scholarly journals Socioeconomic Patterns of Twitter User Activity

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 780
Author(s):  
Jacob Levy Abitbol ◽  
Alfredo J. Morales
Keyword(s):  
Socioeconomic Status ◽  
Large Scale ◽  
Human Mobility ◽  
High Dimensional ◽  
Demographic Information ◽  
Socioeconomic Groups ◽  
Twitter User ◽  
Anonymized Data ◽  
Online Conversation ◽  
Socioeconomic Patterns

Stratifying behaviors based on demographics and socioeconomic status is crucial for political and economic planning. Traditional methods to gather income and demographic information, like national censuses, require costly large-scale surveys both in terms of the financial and the organizational resources needed for their successful collection. In this study, we use data from social media to expose how behavioral patterns in different socioeconomic groups can be used to infer an individual’s income. In particular, we look at the way people explore cities and use topics of conversation online as a means of inferring individual socioeconomic status. Privacy is preserved by using anonymized data, and abstracting human mobility and online conversation topics as aggregated high-dimensional vectors. We show that mobility and hashtag activity are good predictors of income and that the highest and lowest socioeconomic quantiles have the most differentiated behavior across groups.

scholarly journals Trajectory-User Linking via Variational AutoEncoder

2018 ◽  
Author(s):  
Fan Zhou ◽  
Qiang Gao ◽  
Goce Trajcevski ◽  
Kunpeng Zhang ◽  
Ting Zhong ◽  
...  
Keyword(s):  
Latent Variables ◽  
Large Scale ◽  
Human Mobility ◽  
Hierarchical Structures ◽  
High Dimensional ◽  
Generation Process ◽  
Mobility Patterns ◽  
Variational Autoencoder ◽  
Hidden States ◽  
Structural Semantics

Trajectory-User Linking (TUL) is an essential task in Geo-tagged social media (GTSM) applications, enabling personalized Point of Interest (POI) recommendation and activity identification. Existing works on mining mobility patterns often model trajectories using Markov Chains (MC) or recurrent neural networks (RNN) -- either assuming independence between non-adjacent locations or following a shallow generation process. However, most of them ignore the fact that human trajectories are often sparse, high-dimensional and may contain embedded hierarchical structures. We tackle the TUL problem with a semi-supervised learning framework, called TULVAE (TUL via Variational AutoEncoder), which learns the human mobility in a neural generative architecture with stochastic latent variables that span hidden states in RNN. TULVAE alleviates the data sparsity problem by leveraging large-scale unlabeled data and represents the hierarchical and structural semantics of trajectories with high-dimensional latent variables. Our experiments demonstrate that TULVAE improves efficiency and linking performance in real GTSM datasets, in comparison to existing methods.

scholarly journals Uncovering the socioeconomic facets of human mobility

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hugo Barbosa ◽  
Surendra Hazarie ◽  
Brian Dickinson ◽  
Aleix Bassolas ◽  
Adam Frank ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Public Transportation ◽  
Large Scale ◽  
Human Mobility ◽  
Economic Status ◽  
Urban Infrastructure ◽  
Economic Systems ◽  
The Us ◽  
Low Levels

AbstractGiven the rapid recent trend of urbanization, a better understanding of how urban infrastructure mediates socioeconomic interactions and economic systems is of vital importance. While the accessibility of location-enabled devices as well as large-scale datasets of human activities, has fueled significant advances in our understanding, there is little agreement on the linkage between socioeconomic status and its influence on movement patterns, in particular, the role of inequality. Here, we analyze a heavily aggregated and anonymized summary of global mobility and investigate the relationships between socioeconomic status and mobility across a hundred cities in the US and Brazil. We uncover two types of relationships, finding either a clear connection or little-to-no interdependencies. The former tend to be characterized by low levels of public transportation usage, inequitable access to basic amenities and services, and segregated clusters of communities in terms of income, with the latter class showing the opposite trends. Our findings provide useful lessons in designing urban habitats that serve the larger interests of all inhabitants irrespective of their economic status.

