Reliable and accurate localization of mobile nodes is a key enabler for numerous emerging applications in the commercial, public safety, and military sectors. Achieving such location-awareness by conventional techniques is challenging in harsh environments with limited infrastructures. This work proposes a new paradigm called network localization and navigation (NLN), in which mobile nodes exploit spatiotemporal network cooperation for positional inference. To fully understand the cooperation benefits and associated costs, we establish a theoretical foundation for NLN and determine the fundamental limits of localization accuracy using equivalent Fisher information analysis. We then introduce the notion of carry-over information and provide a geometric interpretation for the evolution of localization information in both spatial and temporal domain. Our framework unifies the localization information obtained from spatiotemporal network cooperation, leading to a deep understanding of information evolution and cooperation benefits in cooperative navigation networks.