Identifying the most deprived regions of any country or city is key if policy
makers are to design successful interventions. However, locating areas with the
greatest need is often surprisingly challenging in developing countries. Due to
the logistical challenges of traditional household surveying, official
statistics can be slow to be updated; estimates that exist can be coarse, a
consequence of prohibitive costs and poor infrastructures; and mass
urbanisation can render manually surveyed figures rapidly out-of-date.
Comparative judgement models, such as the Bradley--Terry model, offer a
promising solution. Leveraging local knowledge, elicited via comparisons of
different areas' affluence, such models can both simplify logistics and
circumvent biases inherent to house-hold surveys. Yet widespread adoption
remains limited, due to the large amount of data existing approaches still
require. We address this via development of a novel Bayesian Spatial
Bradley--Terry model, which substantially decreases the amount of data
comparisons required for effective inference. This model integrates a network
representation of the city or country, along with assumptions of spatial
smoothness that allow deprivation in one area to be informed by neighbouring
areas. We demonstrate the practical effectiveness of this method, through a
novel comparative judgement data set collected in Dar es Salaam, Tanzania.Comment: 23 pages, 7 figures, to be published in the journal of the Royal
Statistical Society: Series