MOVEMENT
ANIMAL MOVEMENT ANALYSIS
ARCVIEW EXTENSION
USGS - BRD, Alaska
Science Center - Biological Science Office
Glacier Bay Field Station
Philip N. Hooge Ph.D.
Loading the Extension: Animal Movement is an ArcView program extension. 2 versions
of 1.0 are currently available, one which requires the simultaneous use
of Spatial Analyst. After downloading, unzip/extract the .avx file
directly (or unzip to a temp directory, then copy) to the ArcView EXT32
folder. Start the ArcView program, click on "file" and go to
"extensions". From the list of available extensions, select
Spatial Movement Analysis (which requires Spatial Analyst to be selected
first), or choose Animal Movement Analysis (which does not require Spatial
Analyst), and click "ok". The collection of new functions
will now be available through the menu choices, tools, and buttons
described below.
Loading Version 2.0 or later:
- Unzip or download the animal movement files.
- Copy the animal movement extension (.AVX file) in to the $AVEXT folder.
- Spatial Analyst (Spatial.avx) extension must already be installed in to the $AVEXT directory.
- Place the avdlog.dll file in the $AVBIN directory and the avdlog.dat in to the $AVLIB directory .
- These files have been included in the Zip file on the Web, and can be copied directly in to the folder if not already installed. A sample data shapefile, in UTM Nad27 projection, has been included in the zip file.
- Start-up ArcView, go to the file menu, choose "extensions", and select "Animal Movement SA Version 2".
For any problems or questions regarding this documentation, contact: elizabeth_solomon@nps.gov
Using this documentation: Click on the menu bar above to go to the major headings and then
click on either the subheading list or the image map of menu choices or
buttons. Click on the return button to get back to the subheading
list. Note: The images in this documentation are degraded compared
to the screen and printer output in order to speed up loading.
Citation information:
Please site the Animal Movement program if you use it in a
publication.
P. N. Hooge and B. Eichenlaub. 1997. Animal movement extension to
arcview. ver. 1.1. Alaska Science Center - Biological Science Office, U.S.
Geological Survey, Anchorage, AK, USA.
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The Movement extension is a collection of over 40 functions to aid
in the analysis of animal movement data. The motivation for the creation
of this program was the absence of a collection of tools with real integration
into a GIS (Geographic Information System). In addition, several
functions in this program had not been implemented before or were
specifically developed to address author specific research
problems. The ArcView software program was chosen for development because
of high degree of integration of its GIS environment, the ability to work with
a wide variety of spatial data formats, compatibility with many computer
platforms, and the capability of its object oriented
scripting language Avenue. The ability to cleanly load and unload code
developed for specific functions (extensions) was an added benefit. The
use of Avenue and ArcView has both costs and benefits. Implemented
through a scripting language, several of the functions are significantly slower
than compiled code would be. However most functions work as fast or
faster than similarly compiled programs. On the benefit side the program
has full integration with all other ArcView functions and extensions. The
Movement extension works in multiple projection systems, uses the
selected records (enabling complex queries or selections), inputs point
and attribute data in many formats, and will integrate with many types of
spatial data.
The Movement extension also has considerable utility for the
analysis of other point phenomena and has functions which are useful for many
types of GIS uses.
Requirements: ArcView and Spatial Analyst (for full
functionality). This program will work in versions of ArcView on PC , UNIX and
Macintosh hardware (Note...several Dll's required by V. 2.0 are PC compatible
only, and will not work with the UNIX platform). The random number
generator on the UNIX platform, however is not truly random and caution should
be used with the functions that use random numbers. On the PC platform it is
recommended that a Pentium or Pentium Pro 200 or faster be used with this program
and at least 32 megabytes of memory, especially when simultaneously running
Spatial Analyst.
IMPORTANT NOTICE: We have done extensive testing on our data
sets. However, due to the complexity and size of this application there
will be bugs that we have missed or that only appear in some data sets.
Be aware that there are substantial differences in the values calculated by
many home range programs due to small differences in the way algorithms were
implemented (see Worton 1989, White & Garrott 1990 for examples).
