Geoscience Reference
In-Depth Information
engaged in research spanning all of life since it first emerged approximately 3.8 billion years ago.
This breadth is a potential advantage for GC, offering many useful avenues for research involving
biological applications.
As with geospatial analyses, the application space for biological analyses is enormous. It is
perhaps a gross generalisation but, where geospatial disciplines are focussed on geolocated phe-
nomena, biology is focussed on organisms. The geospatial disciplines and biology both deal with
temporal scales ranging from immediate to astronomical. Both are concerned with processes and
interactions between objects over space, but, where geospatial disciplines are generally concerned
with landscape to global scales, biology is concerned with processes at spatial scales ranging from
within individual virus cells, to Petri dishes, to the planet (see Chapman, 2009). This scale range is
many orders of magnitude greater than in geospatial research.
Clearly the overlap of applications between the geo- and bio-disciplines lies at the spatial
scales typically occupied by the geosciences. However, it is important to remember that many
spatial analyses have broad applicability. Spatial analyses are fundamentally applied to some
set of spatial units, where membership of the set is normally based on proximity to a particular
unit of interest, for example, central or focal unit, but can be defined in possibly arbitrary ways.
Such analyses can be applied at any scale given appropriate modifications. For example, Darcy's
law of fluid flow through porous media, one of the foundations of hydrology, finds application
in cellular-level studies (Chapman et al., 2008). One can even analyse digestive tracts as spatial
entities (Stearns et al., 2011).
GC and biology intersect in two ways. First is the application of analytical methods and simu-
lation environments that are biological analogues. Perhaps the most common examples of these
are evolutionary algorithms and artificial neural networks. Such topics are dealt with in depth in
Heppenstall and Kirkland (2014) and Fischer and Abrahart (2014). Second, and the focus of this
chapter, is the application of GC methods to geolocated biological data.
As will be discussed, some of the GC applications in biology are well established and in several
cases involve the application of methods developed outside the geosciences, GC and biology. Others
represent areas of GC that have potential for biology or, alternately, are application areas in biology
that have potential to generate interesting developments in GC.
The remainder of this chapter is divided into two parts. First is a general outline of the types of
biological data that are relevant to GC, either for direct application or as a source of new research
approaches. This is then followed by a discussion of some spatial and spatio-temporal analyses that
can be applied.
6.2 BIOLOGICAL DATA
Readers will be familiar with the GIS data models most frequently used to represent geographic
phenomena in GC (see Longley et al., 2010). In the object data model, some set of objects posi-
tioned in geographic and/or temporal space is assigned values that describe one or more associated
locational spatio-temporal attributes. In the field data model, one or more continuous surfaces are
located in space and/or time. Geolocated biological data are amenable to such data models but, in
addition, have hierarchical representations of the relatedness between organisms in the form of
taxonomies and phylogenies (Figure 6.1). Of course, one can develop such hierarchical structures to
link non-biological phenomena, soil classifications being an established example; it is just that the
concept is very well defined in biology. Indeed, it is a fundamental part of the discipline. This can
be partly attributed to the fact that considerable research and effort has gone into the understanding
of genes and their use for determining relatedness between organisms.
In terms of the individual unit of analysis, one can analyse geolocated biological data at several
levels. First, there is the individual organism, followed by groups of organisms (e.g. herds, flocks,
crowds), up to populations. A reality is that data are frequently not available at any of these levels
due to difficulties of sampling. In such cases, one can analyse collections of organisms. As analyses
