infarction morbidity and its associations with weather conditions, Medicina, Vol. 50, No. 3, pp. 182-189. crossref(new window)

13.
Ryu, K.S., Park, H.W., Park, S.H., Ishag, I.M., Bae, J.H., and Ryu, K.H. (2015), The discovery of prognosis factors using association rule mining in acute myocardial infarction with ST-Segment elevation, In: Renda, M.E., Bursa, M., Holzinger, A., and Khuri, S. (eds.), Information Technology in Bio-and Medical Informatics, Springer International Publishing, Lecture Notes in Computer Science, pp. 49-55.

14.
Shon, H.S., Hwang, K.K., Bae, J.W., Kim, K.A., Lee, J.Y., and Ryu, K.H. (2013), N-terminal pro-B-type natriuretic peptide as prognostic marker for patients of non ST-segment elevation myocardial infarction, Journal of Central South University, Vol. 20, No. 8, pp. 2226-2232. crossref(new window)

15.
Sinnott, R.W. (1984), Virtues of the Haversine, Sky and Telescope, Vol. 68, No. 2, p. 159.

16.
Vanos, J.K., Hebbern, C., and Cakmak, S. (2014), Risk assessment for cardiovascular and respiratory mortality due to air pollution and synoptic meteorology in 10 Canadian cities, Environmental Pollution, Vol. 185, pp. 322-332. crossref(new window)

17.
WHO reports (2015), The top 10 causes of death, World Health Organization, Switzerland, http://who.int/en/ (last date accessed: 16 October 2015).

18.
Wilson, P.W., D’Agostino, R.B., Levy, D., Belanger, A.M., Silbershatz, H., and Kannel, W.B. (1998), Prediction of coronary heart disease using risk factor categories, Circulation, Vol. 97, No. 18, pp. 1837-1847. crossref(new window)

19.
Yang, B.Y. (2004a), The Application of GIS for Effective Distribution, Master's thesis, Kyung Hee University, Seoul, Korea, 98p. (in Korean with English abstract)

20.
Yang, H.E. (2004b), Generalized Additive Model of Air Pollution to Daily Mortality, Master's thesis, Duk-Sung Women's University, Seoul, Korea, 60p. (in Korean with English abstract)

21.
Yusuf, S., Hawken, S., Ôunpuu, S., Dans, T., Avezum, A., Lanas, F., McQueen, M., Budaj, A., Pais, P., Varigus, J., and Lisheng, L. (2004), Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study, The Lancet, Vol. 364, No. 9438, pp. 937-952. crossref(new window)