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Óscar Fontanelli

Facultad Latinoamericana de Ciencias Sociales

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Maribel Hernández-Rosales

Center for Research and Advanced Studies of the National Polytechnic Institute

Wentian Li

Shanghai East Hospital

Plinio Guzmán

Instituto Politécnico Nacional

Ricardo Mansilla

Hospital Universitari Germans Trias i Pujol

Guillermo de Anda‐Jáuregui

National Institute of Genomic Medicine

Pedro Miramóntes

Universidad Nacional Autónoma de México

Marisol Flores-Garrido

Universidad Nacional Autónoma de México

Amilcar Meneses‐Viveros

Center for Research and Advanced Studies of the National Polytechnic Institute

Gabriela Olmedo-Ãlvarez

Center for Research and Advanced Studies of the National Polytechnic Institute

G. Cocho

Universidad Nacional Autónoma de México

Cooperative Institutions

Universidad Nacional Autónoma de México

Asociación Mexicana de Psicología y Desarrollo Comunitario

Instituto Politécnico Nacional

Center for Research and Advanced Studies of the National Polytechnic Institute

Universidad Autónoma del Estado de Morelos

University of Oxford

Universidad Autónoma de la Ciudad de México

Instituto Tecnológico Superior de Irapuato

Instituto de Estudios Avanzados

Universidad Tecnológica de Aguascalientes

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Human mobility patterns in Mexico City and their links with socioeconomic variables during the COVID-19 pandemic

Research Square (Research Square) (2022)

Óscar Fontanelli Dulce Valdivia Guillermo Romero O. Guerrero Medina Wentian Li

Abstract The availability of cellphone geolocation data provides a remarkable opportunity to study human mobility patterns and how these patterns are affected by the recent pandemic. Two simple centrality metrics allow us to measure two different aspects of mobility in origin-destination networks constructed with this type of data: variety of places connected to a certain node (degree) and number of people that travel to or from a given node (strength). In this contribution, we present an analysis of node degree and strength in daily origin-destination networks for Greater Mexico City during 2020. Unlike what is observed in many complex networks, these origin-destination networks are not scale free. Instead, there is a characteristic scale defined by the distribution peak; centrality distributions exhibit a skewed two-tail distribution with power law decay on each side of the peak. We found that high mobility areas tend to be closer to the city center, have higher population and better socioeconomic conditions. Areas with anomalous behavior are almost always on the periphery of the city, where we can also observe qualitative difference in mobility patterns between east and west. Finally, we study the effect of mobility restrictions due to the outbreak of the COVID-19 pandemics on these mobility patterns.

Pandemic

Geolocation

10.21203/rs.3.rs-1957256/v1

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Citations (2)

Administrative unit population data for 150 countries and territories

Figshare (2017)

Óscar Fontanelli

File official-data.rar includes population data of second level administrative units, from offficial data sources, for Mexico (municipalities), Spain (provinces) and US (counties). It also includes population data for primary and secondary level administrative divisions and major cities for China and India.
File sau-data.rar includes population data for second level administrative units for 150 countries and territories. Data source is the Statoids data base, taken from statoids.com.
File world-population.rar includes population for countries territories from the UN and the World Bank.
All data updated up to january 2017.

10.6084/m9.figshare.4570297

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Lavalatte_distribution_occurrence_examples.rar

Figshare (2016)

Óscar Fontanelli

Examples of occurrence of the Lavalette distributionSAU population for Nigeria.TAU population in Spain.Amino acid to amino acid mutation rates for missense variants.Plant organelle codon usage.Non rodent mammalian nucleus codon usage.

10.6084/m9.figshare.3363961

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Analyzing time series activity of Twitter political spambots

arXiv (Cornell University) (2021)

Óscar Fontanelli Aldo Venegas Ricardo Mansilla

The presence and complexity of political Twitter bots has increased in recent years, making it a very difficult task to recognize these accounts from real, human users. We intended to provide an answer to the following question: are temporal patterns of activity qualitatively different in fake and human accounts? We collected a large sample of tweets during the post-electoral conflict in the US in 2020 and performed supervised and non-supervised statistical learning technique sto quantify the predictive power of time-series features for human-bot recognition. Our results show that there are no substantial differences, suggesting that political bots are nowadays very capable of mimicking human behaviour. This finding reveals the need for novel, more sophisticated bot-detection techniques.

10.48550/arxiv.2105.12734

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Extreme drought threat assessment: ranking analysis and its application to a regional study in central Mexico

Research Square (Research Square) (2024)

Elio Roca-Flores Soto J. Soto-Bernal Berenice Rojo-Garibaldi Óscar Fontanelli Miguel Angel González-González

