Visaya, M.V.Sherwell, D.Sartorius, B.Cromieres, F.2016-07-262016-07-262015-04Visaya, M.V. et al. 2015. Analysis of binary multivariate longitudinal data via 2-dimensional orbits: An application to the Agincourt Health and Socio-Demographic Surveillance System in South Africa. 2015. PLoS ONE 10(4): e0123812.1932-620310.1371/journal.pone.0123812http://hdl.handle.net/10539/20702© 2015 Visaya et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.We analyse demographic longitudinal survey data of South African (SA) and Mozambican (MOZ) rural households from the Agincourt Health and Socio-Demographic Surveillance System in South Africa. In particular, we determine whether absolute poverty status (APS) is associated with selected household variables pertaining to socio-economic determination, namely household head age, household size, cumulative death, adults to minor ratio, and influx. For comparative purposes, households are classified according to household head nationality (SA or MOZ) and APS (rich or poor). The longitudinal data of each of the four subpopulations (SA rich, SA poor, MOZ rich, and MOZ poor) is a five-dimensional space defined by binary variables (questions), subjects, and time. We use the orbit method to represent binary multivariate longitudinal data (BMLD) of each household as a two-dimensional orbit and to visualise dynamics and behaviour of the population. At each time step, a point (x, y) from the orbit of a household corresponds to the observation of the household, where x is a binary sequence of responses and y is an ordering of variables. The ordering of variables is dynamically rearranged such that clusters and holes associated to least and frequently changing variables in the state space respectively, are exposed. Analysis of orbits reveals information of change at both individual- and population-level, change patterns in the data, capacity of states in the state space, and density of state transitions in the orbits. Analysis of household orbits of the four subpopulations show association between (i) households headed by older adults and rich households, (ii) large household size and poor households, and (iii) households with more minors than adults and poor households. Our results are compared to other methods of BMLD analysis.endata visualizationchartsMarkov modelsdemographySouth Africastatistical modelshealthsocio-economicmultivariate data analysisbinary multivariate longitudinal datacluster analysisdata analysisfamily sizehealth care organizationhealth surveyhouseholdintermethod comparisonlongitudinal studymathematical modelpovertyrural areasocial statusArticle