Please send detailed descriptions of the problem and if possible data sets that
they appeared with to Philip N. Hooge. We
will try to fix them as time is available.
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Most of the functions work on point themes, which can be created
in any method that ArcView can use. Examples include making point event themes from
database tables, ascii files or using point shape files, AutoCad point files,
and point coverages from ArcInfo. Data can have any number of attributes which
can be used in several functions (as well as by the standard ArcView
functions). The line coverages that are used for a few functions are best
made with the point to polyline tool in the
program. If there is a date field present it will be used automatically
or if there are multiple date fields the program will query the user. The
program can use multiple projections. The projection units and distance
units are always the units set in the view. However some functions do not
give meaningful measurements in some projections. For example area measurements
in decimal degrees will have limited utility. The bulk of the testing was done
using the UTM projection and this is the suggested projection because of this
testing and the characteristics of the UTM projection for most study sites used
in animal movement studies. Some studies however that have very large
study sites may need to use other projections such as Albers. Even if all
your work is in one projection system we suggest that you leave your vector
data in decimal degrees to take advantage of the ability to reproject your data
as needed to integrate your data with other spatial data sources. The
Movement program expects the data to be sorted or functions that are outputting
time depended data (e.g. speed, autocorrelation) will return nonsense. There is
a function in the program which does a permanent sort of the data. If
your data is not sorted it will be necessary to use this program as the Table
Sort command only works on the Table document and does not extend to the
virtual attribute table which is used by the rest of ArcView. If you wish
to preserve the original ordering use the add record number function first and
then resort latter based on this.
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Click on a button or tool. |
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Buttons |
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V. 1.0
Menu |
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Kernel
Home Range calculates a fixed kernel home range utilization distribution
(Worton1989) as a grid coverage using either ad hoc calculation of a smoothing
parameter, least squares cross validation Silverman (1986), or a user input for
the smoothing parameter (H). The bivariate normal density kernel is used
as suggested by Worton (1989). The least squares cross validation
(LSCV) in this version takes significant processing time despite using
the golden minimizing routine. Most user will find that the adhoc calculations
are very close to the LSCV for exploratory analysis (for most datasets Hopt is
usually between 0 .7 and .9 of Href[Ad hoc] in this implementation). The
adhoc calculation is based on Silverman (1986) rather than Worton(1989) or
Seaman & Powel (1996). The problem of discretization errors (0
length distances caused from rounding location positions giving a minimized h
of zero) are handled slightly different than Tufto (1996). Distance
measures are uniform randomly placed between 0 and (href/100) when and only
when the distance measurements are 0. This only adjust the locations when
necessary and allows for different projection and distance systems.
The kernel is based on the non-corrected data. The program queries the user if
they would like to adjust the .LSCV or the Adhoc H. Worton (1994)
suggests adjusting H by 0.8 for normal distributions and 0.5 for
uniform. Work by Seaman & Powel (1996) suggest that this is not
necessary with the LSCV. It is our experience that the original
Adhoc and LSCV smoothing parameters provide a less biased estimator
than a user selected or Worton's corrections.
Three
things are output from this routine: A grid coverage with the Utilization Distribution
(probabilities), a polygon shapefile containing individual polygons for each
selected probability, an associated attribute table containing
probability and area fields for each set of probability polygons, and a message
box displaying the area calculations of each probability, Note that
the default probabilities are 50 and 95, and that the view must be zoomed out
sufficiently to encompass the larger probability areas to create the polygon
shapefile (95%, etc.)
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This figure shows the green dots (location points from which the kernel was created from using the LSCV) against the utilization distribution grid. |
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This figure is a display of the location points (shown in yellow) within the selected 50, 75, and 90% probability polygons. |
This
program calls four functions Fun.KernelHR, Fun.LSCV, Fun.Golden,
Fun.Score. Fun.LSCV calls function Fun.Golden which in turn calls
Fun.Score. These latter two functions are best used from Fun. LSCV rather
than being called themselves. Fun.KernelHR and Fun.LSCV have the following structure
which can be called from avenue:
Fun.LSCV
1.2 3/20/98
This
implements Least Squares Cross Validation for the Normal Bivariate Kernel
Smoothing Factor
NOTES:
This LSCV is valid only for the bivariate normal kernel but while it is
designed for the Fixed Kernel it will give accurate results for the adaptive
kernel.
