Mobile device tracking technologies based on various positioning systems have made location data collection ubiquitous. The frequency at which location samples are recorded varies across applications, yet it is usually pre-defined and fixed, resulting in redundant information, and draining the battery of mobile devices. In this paper, we first answer the question "at what frequency should individual human movements be sampled so that they can be reconstructed with minimum loss of information?". Our analysis unveils a novel linear scaling law of the localization error with respect to the sampling interval. We then present DuctiLoc, a location sampling mechanism that utilises the law above to profile users and adapt the position tracking frequency to their mobility. DuctiLoc is energy efficient, as it does not rely on powerhungry sensors or expensive computations; moreover, it provides a handy knob to control energy usage, by configuring the target positioning accuracy. Controlling the trade-off between accuracy and sampling rate of human movement is useful in a number of contexts, including mobile computing and cellular networks. Real-world experiments with an Android implementation show that DuctiLoc can effectively adjust the sampling frequency to individual mobility habits and target accuracy level, reducing the energy consumption by 60% to 98% with respect to a baseline periodic sampling.