Mitigating Epidemics through Mobile Micro-measures

Epidemics of infectious diseases are among the largest threats to the quality of life and the economic and social well-being of developing countries. The arsenal of measures against such epidemics is well-established, but costly and insufficient to mitigate their impact. In this paper, we argue that mobile technology adds a powerful weapon to this arsenal, because (a) mobile devices endow us with the unprecedented ability to measure and model the detailed behavioral patterns of the affected population, and (b) they enable the delivery of personalized behavioral recommendations to individuals in real time. We combine these two ideas and propose several strategies to generate such recommendations from mobility patterns. The goal of each strategy is a large reduction in infections, with a small impact on the normal course of daily life. We evaluate these strategies over the Orange D4D dataset and show the benefit of mobile micro-measures, even if only a fraction of the population participates. These preliminary results demonstrate the potential of mobile technology to complement other measures like vaccination and quarantines against disease epidemics.Comment: Presented at NetMob 2013, Bosto

Human Mobility Patterns for Different Regions in Myanmar Based on CDRs Data

Sustainable urban and transportation planning depends greatly on understanding human mobility patterns in urban area. Myanmar is one of the developing countries in ASEAN. It develops more rapidly as compare past years due to its international trade policy change and faces serious traffic problem in major cities. To solve these problem, human mobility pattern need to know for improvement. Therefore, this paper focuses to analyze different human mobility patterns for the different regions in Myanmar by using Call Detail Records (CDRs) Data. Such studies could be useful for creating transport model of mobility pattern. The numbers of trip generated are obtained by using CDRs over seven days period. CDRs of each region can be used to generate trip numbers of townships within certain time frame and time windows. In this study, average distance travelled, preferred days of long distance users and human mobility patterns at different times of weekdays and weekends in Yangon and Mandalay were analyzed. People living in Yangon area are generally more travelled than Mandalay on weekdays and weekends. The results indicated the similarities and differences in mobility patterns for both cities. This information is very useful for transport planning and future transportation developments

A Tool for Appraising Mobility Environment with a Perfect Based Index Measure

Diverse methods, approaches and models have been employed in explaining mobility in both the urban and human context. However, there has been the ever-present drawback premised on data unavailability, “dyrtiness” or scantiness. More so, the techniques and parameters used, does not provide clues about mobility complexities engendered by attributes of “mobility environments”, as a result, determinants of mobility complexities are hardly fully described. To narrow the gap, it is conjectured that systematic evaluation of traveler perception of “mobility environments”, may provide hints about the degree to which specified spatial units enhance or hinder mobility, by rating such environment with a perception based index construct we hope will help improve assessments of “mobility environments”. This need is underscored by the necessity to explore alternative decision support tools, for mobility evaluations, especially where it may be implausible to apply advanced, high end, data hungry models of mobility evaluation. The method involved a two-pronged survey of transport professionals and randomly selected travelers. The professionals helped with “mobility environment” attributes identification and selection of contextually relevant ones from a list of potential attributes of influence, extracted from relevant literature using the Delphi method. Randomly selected travelers were in turn presented with the short listed attributes for rating on a five point Likert scale. Ratings were then used to determine attribute rankings and their commensurate index equivalents, as a basis for classification. Travelers indicated that a high activity mix, high road and pedestrian network density are good mobility enhancing qualities a city should possess. However, aggregate indexing indicated that enhancing development characteristics, mode characteristics, travel and economic attributes, are the most important for the study area. The measures are targeted at facilitating development of cost effective and parsimonious means of identifying urban mobility challenges by local authorities, to provide a strategic pathway for a city’s “mobility environments” qualities to be identified and objectively appraised, in order to satisfactorily target interventions at improving both the “mobility environment” and the quality of life of city inhabitants

Filling the Gaps: On the Completion of Sparse Call Detail Records for Mobility Analysis

International audienceCall Detail Records (CDRs) have been widely used in the last decades for studying different aspects of human mobility. The accuracy of CDRs strongly depends on the user-network interaction frequency: hence, the temporal and spatial spar-sity that typically characterize CDR can introduce a bias in the mobility analysis. In this paper, we evaluate the bias induced by the use of CDRs for inferring important locations of mobile subscribers, as well as their complete trajectories. Besides, we propose a novel technique for estimating real human trajectories from sparse CDRs. Compared to previous solutions in the literature, our proposed technique reduces the error between real and estimated human trajectories and at the same time shortens the temporal period where users' locations remain undefined

Data from mobile phone operators: A tool for smarter cities?

