Quadrat Analysis_RW_Thomas

These last results illustrate well the difficulties

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Unformatted text preview: e of scale effects have tried to fit a number of these models to point patterns of shops and urban populations. However, here again the problem of model specification becomes even more intractable because the number of parameters to be estimated increases with the number of variables. When designing quadrat analysis experiments one is usually faced with a choice between complex theories which are difficult to test, and simple indices which are useful for comparative studies but which have limited explanatory power. Nevertheless, the reciprocal relationship between theory and data which characterises the quadrat method often Provides useful insights into many geographical problems. BIBLIOGRAPHY Table 9 illustrates the application of the G index to the Merseyside factory location data at four different block sizes. We see that only shipbuilding seems to be influenced by cell size, with a G index equal to .238. Here the redundancy of the frequency array becomes progressively larger as the block size is increased and we can interpret this result using GreigSmith...
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