Global Positioning System Reference
In-Depth Information
data, since they provide a well-known spreadsheet-like environment
extended by dynamic data manipulation and automatic aggregation.
On the other hand, the popularization of spatial data by internet
providers, such as Google maps or Spatial Data Infrastructures (SDIs)
developed on a national or global level opens different alternatives to
include spatial data in various kinds of applications, including the ones used
for decision making. However, traditionally, the manipulation and querying
of spatial data rely on (often complex) software of Geographic Information
Systems (GISs) that requires a geo-knowledge, e.g., knowledge about spatial
reference systems, layers, storage formats, operations and/or functions
necessary for spatial data analysis (Yeung and Hall 2007). This situation
makes diffi cult the task of promoting decision-making processes based on
spatial data since personnel responsible for making decisions, especially
at lower administrative levels of municipalities or other administrative
entities, do not always have the necessary geo-knowledge to explore data
using GISs (Malinowski 2013). Furthermore, the available conventional
data provided by many public institutions, e.g., census bureau or research
centers, can be incorporated and joined with spatial data, thereby improving
their diversity and augmenting the circle of users that can explore it for
decision-making initiatives.
Spatial OLAP (SOLAP) is an option to deliver spatial and conventional
data to the decision makers providing analysis capabilities without the
necessity to dominate the geo-concepts required for manipulating spatial
data in GIS software. SOLAP applications are more easily used by non-
expert users than pure GIS applications (Rivest et al. 2005), even though in
some occasions they have to be adjusted to satisfy user needs (Scotch et al.
2007). With a simple click, SOLAP provides aggregated or detailed data of
interest in an analysis environment that includes tables, graphs, and maps
(Rivest et al. 2005). In this way, non-experts users can analyze spatial and
conventional data according to their needs which can be different from what
the geo-specialists require, e.g., geographers, cartographers, and surveyors,
among others. Nevertheless, the decisional process should rely on a wide
variety of high quality data, otherwise, some aspects may be missing
and incorrect decisions may be made that harm company/organization
outcomes (Talhofer et al. 2011). ETL processes can be used for this purpose
integrating, as well as transforming conventional and spatial data before
loading it into spatial DWs (SDWs). Moving data to a SDW makes it
available for analysis within existing organizational/enterprise data using
different kinds of reporting and analytical tools (Badard et al. 2012).
SDWs and SOLAP are developed based on a relatively long and
successful tradition in using conventional DWs and OLAP; however,
SOLAP tools do not always meet users' expectations in providing similar
functionalities as OLAP tools do. This situation is due to the fact that even
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