Data visualization examples include maps, poverty composition graphics, bubble charts of incidence and intensity, and so on. Because the MPI is additive and decomposable, and because the data it uses are directly comparable across populations, the MPI, headcount ratio (percentage of people who are poor), intensity (average deprivation score among poor people), as well as all indicator levels and trends can be disaggregated by any subgroup for which the data are representative, such as subnational region, ethnic group, age group, or other social categories. This supports the SDG goal of "leaving no one behind" and seeing whether the poorest groups are catching up over time.