Needs assessments in disaster-affected communities produce data that express priorities or ordered preferences, such as about urgent needs, or preferred coping strategies. When the items are ranked (and not just independently rated), summary priority measures can be validly computed as sums or averages of the concerned individual values. The validity is derived from the theory of election systems, specifically the so-called Borda count.
Visualizing differences in the priority measures by respondents (e.g. affected communities) or by items (e.g. sectors) is done in a second step. When the number of items or groups to distinguish is considerable, traditional methods like clustered bar charts overwhelm the cognitive capacity of the reader.
Heat maps, pseudo-charts that in essence are colored tables, offer an easier-to-understand alternative. This note demonstrates the steps needed to make such maps, using data that an Acaps assessment generated in a flood zone in Bangladesh. Depending on analytic interest, several flavors of heat maps are feasible. As an additional example, we visualize the relative importance of coping strategies across affected groups, by rating the group-specific item scores to the all-sample averages.
The total number of assessed communities may be small. Special care then has to be taken to safeguard against unrecognized influences of small changes in individual preferences and their possibly considerable effects on the ranking of items or groups. This is a challenge that the quantitative treatment of data that fundamentally originated as qualitative information faces everywhere - and also in the interpretation of heat maps used in needs assessments.