Abundance and population density are fundamental pieces of information for population ecology and species conservation, but they are difficult to estimate for rare and elusive species. Mark-resight models are popular for estimating population abundance because they are less invasive and expensive than traditional mark-recapture. However, density estimation using mark-resight is difficult because the area sampled must be explicitly defined, historically using ad-hoc approaches. We develop a spatial mark-resight model for estimating population density that combines spatial resighting data and telemetry data. Incorporating telemetry data allows us to inform model parameters related to movement and individual location. Our model also allows <100% individual identification of marked individuals. We implemented the model in a Bayesian framework, using a custom-made Metropolis-within-Gibbs Markov chain Monte Carlo algorithm. As an example, we applied this model to a mark-resight study of raccoons on South Core Banks, a barrier island in Cape Lookout National Seashore, North Carolina. We estimated a population of 186.71 ± 14.81 individuals, which translated to a density of 8.29 ± 0.66 individuals per km2. The model presented here will have widespread utility in future applications, especially for species that are not naturally marked.