CALLS:
Function Fun.Golden and Function Fun.Score
RETURNS:
HList = {minH}
H is the minimized smoothing parameter
INPUT:
TheFTab
= Self.Get(0) 'The Ftab of the location points
TheSet
= Self.Get(1) 'The Set of locations
ThePrj
= Self.Get(2) 'The projection of the location points
Href
= Self.Get(3) 'The adhoc determined H value used to create the parameters
for the search and shorten it.
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Minimum Convex Polygon Home Range
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Calculates a minimum convex polygon home range based on either the selected records or if none are selected the entire data set. A polygon shape file theme is created. The location point statistics menu choice will output a MCP as a graphic object if this is desired rather than a shape file if (View Graphics must be selected). If only area is desired then location point statistics with nothing selected will output MCP area. Yellow
circles are locations and the checkered polygon is the MCP |
The
Fun.MCP which this program calls Minimum Convex Polygon Home Range has the
following input and output structure:
RETURNS
retList = {MCP, MCPArea, pList, cornerPts}
MCP
is the polygon graphic object
MCPArea
is the area measurement in view units
pList
is the list of points
CornerPts
are the points making up the corners
INPUT:
theView
= SELF.Get(0) 'The view
theTheme
= SELF.Get(1) 'The theme with the points
theFtab
= SELF.Get(2) 'The Ftab with the points
theFBit
= SELF.Get(3) 'The selected records
thePrj
= SELF.Get(4) 'The view projection
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Creates a grid based on the Harmonic Mean method (Dixon & Chapman 1980). We do not encourage the use of this method for calculating home range areas and thus do not directly output a area measurement. It has many problems in home range calculation (Worton 1987 1989, White & Garrott 1990) these include dependencies on grid origin and spacing. This and the Harmonic Mean Point file are placed here mainly for the purpose of examining activity centers and for obtaining habitat relationships based on a gridded distance measurements. |
Generates a
harmonic mean home range from a gridded point theme of harmonic mean z
values. This program generates a grid based on the harmonic mean z
values. The attribute field VALUE is the z values, the attribute field
PROPORTION is the proportional representation of those grid cells summed over
the entire grid and equal to 1. PROPORTION_CUM is the cumulative total of
PROPORTION. These can be used to generate a psuedo-utilization
distribution as the harmonic mean method does not create a utilization
distribution.
This menu choice used the
Function Fun.HarmonicMean .
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This implements the Jennrich-Turner (1969) Bivariate Normal Home Range. This method of home range calculation is seriously flawed in its dependence on a bivariate normal distribution of locations but is valuable due to its lack of sensitivity with sample size, simplicity of underlying probability theory, ability to get confidence limits on the estimates, and to derive the principle axis of the location coordinates. Other than the rare circumstances where the data is bivariately normal the main utility of this program lies in developing statistically simple home range-habitat models and in comparison with home range estimates using this method in other studies. |
The
program ask the user which probability ellipse to output (current choices are 95,
90, and 50). The program will then take the selected records (or if none
are selected the entire dataset) and generate a bivariate normal probability
ellipse including the major and minor axis and the arithmetic mean as a
graphic object. This graphic object is ungrouped so individual items can be
deleted. The graphics are not erased so multiple ellipses
can be generated on one or many location files. The ellipse can be
selected and copy and pasted into polygon coverage. A message
box with the arithmetic mean X and Y, the area of the ellipse, the major axis
length, the minor axis length and the angle of the major axis in reference to
the X axis.
