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In Japan, the Housing and Land Survey (HLS) provides municipality-levelgrouped data on household incomes. Although these data can be used foreffective local policymaking, their analyses are hindered by severalchallenges, such as limited information attributed to grouping, the presence ofnon-sampled areas, and the very low frequency of implementing surveys. Toaddress these challenges, we propose a novel grouped-data-based spatio-temporalfinite mixture model to model the income distributions of multiple spatialunits at multiple time points. A unique feature of the proposed method is thatall the areas share common latent distributions and that the mixing proportionsthat include the spatial and temporal effects capture the potential area-wiseheterogeneity. Thus, incorporating these effects can smooth out the quantitiesof interest over time and space, impute missing values, and predict futurevalues. By treating the HLS data with the proposed method, we obtain completemaps of the income and poverty measures at an arbitrary time point, which canbe used to facilitate rapid and efficient policymaking with fine granularity.

Spatio-temporal smoothing, interpolation and prediction of income distributions based on grouped data