Sample Bias due to Missing Data in Mobility Surveys

A growing number of companies use mobility information in their day-to-day business. One requirement thereby is that inference about population-wide mobility patterns can be made. Therefore, it is not only important to find mobility patterns in a given data sample but also to assert their validity for the total population. This aspect of analysis has been largely neglected in mobility data mining research, which limits the applicability of the whole algorithmic field. In this paper we will analyze one aspect of sample bias due to incomplete mobility data. We will provide a systematic approach to detect dependencies between mobility behavior, socio-demography and missing data. Further, we apply the approach to a large GPS mobility survey in Switzerland and show that our concerns are justified and require attention in future research. We hope that our paper will raise the awareness that representativity of mobile behavior cannot be taken for granted in mobility surveys due to missing data and is a research direction of utmost importance.