In health and aging studies that include GPS assessments, mobility is often represented by a single indicator such as the number of activity locations, the size of the activity space, or the total distance covered. We argue, however, that mobility is a multi-dimensional construct. In this paper, we first provide a framework to categorize and systematically organize daily-life mobility indicators. We then illustrate this framework by computing several mobility indicators based on GPS data recorded from healthy older adults. We apply a correlational approach to the computed mobility indicators as a first step towards discovering underlying dimensions of daily mobility. We found a trend towards higher correlations between mobility indicators that represent more similar properties of mobility. The framework can be used by health researchers to inform the choice of appropriate mobility indicators in the design of empirical studies.