Open AccessSocial Mobility Patterns in the World's Populated Cities Through COVID-19
Author(s) -
Ana Lorena Jiménez Preciado,
Nora Gavira Durón
Publication year - 2021
Language(s) - English
Resource type - Journals
ISSN - 1665-5346
DOI - 10.21919/remef.v16i3.675
Subject(s) - social distance , individual mobility , kernel density estimation , residence , covid-19 , economic geography , geography , demographic economics , work (physics) , regional science , statistics , econometrics , estimator , economics , mathematics , engineering , medicine , mechanical engineering , disease , pathology , infectious disease (medical specialty)
Objective: identify social mobility patterns in the world's most populated cities from the ravaging pandemic of COVID-19 and the confinement and social distancing measures. Method: ternary diagrams to examine the simultaneous movement to different places (grocery, services, parks, workplaces, residence, and transit). Specifically, we use crosshair ternary plots and a Gaussian Kernel Density Estimator (KDE) for ternary density diagrams. Results: for the most part, the mobility reduction was between 40% and 60% in the selected cities. Nevertheless, there were more significant transit cases, but not workplaces or residences, suggesting that the informal market may absorb part of the labor work. Limitations and implications: the main limitation of this analysis is in scaling the data since the mobility statistics represent negative percentages. Main contribution: the work's principal contribution and originality lie in using ternary diagrams, allowing the identification of social mobility patterns in the largest cities and understanding how displacement of populations has changed since COVID-19.