Advanced SearchSearch Tips
Spatial Analysis Methods for Asbestos Exposure Research
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Spatial Analysis Methods for Asbestos Exposure Research
Kim, Ju-Young; Kang, Dong-Mug;
  PDF(new window)
Objectives: Spatial analysis is useful for understanding complicated causal relationships. This paper focuses trends and appling methods for spatial analysis associated with environmental asbestos exposure. Methods: Literature review and reflection of experience of authors were conducted to know academic background of spatial analysis, appling methods on epidemiology and asbestos exposure. Results: Spatial analysis based on spatial autocorrelation provides a variety of methods through which to conduct mapping, cluster analysis, diffusion, interpolation, and identification. Cause of disease occurrence can be investigated through spatial analysis. Appropriate methods can be applied according to contagiousness and continuity. Spatial analysis for asbestos exposure source is needed to study asbestos related diseases. Although a great amount of research has used spatial analysis to study exposure assessment and distribution of disease occurrence, these studies tend to focus on the construction of a thematic map without different forms of analysis. Recently, spatial analysis has been advanced by merging with web tools, mobile computing, statistical packages, social network analysis, and big data. Conclusions: Because the trend in spatial analysis has evolved from simple marking into a variety of forms of analyses, environmental researchers including asbestos exposure study are required to be aware of recent trends.
asbestos;spatial analysis;spatial epidemiology;geographic information system (GIS);
 Cited by
Spatial analysis of $PM_{10}$ and cardiovascular mortality in the Seoul metropolitan area,;;;;;;

Environmental health and toxicology, 2014. vol.29. pp.5.1-5.7 crossref(new window)
High Incidence of Breast Cancer in Light-Polluted Areas with Spatial Effects in Korea,;;;;

Asian Pacific Journal of Cancer Prevention, 2016. vol.17. 1, pp.361-367 crossref(new window)
Spatial analysis of PM<sub>10</sub> and cardiovascular mortality in the Seoul metropolitan area, Environmental Health and Toxicology, 2014, 29, e2014005  crossref(new windwow)
High Incidence of Breast Cancer in Light-Polluted Areas with Spatial Effects in Korea, Asian Pacific Journal of Cancer Prevention, 2016, 17, 1, 361  crossref(new windwow)
National GIS portal book/pdf_files/Gis-pdf/GIS_1.pdf. [accessed 24 Oct. 2012].

Tobler WA. Computer movie simulating urban growth in the Detroit region. Econ Geogr. 1970; 46(2): 234-40 crossref(new window)

Kim KG. Exploration of spatial autocorrelation and utilization of spatial regression. J Korea Public Adm. 2003; 6: 983-1001.

Kang DM. Health Effects of Environmental Asbestos Exposure. J. Env. Hlth. Sci. 2009; 35(2): 71-7.

Anselin L, Cohen J, Cook D, Gorr W, Tita G. Spatial Analyses of Crime. Measurement And Analysis Of Crime And Justice. 2000; 4: 213-62.

Yoo EH. The study of spatal statistical analysis in GIS environment. [dissertation], [Seoul]; Seoul National University; 1999.

Gesler W. The uses of spatial analysis in medical geography. a review. Soc Sci Med. 1986; 23: 963- 73. crossref(new window)

Hong HP. Discovery of hotspot areas using a spatial clustering method and echelon analysis. J Korean Official Stat. 2003; 8(2): 131-53.

Morrill R, Gaile GL, Thrall GI. Spatial diffusion. Newbury Park, CA, Sage Publications. 1988. p. 86.

Kim JY. A study on Geographical Interpolation Analysis of Korean Asbestos Factories. J Policy Sci. 2011; Vol. 6.

Jeong SY. A spatial statistics application for the study of traffic. J Natl Land Res. 2005; 285: 151-4.

Bailey TC. A review of statistical spatial analysis in geographical information systems, Spatial Analysis and GIS. (Eds) Stewart Fotheringham and Peter Rogerson, Taylor & Francis; 1994. p.18

Anselin L, Syabri E, Kho Y. Da G. An Introduction to Spatial Data Analysis. Geogr Anal. 2006; 38(1): 5-22. crossref(new window)

Anselin L, Getis A. Spatial statistical analysis and geographical information systems. Ann Region Sci. 1992; 26: 19-33. crossref(new window)

Pfeiffer DU, Robinson TP, Stevenson M, Stevens KB, Rogers DJ, Clements ACA. Spatial Analysis in Epidemiology. Oxford University Press. 2008. p. 3.

Robinson, TP, Franceschini G, Wint GRW. FAO's Gridded Livestock of the World. Veterinaria Italiana. 2007. p. 43.

Moore DA. Carpenter TE. Analytical Methods and Geographic Information Systems: Use in Health Research and Epidemiology. Epidemiol Rev. 1999; 21(2): 146-147

Wikipedia, Comparison of geographic information systems software, of _GIS_software [accessed 24 Oct. 2012].

Choi JB, Son IL, Son JH. Health Risk Assessments using GIS Method for the Abandoned Asbestos Mines. J. Miner. Soc. Korea. 2011; 24(1): 43-53. crossref(new window)

Kim YC, Son BH, Kim HY, Hong WH. A study on the Distribution Maps for Asbestos Cement Slate Using GIS, J Korea Inst Ecol Archit Environ. 2011; 11(3): 57-62.

Lee JM, Park JK, Han JY, Choi SY, Kim DM. A Study on the Construction of Asbestos Map Using GIS. Proceedings Korean Soc GeoSpatial Inform Syst. 2010; 131-134.

Han DW, Hwang SS. Spatial Epidemiology and Environmental Health: On the Use of Spatially Referenced Health and Environment Data. J. Env. Hlth. Sci. 2011; 37(1): 1-11. crossref(new window)

Pan XL, Howard W. Wang DW, Beckett LA, Schenker MS. Residential Proximity to Naturally Occurring Asbestos and Mesothelioma Risk in California. Am J Resp Crit Care Med. 2005; 172: 1019-25. crossref(new window)

Maule MM. Modeling Mesothelioma Risk Associated with Environmental Asbestos Exposure. Environ Health Perspect. 2007; 115(7): 1066-71. crossref(new window)

Fazzo L, Santis M, Minelli G, Bruno C, Zona A, Marinaccio A, et al. Pleural Mesothelioma Mortality and Asbestos Exposure Mapping in Italy. Am J Ind Med. 2012; 55: 11-24. crossref(new window)

Petja BM, Twumasi YA, Tengbeh GT, Atanasova M. Spatial epidemiology risk assessment for rehabilitated former asbestos mining areas in Limpopo Province, South Africa, using remote sensing and conventional analytical methods. South Afr J Epidemiol Infect. 2010; 25(3): 32-39. crossref(new window)

Newton R, Deonarine A, Wernisch L. Displaying R spatial statistics on Google dynamic maps with web applications created by Rwui. Int J of Health Geogr. 24 Sep. 2012.