7 research outputs found

    Movement patterns captured at different temporal scales illustrate connectivity between districts in Kenya within a 24-hour time period (N = 90,645 tracks) and during a ten month time period (N = 17,900).

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    <p>Each line represents a movement segment. The long distance tracks indicates population movements by plane or by train within the country. Maps created using ArcGIS 10.2.</p

    Summary of Twitter data used in this analysis that was collected for Kenya between June 2013 and March 2014 (N<sub>unique users</sub> = 28,335; N<sub>tweets</sub> = 720,149).

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    <p>Summary of Twitter data used in this analysis that was collected for Kenya between June 2013 and March 2014 (N<sub>unique users</sub> = 28,335; N<sub>tweets</sub> = 720,149).</p

    A framework and associated data sources useful for capturing human mobility in time and space.

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    <p>Movements are characterized in terms of their spatial and temporal scale, which are defined in terms of physical displacement (<i>spatial</i>) and time spent (<i>temporal</i>, frequency and duration) (Source: adapted from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129202#pone.0129202.ref045" target="_blank">45</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129202#pone.0129202.ref057" target="_blank">57</a>]).</p

    Map illustrating the connectivity between districts and Eigenvector value illustrating the level of influence of each district.

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    <p>For clarity we have only illustrated linkages with more than 100 connections and Eigenvectors greater than 0.50. Map created using ArcGIS 10.2.</p

    Connectivity between locations and human movement within Kenya.

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    <p>(A) Distances travelled daily, monthly and in total by each user and (B) the proportion of user’s radius of gyration (solid line).</p
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