1. Nicholas A. Procopio, Ph.D, GISP
Environmental GIS
Nicholas A. Procopio, Ph.D, GISP

2. Analyzing species point data
Activity Range
Probability range
Nearest Neighbors
Spatial autocorrelation

3. Activity Range 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

4. Activity Range
An activity range, as defined with the minimum convex-polygon method, consists of the area bounded by the outermost telemetry locations of an animal.

5. Core Activity Area
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.

6. Kernal Estimation
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

7. Bandwidth is all important
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”.

8. Use Hawth’s Tools with earlier versions of ArcMap
Use with
ArcMap 10
Use Hawth’s Tools with earlier versions of ArcMap

9. Uses a separate command line interface.
Has good command assistance.
Generates spatial files (shapefile or raster) that can be added into ArcMap.

11. 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");

12. genmcp(in="c:\du\points.shp", out="c:\du\points_mcp.shp");
A single MCP
genmcp(in="c:\du\points.shp", out="c:\du\points_mcp.shp");

13. Requires a unique id field and an order field
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");

14. 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. 

15. Generate Probable Activity Areas 2 step process

16. 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, , ,
3 isopleths processed.

17. Probable activity areas

18. Probable activity areas

19. increased bandwidth (500,000)
Probable activity areas
increased bandwidth (500,000)

20. decreased bandwidth (10,000)
Probable activity areas
decreased bandwidth (10,000)

21. Other meaningful stats.
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).