Micromaps

Abstract

Micromap visualizations are an important tool for the geovisualization of areal data that are based on a series of small maps, the micromaps. The three major variations of micromap visualization designs are linked micromap (LM) plots, conditioned micromaps, and comparative micromaps. The most widely used are LM plots. These are based on several panel columns that consist of multiple small maps, that is, the micromaps, name identifiers of the area units, plotting symbols, and one or more statistical data columns. LM plots overcome some of the limitations of choropleth maps that are frequently used for the display of areal data, but that are usually limited to only one statistical variable at a time and that do not work well for visualizing small areas. Micromaps were originally created for the United States, but have seen extended use for countries in Europe (France and Germany), Asia (Korea and China), and South America (Brazil and Argentina) as well as for different nonadministrative boundaries, such as watersheds and ecoregions. Micromaps have been used for all kinds of areal data, such as environmental, agricultural, and medical, to name only a few.

Effect of Manual Therapy and Splint Therapy in People with Temporomandibular Disorders: A Preliminary Study

“…Descriptive results of continuous data were expressed as mean and standard deviation while categorical data were described as frequencies and percentages. In order to test treatment effect we applied in standardized data, a non-parametric permutational multivariate analysis of variance (PERMANOVA) with a repeated measure design that included three factors [30,31]: factor A, treatment; factor B, subjects; and factor C, time. Although PERMANOVA is robust to heterogeneity of variance in balanced designs, we tested it applying PERMDISP which is a multivariate analogue of Leven's test [31].…”

“…In order to test treatment effect we applied in standardized data, a non-parametric permutational multivariate analysis of variance (PERMANOVA) with a repeated measure design that included three factors [30,31]: factor A, treatment; factor B, subjects; and factor C, time. Although PERMANOVA is robust to heterogeneity of variance in balanced designs, we tested it applying PERMDISP which is a multivariate analogue of Leven's test [31]. Post-hoc pair-wise tests among time treatments were tested through the F-ratio applying Monte Carlo methodology (p-values were obtained using 150 permutations) [30].…”