1. A computer-implemented method, comprising: storing, by one or more data processing apparatuses, data identifying a plurality of geographic entities, wherein each entity of the plurality of geographic entities is associated with a location and a quantification of an attribute, the attribute comprising at least one of a population or an experiment-specific metric; using a first clustering algorithm to cluster, with the one or more data processing apparatuses, the plurality of entities into a first set of clusters according to the location of each of the plurality of geographic entities; determining, with the one or more data processing apparatuses, an accuracy probability for each geographic entity of the plurality of geographic entities clustered into the first set of clusters, wherein the accuracy probability for a particular geographic entity of the plurality of geographic entities is determined according to a distance from the particular geographic entity to a second geographic entity of the plurality of geographic entities, wherein the second geographic entity is placed in a different cluster of the first set of clusters than the particular geographic entity and has a location that is the closest to the location of the particular geographic entity as compared to the other geographic entities of the plurality of geographic entities placed in a different cluster than the particular geographic entity; and determining, with the one or more data processing apparatuses, a first cluster measurement for the first set of clusters, wherein the first cluster measurement is derived from a quantification of the attribute for each of the geographic entities weighted by the accuracy probability for the geographic entity.
1. A computer-implemented method, comprising: storing, by one or more data processing apparatuses, data identifying a plurality of geographic entities, wherein each entity of the plurality of geographic entities is associated with a location and a quantification of an attribute, the attribute comprising at least one of a population or an experiment-specific metric; using a first clustering algorithm to cluster, with the one or more data processing apparatuses, the plurality of entities into a first set of clusters according to the location of each of the plurality of geographic entities; determining, with the one or more data processing apparatuses, an accuracy probability for each geographic entity of the plurality of geographic entities clustered into the first set of clusters, wherein the accuracy probability for a particular geographic entity of the plurality of geographic entities is determined according to a distance from the particular geographic entity to a second geographic entity of the plurality of geographic entities, wherein the second geographic entity is placed in a different cluster of the first set of clusters than the particular geographic entity and has a location that is the closest to the location of the particular geographic entity as compared to the other geographic entities of the plurality of geographic entities placed in a different cluster than the particular geographic entity; and determining, with the one or more data processing apparatuses, a first cluster measurement for the first set of clusters, wherein the first cluster measurement is derived from a quantification of the attribute for each of the geographic entities weighted by the accuracy probability for the geographic entity. 16. The method of claim 1 , wherein the experiment-specific metric is a relevant population for an advertising experiment, wherein the quantification of the attribute for each geographic entity of the plurality of geographic entities is the relevant population for the geographic entity.