1,934 research outputs found

    Spatiotemporal Modeling in Wireless Communication Networks

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    تهدف هذه الدراسة إلى تحليل تدفق هجرة الأفراد بين المحافظات العراقية باستخدام بيانات مجهولة حقيقية من شركة كورك تيليكوم في العراق. الغرض من هذا التحليل هو فهم بنية الاتصال وجاذبية المدن أو المحافظات من خلال فحص هجرة التدفق والكثافة السكانية، لذلك من وجهة النظر هذه يتم تصنيفها على أساس الهجرة البشرية في وقت معين. تمت ملاحظة بيانات الهاتف المحمول من نوع المكالمات التفصيلية للمكالمات ((CDRs، والتي تقع في فترة 6 أشهر خلال COVID-19 في العام 2020-2021. وفقًا لطبيعة CDRs، تم تطبيق الخوارزميات المكانية والزمانية المعروفة: نموذج الإشعاع ونموذج الجاذبية لتحليل هذه البيانات، واتضح أنها مكملة للآخر بناءً على النتائج التي تم الحصول عليها. تم تمثيل النتائج من خلال استكشاف التدفقات لكل محافظة على مستويين من التجريد: الماكروسكوب والميزوسكوب. وجدت النتائج أن نماذج التفاعل الزماني المكاني مكملة للآخر، حيث تم حساب التدفقات بواسطة نموذج الإشعاع الذي سيتم استخدامه في نموذج الجاذبية. كما تم الحصول على ملخص للتدفقات بين المحافظات ولكل محافظة على حدة. واستناداً إلى العينة المأخوذة من إجمالي عدد التدفقات، كانت أعلى نسبة جذب بين محافظتي نينوى وذي قار وبلغت٪ ، بينما كانت أقل نسبة جذب بين محافظتي واسط وكربلاء والتي بلغت   . بالإضافة إلى ذلك، أظهرت الخرائط النسبة المئوية لكل محافظة، في إشارة إلى لون كل محافظة، من اللون الفاتح الذي يعني انخفاض الجذب، إلى الغامق الذي يعني الجذب العالي. في المستقبل، من الممكن الحصول على بيانات أكثر تفصيلاً واستخدام خوارزميات الشبكة المعقدة لتحليل هذه البيانات.This study aims to analyze the flow migration of individuals between Iraqi governorates using real anonymized data from Korek Telecom company in Iraq. The purpose of this analysis is to understand the connection structure and the attractiveness of these governorates through examining the flow migration and population densities. Hence, they are classified based on the human migration at a particular period. The mobile phone data of type Call Detailed Records (CDRs) have been observed, which fall in a 6-month period during COVID-19 in the year 2020-2021. So, according to the CDRs nature, the well-known spatiotemporal algorithms: the radiation model and the gravity model were applied to analyze these data, and they are turned out to be complementary to each other. However, the results explore the flows of each governorate at two levels of abstraction: The Macroscopic and Mesoscopic. These results found that the spatiotemporal interaction models are complementary to the other, as the determined flows based on the radiation model have been used in the gravitational model. Furthermore, flows summary among all the governorates as well as for each of them has been obtained separately. Thus, based on the total number of flows, the highest attraction rate was between Nineveh and Dhi Qar governorates which reached , while the lowest attraction was between Wasit and Karbala governorates which reached . In addition, the extracted geographical maps showed each governorate ratio. Regarding the color of each governorate that degraded from light to dark, which indicated the low to high attraction respectively. In the future, it is possible to obtain more detailed data, and to use complex network algorithms for analyzing this data

    A Comparison of Spatial-based Targeted Disease Containment Strategies using Mobile Phone Data

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    Epidemic outbreaks are an important healthcare challenge, especially in developing countries where they represent one of the major causes of mortality. Approaches that can rapidly target subpopulations for surveillance and control are critical for enhancing containment processes during epidemics. Using a real-world dataset from Ivory Coast, this work presents an attempt to unveil the socio-geographical heterogeneity of disease transmission dynamics. By employing a spatially explicit meta-population epidemic model derived from mobile phone Call Detail Records (CDRs), we investigate how the differences in mobility patterns may affect the course of a realistic infectious disease outbreak. We consider different existing measures of the spatial dimension of human mobility and interactions, and we analyse their relevance in identifying the highest risk sub-population of individuals, as the best candidates for isolation countermeasures. The approaches presented in this paper provide further evidence that mobile phone data can be effectively exploited to facilitate our understanding of individuals' spatial behaviour and its relationship with the risk of infectious diseases' contagion. In particular, we show that CDRs-based indicators of individuals' spatial activities and interactions hold promise for gaining insight of contagion heterogeneity and thus for developing containment strategies to support decision-making during country-level pandemics

