Methods for evaluating the fit of spatial point process models using residual analysis areexplored to study fatal car accidents in Downtown Los Angeles (DTLA). Residual diag- nostics include spatial residual plots, quantile-quantile (Q-Q), and residual density plots to summarize residual distributions. Comparative analysis focuses on homogeneous and dif- ferent structures of the inhomogeneous Poisson point process models, incorporating covari- ates such as freeway proximity cub distance and environmental conditions Smoke.or.Haze. Goodness-of-fit metrics and K-function analyses assess clustering and dispersion patterns, particularly in high-traffic regions, relevant to the covariates involved. Results highlight improvements in model performance when spatial covariates are in- cluded. Residual analyses reveal that homogeneous models fail to capture local clustering, while models with covariates reduce unexplained variability and align residual distributions more closely with theoretical expectations. K-function results show that combining covariates effectively balances clustering and dispersion patterns, particularly at smaller distances. The study is only an introduction to applying locational and environmental factors to enhance the ability of point process models to explain spatial variability in fatal accidents. These findings provide a foundation for improving urban safety planning and traffic policy design. Residual diagnostics and spatial analysis indicate that future models could benefit from additional covariates, thinning techniques, and spatio-temporal extensions to capture evolving accident patterns and further improve model fits.