Composition of Geospatial Visualizations for Scale-aware Views of Multiple Outcome Variables in Population Surveys

Abstract

Population survey data is important for understanding the status of well-being along any dimensions, i.e., social, economic, political, health, etc. This data generates spatial point patterns which can be explored and analyzed using visualization. Given the spatial aspect of the data, there is a requirement of using cartographic maps, which are mostly limited to visualizing a single variable in most cases. Here, it is also important that the choice of visualizations also enable scale-aware analysis when zooming in and out of the maps, since the data is from the smaller political units and can be aggregated to larger political units. Thus, we explore the different visual compositions which use mathematical operators and the composite layouts for visualizing multiple outcome variables in survey data. The mathematical operators allow the use of univariate and bivariate data modeling and representation, and composite layouts of interest are juxtaposition and superimposed views. We demonstrate the inferences from visualizations using a case study on malnutrition in children under five in India. Our work shows that a visual composition of binary relationships represented in a visualization and a juxtaposed layout of such pairwise variables is effective in making inferences from the multivariate spatial point patterns in population data.