Do you work with human mobility data?
Then this publication could be important for you to read, and to reflect on!

A tale of ten cities: data bias in human mobility is pervasive and highly location specific by K. den Nijs, E. Omodei, & V. Sekara published in EPJ Data Science.

In this joint work w. Katinka den Nijs and Elisa Omodei we study ‘data production’ biases in human mobility data. Data production bias is a form of bias which deals not only with whether individuals are represented in datasets, but also how they are represented. Especially in terms of how much data they produce. Put differently, we don’t only care about if people are represented in data, we also care about how they are represented.

We study a GPS mobility dataset, collected from anonymized smartphones for ten major US cities, and find that data points are as, or more, unequally distributed than wealth. Imagine!
Their data production is more unequally distributed than wealth in society!

To understand the interplay between demographic factors and data production bias we build machine learning models to predict the number of data points we can expect to be produced by the composition of demographic groups living in census tracts.  We build one model per city and, generally, find strong effects of wealth, ethnicity, and education on data production.

While we find that bias is a ubiquitous phenomenon, occurring in all ten cities, we also find that each city suffers from its own manifestation of it, and that location-specific models are required to model bias for each city. Our work raises serious questions about general approaches to debiasing human mobility data and highlights the urgent need for further research on the topic.

In summary, be careful with biases in human mobility data (or any other digitally collected datasets). Data production biases can have significant downstream effects, when data is used to model everything from epidemics, behavior, etc.

TLDR; Representativeness does not mean we should only strive for individuals to merely be ‘present’ in data. As our results show, how people are represented, or misrepresented, via the quantity and quality of data the produce is of equal importance. Further, ‘debiasing’ data is no easy task, as it heavily depends on geographical context.