Tweets on the road

The pervasiveness of mobile devices, which is increasing daily, is generating a vast amount of geo-located data allowing us to gain further insights into human behaviors. In particular, this new technology enables users to communicate through mobile social media applications, such as Twitter, anytime and anywhere. Thus, geo-located tweets offer the possibility to carry out in-depth studies on human mobility. In this paper, we study the use of Twitter in transportation by identifying tweets posted from roads and rails in Europe between September 2012 and November 2013. We compute the percentage of highway and railway segments covered by tweets in 39 countries. The coverages are very different from country to country and their variability can be partially explained by differences in Twitter penetration rates. Still, some of these differences might be related to cultural factors regarding mobility habits and interacting socially online. Analyzing particular road sectors, our results show a positive correlation between the number of tweets on the road and the Average Annual Daily Traffic on highways in France and in the UK. Transport modality can be studied with these data as well, for which we discover very heterogeneous usage patterns across the continent.Comment: 15 pages, 17 figure

Touristic site attractiveness seen through Twitter

Tourism is becoming a significant contributor to medium and long range travels in an increasingly globalized world. Leisure traveling has an important impact on the local and global economy as well as on the environment. The study of touristic trips is thus raising a considerable interest. In this work, we apply a method to assess the attractiveness of 20 of the most popular touristic sites worldwide using geolocated tweets as a proxy for human mobility. We first rank the touristic sites based on the spatial distribution of the visitors’ place of residence. The Taj Mahal, the Pisa Tower and the Eiffel Tower appear consistently in the top 5 in these rankings. We then pass to a coarser scale and classify the travelers by country of residence. Touristic site’s visiting figures are then studied by country of residence showing that the Eiffel Tower, Times Square and the London Tower welcome the majority of the visitors of each country. Finally, we build a network linking sites whenever a user has been detected in more than one site. This allow us to unveil relations between touristic sites and find which ones are more tightly interconnected.Partial financial support has been received from the Spanish Ministry of Economy (MINECO) and FEDER (EU) under project INTENSE@COSYP (FIS2012-30634), and from the EU Commission through project INSIGHT. The work of ML has been funded under the PD/004/2013 project, from the Conselleria de Educación, Cultura y Universidades of the Government of the Balearic Islands and from the European Social Fund through the Balearic Islands ESF operational program for 2013-2017. JJR acknowledges funding from the Ramón y Cajal program of MINECO. BG was partially supported by the French ANR project HarMS-flu (ANR-12-MONU-0018).Peer Reviewe

Migrant mobility flows characterized with digital data

Monitoring migration flows is crucial to respond to humanitarian crisis and to design efficient policies. This information usually comes from surveys and border controls, but timely accessibility and methodological concerns reduce its usefulness. Here, we propose a method to detect migration flows worldwide using geolocated Twitter data. We focus on the migration crisis in Venezuela and show that the calculated flows are consistent with official statistics at country level. Our method is versatile and far-reaching, as it can be used to study different features of migration as preferred routes, settlement areas, mobility through several countries, spatial integration in cities, etc. It provides finer geographical and temporal resolutions, allowing the exploration of issues not contemplated in official records. It is our hope that these new sources of information can complement official ones, helping authorities and humanitarian organizations to better assess when and where to intervene on the ground

Sampling uncertainty associated with western Mediterranean pelagic fish abundance estimates derived from acoustic data.

Acoustic surveys are used worldwide for the assessment of pelagic fish stocks. In the Spanish Mediterranean area acoustic surveys are performed annually in late autumn and cover the entire continental shelf between 30 and 200 m depth. This survey was initiated to obtain estimates of anchovy recruitment (Engraulis encrasicolus), but additionally provide a general overview of the whole pelagic fish community in the study area during the time of the year it is performed. Our study area has a diverse assemblage of small and medium-sized pelagic fish (up to nine species) and thus we rely on the proportion of species obtained in middle-water pelagic fishing trawls to attribute the amount of echo corresponding to every single species and estimate their abundance. Although uncertainties may arise from many different sources (e.g. transducer motion, target strength, migration) we focus our study on the estimation of overall sampling uncertainty, one of the main contributors to random error. We apply geostatistical techniques to deal with spatial correlation and discuss benefits and pitfalls in their application. Although transitive geostatistics have seldom been used, most probably due to their inability to produce spatial maps of abundance and variance estimations, they constitute a powerful tool to routinely estimate sampling variance and its variation in time in a multi-specific context. They may also help to assess the effects of varying sampling intensity and could potentially be useful to detect possible processing errors, for example, in echogram scrutinizing. These techniques may therefore potentially help improve both the precision and the accuracy of acoustic dat

Ets homologous factor (EHF) has critical roles in epithelial dysfunction in airway disease

The airway epithelium forms a barrier between the internal and external environments. Epithelial dysfunction is critical in the pathology of many respiratory diseases, including cystic fibrosis. Ets homologous factor (EHF) is a key member of the transcription factor network that regulates gene expression in the airway epithelium in response to endogenous and exogenous stimuli. EHF , which has altered expression in inflammatory states, maps to the 5' end of an intergenic region on Chr11p13 that is implicated as a modifier of cystic fibrosis airway disease. Here we determine the functions of EHF in primary human bronchial epithelial (HBE) cells and relevant airway cell lines. Using EHF ChIP followed by deep sequencing (ChIP-seq) and RNA sequencing after EHF depletion, we show that EHF targets in HBE cells are enriched for genes involved in inflammation and wound repair. Furthermore, changes in gene expression impact cell phenotype because EHF depletion alters epithelial secretion of a neutrophil chemokine and slows wound closure in HBE cells. EHF activates expression of the SAM pointed domain-containing ETS transcription factor, which contributes to goblet cell hyperplasia. Our data reveal a critical role for EHF in regulating epithelial function in lung disease

Cross-Checking Different Sources of Mobility Information

The pervasive use of new mobile devices has allowed a better characterization in space and time of human concentrations and mobility in general. Besides its theoretical interest, describing mobility is of great importance for a number of practical applications ranging from the forecast of disease spreading to the design of new spaces in urban environments. While classical data sources, such as surveys or census, have a limited level of geographical resolution (e.g., districts, municipalities, counties are typically used) or are restricted to generic workdays or weekends, the data coming from mobile devices can be precisely located both in time and space. Most previous works have used a single data source to study human mobility patterns. Here we perform instead a cross-check analysis by comparing results obtained with data collected from three different sources: Twitter, census, and cell phones. The analysis is focused on the urban areas of Barcelona and Madrid, for which data of the three types is available. We assess the correlation between the datasets on different aspects: the spatial distribution of people concentration, the temporal evolution of people density, and the mobility patterns of individuals. Our results show that the three data sources are providing comparable information. Even though the representativeness of Twitter geolocated data is lower than that of mobile phone and census data, the correlations between the population density profiles and mobility patterns detected by the three datasets are close to one in a grid with cells of 2×2 and 1×1 square kilometers. This level of correlation supports the feasibility of interchanging the three data sources at the spatio-temporal scales considered.Partial financial support has been received from the Spanish Ministry of Economy (MINECO) and FEDER (EU) under projects MODASS (FIS2011-24785) and INTENSE@COSYP (FIS2012-30634), and from the EU Commission through projects EUNOIA, LASAGNE and INSIGHT. ML acknowledges funding from the Conselleria d'Educació, Cultura i Universitats of the Government of the Balearic Islands, and JJR from the Ramón y Cajal program of MINECO.Peer Reviewe