In the response phase of sudden-onset natural disasters, information is of crucial importance for relief organisations and their aid workers. Information is necessary for gaining situational awareness in a disaster for effective decision-making in response operations. In my research, an information system is designed that maps the reachability of affected communities in a disaster-struck area, in terms of their connectedness to relief efforts by aid workers. This reachability model can be deployed rapidly and offers aid workers a situational overview in the first moments of the response, giving them relevant information to base their day-to-day decision on. The reachability model is built in Python, using graph theory and OpenStreetMap. The Papua New Guinea earthquake of February 2018 and hurricane Irma on St. Maarten are used as case-studies to evaluate the feasibility of the reachability model. The conclusion of the research is that the reachability model is both technically and practically feasible and that it improves situational awareness for aid workers and therewith improve the effectiveness of relief operations during the response phase of sudden-onset natural disasters.