Mesoscopic Structure and Social Aspects of Human Mobility
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{"title"=>"Mesoscopic structure and social aspects of human mobility", "type"=>"journal", "authors"=>[{"first_name"=>"James P.", "last_name"=>"Bagrow"}, {"first_name"=>"Yu-Ru", "last_name"=>"Lin"}], "year"=>2012, "identifiers"=>{"doi"=>"10.1371/journal.pone.0037676", "arxiv"=>"1202.0224"}, "id"=>"ded07362-0b6a-343f-8678-1276137a45b6", "abstract"=>"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.", "link"=>"http://www.mendeley.com/research/mesoscopic-structure-social-aspects-human-mobility-3", "reader_count"=>8, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>1, "Student > Ph. D. Student"=>2, "Student > Master"=>2, "Other"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>1, "Student > Ph. D. Student"=>2, "Student > Master"=>2, "Other"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Environmental Science"=>1, "Mathematics"=>1, "Medicine and Dentistry"=>1, "Physics and Astronomy"=>2, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Computer Science"=>{"Computer Science"=>2}, "Mathematics"=>{"Mathematics"=>1}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"Colombia"=>1, "Brazil"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/326136"], "description"=>"<div><p>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.</p> </div>", "links"=>[], "tags"=>["mesoscopic", "aspects", "mobility"], "article_id"=>124365, "categories"=>["Physics", "Mathematics"], "users"=>["James P. Bagrow", "Yu-Ru Lin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0037676", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Mesoscopic_Structure_and_Social_Aspects_of_Human_Mobility/124365", "title"=>"Mesoscopic Structure and Social Aspects of Human Mobility", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-05-31 01:12:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/629531"], "description"=>"<p>We characterize the interaction concentration of a user by , the probability for that user to place a call to his or her Most Frequent Contact. (<b>A</b>) The distribution of over the population shows that most users have between approximately and . (See also Fig. 4b.) (<b>B</b>) To connect the concentration with user mobility, we study how the mean varies with the number of habitats each user possesses. We see that gradually decays as the number of habitats grows, indicating that broadly traveled individuals tend to more evenly distribute their calls over their partners. (<b>C</b>) Studying as a function of , we uncover an intriguing relationship. For users with particularly small mobility ranges, is small but grows as grows. This continues until , the same critical radius size observed in Fig. 2b. The mean then decays for . Surprisingly, this implies that the distribution of call activity over a user's partners exhibits different behavior depending on whether that user possess one mobility habitat, or many habitats. (<b>D</b>) The fraction of reciprocated contacts as a function of shows a trend similar to . Not only do those users with small tend to be distinctly less socially concentrated compared with most users, they also tend to make more non-reciprocated contacts (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037676#pone.0037676.s001\" target=\"_blank\">File S1</a> Fig. C for details). Error bars indicate s.e.</p>", "links"=>[], "tags"=>["mobility"], "article_id"=>300030, "categories"=>["Physics", "Mathematics"], "users"=>["James P. Bagrow", "Yu-Ru Lin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0037676.g005", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Communication_and_mobility_dynamics_/300030", "title"=>"Communication and mobility dynamics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-31 00:00:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/629380"], "description"=>"<p>We characterize each habitat's spatial extent by computing the radius of gyration considering only calls placed from locations within habitat . (<b>A</b>) The distribution of habitat radii over the population shows that the primary habitat tends to be more spatially compact than the less frequented habitats, though most are consistently smaller than the total computed using all phone activity. (<b>B</b>) The growth in the radius of the primary habitat as a function of total radius . For km, we see , indicating that those users are characterized by a single habitat. In contrast, for . Since approximately 92% of the population have km, the majority of users exist in a regime where their primary habitat encompasses a potentially far smaller spatial region than their total mobility. (<b>C</b>) For users with multiple habitats, the distance between the first and second habitat's centers of mass is consistently greater than (grey line) and exhibits power law scaling, , with . Taken together, we see that most habitats are both well separated and spatially compact, and that the magnitude of is primarily due to movement between these habitats.</p>", "links"=>[], "tags"=>["properties", "mobility"], "article_id"=>299875, "categories"=>["Physics", "Mathematics"], "users"=>["James P. Bagrow", "Yu-Ru Lin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0037676.g002", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatial_properties_of_mobility_habitats_/299875", "title"=>"Spatial properties of mobility habitats.