Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data
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{"title"=>"Exploring universal patterns in human home-work commuting from mobile phone data", "type"=>"journal", "authors"=>[{"first_name"=>"Kevin S.", "last_name"=>"Kung", "scopus_author_id"=>"55776982600"}, {"first_name"=>"Kael", "last_name"=>"Greco", "scopus_author_id"=>"56227349400"}, {"first_name"=>"Stanislav", "last_name"=>"Sobolevsky", "scopus_author_id"=>"37027058000"}, {"first_name"=>"Carlo", "last_name"=>"Ratti", "scopus_author_id"=>"35230766400"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84903147238", "doi"=>"10.1371/journal.pone.0096180", "pui"=>"373381727", "pmid"=>"24933264", "scopus"=>"2-s2.0-84903147238", "issn"=>"19326203", "arxiv"=>"1311.2911"}, "id"=>"7c40b79c-ede6-31b5-a794-651c0fea89b0", "abstract"=>"Home-work commuting has always attracted significant research attention because of its impact on human mobility. One of the key assumptions in this domain of study is the universal uniformity of commute times. However, a true comparison of commute patterns has often been hindered by the intrinsic differences in data collection methods, which make observation from different countries potentially biased and unreliable. In the present work, we approach this problem through the use of mobile phone call detail records (CDRs), which offers a consistent method for investigating mobility patterns in wholly different parts of the world. We apply our analysis to a broad range of datasets, at both the country (Portugal, Ivory Coast, and Saudi Arabia), and city (Boston) scale. Additionally, we compare these results with those obtained from vehicle GPS traces in Milan. While different regions have some unique commute time characteristics, we show that the home-work time distributions and average values within a single region are indeed largely independent of commute distance or country (Portugal, Ivory Coast, and Boston)-despite substantial spatial and infrastructural differences. Furthermore, our comparative analysis demonstrates that such distance-independence holds true only if we consider multimodal commute behaviors-as consistent with previous studies. In car-only (Milan GPS traces) and car-heavy (Saudi Arabia) commute datasets, we see that commute time is indeed influenced by commute distance. Finally, we put forth a testable hypothesis and suggest ways for future work to make more accurate and generalizable statements about human commute behaviors.", "link"=>"http://www.mendeley.com/research/exploring-universal-patterns-human-homework-commuting-mobile-phone-data", "reader_count"=>143, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>4, "Librarian"=>1, "Researcher"=>19, "Student > Doctoral Student"=>12, "Student > Ph. D. Student"=>49, "Student > Postgraduate"=>3, "Student > Master"=>29, "Other"=>8, "Student > Bachelor"=>9, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>5, "Professor > Associate Professor"=>4, "Librarian"=>1, "Researcher"=>19, "Student > Doctoral Student"=>12, "Student > Ph. D. Student"=>49, "Student > Postgraduate"=>3, "Student > Master"=>29, "Other"=>8, "Student > Bachelor"=>9, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>10, "Agricultural and Biological Sciences"=>3, "Arts and Humanities"=>3, "Philosophy"=>2, "Business, Management and Accounting"=>6, "Computer Science"=>26, "Earth and Planetary Sciences"=>10, "Economics, Econometrics and Finance"=>7, "Engineering"=>16, "Environmental Science"=>9, "Mathematics"=>2, "Medicine and Dentistry"=>2, "Design"=>4, "Physics and Astronomy"=>9, "Psychology"=>5, "Social Sciences"=>29}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Social Sciences"=>{"Social Sciences"=>29}, "Physics and Astronomy"=>{"Physics and Astronomy"=>9}, "Psychology"=>{"Psychology"=>5}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>10}, "Environmental Science"=>{"Environmental Science"=>9}, "Arts and Humanities"=>{"Arts and Humanities"=>3}, "Design"=>{"Design"=>4}, "Engineering"=>{"Engineering"=>16}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>10}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>7}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>3}, "Computer Science"=>{"Computer Science"=>26}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>6}, "Philosophy"=>{"Philosophy"=>2}}, "reader_count_by_country"=>{"United States"=>5, "Sri Lanka"=>1, "Japan"=>1, "United Kingdom"=>1, "Belarus"=>1, "Spain"=>4, "Russia"=>1, "Canada"=>2, "Turkey"=>1, "Belgium"=>1, "Luxembourg"=>1, "Finland"=>1, "Denmark"=>1, "France"=>2, "Germany"=>1}, "group_count"=>9}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1536529"], "description"=>"<p>Statistics from Spearman’s correlation tests on the timing of commute, with data drawn from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096180#pone-0096180-g004\" target=\"_blank\">Figure 4</a>.</p>", "links"=>[], "tags"=>["geoinformatics", "Spatial analysis", "Network Analysis", "social networks", "geography", "Human geography", "Social geography", "Sociology", "communications", "Social mobility", "drawn"], "article_id"=>1058755, "categories"=>["Science Policy"], "users"=>["Kevin S. Kung", "Kael Greco", "Stanislav Sobolevsky", "Carlo Ratti"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096180.t002", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistics_from_Spearman_s_correlation_tests_on_the_timing_of_commute_with_data_drawn_from_Figure_4_/1058755", "title"=>"Statistics from Spearman’s correlation tests on the timing of commute, with data drawn from Figure 4.