Geoscience Reference
In-Depth Information
observednearest =
0.5078
In our example the mean nearest distance
observednearest
comes to 0.5078.
We next calculate the area of the map. h e expected mean nearest-neighbor
distance is half the square root of the map area divided by the number of
observations.
maparea = (max(data(:,1)-min(data(:,1)))) ...
*(max(data(:,2)-min(data(:,2))));
expectednearest = 0.5 * sqrt(maparea/length(data))
expectednearest =
0.4875
In our example the expected mean nearest-neighbor distance
expectednearest
is 0.4875. Finally, we compute the standard error of the mean nearest-
neighbor distance
se
se = 0.26136/sqrt(length(data).^2/maparea)
se =
0.0255
and the test statistic
Z
.
Z = (observednearest - expectednearest)/se
Z =
0.7954
Since -1.96<
Z
<+1.96, we cannot reject the null hypothesis and conclude that
the data are randomly distributed, but not clustered.
7.10 Analysis of Digital Elevation Models (by R. Gebbers)
Digital elevation models (DEMs) and their derivatives (e.g., slope and aspect)
can indicate surface processes such as lateral water l ow, solar irradiation, or
erosion. h e simplest derivatives of a DEM are the slope and the aspect. h e
slope
(or
gradient
) is a measure of the steepness, the incline or the grade of a
surface measured in either percentages or degrees. h e
aspect
(or
exposure
)
refers to the direction in which a slope faces.
We use the SRTM data set introduced in Section 7.5 to illustrate the
analysis of a digital elevation model for slope, aspect and other derivatives.
h e data are loaded by
clear
