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[HTT19-02] Map representation of estimated concentration distribution of PM2.5 using WebGIS
Keywords:WebGIS, jSTAT MAP, Kriging, PM2.5, Google Colab
Interval estimation is important information in the examination of PM2.5 concentration distribution estimated by geostatistical methods. Especially when comparing the estimated concentration with other geographical information and statistical information such as national census, it is indispensable to examine the probability of the impact of the estimated concentration. In the study, a method of expressing the estimated concentration distribution of PM2.5 and its confidence interval in Okayama city obtained by the Kriging method, which is one of the methods of geostatistics, using the WebGIS application was examined.
The target area in the study was the entire area of Okayama City, Okayama Prefecture, and the PM2.5 estimation was base on the observation data were for 2017 FY (monthly values) published by the National Institute for Environmental Studies (http: //www.nies.go.jp/igreen/). JPGIS (Geographical Survey Institute, 2020), which is government-affiliated open data, was used as the geographic information data required for the analysis. GNU R (R core team, 2020) and Google Colab (Google, 2020) were used for geostatistical analysis. The jSTAT MAP (Statistics Bureau, Ministry of Internal Affairs and Communications, 2020) was used as the WebGIS in the study.
The spatial dependence of PM2.5 observation data was estimated using a variogram, and the PM2.5 estimated concentration distribution and its variance throughout Okayama City were obtained by the Kriging method. The mesh grid used for estimation used the Japanese 3rd Mesh code. Then, the estimated concentration distribution and its confidence interval were mapped using the jSTAT MAP. The exposure risk examined together with the census data was also expressed in the jSTAT MAP.
Since jSTAT MAP has built-in various geographic information and statistical information, it was possible to perform easily the qualitative comparison with the estimated concentration of PM2.5, its confidence interval, and exposure risk with it. Also, since jSTAT MAP is a WebApp, no proprietary GIS application is required to mapping the PM2.5 exposure risk assessment data. It is expected that it will be used more as one of the information disclosure means expressed on a map.
The target area in the study was the entire area of Okayama City, Okayama Prefecture, and the PM2.5 estimation was base on the observation data were for 2017 FY (monthly values) published by the National Institute for Environmental Studies (http: //www.nies.go.jp/igreen/). JPGIS (Geographical Survey Institute, 2020), which is government-affiliated open data, was used as the geographic information data required for the analysis. GNU R (R core team, 2020) and Google Colab (Google, 2020) were used for geostatistical analysis. The jSTAT MAP (Statistics Bureau, Ministry of Internal Affairs and Communications, 2020) was used as the WebGIS in the study.
The spatial dependence of PM2.5 observation data was estimated using a variogram, and the PM2.5 estimated concentration distribution and its variance throughout Okayama City were obtained by the Kriging method. The mesh grid used for estimation used the Japanese 3rd Mesh code. Then, the estimated concentration distribution and its confidence interval were mapped using the jSTAT MAP. The exposure risk examined together with the census data was also expressed in the jSTAT MAP.
Since jSTAT MAP has built-in various geographic information and statistical information, it was possible to perform easily the qualitative comparison with the estimated concentration of PM2.5, its confidence interval, and exposure risk with it. Also, since jSTAT MAP is a WebApp, no proprietary GIS application is required to mapping the PM2.5 exposure risk assessment data. It is expected that it will be used more as one of the information disclosure means expressed on a map.