Forecasting socio economic distributions on small area spatial domains for count data

Abstract: Statistical information for empirical analysis is very frequently available at a higher level of aggregation than it would be desired. The spatial disaggregation of the socio-economic data is considered complex owing to the inherent spatial properties and relationships of the spatial data, namely, spatial dependence and spatial heterogeneity. Spatial dependence is where the variables display some degree of local similarity; and spatial heterogeneity is where the relationships between variables change across space. In addition, the relationships of spatial data are very scale dependent; they are inherently inconsistent at the different scales. The spatial dependence, spatial heterogeneity and the effect of scale formed major technical issues that largely impact on the accuracy of the regional forecast disaggregation. In this paper we propose entropy-based spatial forecast disaggregation methods for count data that use all available information at each level of aggregation even if it is incomplete. The proposed methods are validated through the diagnostic analysis of the methods using ancillary information.