Some thoughts on inference in the analysis of spatial data

Abstract

Statistical inference is important for all those who engage in the analysis of spatial data. The issue is becoming increasingly important given the explosion in the availability of spatial data and the proliferation of Geographic Information Systems (GIS) across different academic disciplines and application areas. The aim of this paper is to provide a brief overview of some of the concepts and controversies inherent in statistical inference in the hope of raising the level of awareness within the geographic information science community that different points of view exist when it comes to inference. We argue that the concept of statistical inference in spatial data analysis and spatial modelling is perhaps broader than many GIS users imagine. In particular, we argue that different types of inference exist and that process inference is just as valid as sample inference, even though the latter appears to dominate the GIS literature.

Acknowledgement

The first author acknowledges the generous support of Science Foundation Ireland in the form of a Research Professorship.

Notes

¹Given that 100 is not an integer multiple of 6, it is impossible to obtain equal or ‘fair’ proportions of each possible score in this population data set, so a simple description of the population would automatically lead to the conclusion that the die is not fair, regardless or whether it is or not!

Strictly speaking, this should be ‘failure to reject’.