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A Model-based Geostatistical Approach for Modelling Spatially Aggregated Misaligned Data
Olatunji Johnson (CHICAS, Lancaster University)
Spatially Aggregated data are nowadays increasingly common. This is usually because of ethical concern of data use as well as preserving confidentiality. Different Government agencies often release data at different spatial scales. Our main focus in the work is to study the relationship between life expectancy at birth (LEB) and index of multiple deprivation (IMD) in Liverpool, UK when they are spatially misaligned and carry out spatially continuous prediction of LEB. To this end, we developed a model-based geostatistical approach for the joint analysis of LEB and IMD, when these are available over different partitions of the study region. We model the spatial correlation in LEB and IMD across the areal units using inter-point distances based on a regular grid covering the whole of the study area. We found that the effect of IMD on LEB is stronger in males than in females, explaining about 63.35% of the spatial variation in LEB in the former group and 38.92% in the latter. We also estimate that LEB is about 8.5 years lower between the most and least deprived area of Liverpool for men, and 7.1 years for women. Finally, we find that LEB, both in males and females, is at least 80% likely to be above the England wide average only in some areas falling in the electoral wards of Childwall, Woolton and Church. The novel model-based geostatistical framework provides a feasible solution to any spatial misalignment problem. More importantly, the proposed methodology has the following advantages over existing methods: 1) it can deliver spatially continuous inferences using spatially aggregated data; 2) it can be applied to any form of misalignment with information provided at a range of spatial scales, from areal-level to pixel-level.