"In this paper, [the authors] suggest an alternative method for fitting the gravity model. In this method, the interaction variable is treated as the outcome of a discrete probability process, whose mean is a function of the size and distance variables. This treatment seems appropriate when the dependent variable represents a count of the number of items (people, vehicles, shipments) moving from one place to another. It would seem to have special advantages where there are some pairs of places between which few items move. The argument will be illustrated with reference to data on the numbers of migrants moving in 1970-1971 between pairs of the 126 labor market areas defined for Great Britain...."
Previous evidence for spatial clustering of amyotrophic lateral sclerosis is inconclusive. Studies that have identified apparent clusters have often been based on a small number of cases, which means the results may have occurred by chance processes. Also, most studies have used the geographic location at the time of death as the basis for cluster detection, rather than exploring clusters at other points in the life cycle. In this study, the authors examine 1,000 cases of amyotrophic lateral sclerosis distributed throughout Finland who died between June 1985 and December 1995. Using a spatial-scan statistic, the authors examine whether there are significant clusters of the disease at both time of birth and time of death. Two significant, neighboring clusters were identified in southeast and south-central Finland at the time of death. A single significant cluster was identified in southeast Finland at the time of birth, closely matching one of the clusters identified at the time of death. These results are based on a large sample of cases, and they provide convincing evidence of spatial clustering of this condition. The results demonstrate also that, if the cluster analysis is conducted at different stages of the cases' life cycle, different conclusions about where potential risk factors may exist might result.
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