The
Fun.Jennrich_Turner which this program calls Jennrich Turner Home Range has the
following input and output structure:
RETURNS:
JTList = {ArithmaticMean,Area, MajorAxisLength, MinorAxisLength, Angle, MajorAxis,
MinorAxis, Ellipse}
ARITHMATICMEAN
is equal to the arithmetic mean point
AREA
is equal to the area of the probability ellipse
MAJORAXISLENGTH
is equal to the length of the major axis
MINOR
AXISLENGTH is equal to the length of the minor axis
ANGLE
is equal to the angle which the major axis is inclined from the X axis
MAJORAXIS
is equal to a line representing the MajorAxis
MINORAXIS
is equal to a line representing the MinorAxis
ELLIPSE
is equal to the probability ellipse
INPUT:
TheFTab
= Self.Get(0) 'The Ftab of the location points
TheSet
= Self.Get(1) 'The Set of locations
ThePrj
= Self.Get(2) 'The projection of the location points
Probability
= Self.Get(3) 'The probability ellipse 95%, 90%, or 50%
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Bootstraps the points in a dataset given the user- selected parameters to test the effects of sample size on mcp area. |
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Classify the
legend using the z value. |
The
fun.HarmonicMean which this program calls Harmonic Mean Point Theme has the
following input and output structure:
RETURNS:
Hmlist = {HarmonicMean, HMDict, zmin, zmax, MaxPoint, MinPoint,theInc,URPt} a list
with the Harmonic Mean location (a point), the dictionary containing all the
grid points and the z values (can be used to create a harmonic mean home range
or to find multiple centersof activity, the minimum z value, the maximum z
value, and the location of the Max z value (a record when the points themselves
are used. MinPoint
will equal HarmonicMean except for when the points are used. Then it will
return the Record with the minimum value. theInc is the
distance between points. URPt is the upper right corner pt these have been
place in here to aid in the production of grid coverages from the data origin
which is also useful can be obtained from the rect passed to this function.
NOTES:
if both TheRect and TheXdiv are nil then the points themselves are used to
generate the Harmonic Mean. This is useful for eliminating outliers
from the data set in a meaningful way. In this case MinPoint &
MaxPoint will be a record number. This is one of the methods used in outlier removal.
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Creates
random angles and uses distances between existing sequential points to
determine walk points.
Requires
an active point theme. Prompts the user to select one of the following starting
points for the simulation: First Point, Last Point, Arithmatic Mean, and Harmonic
Mean (Defaults to the First Point).
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Site
fidelity test without the use of a bounding graphic. |
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Table
represents the number of replicates usedin the analysis and the corresponding
R2 and linearity values. Lower r2 value = higher site fidelity
The
simulation compares the observed pattern with a user selectable number of
random walks. This uses a Monte Carlo simulation and parameters from
the original data to determine if the observed movement pattern has more site fidelity
than should occur randomly, is a random pattern or is overly dispersed. It is
suggested that a 100 simulations be run first and if the data is close to
chosen probability level break point then run it with a 1000 simulations which
will more accurately reflect the random walk distribution. Outputs
a table with 3 fields, Replicate, R2 and Linearity. Outputs a polyline theme
containing polylines of random walks. The polyline attribute table
and the R2 table are linked by Replicate and LinkID. If replicates < 100,
outputs a chart showing the r2 sorted in ascending order. To
identify which r2 belongs with which polyline, select the record from the
output table and view the selected polyline in the view. However,as the chart
is dynamically linked to the table, selecting individual records will modify
the charts color scheme. To save the original chart, add it to a static
layout before viewing individual records in the r2 table.The O replicate in the
output table and chart is the observed data the others are replicates from
simulation runs.
Works
on selected point records or if none selected the entire table.A single
selected polygon or polyline may be use to limit the extent of the random
walks. Progress
bar tells the progress through the simulation loop.
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Create Polyline From Point File
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Creates a an ordered polyline theme from a point file Point
theme file must be pre-sorted. You
must classify the theme with the arrow line symbol in order to see direction
of travel.
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Sets the characteristics for the movement path function button.
