Key concepts 1 GIS - Spatial Data Analysis: An Introduction for GIS Users - page 23

to two sets. In these cases, one, either, both, or mutually exclusive sets are selected.
h e Boolean AND selects objects fuli lling two criteria, OR selects objects fuli lling
either of the criteria, NOT selects objects fuli lling one criterion but not the other and
XOR (exclusive OR) selects objects fuli lling one criterion or the other, but not both.
Figure 2.10 shows a simple example of a query in practice.
Queries constructed using Boolean logic can be used to select features in any desired
combination. In the case of two or more criteria, query statements are easily extended.
h e application of Boolean logic for the overlay of multiple data layers (the identii ca-
tion of common areas of two or more sets of polygons) is discussed in Chapter 5. With
Boolean logic, membership of a class is dei nite. An alternative approach is fuzzy logic,
which recognizes uncertainty in assigning features to classes (see Longley et al. (2005a)
for a summary). For example, boundaries between two soil types are not likely to be
clearly dei ned and instead some form of classii cation that accounts for the probabil-
ity of there being one soil type or another at a particular location is likely to be more
appropriate than a 'hard' classii cation of the type described above (see Section 3.4 for
a discussion about probabilities).
Summary
This chapter covers a wide variety of concepts that are important in the analysis of spatial
data. The focus was on key GIS concepts, including data models, databases, projections,
georeferencing and geocoding, spatial scale, spatial data collection, errors, visualization,
and querying spatial data. Such issues are central to understanding the material cov-
ered in the rest of the topic. Knowledge of data models is important as the data provide
the basis of any analysis. Some understanding of database principles and data extrac-
tion (querying) is also central to a large proportion of analyses. Understanding of how
data are collected, and the limitations of particular approaches, is essential background
to the application of spatial data. No data are perfect representations of reality and so an
awareness of potential sources of error is crucial. Finally, visualization and querying of
spatial databases are common fi rst steps in any spatial analysis.
Further reading
The further reading section of the previous chapter cited some useful introductions to
GIS. Some of the topics listed in that section provide in-depth material on some of the
topics outlined in this chapter. In particular, issues such as databases, query of spatial
data, and errors are dealt with by Burrough and McDonnell (1998) , Longley et al . (2005a) ,
and Heywood et al . (2006) . Descriptions of spatial data formats and storage are given by
Wise (2002) . Spatial data collection is a vast topic; useful introductions to survey and remote
sensing are provided by Bannister et al . (1998) and Lillesand et al . (2007) , respectively.