There are a number of more highly-modeled methods, including dasymetric modeling and smart interpolation, that incorporate additional geographic data. These data are used to produce weight matrices for determining how to apportion population by pixel. Several global data products use ancillary data in their spatial modeling, incorporating remotely sensed data on land cover, urban extent, accessibility, or all of the above in order to delineate population surfaces. GPWv4, however, uses the areal-weighting approach. The main benefit of disaggregating demographic variables by areal-weighting is fidelity to the input data. Census information modeled with this approach may be analyzed in conjunction with ancillary data sets such as land cover without concern for endogeneity - meaning, without worrying that the population data are already using land cover as a means of reallocating the population in a given area. The modeled census information are also suitable for use in dasymetric and other modeling approaches—the population counts or densities can be reallocated based on other layers.