  An integrated approach of spatial data analysis and Geographically Weighted
Poisson Regression (GWPR) along with global regression techniques are used in
this study. This approach aims to model relationships between dependent
variable Covid-19 and independent variables from socioeconomic and pre-existing
health conditions within the local government area (LGA) in New South Wales
(NSW)-Australia. Based on geospatial data analysis and a step-by-step procedure
in building both global and GWPR models, four (4) independent variables are
finally selected to investigate relationships between dependent and independent
variables at the local scale. The GWPR model's results with the Goodness-of-Fit
(R2) range between 45-73% exhibit positive relationships between Covid-19 and
the total population, the cancers, and the people with ages between 60 and 85
in most of the NSW state. Meanwhile, a negative relationship is observed
between Covid-19 and the ischaemic heart disease; however, the estimated
coefficients for this relationship are very low and close to zero; hence
further investigation, including assessment from a different perspective, is
necessary for validation. In conclusion, the model suggests that the
relationships between the dependent variable and independent variables are
nonstationary. Therefore, GWPR model calibration plays a vital role in
geographic modelling at the local scale.

Physics and Society