My student Tianxing has been working hard to decipher cellphone data for some time now. Earlier this year, we have completed a paper, in collaboration with Amanda’s group,  showing unexpected representative biases in cellphone data that appears to have direct link with privacy regulations.  In this study, we uncovered work types of cellphone users using a clustering algorithm, and validated the results against  surveys data and regression analysis.  You may download a preprint here. The abstract follows.

In a short period, the COVID-19 pandemic has transformed telework into a common practice for a significant portion of the workforce. This shift has profound implications for land use, urban development, and transportation. Traditional survey-based methods for tracking these changes are struggling to keep pace with the rapidity of this transformation. Here, we propose a method to identify different types of workers from mobile phone data, which allows us to closely examine the correlation between work arrangements, mobility patterns and key socio-demographic attributes. By applying a hierarchical clustering algorithm to a set of features extracted from a mobile phone data set, six different work types are identified and their validity is confirmed using different approaches. We find teleworkers tend to travel slower than regular workers but faster than non-workers. They also travel a shorter distance to reach their primary activity location than regular workers, but a longer distance to reach other activity locations than both regular and non-workers. Our regression analysis further shows that, largely in agreement with findings in literature, racial minority and low income groups are less likely to telework. Implications for the use of trace data to model the evolving relationship between mobility and worker-classification are discussed.