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A Short Overview of the Methods for Spatial Data Analysis

  • Masaharu Tanemura
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

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.

Keywords

Markov Chain Monte Carlo Spatial Data Neighbour Distance Point Pattern Markov Chain Monte Carlo Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Japan 1998

Authors and Affiliations

  • Masaharu Tanemura
    • 1
  1. 1.The Institute of Statistical MathematicsMinato-ku, Tokyo 106Japan

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