Geoscience Reference
In-Depth Information
Geospatial data is used by a plethora of thematic areas and scientific disciplines
and now underpins many of them. Such disciplines include environmental monitor-
ing; rapid response disaster mapping; climate modeling; census and political map-
ping; remote sensing, logistics and urban planning. Although the spatial data used in
each of the disciplines differ, the underlying principles and paradigms are consistent
to most. The main objective of geospatial data is to have a digital representation
of the reality that exists. This requires an abstraction of the real world using a data
model, which in turn is defined by data structure that represents the data model using
arrays and programming structures. However, with respect to geospatial data, the
data model is a key aspect and impacts the type of geospatial analysis.
1.2 Projections and Coordinate Reference Systems
We have mentioned that geospatial data is unique in that it references information to
a location or locations on the Earth's surface. Consequently a coordinate reference
system is used to transform three dimensional surfaces of the Earth into a two dimen-
sional plane. For instance, if you plot a series of lines using coordinates in latitude
and longitude of a Cartesian system, the straight line will appear bent and areas will
be distorted.
There are many coordinate reference systems and geographic projections, each of
which can be defined by amathematical function. Themap projections can be broadly
divided into four groups that include: the conical; cylindrical; planar and interrupted
projections. They each have their advantages and disadvantages depending on the
spatial extent and requirements of the purpose. However, it suffices to say that they
all distort at least one of the five geographic relationships which are:
•
Areas
•
Angles
•
Gross shapes
•
Distances
•
Directions.
1.3 Spatial Data Models
Much of the data available today, be it environmental, socio-economic or climatic, can
be spatially referenced, thereby providing opportunities to spatially analyze trends
and relationships. At a very superficial level, geospatial information can be described
as an abstract representation of the Earth's surface, which is frequently stored dig-
itally. The data model is a simplification of the real world that incorporates the
properties considered relevant to the application. The data model copes with the
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