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Statistical Analysis and Modelling of Spatial Point Patterns
John Wiley and Sons Ltd, Jan 2008, Pages: 560
Statistics in Practice is an important international series of texts which provide detailed coverage of statistical concepts, methods and worked case studies in specific fields of investigation and study.
With sound motivation and many worked practical examples, the books show in down-to-earth terms how to select and use an appropriate range of statistical techniques in a particular practical field within each title’s special topic area.
The books provide statistical support for professionals and research workers across a range of employment fields and research environments. Subject areas covered include medicine and pharmaceutics; industry, finance and commerce; public services; the earth and environmental sciences, and so on.
The books also provide support to students studying statistical courses applied to the above areas. The demand for graduates to be equipped for the work environment has led to such courses becoming increasingly prevalent at universities and colleges.
It is our aim to present judiciously chosen and well-written workbooks to meet everyday practical needs. Feedback of views from readers will be most valuable to monitor the success of this aim.
Excerpt from Preface:
Spatial point processes are mathematical models that describe the arrangement of objects that are irregularly or randomly distributed in the plane or in space. The patterns formed by the objects are analysed in many scientific disciplines; hence a great variety of objects may be considered such as atoms, molecules, biological cells, animals, plants, trees, particles, pores, or stars and galaxies. At a basic level the data simply consist of point coordinates describing the locations of the objects, but additional characteristics of the objects can also be included in the analysis.
These additional characteristics may, for instance, describe the size or type of an object and are usually referred to as ‘marks’. Point process analysis is in many ways distinct from the classical statistical methodologies presented in undergraduate textbooks. However, some of the more fundamental classical statistical issues remain influential; for example, sampling, exploratory data analysis, parameter estimation, model fitting, testing of hypotheses and separating signal from noise may all form part of a point process analysis.
Point process statistics is perhaps the most developed and beautiful branch of the modern field of spatial statistics; this is perhaps because points are the most elementary of geometrical objects and lead to data structures that are particularly clear and useful. Sometimes, however, point data have to be analysed in combination with other data from variables that vary continuously in space. This requires an application of spatial statistical methods that fall outside the realm of spatial point processes.
Recent decades have seen a strong increase in the development of point process methodology, based on a profound theoretical development and driven by applications from many different fields of science. In addition to the classical fields of application such as archaeology, astronomy, particle physics and forestry, today other fields such as ecology, biology, medicine and materials science extensively apply point process methods. This development is facilitated by the advent of new and improved technologies that may be used to collect point pattern data.
Whereas the first point patterns were small and collected manually, modern data sets tend to be much larger and are collected using automated methods. Ecologists have found fascinating relations in plant communities by considering plants as points marked by characteristics such as size, species or genotype.
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