Sublinear scaling of country attractiveness observed from Flickr dataset

The number of people who decide to share their photographs publicly increases every day, consequently making available new almost real-time insights of human behavior while traveling. Rather than having this statistic once a month or yearly, urban planners and touristic workers now can make decisions almost simultaneously with the emergence of new events. Moreover, these datasets can be used not only to compare how popular different touristic places are, but also predict how popular they should be taking into an account their characteristics. In this paper we investigate how country attractiveness scales with its population and size using number of foreign users taking photographs, which is observed from Flickr dataset, as a proxy for attractiveness. The results showed two things: to a certain extent country attractiveness scales with population, but does not with its size; and unlike in case of Spanish cities, country attractiveness scales sublinearly with population, and not superlinearly.Singapore-MIT Alliance for Research and Technology (SMART)Accenture (Firm)Air liquide (Firm)Coca-Cola CompanyEricsson (Firm)Volkswagen Electronics Research LabUber (Firm)MIT Senseable City Lab Consortiu

Scaling of city attractiveness for foreign visitors through big data of human economical and social media activity

Scientific studies investigating laws and regularities of human behavior are nowadays increasingly relying on the wealth of widely available digital information produced by human social activity. In this paper we leverage big data created by three different aspects of human activity (i.e., bank card transactions, geotagged photographs and tweets) in Spain for quantifying city attractiveness for the foreign visitors. An important finding of this papers is a strong superlinear scaling of city attractiveness with its population size. The observed scaling exponent stays nearly the same for different ways of defining cities and for different data sources, emphasizing the robustness of our finding. Temporal variation of the scaling exponent is also considered in order to reveal seasonal patterns in the attractivenessComment: 8 pages, 3 figures, 1 tabl

Uncovering Urban Temporal Patterns from Geo-Tagged Photography

We live in a world where digital trails of different forms of human activities compose big urban data, allowing us to detect many aspects of how people experience the city in which they live or come to visit. In this study we propose to enhance urban planning by taking into a consideration individual preferences using information from an unconventional big data source: dataset of geo-tagged photographs that people take in cities which we then use as a measure of urban attractiveness. We discover and compare a temporal behavior of residents and visitors in ten most photographed cities in the world. Looking at the periodicity in urban attractiveness, the results show that the strongest periodic patterns for visitors are usually weekly or monthly. Moreover, by dividing cities into two groups based on which continent they belong to (i.e., North America or Europe), it can be concluded that unlike European cities, behavior of visitors in the US cities in general is similar to the behavior of their residents. Finally, we apply two indices, called “dilatation attractiveness index” and “dilatation index”, to our dataset which tell us the spatial and temporal attractiveness pulsations in the city. The proposed methodology is not only important for urban planning, but also does support various business and public stakeholder decision processes, concentrated for example around the question how to attract more visitors to the city or estimate the impact of special events organized there.Singapore-MIT Alliance for Research and Technology (SMART)Center for Complex Engineering Systems (CCES) at KACST and MITCoca-Cola CompanyAccenture (Firm

Geographical veracity of indicators from mobile phone data : a study of call detail records data in France

PhD ThesisThe study of mobile phone data opens opportunities in many research domains and for many applications. One point of critique is that, within current analyses, mobile phone users are considered uniform and interchangeable. To counter this social atom problem, good research practice demands an increasing contextualization of research results, for example by confrontation with auxiliary datasets or geographical knowledge. The latter forms the starting point of this thesis. The main argument is that there exists a spatial knowledge gap when it comes to the use of indicators derived from mobile phone data. The presented studies assess the geographical veracity of indicators derived from Call Detail Record (CDR) data and the underlying methods used. Based on a CDR dataset of almost 18.5 million users in France captured during a 154-day period in 2007, they show how mobile phone indicators can be constructed for all individual users using big data technologies. Investigation then is on the performance, sensitivity to user choices, and error estimations of home detection methods, which form a primordial step for the aggregation of users in space. Next, a spatial analysis of the popular Mobility Entropy (ME) indicator is performed, revealing its bias to cell tower density, for which a correction is then proposed. Ultimately, the relations between mobile phone indicators, indicators from other data sources and city definitions in France are explored. The main contribution of the thesis is that it reveals multiple limits of the common practices, results, and interpretations that govern mobile phone data research. The presented studies challenge the veracity of mobile phone indicators in different, predominantly geographical, ways and open up discussion on what should be done to improve trustworthiness

Social informatics

5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013, Proceedings</p