Spatial correlation analysis of the indicators of anthropocene using urban data

Abstract

As the impact of human development on earth system change has been rapidly increased, a lot of debates going on to define a new geological epoch called "Anthropocene" in academia. The noticeable global system changes detected in urban areas are a great deal of ground-level ozone and fine particulate matters (PM2.5). Those are having negative effects on humans again. In this research, open-source data which can correspond to ‘great acceleration’ indicators were collected to perform correlation analysis between socioeconomic indicators and earth system indicators in the local urban area spatially. A total of 29 variables which are representing urban infrastructure data were collected as explanatory variables, and ground-level ozone ($O_3$) and fine particulate matters (PM2.5) data representing earth system change in an urban environment were collected as dependent variables in the 424 administrative districts of Seoul. The regression model using ground-level ozone ($O_3$) as a dependent variable showed that air pollution emission facilities, the number of parking lots, the number of residential facilities, and the size of reconstruction were highly correlated. The geographically weighted regression model was more effective than the ordinary least squares model to explain the spatial distribution of ground-level ozone. Also, the four significant explanatory variables have regional differences in their influence on the ground-level ozone ($O_3$). The regression model using fine particulate matters (PM2.5) as a dependent variable had not much correlation with the collected explanatory data. The results of this research could be used as a basis and reference for urban planning and development projects or for policies to reduce air pollution in autonomous administrative areas.