Faster indicators of chikungunya incidence using Google searches.

Chikungunya, a mosquito-borne disease, is a growing threat in Brazil, where over 640,000 cases have been reported since 2017. However, there are often long delays between diagnoses of chikungunya cases and their entry in the national monitoring system, leaving policymakers without the up-to-date case count statistics they need. In contrast, weekly data on Google searches for chikungunya is available with no delay.

Using aircraft location data to estimate current economic activity.

Aviation is a key sector of the economy, contributing at least 3% to gross domestic product (GDP) in the UK and the US. Currently, airline performance statistics are published with a three month delay. However, aircraft now broadcast their location in real-time using the Automated Dependent Surveillance Broadcast system (ADS-B).

Using big data to map the relationship between time perspectives and economic outputs.

Recent studies have shown that population-level time perspectives can be approximated using "big data" on search engine queries, and that these indices, in turn, predict the per-capita Gross Domestic Product of countries. Although these findings seem to support Baumard's suggestion that affluence makes people more future-oriented, they also reveal a more complex relationship between time perspectives and economic outputs.

Happiness is Greater in More Scenic Locations.

Does spending time in beautiful settings boost people's happiness? The answer to this question has long remained elusive due to a paucity of large-scale data on environmental aesthetics and individual happiness. Here, we draw on two novel datasets: first, individual happiness data from the smartphone app, Mappiness, and second, crowdsourced ratings of the "scenicness" of photographs taken across England from the online game Scenic-Or-Not. We find that individuals are happier in more scenic locations, even when we account for a range of factors such as the activity the individual was engaged in at the time, weather conditions and the income of local inhabitants.

Using deep learning to quantify the beauty of outdoor places.

Beautiful outdoor locations are protected by governments and have recently been shown to be associated with better health. But what makes an outdoor space beautiful? Does a beautiful outdoor location differ from an outdoor location that is simply natural? Here, we explore whether ratings of over 200 000 images of Great Britain from the online game , combined with hundreds of image features extracted using the Places Convolutional Neural Network, might help us understand what beautiful outdoor spaces are composed of. We discover that, as well as natural features such as 'Coast', 'Mountain' and 'Canal Natural', man-made structures such as 'Tower', 'Castle' and 'Viaduct' lead to places being considered more scenic.

Searching Choices: Quantifying Decision-Making Processes Using Search Engine Data.

When making a decision, humans consider two types of information: information they have acquired through their prior experience of the world, and further information they gather to support the decision in question. Here, we present evidence that data from search engines such as Google can help us model both sources of information. We show that statistics from search engines on the frequency of content on the Internet can help us estimate the statistical structure of prior experience; and, specifically, we outline how such statistics can inform psychological theories concerning the valuation of human lives, or choices involving delayed outcomes.

Quantifying the link between art and property prices in urban neighbourhoods.

Is there an association between art and changes in the economic conditions of urban neighbourhoods? While the popular media and policymakers commonly believe this to be the case, quantitative evidence remains lacking. Here, we use metadata of geotagged photographs uploaded to the popular image-sharing platform Flickr to quantify the presence of art in London neighbourhoods. We estimate the presence of art in neighbourhoods by determining the proportion of Flickr photographs which have the word 'art' attached.

Tracking Protests Using Geotagged Flickr Photographs.

Recent years have witnessed waves of protests sweeping across countries and continents, in some cases resulting in political and governmental change. Much media attention has been focused on the increasing usage of social media to coordinate and provide instantly available reports on these protests. Here, we investigate whether it is possible to identify protest outbreaks through quantitative analysis of activity on the photo sharing site Flickr.

Quantifying the Search Behaviour of Different Demographics Using Google Correlate.

Vast records of our everyday interests and concerns are being generated by our frequent interactions with the Internet. Here, we investigate how the searches of Google users vary across U.S.

Quantifying the Impact of Scenic Environments on Health.

Few people would deny an intuitive sense of increased wellbeing when spending time in beautiful locations. Here, we ask: can we quantify the relationship between environmental aesthetics and human health? We draw on data from Scenic-Or-Not, a website that crowdsources ratings of "scenicness" for geotagged photographs across Great Britain, in combination with data on citizen-reported health from the Census for England and Wales. We find that inhabitants of more scenic environments report better health, across urban, suburban and rural areas, even when taking core socioeconomic indicators of deprivation into account, such as income, employment and access to services.

The advantage of short paper titles.