Enter the following travel path graphic
characteristics:
· Line color
· Line width
· End point size
· End point color
· End point symbol
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Calculates
a series of descriptive statistics of the animal location point pattern and can
output graphical representations of some statistics. Select a method for
the output of location statistics. If nothing is selected then the routine will
default to basic statistics and a screen report of the results. Multiple
items from the list can be selected simultaneously. If any item is
selected from the list and a screen report is desired then Screen Report must
be selected. View Graphics will output the graphical results to the current
view. Advanced Statistics contains those functions that require significant
processing time namely the nearest neighbor analysis, Cramer-von mises, and the
harmonic mean calculations. The harmonic mean calculation will query the
user with the desired grid size which substantially effects the speed of the
routine. The box showing the statistical boundaries is useful for evaluating
the nearest neighbor analysis and Cramer-von mises both tests of complete
spatial randomness. These boundaries can be adjusted in the individual
menu choices for these functions.
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Minimum X |
Minimum value of x coordinate (utmx - northing) |
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Minimum Y |
Minimum value of y coordinate (utmy - easting) |
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Maximum X |
Maximum value of x coordinate (utmx- northing) |
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Maximum Y |
Maximum value of Y coordinate (utmy- easting) |
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Sample Size |
Total number or records (observations) in the dataset |
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Mean of X |
Mean value of x coordinate (utmx - northing) |
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Mean of Y |
Mean value of y coordinate (utmy - easting) |
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X Variance |
Variance of X coordinates (utmx - northing) |
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Y Variance |
Variance of Y coordinates (utmy - easting) |
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XY Variance |
The XY variance (not the covariance) |
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Minimum distance |
Minimum distance (m) between observations |
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Maximum distance |
Maximum distance (m) between observations |
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Total distance |
Total distance (m) traveled per dataset |
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Mean distance |
Mean distance (m) traveled per dataset |
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Number of bearings |
Total number of bearings (angles) per dataset |
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Mean bearing |
Mean bearing per dataset (azimuth) |
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R Concentration of angles |
The concentration of angles 1-r is the "circular variance" |
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Angular deviation |
The angular equivalent of linear standard deviation |
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Rayleigh's z for angles |
The z value for Rayleigh's test for significant angles |
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Minimum date |
Earliest observation date per dataset (yymmdd) |
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Maximum date |
Latest observation date per dataset (yymmdd) |
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Duration of Study |
Total number of days per dataset |
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Minimum speed (units/day) |
Minimum number of meters traveled per day |
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Maximum speed (units/day) |
Maximum number of meters traveled per day |
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Mean daily speed |
Mean number of meters traveled per day; distance/number of days in dataset |
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Linearity |
The distance between travel path endpoints and the total distance traveled |
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R2 |
A measure of dispersion of the data, mean squared distance (MSD) from the center of activity |
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T2/R2 ratio |
Schoener's ratio for examining autocorrelation. R2/MSD between successive observations |
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Primary axis length |
The major axis length (bivariate normal) |
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Secondary axis length |
The minor axis length (bivariate normal) |
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Primary axis angle |
The angle which the primary axis is offset from the X axis (90 to -90) |
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Eccentricity |
The ratio between the minor and the major axis |
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Cramer-von Mises |
Test statistic for Complete Spatial Randomness (CSR)(see Cramer-von Mises menu selection) |
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CM heterogeneity p. |
The probability value for rejecting the null hypothesis of CSR using the Cramer-von Mises |
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MCP area |
Minimum Convex Polygon area (m); calculates area between all points in dataset |
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95% Ellipse Area |
The bivariate normal 95% ellipse |
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Harmonic mean X Value |
The X coordinate position of the harmonic mean |
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Harmonic mean Y Value |
The Y coordinate position of the harmonic mean |
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Nearest-Neighbor R |
The nearest neighbor test statistic (see Nearest-Neighbor menu selection) |
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Nearest-Neighbor z |
The z value of R |
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Nearest-Neighbor p |
The probability value for rejecting the null hypothesis of CSR using Nearest-Neighbor R |
View Graphics
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Advanced Statistics Graphic Output
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Nearest Neighbor Analysis Test For Complete Spatial
Randomness
The Nearest Neighbor Analysis program tests for complete spatial randomness using a selected graphic or polygon feature from a polygon shapefile. Returns a message box displaying the area of the chosen study site and the z and r values.
