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Abstract COVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance ( $$R^{2}=0.97$$ R 2 = 0.97 ) with smaller Akaike Information Criterion (AICc $$=-66.42$$ = - 66.42 ) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran’s $$I=-0.0395$$ I = - 0.0395 and $$p < 0.01$$ p < 0.01 ) in the residuals. It is found that more than 86% of local $$R^{2}$$ R 2 values are larger than 0.60 and almost 68% of $$R^{2}$$ R 2 values are within the range 0.80–0.97. Moreover, some interesting local variations in the relationships are also found.