Potgieter, AFabris-Rotelli, INKimmie, NDudeni-Tlhone, NontembekoHolloway, Jennifer PJanse van Rensburg, CThiede, RDebba, PraveshManjoo-Docrat, RAbdelatif, NMakhanya, S2021-11-172021-11-172021-10Potgieter, A., Fabris-Rotelli, I., Kimmie, N., Dudeni-Tlhone, N., Holloway, J.P., Janse van Rensburg, C., Thiede, R. & Debba, D. et al. 2021. Modelling representative population mobility for COVID-19 spatial transmission in South Africa. <i>Frontiers in Big Data, 4.</i> http://hdl.handle.net/10204/121502624-909Xhttps://doi.org/10.3389/fdata.2021.718351http://hdl.handle.net/10204/12150The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices and further compares the results through hierarchical clustering. This provides insight for the user into which data provides what type of information and in what situations a particular source is most useful.FulltextenCovid-19Reduced mobilitySpatial weight matricesPrincipal component analysisHierarchical clusteringArticlePotgieter, A., Fabris-Rotelli, I., Kimmie, N., Dudeni-Tlhone, N., Holloway, J. P., Janse van Rensburg, C., ... Makhanya, S. (2021). Modelling representative population mobility for COVID-19 spatial transmission in South Africa. <i>Frontiers in Big Data, 4</i>, http://hdl.handle.net/10204/12150Potgieter, A, IN Fabris-Rotelli, N Kimmie, Nontembeko Dudeni-Tlhone, Jennifer P Holloway, C Janse van Rensburg, R Thiede, et al "Modelling representative population mobility for COVID-19 spatial transmission in South Africa." <i>Frontiers in Big Data, 4</i> (2021) http://hdl.handle.net/10204/12150Potgieter A, Fabris-Rotelli I, Kimmie N, Dudeni-Tlhone N, Holloway JP, Janse van Rensburg C, et al. Modelling representative population mobility for COVID-19 spatial transmission in South Africa. Frontiers in Big Data, 4. 2021; http://hdl.handle.net/10204/12150.TY - Article AU - Potgieter, A AU - Fabris-Rotelli, IN AU - Kimmie, N AU - Dudeni-Tlhone, Nontembeko AU - Holloway, Jennifer P AU - Janse van Rensburg, C AU - Thiede, R AU - Debba, Debba AU - Manjoo-Docrat, R AU - Abdelatif, N AU - Makhanya, S AB - The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices and further compares the results through hierarchical clustering. This provides insight for the user into which data provides what type of information and in what situations a particular source is most useful. DA - 2021-10 DB - ResearchSpace DP - CSIR J1 - Frontiers in Big Data, 4 KW - Covid-19 KW - Reduced mobility KW - Spatial weight matrices KW - Principal component analysis KW - Hierarchical clustering LK - https://researchspace.csir.co.za PY - 2021 SM - 2624-909X T1 - Modelling representative population mobility for COVID-19 spatial transmission in South Africa TI - Modelling representative population mobility for COVID-19 spatial transmission in South Africa UR - http://hdl.handle.net/10204/12150 ER -25067