Exploring spatial data analysis techniques using R: the case of observations with no neighbours

It is widely acknowledged that one of the impediments to a broader acceptance of techniques for spatial data analysis is that handling spatial data involves more insight and possibly the use of additional applications than other forms of data (Anselin, 2000, p. 217). We are perhaps more familiar with the potential difficulties caused by the inadequate mapping of data into temporal reference frameworks, such as the predicted complications attributed to the year 2000 problem, when a circular measure (99 1 0) was treated as linear. Spatial data come with many assumptions about their reference frameworks, including projection metadata, and are often derived from geographical information systems or other archives of spatial position data. Some of these are also time-specific, where boundary segents are introduced to or removed from maps of polygon representations of spatial objects.