Key concepts 1 GIS - Spatial Data Analysis: An Introduction for GIS Users - page 8

Valu e
High : 45
Low : 28
Figure 2.1 Raster image used to illustrate various methods. Units are elevations in metres.
2.2.2 Vector data
As with the raster model, it is assumed that readers are familiar with the vector data
model, but some background is given about vector data storage formats and their
relevance for spatial data analysis. Whereas features in raster grids are identii ed sim-
ply by the row and column position of cells, vector data comprise explicit spatial
coordinates of the features that make up objects. Vector data comprise points (with
x - and y -coordinates), lines (line segments (or arcs) connected by points), and area
polygons (lines with the same start and end point). An example of some line features
is given in Figure 2.2. h is example shows that line features comprise two forms of
point locations—vertices, which represent change in direction of arcs, and nodes,
which represent the start or end of arcs, including locations where dif erent arcs con-
nect. Of course, vectors representing real-world features are usually much more comp-
lex than those shown in Figure 2.2 (and may have many vertices). Note that, while
x and y are used to represent two-dimensional position, z is ot en used to indicate
the third dimension (elevation) and also, as in this topic, values of any property (e.g.
precipitation amount) that are associated with particular x - and y -coordinates.
Vector data can be stored as what are sometimes called 'spaghetti' data—that is,
strings of unconnected line segments. In this case, relationships between objects (e.g.
which line is connected to which other lines) are not encoded. However, explicit infor-
mation on the relationships between objects reduces the computational demands of
subsequent analyses and analysis of vector data is usually preceded by the construc-
tion of topology, as discussed in the next section. Conventionally, vector data are divided