Range maps are time-consuming for experts to produce, but georeferenced records offer an alternative foundation for range mapping. To test data ranges from records for abundant species, I compared data ranges for 3,960 species globally to expert ranges, which are subjective approximations with errors. I produced kernels that contained 99% of georeferenced records, contributing high-certainty range areas limited to sampling extents. To extend kernels, I merged kernels with aggregated polygons of records, which provided undersampled areas. Selecting one of six different aggregation distances ranging from 200 to 5,000 km for complex images was based on supervised classification, guided by proximity to kernels and resemblance to International Union for Conservation of Nature (IUCN) expert ranges. Maximizing overlap with expert ranges using intersection metrics resulted in similar scores to supervised image classification. Using supervised classification, median intersection of data ranges with expert ranges (relative to expert range areas) varied by taxa, from 86% for mammals, 91% for anurans and reptiles, 95% for birds, and 93% for plants. As a baseline, median intersection percentage was 93% for IUCN ranges with another source of expert range maps (Mammal Diversity Database). Data range areas were larger by factors (median) of 1.2 to 1.4 for animal taxa and 2.0 for plants (driven by non-native plant locations) than IUCN ranges, resulting in the median intersection relative to total union area of 59% to 66% for animals and 41% for plants. For IUCN ranges with ≥95% intersection with georeferenced records, data ranges were greater in area by a factor of 1.1 (median), but for IUCN ranges with <80% intersection with georeferenced records, data ranges were greater by a factor of 2.2. Because of range complexity, simple rulesets based on information from records and kernels are unlikely to provide consistent classification of aggregation distances, and nonlinear modeling is the next development phase for mapping of species without ranges. Data ranges, which overlapped with most of the expert ranges and 99% of records, can supply provisional range maps, with selection of aggregation distances based on expertise or modeled distance, to assist conservation planning.