A Fast Clustering Algorithm for Large-scale and High Dimensional Data

2009 ◽  
Vol 35 (7) ◽  
pp. 859-866
Author(s):  
Ming LIU ◽  
Xiao-Long WANG ◽  
Yuan-Chao LIU
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
High Dimensional

scholarly journals Changes in cannabis use according to socioeconomic status among Finnish adolescents from 2000 to 2015

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Noora Knaappila ◽  
Mauri Marttunen ◽  
Sari Fröjd ◽  
Nina Lindberg ◽  
Riittakerttu Kaltiala
Keyword(s):  
Socioeconomic Status ◽  
Parental Education ◽  
Population Based ◽  
Cannabis Use ◽  
Chi Square ◽  
Socioeconomic Groups ◽  
Parental Unemployment ◽  
The Usa ◽  
Chi Square Test ◽  
School Survey

Abstract Background Despite reduced sanctions and more permissive attitudes toward cannabis use in the USA and Europe, the prevalences of adolescent cannabis use have remained rather stable in the twenty-first century. However, whether trends in adolescent cannabis use differ between socioeconomic groups is not known. The aim of this study was to examine trends in cannabis use according to socioeconomic status among Finnish adolescents from 2000 to 2015. Methods A population-based school survey was conducted biennially among 14–16-year-old Finns between 2000 and 2015 (n = 761,278). Distributions for any and frequent cannabis use over time according to socioeconomic adversities were calculated using crosstabs and chi-square test. Associations between any and frequent cannabis use, time, and socioeconomic adversities were studied using binomial logistic regression results shown by odds ratios with 95% confidence intervals. Results At the overall level, the prevalences of lifetime and frequent cannabis use varied only slightly between 2000 and 2015. Cannabis use was associated with socioeconomic adversities (parental unemployment in the past year, low parental education, and not living with both parents). The differences in any and frequent cannabis use between socioeconomic groups increased significantly over the study period. Conclusions Although the overall changes in the prevalence of adolescent cannabis use were modest, cannabis use increased markedly among adolescents with the most socioeconomic adversities. Socioeconomic adversities should be considered in the prevention of adolescent cannabis use.

scholarly journals Tracking the global reduction of marine traffic during the COVID-19 pandemic

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
David March ◽  
Kristian Metcalfe ◽  
Joaquin Tintoré ◽  
Brendan J. Godley
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Western Mediterranean ◽  
Western Mediterranean Sea ◽  
Marine Traffic ◽  
Human Movements ◽  
Exclusive Economic Zones ◽  
Global Impacts ◽  
Economic Zones

AbstractThe COVID-19 pandemic has resulted in unparalleled global impacts on human mobility. In the ocean, ship-based activities are thought to have been impacted due to severe restrictions on human movements and changes in consumption. Here, we quantify and map global change in marine traffic during the first half of 2020. There were decreases in 70.2% of Exclusive Economic Zones but changes varied spatially and temporally in alignment with confinement measures. Global declines peaked in April, with a reduction in traffic occupancy of 1.4% and decreases found across 54.8% of the sampling units. Passenger vessels presented more marked and longer lasting decreases. A regional assessment in the Western Mediterranean Sea gave further insights regarding the pace of recovery and long-term changes. Our approach provides guidance for large-scale monitoring of the progress and potential effects of COVID-19 on vessel traffic that may subsequently influence the blue economy and ocean health.

scholarly journals Mining Vehicle Trajectories to Discover Individual Significant Places: Case Study using Floating Car Data in the Paris Region

2021 ◽  
pp. 036119812199550
Author(s):  
Danyang Sun ◽  
Fabien Leurent ◽  
Xiaoyan Xie
Keyword(s):  
Large Scale ◽  
City Planning ◽  
Human Mobility ◽  
Work Place ◽  
Trajectory Data ◽  
Floating Car Data ◽  
Vehicle Trajectories ◽  
Activity Types ◽  
Paris Region

