Dot mapping is a traditional method for visualizing quantitative data, but current automateddot mapping techniques are limited. The most common automated method places dotspseudo-randomly within enumeration areas, which can result in overlapping dots and verydense dot clusters for areas with large values. These issues affect users’ ability to estimatevalues. Graduated dot maps use dots with different sizes that represent different values.With graduated dot maps the number of dots on a map is smaller and the likelihood ofoverlapping dots is smaller. This research introduces an automated method of generatinggraduated dot maps that arranges dots with blue noise patterns to avoid overlapping dotsand uses clustering algorithms to replace densely-packed dots with dots of larger sizes. Auser study comparing graduated dot maps, pseudo-random dot maps, blue noise dot maps,and area-proportional circle maps with almost 300 participants was conducted. Resultsindicate that map-users can interpret graduated dot maps more accurately than the othermap types. In addition, map users appear to prefer graduated dot maps to the other maptypes. These findings suggest that graduated dot maps are more effective and more appealingthan conventional dot maps.