Street-Weighted Interpolation Techniques for Demographic Count Estimation in Incompatible Zone Systems

Abstract

Data processing for the spatial analysis of small-area social, demographic, and economic data often requires the combination of data spatially aggregated to two or more incompatible zone systems in a region, such as a set of enumeration districts that changes over time. Such situations can be addressed by areal interpolation—the transfer of data between zonal systems according to spatial algorithms. The authors test a technique of areal interpolation using geographic information systems (GIS) that employs a digital map layer representing streets and roads to derive varying density weights for small areas within aggregation zones. The technique reduces errors in estimation compared with estimates derived using the commonly applied area-weighting technique, with its assumption of uniform density. The street-weighting technique is much easier to use than other interpolation techniques that have also been shown to reduce error compared with area-based weighting.