In this study we discovered significant places in individual mobility by exploring vehicle trajectories from floating car data. The objective was to detect the geo-locations of significant places and further identify their functional types. Vehicle trajectories were first segmented into meaningful trips to recover corresponding stay points. A customized density-based clustering approach was implemented to cluster stay points into places and determine the significant ones for each individual vehicle. Next, a two-level hierarchy method was developed to identify the place types, which firstly identified the activity types by mixture model clustering on stay characteristics, and secondly discovered the place types by assessing their profiles of activity composition and frequentation. An applicational case study was conducted in the Paris region. As a result, five types of significant places were identified, including home place, work place, and three other types of secondary places. The results of the proposed method were compared with those from a commonly used rule-based identification, and showed a highly consistent matching on place recognition for the same vehicles. Overall, this study provides a large-scale instance of the study of human mobility anchors by mining passive trajectory data without prior knowledge. Such mined information can further help to understand human mobility regularities and facilitate city planning.

scholarly journals Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

2021 ◽  
Vol 11 (2) ◽  
pp. 472
Author(s):  
Hyeongmin Cho ◽  
Sangkyun Lee
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Large Scale ◽  
High Dimensional Data ◽  
Quality Measures ◽  
Training Data ◽  
Measure Data ◽  
High Dimensional ◽  
Small Scale ◽  
Class Separability

Machine learning has been proven to be effective in various application areas, such as object and speech recognition on mobile systems. Since a critical key to machine learning success is the availability of large training data, many datasets are being disclosed and published online. From a data consumer or manager point of view, measuring data quality is an important first step in the learning process. We need to determine which datasets to use, update, and maintain. However, not many practical ways to measure data quality are available today, especially when it comes to large-scale high-dimensional data, such as images and videos. This paper proposes two data quality measures that can compute class separability and in-class variability, the two important aspects of data quality, for a given dataset. Classical data quality measures tend to focus only on class separability; however, we suggest that in-class variability is another important data quality factor. We provide efficient algorithms to compute our quality measures based on random projections and bootstrapping with statistical benefits on large-scale high-dimensional data. In experiments, we show that our measures are compatible with classical measures on small-scale data and can be computed much more efficiently on large-scale high-dimensional datasets.

scholarly journals Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 146
Author(s):  
Aleksei Vakhnin ◽  
Evgenii Sopov
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithms ◽  
Numerical Experiments ◽  
Large Scale ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fixed Number ◽  
High Dimensional ◽  
Cooperative Coevolution ◽  
Large Scale Problems

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.

scholarly journals Association of the Yips and Musculoskeletal Problems in Highly Skilled Golfers: A Large Scale Epidemiological Study in Japan

Sports ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 71
Author(s):  
Yasufumi Gon ◽  
Daijiro Kabata ◽  
Sadahito Kawamura ◽  
Masahito Mihara ◽  
Ayumi Shintani ◽  
...  
Keyword(s):  
Large Scale ◽  
Detrimental Effect ◽  
Multivariate Logistic Regression Analysis ◽  
Positive Association ◽  
Demographic Information ◽  
Multivariate Logistic Regression ◽  
Factors Associated ◽  
Musculoskeletal Problems ◽  
Large Scale Epidemiological Study ◽  
Motor Disturbances

The yips are a set of conditions associated with intermittent motor disturbances that affect precision movement, especially in sports. Specifically, skilled golfers suffer from the yips, although its clinical characteristics and pathophysiology have not been well-studied. We surveyed skilled golfers to characterize their yips-related symptoms, to explore potential confounding factors associated with the yips. Golfers’ demographic information, golfing-career-related history, musculoskeletal status and manifestations of the yips are surveyed. Among the 1576 questionnaires distributed, 1457 (92%) responses were received, of which 39% of golfers had experienced the yips. The median age and golfing careers were 48 and 28 years, respectively. Golfers who had experienced the yips were older and had longer golfing careers and more frequent musculoskeletal problems than those without experience of the yips. The multivariate logistic regression analysis revealed that a longer golfing career and musculoskeletal problems were independent factors associated with yips experience. More severe musculoskeletal problems were associated with higher odds of experiencing the yips. A positive association between the yips and musculoskeletal problems was also observed. The yips have similar characteristics to task-specific movement disorders, with a detrimental effect caused by excessive repetition of a routine task. These findings support the notion that the yips are a type of task-specific dystonia.

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