pop_den

<p>A dataset containing the population density of Newcastle upon Tyne of 300 X 300 metres</p>

datasets

Provides a computationally efficient discrete approximation to log-Gaussian Cox process model for spatially aggregated disease count data. It uses Monte Carlo Maximum Likelihood for model parameter estimation as proposed by Christensen (2004) <doi: 10.1198/106186004X2525> and delivers prediction of spatially discrete and continuous relative risk. It performs inference for static spatial and spatio-temporal dataset. The details of the methods are provided in Johnson et al (2019) <doi:10.1002/sim.8339>.

Olatunji Johnson

SDALGCP

Spatially Discrete Approximation to Log-Gaussian Cox Processes
for Aggregated Disease Count Data

Emanuele Giorgi

Peter Diggle

pop_den function

<p>This is a raster file of population density, 300 X 300 meters.</p>

pop_den: Population density raster shapefile of Newcastle Upon Tyne in the UK.

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