Abstract The use of mobile phone data provides new spatio-temporal tools for improving urban planning, and for reducing inefficiencies in present-day urban systems. Data from mobile phones, originally intended as a communication tool, are increasingly used as innovative tools in geography and social sciences research. Empirical studies on complex city systems from human-centred and urban dynamics perspectives provide new insights to develop promising applications for supporting smart city initiatives. This paper provides a comprehensive review and a typology of spatial studies on mobile phone data, and highlights the applicability of such digital data to develop innovative applications for enhanced urban management

Relevance of Context for the Temporal Completion of Call Detail Record

Call Detail Records (CDRs) are an important source of information in the study of different aspects of human mobility. However, their utility is often limited by spatio-temporal sparsity. In this paper, we first evaluate the effectiveness of CDRs in measuring relevant mobility features. We then investigate whether the information of user's instantaneous whereabouts provided by CDRs enables us to estimate positions over longer time spans. Our results confirm that CDRs ensure a good estimation of radii of gyration and important locations, yet they lose some location information. Most importantly, we show that temporal completion of CDRs is straightforward and efficient: thanks to the fact that they remain fairly static before and after mobile communication activities, the majority of users' locations over time can be accurately inferred from CDRs. Finally, we observe the importance of user's context, i.e., of the size of the current network cell, on the quality of the CDR temporal completion.Les statistiques d’appel (ou en anglais Call Detail Records - CDR) sont une importante source d’information dans l’étude des différents aspects de la mobilité humaine. Cependant,leur utilité est souvent limitée par son spartiété spatio-temporelle. Dans cet article, nous évaluons d’abord l’efficacité de l’utilisation des CDR pour la mesure des caractéristiques de mobilité pertinentes. Nous nous demandons ensuite si les informations de localisation instantanée de l’utilisateur fournies par les CDR nous permettent d’estimer leurs positions sur des périodes longues. Nos résultats confirment que les CDR assurent une bonne estimation des rayons de giration et des emplacements importants, mais ils perdent certaines informations de localisation.Plus important encore, nous montrons que l’achèvement temporel des CDR est simple et efficace:grâce au fait qu’ils restent relativement statiques avant et après les activités de communication mobile, la majorité des emplacements des utilisateurs dans le temps peut être correctement dé-duite des CDR. Enfin, on observe l’importance du contexte de l’utilisateur, c’est-à-dire de la taille de la cellule de réseau actuelle, sur la qualité de l’achèvement temporel des CDR

Message Passing for Collective Graphical Models

National Research Foundation (NRF) Singapore under Corp Lab @ University schem

Human Mobility Flows in the City of Abidjan

International audienceThe growing ubiquity of mobile communications has offered researchers new possibilities to understand human mobility over the last few years. In this work, we analyze Call Detail Records (CDR) made available within the context of the Orange D4D Challenge, focusing on calls of individuals in the city of Abidjan over a period of five months. Our results illustrate how aggregated CDR can be used to tell apart typical and special mobility behaviors, and demonstrate how macroscopic mobility flows extracted from these cellular network data reflect the daily dynamics of a highly populated city. We discuss how these macroscopic mobility flows can help solve problems in developing urban areas

Differentiating population spatial behaviour using a standard feature set

Moving through space, consuming services at locations, transitioning and dwelling are all aspects of spatial behavior that can be recorded with unprecedented ease and accuracy using the GPS and other sensor systems on commodity smartphones. Collection of GPS data is becoming a standard experimental method for studies ranging from public health interventions to studying the browsing behavior of large non-human mammals. However, the millions of records collected in these studies do not lend themselves to traditional geographic analysis. GPS records need to be reduced to a single feature or combination of features, which express the characteristic of interest. While features for spatial behavior characterization have been proposed in different disciplines, it is not always clear which feature should be appropriate for a specific dataset. The substantial effort on subjective selection or design of feature may or may not lead to an insight into GPS datasets. In this thesis we describe a feature set drawn from three different mathematical heritages: buffer area, convex hull and its variations from activity space, fractal dimension of the recorded GPS traces, and entropy rate of individual paths. We analyze these features against six human mobility datasets. We show that the standard feature set could be used to distinguish disparate human mobility patterns while single feature could not distinguish them alone. The feature set can be efficiently applied to most datasets, subject to the assumptions about data quality inherent in the features