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-06-16 03:23:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536528"], "description"=>"<p>The first column of figures shows the probability density functions of morning commute times based on mobile phone signaling data, in Ivory Coast (a–b), Portugal (c–d), Saudi Arabia (e–f), Boston (g–h), and Milan (i–j), for individuals binned by their commute distances: <5 km (blue solid line), 5–10 km (red dashed line), 10–20 km (green dash-dotted line), 20–40 km (black solid line), and 40–80 km (magenta dashed line). The inset plots show the cumulative distribution function of the same quantities. The second column of figures shows the probability density functions of evening commute times the respective regions.</p>", "links"=>[], "tags"=>["geoinformatics", "Spatial analysis", "Network Analysis", "social networks", "geography", "Human geography", "Social geography", "Sociology", "communications", "Social mobility", "functions", "commute"], "article_id"=>1058754, "categories"=>["Science Policy"], "users"=>["Kevin S. Kung", "Kael Greco", "Stanislav Sobolevsky", "Carlo Ratti"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096180.g006", "stats"=>{"downloads"=>1, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probability_density_functions_of_commute_times_/1058754", "title"=>"Probability density functions of commute times.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:23:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536572", "https://ndownloader.figshare.com/files/1536573", "https://ndownloader.figshare.com/files/1536574", "https://ndownloader.figshare.com/files/1536575"], "description"=>"<div><p>Home-work commuting has always attracted significant research attention because of its impact on human mobility. One of the key assumptions in this domain of study is the universal uniformity of commute times. However, a true comparison of commute patterns has often been hindered by the intrinsic differences in data collection methods, which make observation from different countries potentially biased and unreliable. In the present work, we approach this problem through the use of mobile phone call detail records (CDRs), which offers a consistent method for investigating mobility patterns in wholly different parts of the world. We apply our analysis to a broad range of datasets, at both the country (Portugal, Ivory Coast, and Saudi Arabia), and city (Boston) scale. Additionally, we compare these results with those obtained from vehicle GPS traces in Milan. While different regions have some unique commute time characteristics, we show that the home-work time distributions and average values within a single region are indeed largely independent of commute distance or country (Portugal, Ivory Coast, and Boston)–despite substantial spatial and infrastructural differences. Furthermore, our comparative analysis demonstrates that such distance-independence holds true only if we consider multimodal commute behaviors–as consistent with previous studies. In car-only (Milan GPS traces) and car-heavy (Saudi Arabia) commute datasets, we see that commute time is indeed influenced by commute distance. Finally, we put forth a testable hypothesis and suggest ways for future work to make more accurate and generalizable statements about human commute behaviors.</p></div>", "links"=>[], "tags"=>["geoinformatics", "Spatial analysis", "Network Analysis", "social networks", "geography", "Human geography", "Social geography", "Sociology", "communications", "Social mobility", "patterns", "home-work", "commuting"], "article_id"=>1058795, "categories"=>["Science Policy"], "users"=>["Kevin S. Kung", "Kael Greco", "Stanislav Sobolevsky", "Carlo Ratti"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0096180.s001", "https://dx.doi.org/10.1371/journal.pone.0096180.s002", "https://dx.doi.org/10.1371/journal.pone.0096180.s003", "https://dx.doi.org/10.1371/journal.pone.0096180.s004"], "stats"=>{"downloads"=>0, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Exploring_Universal_Patterns_in_Human_Home_Work_Commuting_from_Mobile_Phone_Data_/1058795", "title"=>"Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-06-16 03:23:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536522"], "description"=>"<p>The peak times of morning (a, c) and evening (b, d) commutes for Ivory Coast (blue solid line with open circles) and Portugal (red dashed line with closed dots), as a function of commute distance. There are two methods of calculating this peak time: the median time (a, b), and the fitted Gaussian mean time (c, d). Note the stronger distance-dependent behaviors in the morning. The individual’s commute times in the morning and evening are estimated, respectively, by the time of the last call from home in the morning, and by the time of the first call from home in the evening. <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096180#pone.0096180.s003\" target=\"_blank\">Fig. S3</a> shows the procedure whereby Gaussian distributions are fitted to the distributions plotted in Fig. 3 in order to produce the peak commute time values. The statistics from the Spearman’s rank correlation tests on these relationships are summarized in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096180#pone-0096180-t002\" target=\"_blank\">Table 2</a>.</p>", "links"=>[], "tags"=>["geoinformatics", "Spatial analysis", "Network Analysis", "social networks", "geography", "Human geography", "Social geography", "Sociology", "communications", "Social mobility", "commute", "times"], "article_id"=>1058748, "categories"=>["Science Policy"], "users"=>["Kevin S. Kung", "Kael Greco", "Stanislav Sobolevsky", "Carlo Ratti"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096180.g004", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Peak_commute_times_as_a_function_of_commute_distance_/1058748", "title"=>"Peak commute times as a function of commute distance.