2017 Impact factor 2.982

News / Highlights / Colloquium

EPJ Data Science Highlight - Listening to the changes in the urban rhythm

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Cities evolve and undergo constant re-organisation as their population grow. This evolving process makes cities resilient and adaptive but also poses a challenge to analyse urban phenomena. For a long time, there has been evidence that suggests temporal and spatial regularities in crime, but so far studies about this have been based on the assumption that cities are static. A new study published in EPJ Data Science takes these factors into consideration and analyses spatio-temporal variation in criminal occurrences.

(Guest post by Marcos Oliveira & Ronaldo Menezes, originally published on the SpringerOpen blog)

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EPJ Data Science Highlight - A model to predict Airbnb distribution in cities

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The distribution of Airbnb listings has been the topic of much discussion among citizens and policy-makers, particularly in major cities. In an article published in EPJ Data Science, Giovanni Quattrone and colleagues looked into the many factors determining the spacial penetration of Airbnb in urban centers and developed a model that aims to predict this distribution in other cities. Among others, the presence of creative communities emerges as an important factor in the adoption of the housing plaftform.

(Guest post by Giovanni Quatronne, originally published on the SpringerOpen blog)

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EPJ Data Science Highlight - Controlling epidemics using mobile phone data

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Mobile data can be (and has been) used to study a vast number of subjects related to human behavior. One of its potential applications is on epidemics, a complex field that is informed not only by healthcare, but also social interactions and human mobility. In this blog post, Stefania Rubrichi explains the context in which her team used a real mobile phone dataset in an attempt to better understand and tackle the spread of diseases. Their study was just published in the journal EPJ Data Science.

(Guest post by Stefania Rubrichi, originally published on the SpringerOpen blog)

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EPJ Data Science Highlight - Academic performance and behavioral patterns

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In an article just published in EPJ Data Science, Valentin Kassarnig, Sune Lehmann and Andreas Bjerre-Nielsen look into smartphone data of undergraduate students to assess factors influencing social behavior and educational performance.

(Guest post by Valentin Kassarnig, Sune Lehmann & Andreas Bjerre-Nielsen, originally published on the SpringerOpen blog)

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EPJ Data Science Highlight - Discovering temporal regularities in retail customers’ shopping behaviour

(image via Pixabay, CC0 Creative Commons)

Why do we buy certain items when we buy them? A new study published in EPJ Data Science analyzes personal retail data to extract a temporal purchasing profile, which is able to summarize whether and when a customer makes a purchase. Its results show that certain patterns and types of shoppers are detectable, which can be used both by customers to enable personalized services, and by the retail market chain for providing offers and discounts tailored to the individual shoppers personal temporal profile.

(Guest post by Riccardo Guidotti and Anna Monreale, originally published on the SpringerOpen blog)

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EPJ Data Science Highlight – The science behind what makes a bestselling book

Photo by Anastasia Zhenina on Unsplash

Books that are fiction, thrillers or mysteries, have high initial sales numbers and are released around Christmas are more likely to be bestsellers, according to a study published in EPJ Data Science

(This post was originally published on the SpringerOpen blog)

A team of researchers from Northeastern University, Boston, used a big data approach to investigate what makes a book successful. By evaluating data from the New York Times Bestseller Lists from 2008 to 2016, they developed a formula to predict if a book would be a bestseller.

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EPJ Data Science Highlight - Urban form and socio-economics: what is the link?

Although urbanization has many advantages, one of its biggest drawbacks is the rise in socio-economic inequality. There have been some attempts at a qualitative analysis of the relationship between certain city features and social inequality, but these kinds of analyses are hard to replicate. A new research article published in EPJ Data Science proposes a new quantitative computer-based method for how to better understand the link between cites and social inequalities.

(Guest post by Alessandro Venerandi, originally published on the SpringerOpen blog)

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EPJ Data Science Highlight - The nexus of attention shift in the wake of a disaster

Nowadays, platforms like Twitter play a big role in the aftermath of disasters, such as natural disasters, mass shootings, or terror attacks, as people try to receive the latest information on what happened through social media channels. A new study published in EPJ Data Science shows how an analysis of social media responses to disasters might help us better understand the dynamic of the public’s attention during these events, what such an analysis shows about people’s attention spans and focus points in the aftermath of disasters, and how analyses like these could be performed in a cost-effective way.

(Guest post by Yu-Ru Lin, originally published on SpringerOpen blog)

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EPJ Data Science Highlight - Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs

Due to the emergence and continuously increasing usage of social media services all over the world, it is now possible to estimate in real-time how entire groups of people are feeling at a given point. However, in order to be able interpret the available data correctly, the right tools and methods need to be used. A new article EPJ Data Science examines a range of such methods and shows their ability but also their limitations.

(Guest post by Andrew Reagan, originally published on SpringerOpen blog

As a grad student trying to understand the emotional content of some unreadably large collection of texts, a typical night in grad school can often go something like this: You’re up late at night planning a new research study, thinking about trying some of this fancy sentiment-based text analysis. You resort to your favorite search engine with the query “sentiment analysis package python.” We have all been there, except maybe with R instead of Python (the latter being my favorite).

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EPJ Data Science Highlight - Gaining historical and international relations insights from social media

As more and more people get their news from social media platforms, these become hosts to vast amounts of information on human behavior in relation to real-time events around the world. In a study published in EPJ Data Science, Vanessa Peña-Araya and team successfully match geopolitical interactions obtained from Twitter activity with real-world historical international relations.

(Guest post by Vanessa Peña-Araya, Mauricio Quezada, Denis Parra and Barbara Poblete, originally published on the SpringerOpen blog

Online social media platforms, like Twitter, Sina Weibo, or Facebook, have become very popular in recent years. They are primarily used to share personal experiences and to keep in touch with friends. Nevertheless, many users turn to these platforms as reliable sources to find real-time information about world events, such as the Ukrainian Crisis or recent natural disasters. In particular, Twitter has become one of the prefered sources on the Web for breaking news updates

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Editors-in-Chief
M. Strohmaier