In spatial analysis, population frequently is aggregated into source units having an areal extent. When using such data in a flow model, distances are calculated as an average between each source unit and a set of destinations. In a network, this average distance might be the shortest path between a destination and the centroid of a source unit. However, population is never concentrated at centroids nor is it uniformly distributed within each spatial unit. In urban areas, it is more likely located proximate to the road system that traverses most areal units. This article presents a method for interpolating areally aggregated data to the segments of the road network bounding an areal unit using a dasymetric approach. A case study for Phoenix, Arizona, compares using a network‐interpolated population distribution with the area‐based approach for the problem of defining service areas for a given set of facilities. A comparison of the road network used, the total demand within each service area, and the total weighted travel distance to facilities shows that the areal‐based method underestimates the portion of road network used in travel and misestimates both the expected demand of each service area and the overall travel distance to a facility. 在空间分析中，群体频繁的集聚于具有一定面积范围的源区域。当在流模型中应用这类数据时，距离计算往往采用每个源区域与目标区域之间的平均值。在一个网络中，这种平均距离可能是终点区域和源区域质心的最短路径。然而，人口并不集中于质心或均匀分布于每个空间单元中。在城市地区，人口最有可能位于横贯多数空间单元的道路系统周围。基于密度插值的方法，本文提出了将空间聚集数据插值至以道路网络为边界的区域单元。以亚利桑那州菲尼克斯进行实证分析，针对给定服务设施集合下提供服务范围的问题，比较了道路网络插值和基于区域插值方法得到的人口分布。利用道路网络的每个服务区的总需求和到服务设施的总体加权通行距离进行比较，基于区域的插值方法低估了用于通行的道路网络比例，同时也误估了每个服务区的期望需求和到一个服务设施的总体通行距离。