  The main goal of disease mapping is to estimate disease risk and identify
high-risk areas. Such analyses are hampered by the limited geographical
resolution of the available data. Typically the available data are counts per
spatial unit and the common approach is the Besag--York--Molli{\'e} (BYM)
model. When precise geocodes are available, it is more natural to use
Log-Gaussian Cox processes (LGCPs). In a simulation study mimicking childhood
leukaemia incidence using actual residential locations of all children in the
canton of Z\"urich, Switzerland, we compare the ability of these models to
recover risk surfaces and identify high-risk areas. We then apply both
approaches to actual data on childhood leukaemia incidence in the canton of
Z\"urich during 1985-2015. We found that LGCPs outperform BYM models in almost
all scenarios considered. Our findings suggest that there are important gains
to be made from the use of LGCPs in spatial epidemiology.
