Summary
Some methods of spatial data analysis are given in a manner of short overview. Here the recent development of this field is also included. At first, it is shown that spatial indices based on quadrat counts or nearest neighbour distances, which have been devised mostly by ecologists, are still useful for preliminary analysis of spatial data. Then, it is discussed that distance functions such as nearest neighbour distribution and K function are useful to the diagnostic analysis of spatial data. Further, it is shown that the maximum likelihood procedures for estimating and fitting pair interaction potential models are very useful for a wide class of spatial patterns. Finally, it is pointed out that Markov chain Monte Carlo (MCMC) methods are powerful tools for spatial data analysis and for other fields.
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© 1998 Springer Japan
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Tanemura, M. (1998). A Short Overview of the Methods for Spatial Data Analysis. In: Hayashi, C., Yajima, K., Bock, HH., Ohsumi, N., Tanaka, Y., Baba, Y. (eds) Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Tokyo. https://doi.org/10.1007/978-4-431-65950-1_30
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DOI: https://doi.org/10.1007/978-4-431-65950-1_30
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