Global Positioning System Reference
In-Depth Information
repositories or creating it from aerial photographs and satellite images
(Mäkelä 2006). As a consequence, many users and producers of spatial
data are not trained in geo-field and may not be aware of the existence and
number of problems that this kind of data may have (Delavar and Devillers
2010; Boin and Hunter 2005; Wang and Strong 1996).
On the other hand, even though the research related to spatial data
quality has a 30-year long tradition, it introduces concepts and techniques
that are complex and diffi cult to understand by typical users and there is lack
of approaches for solving real problems (Devillers et al. 2010). Furthermore,
spatial data quality can also be seen as a relative concept depending on user
expectations (Olsen 2003; ISO19113 2002), e.g., a detailed representation
of different spatial objects may be required for analyzing hazard risks,
but may not be crucial to represent population for different districts for
decision-making users. Several works refer to spatial data quality in SDWs
and SOLAP proposing different mechanisms (1) to improve the prevention
of inappropriate uses of spatial data (Gervais et al. 2010), (2) to facilitate
the reuse of spatial data in a distributed and heterogeneous environment
(Sboui et al. 2010), (3) to establish an iterative process during the phases
of spatial cube development for identifying possible risks in using spatial
data by decision-making users (Levesque et al. 2007), or (4) to include
tolerant integrity constraints for merging spatial objects with different
representations (Bejaoui et al. 2009).
Different international standards and specifi cations (e.g., ISO19113.2002,
ISO19114.2003, ISO19138.2006, OGC 2011) were established to tackle the
problem of spatial data quality. These standards can serve as a basis for the
development of national standards. The standardization must not only refer
to spatial object representations, but also used measures, town names, and
zip codes, among others (Badard et al. 2012). However, this task demands
the leadership and coordination of various government authorities with
long-term fi nancial support, which is not always readily available.
The shortage of national standards, gazetteers, domain-specific
dictionaries, and metadata that help data providers and consumers to
count on better spatial data quality, decreases users' trust in using data
for informed decisions. Changing this situation is not an easy task and,
meanwhile, different techniques and tools may be applied, such as spatial
ETL tools. However, although our implementation of the ETL processes,
based on a graphical interface provided by GeoKettle, decreases the
programming effort, the cleaning process in some occasions requires the
Java code or complex SQL statements. In addition, each implementer must
rely on her/his own knowledge about spatial data cleaning methods,
even though some patterns may be common for different applications.
Therefore, there is a need to consider already existing research related to
ETL processes applied for conventional data and extend them with spatial
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