GeoAlign: Interpolating Aggregates over Unaligned Partitions

Answering crucial socioeconomic questions often requires combining and comparing data across two or more independently collected data sets. However, these data sets are often reported as aggregates over data collection units, such as geographical units, which may differ across data sets. Examples of geographical units include county, zip code, school district, etc., and as such, they can be incongruent. To be able to compare these data, it is necessary to realign the aggregates from the source units to a set of target spatially congruent geographical units. Existing intelligent areal interpolation/realignment methods, however, make strong assumptions about the spatial properties of the attribute of interest based on domain knowledge of its distribution. A more practical approach is to use available reference data sources to aid in this alignment. The selection of the references is vital to the quality of prediction. In this paper, we devise GeoAlign, a novel multi-reference crosswalk algorithm that estimates aggregates in desired target units. GeoAlign is adaptive to new attributes with need for neither distribution-related domain knowledge of the attribute of interest nor knowledge of its spatial properties in Geographic Information System (GIS). We show that GeoAlign can easily be extended to perform aggregate realignment in multi-dimensional space for general use. Experiments on real, public government datasets show that GeoAlign achieves equal or better accuracy in root mean square error (RMSE) than the leading state-of-the-art approach without sacrificing scalability and robustness.