Information, ideas, and diseases, or more generally, contagions, spread over time and space through individual transmissions via social networks, as well as through external sources. A detailed picture of any diffusion process can be achieved only when both a detailed network structure and individual diffusion pathways are obtained. Studying such diffusion networks provides valuable insights to understand important actors in carrying and spreading contagions and to help predict occurrences of new infections. Most prior research focuses on modeling diffusion process only in the temporal dimension. The advent of rich social, media and geo-tagged data now allows us to study and model this diffusion process in both temporal and spatial dimensions than previously possible. Nevertheless, how information, ideas or diseases are propagated through the network as an overall spatiotemporal process is difficult to trace. This propagation is continuous over time and space, where individual transmissions occur at different rates via complex and latent connections. To tackle this challenge, a probabilistic spatiotemporal algorithm for network diffusion simulation (STAND) is developed based on the survival model in this research. Both time and geographic distance are used as explanatory variables to simulate the diffusion process over two different network structures. The aim is to provide a more detailed measure of how different contagions are transmitted through various networks where nodes denote geographic locations at a large scale.