STS-EPR: Modelling individual mobility considering the spatial, temporal, and social dimensions together

Cornacchia, Giuliano; Pappalardo, Luca

doi:10.1016/j.procs.2021.03.035

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…In this study, we use data from one of the more relevant location-based social networks: Foursquare 25 . This LBSN has been used previously in several studies 23,24,[26][27][28][29][30][31][32][33][34] , of human mobility and social relationships, encounter networks due to human mobility, spatial and temporal activity patterns, among others. A more detailed OPEN 1 description of Foursquare will be given in the next sections.…”

Section: Temporal Visitation Patterns Of Points Of Interest In Cities...mentioning

confidence: 99%

Temporal visitation patterns of points of interest in cities on a planetary scale: a network science and machine learning approach

Betancourt

Riascos

Mateos

2023

Sci Rep

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We aim to study the temporal patterns of activity in points of interest of cities around the world. In order to do so, we use the data provided by the online location-based social network Foursquare, where users make check-ins that indicate points of interest in the city. The data set comprises more than 90 million check-ins in 632 cities of 87 countries in 5 continents. We analyzed more than 11 million points of interest including all sorts of places: airports, restaurants, parks, hospitals, and many others. With this information, we obtained spatial and temporal patterns of activities for each city. We quantify similarities and differences of these patterns for all the cities involved and construct a network connecting pairs of cities. The links of this network indicate the similarity of temporal visitation patterns of points of interest between cities and is quantified with the Kullback-Leibler divergence between two distributions. Then, we obtained the community structure of this network and the geographic distribution of these communities worldwide. For comparison, we also use a Machine Learning algorithm—unsupervised agglomerative clustering—to obtain clusters or communities of cities with similar patterns. The main result is that both approaches give the same classification of five communities belonging to five different continents worldwide. This suggests that temporal patterns of activity can be universal, with some geographical, historical, and cultural variations, on a planetary scale.

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Section: Temporal Visitation Patterns Of Points Of Interest In Cities...mentioning

confidence: 99%

Temporal visitation patterns of points of interest in cities on a planetary scale: a network science and machine learning approach

Betancourt

Riascos

Mateos

2023

Sci Rep

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“…Para ciertos investigadores los datos de la movilidad son cruciales en diferentes contextos (Cornacchia y Pappalardo, 2021). En particular, algunos mencionan (Sun, Lei y Liu, 2021;Dribe, Helgertz y Putte, 2015) que el desarrollo a largo plazo de la movilidad social ha sido un tema de investigación relevante por mucho tiempo en la economía y la sociología.…”

Section: Problematización Del Concepto De Movilidad Socialunclassified

Movilidad social intergeneracional en la migración internacional: el caso Huandacareo, Michoacán

Guzmán¹

2022

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Los desplazamientos humanos son un tema de investigación muy relevante para la ciencia histórica, de suma relevancia para México por su robusta tradición, la cuestión de movilidad social vista históricamente en la migración amerita un análisis exhaustivo. Esta investigación examina un tema escasamente estudiado en México, revela que los entrevistados manifiestan movilidad intergeneracional ascendente, ocupacional, de riqueza y socioeconómica para el caso de los empresarios, con ingresos, capacidad de compra y estilo de vida muy superior a la de sus padres; y educativa, subjetiva para la segunda generación. La contribución de esta investigación, de corte comparativo con un enfoque de la migración como proceso —con perspectiva de la historia inmediata, metodológicamente basada en la historia oral—, tiene la peculiaridad de ponderar las características sociodemográficas y sociohistóricas de los empresarios y la segunda generación con estudios universitarios, bajo el concepto de "movilidad social intergeneracional". En este trabajo las historias de vida y una prolongada observación participante nos permitieron acceder a este grado de información.

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“…In this study we use data from Foursquare 32 , a location-based social network that has been used previously in some studies 30,31,[33][34][35][36][37][38][39][40] . A more detail description of Foursquare will be given in the next sections.…”

Section: Introductionmentioning

confidence: 99%

Temporal visitation patterns of points of interest in cities on a planetary scale: a network science and machine learning approach

Betancourt¹,

Riascos²,

Mateos³

2023

Preprint

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In this work, we aim at studying the diversity of human activity patterns in cities around the world. In order to do so, we use, as a proxy, the data provided by the online location-based social network Foursquare, where users make check-ins that indicate points of interest in the city. The data set comprise more than 90 million check-ins in 632 cities of 84 countries in 5 continents. We analyzed more than 11 million points of interest including all sort of places: airports, train stations, restaurants, cafes, bars, parks, hospitals, gyms and many others. With this information we obtained spatial and temporal patterns of activities of each city. We quantify similarities and differences of these patterns for all the cities involved, and construct a network connecting pairs of cities. The links of this network indicate the similarity of patterns between cities and is quantified with the relative entropy between two distributions. Then, we obtained the community structure of this network and the geographic distribution of communities. For comparison, we use a Machine Learning algorithm -unsupervised agglomerative clustering -to obtained clusters or communities of cities with similar patterns. The main result is that both approaches give the same classification of five communities belonging to five different continents worldwide. This suggest that human activity patterns can be universal with some geographical, historical and cultural variations on a planetary scale.

show abstract

STS-EPR: Modelling individual mobility considering the spatial, temporal, and social dimensions together

Cited by 8 publications

References 15 publications

Temporal visitation patterns of points of interest in cities on a planetary scale: a network science and machine learning approach

Temporal visitation patterns of points of interest in cities on a planetary scale: a network science and machine learning approach

Movilidad social intergeneracional en la migración internacional: el caso Huandacareo, Michoacán

Temporal visitation patterns of points of interest in cities on a planetary scale: a network science and machine learning approach

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