Abstract
Abstract Statistical information for empirical analysis is frequently available at a higher level of aggregation than is desired. The spatial disaggregation of the socioeconomic data is considered complex due to the inherent spatial properties and relationships of the spatial data, namely, spatial dependence and spatial heterogeneity. The spatial dependence, spatial heterogeneity, and effect of scale produce major technical issues that largely impact the accuracy of the regional forecast disaggregation. In this chapter, we propose entropy-based spatial forecast disaggregation methods for count areal data that use all available information at each level of aggregation even if it is incomplete. The proposed methods are validated through Monte Carlo simulations using ancillary information. An empirical application to real data is also presented.
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