Vast numbers of scientific articles are published each year, some of which attract considerable attention, and some of which go almost unnoticed. Here, we investigate whether any of this variance can be explained by a simple metric of one aspect of the paper's presentation: the length of its title. Our analysis provides evidence that journals which publish papers with shorter titles receive more citations per paper.

Modelling human mobility patterns using photographic data shared online.

Humans are inherently mobile creatures. The way we move around our environment has consequences for a wide range of problems, including the design of efficient transportation systems and the planning of urban areas. Here, we gather data about the position in space and time of about 16 000 individuals who uploaded geo-tagged images from locations within the UK to the Flickr photo-sharing website.

Quantifying Stock Return Distributions in Financial Markets.

Being able to quantify the probability of large price changes in stock markets is of crucial importance in understanding financial crises that affect the lives of people worldwide. Large changes in stock market prices can arise abruptly, within a matter of minutes, or develop across much longer time scales. Here, we analyze a dataset comprising the stocks forming the Dow Jones Industrial Average at a second by second resolution in the period from January 2008 to July 2010 in order to quantify the distribution of changes in market prices at a range of time scales.

Quantifying International Travel Flows Using Flickr.

Online social media platforms are opening up new opportunities to analyse human behaviour on an unprecedented scale. In some cases, the fast, cheap measurements of human behaviour gained from these platforms may offer an alternative to gathering such measurements using traditional, time consuming and expensive surveys. Here, we use geotagged photographs uploaded to the photo-sharing website Flickr to quantify international travel flows, by extracting the location of users and inferring trajectories to track their movement across time.

Quantifying crowd size with mobile phone and Twitter data.

Being able to infer the number of people in a specific area is of extreme importance for the avoidance of crowd disasters and to facilitate emergency evacuations. Here, using a football stadium and an airport as case studies, we present evidence of a strong relationship between the number of people in restricted areas and activity recorded by mobile phone providers and the online service Twitter. Our findings suggest that data generated through our interactions with mobile phone networks and the Internet may allow us to gain valuable measurements of the current state of society.

Adaptive nowcasting of influenza outbreaks using Google searches.

Seasonal influenza outbreaks and pandemics of new strains of the influenza virus affect humans around the globe. However, traditional systems for measuring the spread of flu infections deliver results with one or two weeks delay. Recent research suggests that data on queries made to the search engine Google can be used to address this problem, providing real-time estimates of levels of influenza-like illness in a population.

Quantifying regional differences in the length of Twitter messages.

The increasing usage of social media for conversations, together with the availability of its data to researchers, provides an opportunity to study human conversations on a large scale. Twitter, which allows its users to post messages of up to a limit of 140 characters, is one such social media. Previous studies of utterances in books, movies and Twitter have shown that most of these utterances, when transcribed, are much shorter than 140 characters.

Characterizing the time-perspective of nations with search engine query data.

Vast quantities of data on human behavior are being created by our everyday internet usage. Building upon a recent study by Preis, Moat, Stanley, and Bishop (2012), we used search engine query data to construct measures of the time-perspective of nations, and tested these measures against per-capita gross domestic product (GDP). The results indicate that nations with higher per-capita GDP are more focused on the future and less on the past, and that when these nations do focus on the past, it is more likely to be the distant past.

Using big data to predict collective behavior in the real world.

Recent studies provide convincing evidence that data on online information gathering, alongside massive real-world datasets, can give new insights into real-world collective decision making and can even anticipate future actions. We argue that Bentley et al.'s timely account should consider the full breadth, and, above all, the predictive power of big data.

Quantifying the relationship between financial news and the stock market.

The complex behavior of financial markets emerges from decisions made by many traders. Here, we exploit a large corpus of daily print issues of the Financial Times from 2(nd) January 2007 until 31(st) December 2012 to quantify the relationship between decisions taken in financial markets and developments in financial news. We find a positive correlation between the daily number of mentions of a company in the Financial Times and the daily transaction volume of a company's stock both on the day before the news is released, and on the same day as the news is released.

Quantifying the digital traces of Hurricane Sandy on Flickr.

Society's increasing interactions with technology are creating extensive "digital traces" of our collective human behavior. These new data sources are fuelling the rapid development of the new field of computational social science. To investigate user attention to the Hurricane Sandy disaster in 2012, we analyze data from Flickr, a popular website for sharing personal photographs.

Quantifying trading behavior in financial markets using Google Trends.

Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements.