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{"title"=>"Place-Based Attributes Predict Community Membership in a Mobile Phone Communication Network", "type"=>"journal", "authors"=>[{"first_name"=>"T. Trevor", "last_name"=>"Caughlin", "scopus_author_id"=>"35285537200"}, {"first_name"=>"Nick", "last_name"=>"Ruktanonchai", "scopus_author_id"=>"36440324300"}, {"first_name"=>"Miguel A.", "last_name"=>"Acevedo", "scopus_author_id"=>"13410360600"}, {"first_name"=>"Kenneth K.", "last_name"=>"Lopiano", "scopus_author_id"=>"37048737500"}, {"first_name"=>"Olivia", "last_name"=>"Prosper", "scopus_author_id"=>"43761607700"}, {"first_name"=>"Nathan", "last_name"=>"Eagle", "scopus_author_id"=>"8607117800"}, {"first_name"=>"Andrew J.", "last_name"=>"Tatem", "scopus_author_id"=>"6603035928"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84874307534", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "scopus"=>"2-s2.0-84874307534", "pui"=>"368409714", "pmid"=>"23451034", "doi"=>"10.1371/journal.pone.0056057", "issn"=>"19326203"}, "id"=>"3aa0db9d-f8ac-327e-9c61-457a8e377d21", "abstract"=>"Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97) between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.", "link"=>"http://www.mendeley.com/research/placebased-attributes-predict-community-membership-mobile-phone-communication-network", "reader_count"=>29, "reader_count_by_academic_status"=>{"Researcher"=>7, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>10, "Student > Postgraduate"=>1, "Other"=>2, "Student > Master"=>2, "Student > Bachelor"=>3, "Professor"=>3}, "reader_count_by_user_role"=>{"Researcher"=>7, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>10, "Student > Postgraduate"=>1, "Other"=>2, "Student > Master"=>2, "Student > Bachelor"=>3, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Engineering"=>3, "Environmental Science"=>1, "Medicine and Dentistry"=>3, "Agricultural and Biological Sciences"=>3, "Arts and Humanities"=>1, "Physics and Astronomy"=>1, "Psychology"=>1, "Social Sciences"=>6, "Computer Science"=>8, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Social Sciences"=>{"Social Sciences"=>6}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>3}, "Computer Science"=>{"Computer Science"=>8}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Netherlands"=>1, "United States"=>4, "Senegal"=>1, "Sri Lanka"=>1, "Mexico"=>1, "Thailand"=>1, "India"=>1}, "group_count"=>1}

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• http://doi.org/10.1007/s11042-016-3488-x
• http://doi.org/10.3233/JIFS-169097
• http://doi.org/10.1186/s40649-015-0020-9

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• {"id"=>"305079450201120768", "text"=>"As it turns out, socioeconomic context is an important predictor of community structure: http://t.co/gcGR1X6z1F @PLOSONE", "created_at"=>"2013-02-22T22:19:43Z", "user"=>"w4ffl3", "user_name"=>"Nick Ruktanonchai", "user_profile_image"=>"http://a0.twimg.com/profile_images/2532343045/52embjdywmq0m9tnsj4o_normal.jpeg"}
• {"id"=>"306522330182582273", "text"=>"excited to have a paper in @PLOSONE Nice to be able to share the link with friends and family not in academia: http://t.co/Yz85gnLiCw", "created_at"=>"2013-02-26T21:53:13Z", "user"=>"TrevorCaughlin", "user_name"=>"Trevor Caughlin", "user_profile_image"=>"http://a0.twimg.com/profile_images/2403157411/1vrxkatq1odl191p1a09_normal.jpeg"}
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• {"id"=>"475266366657015809", "text"=>"RT @aroopsind: #PLOSONE: Place-Based Attributes Predict Community Membership in a Mobile Phone Communication Network http://t.co/su3ISRMZjF", "created_at"=>"2014-06-07T13:21:46Z", "user"=>"BetteLoiselle", "user_name"=>"Bette Loiselle", "user_profile_image"=>"http://pbs.twimg.com/profile_images/423846740437897217/uTb31mb2_normal.jpeg"}

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• {"title"=>"File:Place-Based-Attributes-Predict-Community-Membership-in-a-Mobile-Phone-Communication-Network-pone.0056057.s002.ogv", "url"=>"http://commons.wikimedia.org/wiki/File:Place-Based-Attributes-Predict-Community-Membership-in-a-Mobile-Phone-Communication-Network-pone.0056057.s002.ogv", "timestamp"=>"2018-08-16T00:05:33Z"}

