  Spatial areal models encounter the well-known and challenging problem of
spatial confounding. This issue makes it arduous to distinguish between the
impacts of observed covariates and spatial random effects. Despite previous
research and various proposed methods to tackle this problem, finding a
definitive solution remains elusive. In this paper, we propose a simplified
version of the spatial+ approach that involves dividing the covariate into two
components. One component captures large-scale spatial dependence, while the
other accounts for short-scale dependence. This approach eliminates the need to
separately fit spatial models for the covariates. We apply this method to
analyse two forms of crimes against women, namely rapes and dowry deaths, in
Uttar Pradesh, India, exploring their relationship with socio-demographic
covariates. To evaluate the performance of the new approach, we conduct
extensive simulation studies under different spatial confounding scenarios. The
results demonstrate that the proposed method provides reliable estimates of
fixed effects and posterior correlations between different responses.
