Environmental GIS
Nicholas A. Procopio, Ph.D, GISP
nick@drexel.edu
Analyzing species point data
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Activity Range
Probability range
Nearest Neighbors
Spatial autocorrelation
Activity Range
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Minimum Convex Polygons
• completely encloses all data points by
connecting the outer locations in such a
way as to create a convex polygon.
• Describes the Activity Range
Activity Range
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An activity range, as defined with the
minimum convex-polygon method,
consists of the area bounded by the
outermost telemetry locations of an
animal.
Core Activity Area
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The kernel method can be used to
estimate general (95% isopleth) and
core (50% isopleth) activity areas
corresponding to the intensity of
activity in an area
Other percentiles can also be used.
Uses a kernaling methodolgy which
is dependent on a SELECTED
bandwidth.
Kernal Estimation
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weights points that are further
away less than those that are
close.
Kernel estimation attempts to
obtain a smooth estimate of the
probability density (aka a
smoothed histogram).
Issues: choice of ‘kernel’,
bandwidth
Bandwidth is all important
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This raises an obvious issue with modelling
continuous processes…..the scale (in this case
the bandwidth) must be carefully considered.
Striking a balance between too small a
bandwidth (possible high errors for some
predictions) and a large bandwidth (low errors
in all predictions) is essential.
Knowledge of the biology and ecology of the
system is important to making these types of
judgments.
It really becomes “plug and play”.
Use with
ArcMap 10
Use Hawth’s
Tools with
earlier versions
of ArcMap
http://www.spatialecology.com/gme/
•Uses a separate
command line
interface.
•Has good
command
assistance.
•Generates
spatial files
(shapefile or
raster) that can
be added into
ArcMap.
Individual MCPs
genmcp(in="c:\du\points.shp", out="c:\du\points_mcp.shp", uidfield="Id");
or
genmcp(in="c:\du\points.shp", out="c:\du\points_mcp2.shp", where="Id=2");
A single MCP
genmcp(in="c:\du\points.shp", out="c:\du\points_mcp.shp");
Location points to movement path
Requires a unique id field and an order field
convert.pointstolines(in="c:\du\points.shp", uidfield="Id",
orderfield="rec", out="c:\du\point_lines.shp");
Generate Probable Activity Areas
•Note that the 50%, 90% or 95% polygon area
that is generated represents that percentage of the
area (or volume) of coincidence.
•This is based on the distribution of the points and
will not necessarily include i.e. 50% of the points.
•It shows an estimate of where the critter could
have been found i.e. 50% of the time.
•It is an interpolation of the data.
Generate Probable Activity Areas
2 step process
Generate Probable Activity Areas
2 step process
Generate the Kernal Density raster:
kde(in="C:\du\points.shp", out="C:\du\kde5_30",
bandwidth=100000, cellsize = 30, where="Id=1");
WHERE clause: Id=1
KDE completed successfully
Generate the Isopleth layer
isopleth(in="C:\du\kde5_30",
out="C:\du\isopleths5_30.shp", quantiles=c(0.5, 0.9, 0.95));
The input quantiles were used to determine the raster value at which
to calculate the isopleth lines as follows:
Quantile, Isopleth Value
0.5, 0.103690887913028
0.9, 0.0316148920235371
0.95, 0.0180086184523985
3 isopleths processed.
Probable activity areas
Probable activity areas
Probable activity areas
increased bandwidth (500,000)
Probable activity areas
decreased bandwidth (10,000)
Other meaningful stats.
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total distance moved (the sum of all linear
distances between locations)
range length (linear distance between the
two most distant locations)
mean distance moved per day (total
distance moved, divided by the total
number of days monitored)
and mean distance per move (average of
all distances between locations).
The distance moved from hibernaculum
(linear distance between the hibernaculum
and the most distant location).