    Multi-scale Population and Mobility Estimation with Geo-tagged Tweets

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    Recent outbreaks of Ebola and Dengue viruses have again elevated the significance of the capability to quickly predict disease spread in an emergent situation. However, existing approaches usually rely heavily on the time-consuming census processes, or the privacy-sensitive call logs, leading to their unresponsive nature when facing the abruptly changing dynamics in the event of an outbreak. In this paper we study the feasibility of using large-scale Twitter data as a proxy of human mobility to model and predict disease spread. We report that for Australia, Twitter users' distribution correlates well the census-based population distribution, and that the Twitter users' travel patterns appear to loosely follow the gravity law at multiple scales of geographic distances, i.e. national level, state level and metropolitan level. The radiation model is also evaluated on this dataset though it has shown inferior fitness as a result of Australia's sparse population and large landmass. The outcomes of the study form the cornerstones for future work towards a model-based, responsive prediction method from Twitter data for disease spread.Comment: 1st International Workshop on Big Data Analytics for Biosecurity (BioBAD2015), 4 page

    Predicting human mobility through the assimilation of social media traces into mobility models

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    Predicting human mobility flows at different spatial scales is challenged by the heterogeneity of individual trajectories and the multi-scale nature of transportation networks. As vast amounts of digital traces of human behaviour become available, an opportunity arises to improve mobility models by integrating into them proxy data on mobility collected by a variety of digital platforms and location-aware services. Here we propose a hybrid model of human mobility that integrates a large-scale publicly available dataset from a popular photo-sharing system with the classical gravity model, under a stacked regression procedure. We validate the performance and generalizability of our approach using two ground-truth datasets on air travel and daily commuting in the United States: using two different cross-validation schemes we show that the hybrid model affords enhanced mobility prediction at both spatial scales.Comment: 17 pages, 10 figure

    Mesoscopic structure and social aspects of human mobility

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    The individual movements of large numbers of people are important in many contexts, from urban planning to disease spreading. Datasets that capture human mobility are now available and many interesting features have been discovered, including the ultra-slow spatial growth of individual mobility. However, the detailed substructures and spatiotemporal flows of mobility - the sets and sequences of visited locations - have not been well studied. We show that individual mobility is dominated by small groups of frequently visited, dynamically close locations, forming primary "habitats" capturing typical daily activity, along with subsidiary habitats representing additional travel. These habitats do not correspond to typical contexts such as home or work. The temporal evolution of mobility within habitats, which constitutes most motion, is universal across habitats and exhibits scaling patterns both distinct from all previous observations and unpredicted by current models. The delay to enter subsidiary habitats is a primary factor in the spatiotemporal growth of human travel. Interestingly, habitats correlate with non-mobility dynamics such as communication activity, implying that habitats may influence processes such as information spreading and revealing new connections between human mobility and social networks.Comment: 7 pages, 5 figures (main text); 11 pages, 9 figures, 1 table (supporting information

    A review of urban computing for mobile phone traces

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    In this work, we present three classes of methods to extract information from triangulated mobile phone signals, and describe applications with different goals in spatiotemporal analysis and urban modeling. Our first challenge is to relate extracted information from phone records (i.e., a set of time-stamped coordinates estimated from signal strengths) with destinations by each of the million anonymous users. By demonstrating a method that converts phone signals into small grid cell destinations, we present a framework that bridges triangulated mobile phone data with previously established findings obtained from data at more coarse-grained resolutions (such as at the cell tower or census tract levels). In particular, this method allows us to relate daily mobility networks, called motifs here, with trip chains extracted from travel diary surveys. Compared with existing travel demand models mainly relying on expensive and less-frequent travel survey data, this method represents an advantage for applying ubiquitous mobile phone data to urban and transportation modeling applications. Second, we present a method that takes advantage of the high spatial resolution of the triangulated phone data to infer trip purposes by examining semantic-enriched land uses surrounding destinations in individual's motifs. In the final section, we discuss a portable computational architecture that allows us to manage and analyze mobile phone data in geospatial databases, and to map mobile phone trips onto spatial networks such that further analysis about flows and network performances can be done. The combination of these three methods demonstrate the state-of-the-art algorithms that can be adapted to triangulated mobile phone data for the context of urban computing and modeling applications.BMW GroupAustrian Institute of TechnologySingapore. National Research FoundationMassachusetts Institute of Technology. School of EngineeringMassachusetts Institute of Technology. Dept. of Urban Studies and PlanningSingapore-MIT Alliance for Research and Technology (Center for Future Mobility
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