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:23:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536519"], "description"=>"<p>The timing of morning (a, c) and evening commutes (b, d) for Ivory Coast (a, b) and Portugal (c, d), for individuals binned by their commute distances: <2.5 km (blue solid line), 2.5–5 km (red dashed line), 5–10 km (green dash-dotted line), 10–20 km (black solid line), and >20 km (cyan dashed line). The individual’s commute times in the morning and evening are estimated, respectively, by the time of the last call from home in the morning, and by the time of the first call from home in the evening. <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096180#pone.0096180.s003\" target=\"_blank\">Fig. S3</a> shows the sample fit of such distribution to a Gaussian distribution.</p>", "links"=>[], "tags"=>["geoinformatics", "Spatial analysis", "Network Analysis", "social networks", "geography", "Human geography", "Social geography", "Sociology", "communications", "Social mobility", "commute"], "article_id"=>1058745, "categories"=>["Science Policy"], "users"=>["Kevin S. Kung", "Kael Greco", "Stanislav Sobolevsky", "Carlo Ratti"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096180.g003", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distributions_of_commute_timing_/1058745", "title"=>"Distributions of commute timing.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:23:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536517"], "description"=>"<p>The distributions are plotted for Ivory Coast (blue solid line with closed dots), Portugal (red dashed line with x’s), Saudi Arabia (green solid line with open circles), Boston (black solid line with open diamonds), and Milan (cyan dashed line with open triangles). The inset plot is the same plot, reproduced on a log-log scale to show long-tail behaviors of the distributions. The same plots, as cumulative density functions, are shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096180#pone.0096180.s004\" target=\"_blank\">Figure S4</a> for comparative purposes.</p>", "links"=>[], "tags"=>["geoinformatics", "Spatial analysis", "Network Analysis", "social networks", "geography", "Human geography", "Social geography", "Sociology", "communications", "Social mobility", "home-work", "commuting", "aggregated"], "article_id"=>1058743, "categories"=>["Science Policy"], "users"=>["Kevin S. Kung", "Kael Greco", "Stanislav Sobolevsky", "Carlo Ratti"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096180.g002", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distributions_of_home_work_commuting_distances_aggregated_by_countries_cities_/1058743", "title"=>"Distributions of home-work commuting distances, aggregated by countries/cities.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:23:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536516"], "description"=>"<p>This is quantified by the mean daily dwell-time that an average individual in Ivory Coast (right plot) and Portugal (left plot) spends in each of his/her ranked places in the set of non-overlapping frequented places, plotted on a log-log scale, during the day (solid red lines with crosses) and during the night (dashed blue lines with open circles). While the daytime curves follow roughly Zipf’s law, the nighttime curves show a distinct sigmoidal behavior.</p>", "links"=>[], "tags"=>["geoinformatics", "Spatial analysis", "Network Analysis", "social networks", "geography", "Human geography", "Social geography", "Sociology", "communications", "Social mobility", "mobility"], "article_id"=>1058742, "categories"=>["Science Policy"], "users"=>["Kevin S. Kung", "Kael Greco", "Stanislav Sobolevsky", "Carlo Ratti"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096180.g001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Range_of_mobility_during_day_and_night_/1058742", "title"=>"Range of mobility during day and night.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:23:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536530"], "description"=>"<p>The fraction represented shows the approximated ratio of the number of users tracked by each dataset to the total number of potential users in the regions concerned.</p>", "links"=>[], "tags"=>["geoinformatics", "Spatial analysis", "Network Analysis", "social networks", "geography", "Human geography", "Social geography", "Sociology", "communications", "Social mobility"], "article_id"=>1058756, "categories"=>["Science Policy"], "users"=>["Kevin S. Kung", "Kael Greco", "Stanislav Sobolevsky", "Carlo Ratti"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096180.t001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_Datasets_/1058756", "title"=>"Summary of Datasets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-06-16 03:23:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536524"], "description"=>"<p>The means are for the morning (a) and evening (b) for Ivory Coast (blue solid line with closed dots), Portugal (red dashed line with x’s), Saudi Arabia (green solid line with open circles), Boston (black solid line with open diamonds), and Milan (cyan dashed line with open triangles). While Ivory Coast, Portugal, and Boston consist of mobile phone datasets that cover aggregate commute patterns, the Milan data are GPS traces, which provide a comparative insight into car-only commute patterns.</p>", "links"=>[], "tags"=>["geoinformatics", "Spatial analysis", "Network Analysis", "social networks", "geography", "Human geography", "Social geography", "Sociology", "communications", "Social mobility", "commute", "times"], "article_id"=>1058751, "categories"=>["Science Policy"], "users"=>["Kevin S. Kung", "Kael Greco", "Stanislav Sobolevsky", "Carlo Ratti"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096180.g005", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_commute_times_as_a_function_of_commute_distance_/1058751", "title"=>"Mean commute times as a function of commute distance.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:23:19"}

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Relative Metric

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