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Enrique Frias-Martinez

Bio: Enrique Frias-Martinez is an academic researcher from Telefónica. The author has contributed to research in topics: Population & Mobile phone. The author has an hindex of 33, co-authored 90 publications receiving 3500 citations. Previous affiliations of Enrique Frias-Martinez include Brunel University London & New York University.


Papers
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Journal ArticleDOI
TL;DR: An urban dilatation index is defined which measures how the average distance between individuals evolves during the day, allowing us to highlight different types of city structure, and a parameter free method to detect hotspots, the most crowded places in the city is proposed.
Abstract: Pervasive infrastructures, such as cell phone networks, enable to capture large amounts of human behavioral data but also provide information about the structure of cities and their dynamical properties. In this article, we focus on these last aspects by studying phone data recorded during 55 days in 31 Spanish cities. We first define an urban dilatation index which measures how the average distance between individuals evolves during the day, allowing us to highlight different types of city structure. We then focus on hotspots, the most crowded places in the city. We propose a parameter free method to detect them and to test the robustness of our results. The number of these hotspots scales sublinearly with the population size, a result in agreement with previous theoretical arguments and measures on employment datasets. We study the lifetime of these hotspots and show in particular that the hierarchy of permanent ones, which constitute the 'heart' of the city, is very stable whatever the size of the city. The spatial structure of these hotspots is also of interest and allows us to distinguish different categories of cities, from monocentric and "segregated" where the spatial distribution is very dependent on land use, to polycentric where the spatial mixing between land uses is much more important. These results point towards the possibility of a new, quantitative classification of cities using high resolution spatio-temporal data.

384 citations

Proceedings ArticleDOI
03 Sep 2012
TL;DR: This paper evaluates the use of geolocated tweets as a complementary source of information for urban planning applications and applies techniques to automatically determine land uses in a specific urban area based on tweeting patterns.
Abstract: The pervasiveness of cell phones and mobile social media applications is generating vast amounts of geolocalized user-generated content. Since the addition of geotagging information, Twitter has become a valuable source for the study of human dynamics. Its analysis is shedding new light not only on understanding human behavior but also on modeling the way people live and interact in their urban environments. In this paper, we evaluate the use of geolocated tweets as a complementary source of information for urban planning applications. Our contributions are focussed in two urban planing areas: (1) a technique to automatically determine land uses in a specific urban area based on tweeting patterns, and (2) a technique to automatically identify urban points of interest as places with high activity of tweets. We apply our techniques in Manhattan (NYC) using 49 days of geolocated tweets and validate them using land use and landmark information provided by various NYC departments. Our results indicate that geolocated tweets are a powerful and dynamic data source to characterize urban environments.

212 citations

Journal ArticleDOI
TL;DR: The proposed technique uses unsupervised learning and automatically determines land uses in urban areas by clustering geographical regions with similar tweeting activity patterns, indicating that geolocated tweets can be used as a powerful data source for urban planning applications.

181 citations

Proceedings ArticleDOI
01 Oct 2011
TL;DR: An agent-based system that uses social interactions and individual mobility patterns extracted from call detail records to accurately model virus spreading is proposed and applied to study the 2009 H1N1 outbreak in Mexico and to evaluate the impact that government mandates had on the spreading of the virus.
Abstract: The recent adoption of ubiquitous computing technologies has enabled capturing large amounts of human behavioral data The digital footprints computed from these datasets provide information for the study of social and human dynamics, including social networks and mobility patterns, key elements for the effective modeling of virus spreading Traditional epidemiologic models do not consider individual information and hence have limited ability to capture the inherent complexity of the disease spreading process To overcome this limitation, agent-based models have recently been proposed as an effective approach to model virus spreading However, most agent-based approaches to date have not included real-life data to characterize the agents' behavior In this paper we propose an agent-based system that uses social interactions and individual mobility patterns extracted from call detail records to accurately model virus spreading The proposed approach is applied to study the 2009 H1N1 outbreak in Mexico and to evaluate the impact that government mandates had on the spreading of the virus Our simulations indicate that the restricted mobility due the government mandates reduced by 10% the peak number of individuals infected by the virus and postponed the peak of the pandemic by two days

168 citations

Journal ArticleDOI
TL;DR: A versatile method is proposed, which extracts a coarse-grained signature of mobility networks, under the form of a 2 × 2 matrix that separates the flows into four categories, and allows the determination of categories of networks, and in the mobility case, the classification of cities according to their commuting structure.
Abstract: The extraction of a clear and simple footprint of the structure of large, weighted and directed networks is a general problem that has many applications. An important example is given by origin-destination matrices which contain the complete information on commuting flows, but are difficult to analyze and compare. We propose here a versatile method which extracts a coarse-grained signature of mobility networks, under the form of a 2 × 2 matrix that separates the flows into four categories. We apply this method to origin-destination matrices extracted from mobile phone data recorded in thirty-one Spanish cities. We show that these cities essentially differ by their proportion of two types of flows: integrated (between residential and employment hotspots) and random flows, whose importance increases with city size. Finally the method allows to determine categories of networks, and in the mobility case to classify cities according to their commuting structure. The increasing availability of pervasive data in various fields has opened exciting possibilities of renewed quanti-tative approaches to many phenomena. This is particu-larly true for cities and urban systems for which different devices at different scales produce a very large amount of data potentially useful to construct a 'new science of cities' [1].

168 citations


Cited by
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01 Jan 2012

3,692 citations

01 Jan 1990
TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.
Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

2,933 citations

01 Jan 2008
TL;DR: In this article, the authors argue that rational actors make their organizations increasingly similar as they try to change them, and describe three isomorphic processes-coercive, mimetic, and normative.
Abstract: What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

2,134 citations

Journal ArticleDOI
01 Nov 2010
TL;DR: The most relevant studies carried out in educational data mining to date are surveyed and the different groups of user, types of educational environments, and the data they provide are described.
Abstract: Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

1,723 citations

Journal ArticleDOI
TL;DR: The detection of phase transitions constitutes the first objective method of characterising endogenous, natural scales of human movement and allows us to draw discrete multi-scale geographical boundaries, potentially capable of providing key insights in fields such as epidemiology or cultural contagion.
Abstract: Human mobility is known to be distributed across several orders of magnitude of physical distances , which makes it generally difficult to endogenously find or define typical and meaningful scales. Relevant analyses, from movements to geographical partitions, seem to be relative to some ad-hoc scale, or no scale at all. Relying on geotagged data collected from photo-sharing social media, we apply community detection to movement networks constrained by increasing percentiles of the distance distribution. Using a simple parameter-free discontinuity detection algorithm, we discover clear phase transitions in the community partition space. The detection of these phases constitutes the first objective method of characterising endogenous, natural scales of human movement. Our study covers nine regions, ranging from cities to countries of various sizes and a transnational area. For all regions, the number of natural scales is remarkably low (2 or 3). Further, our results hint at scale-related behaviours rather than scale-related users. The partitions of the natural scales allow us to draw discrete multi-scale geographical boundaries, potentially capable of providing key insights in fields such as epidemiology or cultural contagion where the introduction of spatial boundaries is pivotal.

1,543 citations