title: Adjusting Moran's I for population density.
abstract: I derive two new statistics, Ipop and Ipop*, that adjust Moran's I to study clustering of disease cases in areas (for example, counties) with different, known population densities. A simulation of Lyme disease in Georgia suggests that these new statistics can be more powerful than those currently in use. This is because they consider both spatial pattern and non-binomial variance in rates as evidence supporting disease clusters.
authors.affiliation: EMMES Corporation, Potomac, MD 20854.
authors.forename: N
authors.initials: N
authors.lastname: Oden
mesh_terms: D000704:Analysis of Variance; D016000:Cluster Analysis; D005845:Georgia / Q000453:epidemiology; D006801:Humans; D015994:Incidence; D008193:Lyme Disease / Q000453:epidemiology; D015233:Models, Statistical; D011156:Population Density; D011897:Random Allocation; D012307:Risk Factors; D013029:Space-Time Clustering