Animal tracking technologies have advanced rapidly over the past two decades, leading growing stockpiles of high quality, high resolution movement data. Analytical techniques for properly answering ecological questions based on this rich data resource have, however, lagged behind. I describe ongoing work in my lab aimed at establishing a modern and rigorous statistical foundation for analyzing animal tracking data. Based on continuous-time stochastic process models, these methods fully embrace the multiscale autocorrelation structure typical of modern tracking data, come equipped with reliable confidence intervals, are computationally efficient, and are fully implemented in the ctmm R package. In particular, I will focus on animal space use estimation including home range analysis, and will also lay out a roadmap for future analytical developments.