National census data represent the “gold standard” for authoritatively portraying a country's residential population distribution, but their aggregated counts for fixed administrative areas present problems for many geographic information system (GIS) analyses. Intelligent areal interpolation algorithms assist by transferring data from one zonal system to another using ancillary data to improve accuracy. All areal interpolation methods make assumptions and generate errors, and performance varies with both specific location and the data inputs used. This study adds to our understanding of the relative merits of alternative methods by comparing dasymetric, street network, and surface-based models interpolating across two spatial resolutions. It examines the importance of the ancillary data source used to drive the process, particularly the efficacy of open access products. Results from an empirical study show that interpolation accuracy is influenced by the choice of ancillary data input as well as the methodological approach adopted. The strongest overall performance is delivered by dasymetric mapping combined with open access data identifying the locations of buildings. Open access data sets offer considerable potential for widening the use of intelligent population interpolation tools, especially if plug-in tools to execute these algorithms can be made available for commonly used GIS software packages.