Visual exploration of CO2 emissions in chinese cities

As the primary source of carbon dioxide (CO2) emissions, cities are the key to solving climate change. Prior works focus on CO2 emission drivers and CO2 emission city clusters. However, the methods of mining CO2 emission drivers include narrow factors affecting CO2 emissions, and ignore the interactions among these factors. The intelligibility of the results of the Geographical Weighted Regression (GWR) that is used to analyze the spatial heterogeneity of drivers is poor. Moreover, the relations between CO2 emission city clusters and economic ties among cities are ignored. The lack of visual analysis tools is also a problem that needs to be fixed. Hence we develop a novel analysis framework. Thereinto, a novel method based on the stepwise regression (SR) is used to mine drivers; the biclustering algorithm is introduced into the traditional GWR to improve the intelligibility of the GWR; the gravity-entropy model is adopted to analyze the relations between CO2 emission city clusters and economic correlation strength among cities. Furthermore, a visual analytics system is implemented to explore the situation of CO2 emissions. We demonstrate the effectiveness of our approach through case studies conducted with socioeconomic data from 169 Chinese cities and positive feedback from experts and volunteers.