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-31 02:44:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/629483"], "description"=>"<p>(<b>A</b>) The Zipf law governing the probability for a user to visit his or her -th most visited location, as first observed by González et al. <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037676#pone.0037676-Gonzlez1\" target=\"_blank\">[7]</a>. The solid line indicates (<b>B</b>) Interestingly, we observe an identical Zipf law for the probability for a user to call his or her -th most contacted partner. This holds regardless of the total number of contacts for a user. This implies that the same underlying mechanism may govern how users choose both locations to visit and friends to contact. (<b>C</b>) The habitat similarity, related to the number of common locations, between a user's primary habitat and the primary habitat of their contacts, averaged over pairs where both users are present in our data. We see that, despite the Zipf law in B, user's habitats tend to be surprisingly similar to their most contacted ties, even for those less frequently contacted users. Control habitats, generated by randomly shuffling a user's visited locations between his or her original habitats, exhibit lower similarity. See Methods for habitat similarity and controls.</p>", "links"=>[], "tags"=>["physics", "mathematics"], "article_id"=>299983, "categories"=>["Physics", "Mathematics"], "users"=>["James P. Bagrow", "Yu-Ru Lin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0037676.g004", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Contact_activity_and_habitats_/299983", "title"=>"Contact activity and habitats.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-31 02:46:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/629309"], "description"=>"<p>(<b>A</b>) Spatial trajectories of two users, one traveling to a large number of locations and another covering a smaller range. Node size indicates the amount of time spent at a particular location (as quantified by mobile phone activity), node color represents the location's habitat detected using Infomap (see Methods), and line width approximates the number of trips between locations. Habitats are ordered by call volume such that Habitat 1 contains the most calls. (<b>B</b>) Exploding the spatial trajectories from A in time (vertical axis), the recurrent nature of human mobility becomes evident, with a number of trips featuring both consistent destinations and consistently repetitive occurrence (zoom). These features are the root cause of the high predictability that human motion is known to possess. (<b>C</b>) The daily call dynamics of the three most active habitats, as well as the overall dynamics (summed over all habitats). The primary habitat contains the majority of temporal activity. We see that User 1 tends to occupy his or her second and third habitats primarily at night, while User 2 is more evenly distributed. (<b>D</b>) The distribution of the number of habitats per user. The median number of habitats is 11. Due to their typical heterogeneity, we characterize population distributions using percentiles, proportional to the cumulative distribution.</p>", "links"=>[], "tags"=>["spatiotemporal", "substructure", "mobility"], "article_id"=>299805, "categories"=>["Physics", "Mathematics"], "users"=>["James P. Bagrow", "Yu-Ru Lin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0037676.g001", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Habitats_reveal_the_spatiotemporal_substructure_of_human_mobility_patterns_/299805", "title"=>"Habitats reveal the spatiotemporal substructure of human mobility patterns.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-31 02:43:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/629436"], "description"=>"<p>(<b>A</b>) The time evolution of compared with , where both are normalized by their final values at the end of the observation window. We see that the primary habitat tends to reach saturation faster than the overall gyradius, indicating different temporal dynamics. (<b>B</b>) To quantify the saturation rate, we plot the ratio of the two curves from A, for groups of users with different . We see that the primary habitat saturates more quickly as the overall grows. Solid lines of the form provide a guide for the eye. (<b>C</b>) The unnormalized growth in habitat size for the first three habitats. The primary habitat shows a distinct, approximately logarithmic temporal scaling. The other habitats show a longer delay before begins to grow polylogarithmically. (<b>D</b>) Given this delay, we now shift the time series of for each habitat by , the time when the user first entered habitat . Doing so we recover pure logarithmic scaling for all habitats, , indicating that a major factor in the scaling of human mobility is the delay it takes for a user to transition to his or her non-primary habitats.</p>", "links"=>[], "tags"=>["physics", "mathematics"], "article_id"=>299932, "categories"=>["Physics", "Mathematics"], "users"=>["James P. Bagrow", "Yu-Ru Lin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0037676.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Temporal_evolution_of_human_mobility_/299932", "title"=>"Temporal evolution of human mobility.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-31 02:45:32"}

PMC Usage Stats | Further Information

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Relative Metric

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