Abstract This work presents a novel method of threat assessment applied to the drought-prone region of Aguascalientes in Mexico. Extreme drought poses a risk to human health, economy, and survival, aside from the ecological effects. A way to study and assess the threat of complex catastrophic events such as earthquakes, hurricanes, etc. is to use ranking statistics. Such a method has not been used to analyze droughts. Here, such a tool is introduced to study an important case example in central Mexico: Aguascalientes City which recently suffered from extreme drought conditions in 2022 and 2023. Therefore, in this work, the ranking of annual precipitations in Aguascalientes City is obtained. This analysis indicates that meteorological droughts can be classified into different phases, which are clearly separated by gaps in the ranking profile on a log-log scale. Thereafter, several ranking laws that fit both tails corresponding to dramatic events were compared to the data using nonlinear regression. It is found that the Weibull function provides a better fit to the data than other twoparameter fitting functions. An empirical analysis of the fitting parameters of such a function for some other nearby communities shows a linear relation between both parameters. Such a relationship has limited spatial validity. This suggests that the ranking fit can be effectively characterized by a single independent parameter within a region. This provides a quantitative measure of the meteorological drought threat that can serve as a climatic index. The ranking analysis presented here aids in the hydrological management of water basins, including the assistance for agricultural producers, and serves to assess the risk associated with extreme drought.

10.21203/rs.3.rs-3967838/v1

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arXiv (Cornell University) (2020)

Óscar Fontanelli Ricardo Mansilla

In this work we introduce a model based on master equations to describe the time evolution of the popularity of topics and hashtags on the Twitter social network. Specifically, we model the number of times a certain hashtag appears on the network as a function of time. In our model, the behavior of this quantity depends on the degree distribution of the network and the extrinsic interest the community has for the topic or hashtag. From the master equation, we are able to obtain explicit solutions for the mean and variance. We propose a gamma kernel function to model the topic popularity, which is quite simple and yields reasonable results. Finally, we validate the plausibility of the model by analyzing actual Twitter data obtained through the public API.

Popularity

Kernel (algebra)

Social network (sociolinguistics)

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The Contact and Mobility Networks of Mexico City

arXiv (Cornell University) (2020)

Guillermo de Anda‐Jáuregui Plinio Guzmán Óscar Fontanelli Amilcar Meneses‐Viveros Alfredo Hernández

Mexico City, the largest city in Mexico, is also one of the largest cities in the world. It has over 9 million inhabitants and concentrates the vast majority of government and business centers. In this work we describe algorithms that use anonymized location data from mobile devices to construct Mexico City's contact and mobility networks aiming to help the analysis of the city's complexity by understanding movement and physical interaction patterns between its inhabitants. We show the effectiveness and usefulness of our approach by building networks with data collected in February 2020 and performing a general descriptive analysis on them. We found that contact networks in Mexico City are very sparse, characterized by a largest connected component, and with a heavy-tailed degree distribution. On the other hand, we observed that paths conformed by the highest-degrree nodes of mobility networks resemble Mexico City's street network; moreover, we found interesting qualitative differences in the degree distribution of these networks between weekends and weekdays. We present these results along with the release of contact and mobility networks.

Mexico city

Degree distribution

10.48550/arxiv.2007.14596

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Intermunicipal Travel Networks of Mexico (2020-2021)

arXiv (Cornell University) (2022)

Óscar Fontanelli Plinio Guzmán Amílcar Meneses Alfredo Hernández Marisol Flores-Garrido

We present a collection of networks that describe the travel patterns between municipalities in Mexico between 2020 and 2021. Using anonymized mobile device geo-location data we constructed directed, weighted networks representing the (normalized) volume of travels between municipalities. We analysed changes in global (graph total weight sum), local (centrality measures), and mesoscale (community structure) network features. We observe that changes in these features are associated with factors such as Covid-19 restrictions and population size. In general, events in early 2020 (when initial Covid-19 restrictions were implemented) induced more intense changes in network features, whereas later events had a less notable impact in network features. We believe these networks will be useful for researchers and decision makers in the areas of transportation, infrastructure planning, epidemic control and network science at large.

Spatial network

10.48550/arxiv.2203.13916

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Citations (1)

Social Network Analysis and Mining (2022)

Óscar Fontanelli Demian Hernández Ricardo Mansilla

Popularity

Time line

Kernel (algebra)

10.1007/s13278-022-00861-4

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Intermunicipal travel networks of Mexico during the COVID-19 pandemic

Scientific Reports (2023)

Óscar Fontanelli Plinio Guzmán Amilcar Meneses‐Viveros Alfredo José Hernández-Álvarez Marisol Flores-Garrido

Human mobility networks are widely used for diverse studies in geography, sociology, and economics. In these networks, nodes usually represent places or regions and links refer to movement between them. They become essential when studying the spread of a virus, the planning of transit, or society's local and global structures. Therefore, the construction and analysis of human mobility networks are crucial for a vast number of real-life applications. This work presents a collection of networks that describe the human travel patterns between municipalities in Mexico in the 2020-2021 period. Using anonymized mobile location data, we constructed directed, weighted networks representing the volume of travels between municipalities. We analysed changes in global, local, and mesoscale network features. We observe that changes in these features are associated with factors such as COVID-19 restrictions and population size. In general, the implementation of restrictions at the start of the COVID-19 pandemic in early 2020, induced more intense changes in network features than later events, which had a less notable impact in network features. These networks will result very useful for researchers and decision-makers in the areas of transportation, infrastructure planning, epidemic control and network science at large.

Pandemic

Network Analysis

10.1038/s41598-023-35542-5

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Citations (6)