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• {"files"=>["https://ndownloader.figshare.com/files/968689"], "description"=>"<p>Modularity values closer to zero represent runs with fewer differences in connectivity between communities than within communities. Predictive power of LDA represents the percentage of towers with community membership correctly predicted.</p>", "links"=>[], "tags"=>["predictive", "lda", "modularity", "100", "runs"], "article_id"=>638201, "categories"=>["Mathematics"], "users"=>["T. Trevor Caughlin", "Nick Ruktanonchai", "Miguel A. Acevedo", "Kenneth K. Lopiano", "Olivia Prosper", "Nathan Eagle", "Andrew J. Tatem"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056057.g004", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_between_predictive_power_of_LDA_and_modularity_score_for_100_runs_of_the_modularity_algorithm_/638201", "title"=>"Correlation between predictive power of LDA and modularity score for 100 runs of the modularity algorithm.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-23 11:00:13"}
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• {"files"=>["https://ndownloader.figshare.com/files/1005229"], "description"=>"<p>Output from a linear discriminant analysis for the realization of the modularity algorithm with the highest modularity score.</p>", "links"=>[], "tags"=>["linear", "discriminant", "realization", "modularity", "algorithm", "highest"], "article_id"=>665846, "categories"=>["Mathematics"], "users"=>["T. Trevor Caughlin", "Nick Ruktanonchai", "Miguel A. Acevedo", "Kenneth K. Lopiano", "Olivia Prosper", "Nathan Eagle", "Andrew J. Tatem"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056057.t002", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Output_from_a_linear_discriminant_analysis_for_the_realization_of_the_modularity_algorithm_with_the_highest_modularity_score_/665846", "title"=>"Output from a linear discriminant analysis for the realization of the modularity algorithm with the highest modularity score.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-22 01:37:26"}
• {"files"=>["https://ndownloader.figshare.com/files/968681"], "description"=>"<p>Each dot represents a mobile phone tower, and each color represents a unique community. The capital city, Santo Domingo, is located in the center of the southern coast, represented by the cluster of red and blue dots. Panel a (top) shows the result from the run of the modularity algorithm with the highest modularity score. Panel b (bottom) shows predicted community membership of mobile phone towers from the LDA using four place-based attributes for the modularity realization with the top modularity score.</p>", "links"=>[], "tags"=>["170", "towers", "dominican"], "article_id"=>638195, "categories"=>["Mathematics"], "users"=>["T. Trevor Caughlin", "Nick Ruktanonchai", "Miguel A. Acevedo", "Kenneth K. Lopiano", "Olivia Prosper", "Nathan Eagle", "Andrew J. Tatem"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056057.g001", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Community_membership_of_170_mobile_phone_towers_in_the_Dominican_Republic_/638195", "title"=>"Community membership of 170 mobile phone towers in the Dominican Republic.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-23 10:58:33"}
• {"files"=>["https://ndownloader.figshare.com/files/968766", "https://ndownloader.figshare.com/files/968769"], "description"=>"<div><p>Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97) between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.</p> </div>", "links"=>[], "tags"=>["place-based", "attributes", "network"], "article_id"=>638244, "categories"=>["Mathematics"], "users"=>["T. Trevor Caughlin", "Nick Ruktanonchai", "Miguel A. Acevedo", "Kenneth K. Lopiano", "Olivia Prosper", "Nathan Eagle", "Andrew J. Tatem"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0056057.s001", "https://dx.doi.org/10.1371/journal.pone.0056057.s002"], "stats"=>{"downloads"=>5, "page_views"=>34, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Place_Based_Attributes_Predict_Community_Membership_in_a_Mobile_Phone_Communication_Network__/638244", "title"=>"Place-Based Attributes Predict Community Membership in a Mobile Phone Communication Network", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-02-23 11:13:23"}
• {"files"=>["https://ndownloader.figshare.com/files/1005204"], "description"=>"<p>Characteristics of place-based attributes used to quantify geographic context.</p>", "links"=>[], "tags"=>["place-based", "attributes", "quantify", "geographic"], "article_id"=>665825, "categories"=>["Mathematics"], "users"=>["T. Trevor Caughlin", "Nick Ruktanonchai", "Miguel A. Acevedo", "Kenneth K. Lopiano", "Olivia Prosper", "Nathan Eagle", "Andrew J. Tatem"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056057.t001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characteristics_of_place_based_attributes_used_to_quantify_geographic_context_/665825", "title"=>"Characteristics of place-based attributes used to quantify geographic context.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-22 01:37:05"}

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• {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
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• {"unique-ip"=>"2", "full-text"=>"1", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
• {"unique-ip"=>"4", "full-text"=>"3", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
• {"unique-ip"=>"6", "full-text"=>"4", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}

Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[269, 466, 588, 697, 800, 896, 988, 1076, 1165, 1254, 1340, 1417]}, {"subject_area"=>"/Computer and information sciences/Network analysis", "average_usage"=>[340, 548, 701, 844, 945, 1073, 1189, 1282, 1391, 1488, 1562, 1660, 1731]}, {"subject_area"=>"/Earth sciences", "average_usage"=>[296, 488, 620, 717, 828, 938, 1038, 1130, 1230, 1328, 1414, 1502, 1592]}, {"subject_area"=>"/Earth sciences/Geography", "average_usage"=>[285, 472, 614, 714, 817, 928, 1028, 1122, 1225, 1327, 1410, 1482, 1549]}, {"subject_area"=>"/Ecology and environmental sciences/Community ecology", "average_usage"=>[285, 478]}, {"subject_area"=>"/Engineering and technology/Transportation", "average_usage"=>[324, 555, 709, 879, 1033, 1141, 1244, 1354, 1508, 1618, 1712, 1778, 1847]}, {"subject_area"=>"/People and places", "average_usage"=>[270, 456, 573, 676, 773, 867, 955, 1037, 1120, 1205, 1286, 1363, 1436]}, {"subject_area"=>"/People and places/Geographical locations", "average_usage"=>[263, 456, 571, 673, 776, 866, 955, 1040, 1132, 1217, 1310, 1391, 1454]}, {"subject_area"=>"/Physical sciences", "average_usage"=>[254, 431, 547, 651, 748, 842, 932, 1017, 1098, 1178, 1259, 1336, 1404]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[259, 431, 541, 639, 727, 816, 898, 980, 1061, 1136, 1214, 1294, 1356]}]}

F1000Prime