Page 42 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

38 Chapter 2

line generalisations, are important in capturing spatial data (McMaster and Shea, 1992;
Garcia and Fdez-Valdivia, 1994). There are many other types of transformations in a
GIS. Bonham-Carter (1994) provides elaborate discussions on the concepts and
algorithms of many different types of spatial data transformations that are applicable to
geoscience modeling in general.
The discussion here concentrates on spatial data transformations that are more
directly and usually involved in mapping of geochemical anomalies and mineral
prospectivity. These are point-to-area, point-to-surface, line-to-area, line-to-surface,
area-to-point, area-to-area and surface-to-area transformations. The last two
transformations are handled via re-classification operations (see above). Some of these
transformations may require conversion from a vector data model to a raster data model
and vice versa. Detailed discussions on vector-to-raster and raster-to-vector conversions
can be found in Clarke (1995), Mineter (1998) and Sloan (1998). Area-to-point
transformation, for example, can be handled by vector-to-raster conversion, whereby
polygonal geo-objects are converted to pixels and each pixel can treated as a point.
Most geoscience spatial data used in mapping of geochemical anomalies and mineral
prospectivity are recorded as attributes of sampling points (Fig. 2-12A). Because the
objective of most mineral exploration activities is to define anomalous zones rather than
points (except in defining locations for drilling), point-to-area and/or point-to-surface
transformations are required to analyse and model spatial information from point data.
The types of transformations performed depend on the type of geo-objects represented
by point data and on the nature of attribute data. On the one hand, point-to-area
transformations of point data representing geo-objects such as intersections of curvi-
linear structures or locations of mineral deposits can be modeled appropriately by, for
example, point density calculations. On the other hand, point-to-area and point-to-
surface transformations of point data representing qualitative or quantitative attributes
can be modeled by, respectively, non-interpolative transformations or spatial
interpolations. The objective of such transformations is to reconstruct the continuous
field, respectively, which was measured at the sampling points.
Non-interpolative transformations are suitable for point data measured on a nominal
scale. In some cases, such transformations are also applicable to point data measured on
ordinal, interval or ratio scale. Non-interpolative transformations involve creation of
zones of influence around points with assumption of homogeneity of attribute data in
each zone. Bonham-Carter (1994) describes a number of methods of non-interpolative
point-to-area transformations, which are briefly reviewed here. The simplest method is
to associate attributes of each point to a rectangular cell in a regular grid. Cells with
more than one point are assigned attributes that are aggregated according to some rule
and depending on measurement scale, whilst cells without points are assigned null
attributes (Fig. 2-12B). This method has been used for regional geochemical mapping
(e.g., Garrett et al., 1990; Fordyce et al., 1993). A modification of representing point data
as rectangular cells is to draw equal-area circular cells centred on points and to assign
attributes of each point to the corresponding circle; zones outside the circles are assigned
null attributes (Fig 2-12C). An advantage of this method is that the size of the circle can