Geography Reference
In-Depth Information
The online mapping systems facilitate many geospatial datasets which are used,
created, edited and maintained including the use of GeoNames as the layer for
populated places (Chang and Park
2006
; Safra et al.
2010
; Safra and Doytsher
2006a
). Many of them are not classified for importance because of the lack of
additional information such as population or administrative level. A way to give an
importance scale to the names is by linking the GeoNames to other datasets (OSM,
natural earth). OpenStreetMap data provides a limited number of place classifi-
cations (such as city, town, village). For the best cartographic results we need classes
that are more comprehensive about how they rank cities. “Which of these labels
should be visible?” and “how much should this label be emphasized?” are important
decisions that need to be made in cartographic design. To do this the present
research is to join additional information from Natural Earth, OSM and Geonames
and keep the path open for other datasets. Hence the objectives of this study are:
To develop an algorithm for joining open-source geospatial datasets.
To find a common link between the datasets considering the parameters of the
geospatial databases.
To establish accurate and efficient results.
This brings to the forefront the fact that each dataset has a unique set of
properties and also attributes that are common to others but only a properly studied
union of these can yield a completely versatile and comprehensively pervasive
body of single point reference repository of open-source geospatial data. The
requirement analysis reveals several challenges that have not been addressed in
past and recent researches as shown in the next section of literature review that
builds up the background of our study.
Background
A careful study of relevant literature brings forward the fact that in this area the
major thrust has been on joining multisource geodata for localized areas rather than
take-up the challenge of integrating globally used opensource databases and study
their strengths when they are combined. So numerous studies have laid down the
architecture for joining varied data ranging from remotely-sensed, scanned maps,
geographical information system (GIS) layers, global positioning system (GPS)
acquired navigation data and more (Butenuth and Heipke
2005
; Doytsher
2000
).
This has brought about a localized effect of study to this domain since the datasets
acquired were at best of a very limited extent (Egenhofer et al.
1989
; Goesseln and
Sester
2004
; Rigeaux et al.
2001
). The need for integration of heterogeneous data
sources arises in many different cases, one example of which is interoperability of
information systems (Butenuth and Heipke
2005
; Doytsher
2000
; Egenhofer
et al.
1989
), including geographical/geospatial information systems. Another exam-
ple is mediator systems (Goesseln and Sester
2004
; Rigeaux et al.
2001
; Sattler
et al.
2000
) that help in data acceptance and understanding from one system to
another or from a subsystem to next. For geo-spatial information systems, the
data integration problem has two important sub-problems. In map merging or
