EPJ Data Science Highlight - What can we learn from billions of food purchases derived from fidelity cards?
- Published on 23 May 2019
For your health, what you eat is more important than what you earn.
This result comes from our latest project “Poor but Healthy”, which was published in EPJ Data Science, and comes with a @tobi_vierzwo’s stunningly “beautiful map of London” that author Daniele Quercia invites everyone to explore.
By combining 1.6B food item purchases with 1.1B medical prescriptions for the entire city of London for one year, researchers discovered that, to predict health outcomes, socio-economic conditions matter less than what previous research has shown: despite being of lower-income, certain areas are healthy, and that is because of what their residents eat.
Read the full blog post on Medium.
- Published on 22 May 2019
The mass media is one of the social forces with the most active transformative power. However, news reach people unequally. Many factors shape the distribution and influence of news media coverage. Some of these factors are the geographic reach of newspapers (national versus regional newspapers), the direct targeting of specific sectors of the population, and/or the political ideology of the media outlet itself.
In a recent article in EPJ Data Science, Erick Elejalde, from the L3S Research Centre in Hannover, Germany, explains how their work helps to identify whether or not an outlet’s coverage deviates from the purely geographic influence to a more sophisticated behavior involving the weight of political and socioeconomic interests.
Read the post on the SpringerOpen blog.
- Published on 09 April 2019
Online booking platforms such as Airbnb or Uber present themselves as and strive to be inclusive, but there is an increasing amount of both anecdotal and scientific evidence of discriminatory behavior among their users. In a study published in EPJ Data Science, researchers at University College London set out to evaluate interaction patterns within Airbnb, in an effort to understand the extent to which offline human biases influence affects their users.
Read the guest post by Giacomo Livan, Licia Capra, Weihua Li and Victoria Koh on the SpringerOpen blog