This program references the FunNNDCSR,
which implements the Clark and Evans (1954, Ecology 35. pp445-453) algorithm
and allows for either points beyond the boundary to be used for correcting edge
effects or uses the correction of Donnely (1978, Holder (eds) Simulation
methods in archaeology. pp.91-95). This allows considerable flexibility.
If the population has been completely sampled, e.g. animal locations from
radio tracking or tag returns, then choose FALSE for correction and ignore the
boundary boolean (i.e. set it to TRUE or FALSE as it won't matter). The
program defaults to using the boundary correction for anlaysis. If you
have not sampled the complete population then select either the edge
correction or the boundary correction (if you have sample points beyond the
boundary). It checks to see if the sample size is too small (from
Donnely 1978) for the normal distribution.
The FunNNDCSR
which this program calls Nearest Neighbor Analysis has the following input and
output structure:
RETURNS: NNDCSR = {R, z, Significance}
The R value, a z statistic, the probability level,
INPUT:
AFtab
= Self.Get(0) 'The Ftab. A Ftab
ASet
= Self.Get(1) 'The selected records. A bitmap
APrj
= Self.Get(2) 'The projection or nil if it doesn't exist
ChosenShape
= Self.Get(3) 'The shape where the points are
Correction
= Self.Get(4) 'Do a edge correction? A boolean.
Boundary
= Self.Get(5) 'Points outside the boundary to do an edge correction?
OutputSignificance
= Self.Get(6) 'Output the significance? A boolean.
Cramer-Von
Mises Test for Complete Spatial Randomness
Description: Works from a menu on either a
selected point theme and a selected rectangle graphic or from a rectangle
in a shape file. This function is only valid with a rectangular study
plot. The wait cursor will appear and then a messagebox will tell you the
W value and how many features were accounted for in the analysis. W
values relate how clustered or dispersed points are within the graphic
rectangle or rectangle theme you specified. A W value of greater than .3
indicates a tendency towards a clumped (clustered) pattern. A W value of
0.3-.06 indicates a random distribution. An R value of less than
.06 indicates an organized (uniform) pattern. This statistical test
is insensitive to origin but
highly sensitive to the upper right corner of the study plot. Its primary
usefulness is in detecting heterogeneity that the nearest neighbor analysis
(NNA) misses especially NNA base purely on distance measurements.
Zimmerman, D.L. 1993, A bivariate Cramer- von Mises type of test for spatial
randomness, Appl. Statist. V. 42. pp. 43-54.
The funCramer which this program
calls Cramer_von Mises has the following input and output structure:
RETURNS: cramer =
{w,HeteroSign,OrderedSign}
W
is the Cramer-von Mises statistic
HeteroSign
is the significance of acceptance of the alternative hypothesis of
heterogeneity.
OrderedSign
is the significance of acceptance of the alternative hypothesis of
ordering of the data.
INPUT:
AFtab
= Self.Get(0) 'The table. A Ftab
ASet
= Self.Get(1) 'The selected records. A bitmap.
APrj
= Self.Get(2) 'The projection. The projection or nil if none.
ARect
= Self.Get(3) 'The rectangle to do the statistics in. A rect
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Conducts circular point
statistics and outputs a graphic representation of the bearings. The red
line = mean bearing, which is scaled proportionately to the r value of
the angles; r value = 0 to 1, (1=all angles the same) multiplied by the longest
line segment.
This implements circular statistics
(Batschlet 1981) for the sequence of points in a point coverage.
Useful for determining the travel directions from animal movement
locations and the significance of direction of travel.. This function requires
that the data be ordered in the sequence desired. The function works on the
selected records (or all if none are selected) which is useful for examining
parts of the movement path. The program will not tell the
probability level of rejecting the null hypothesis of directed movement but
will give the Z value and sample size which can be looked up in a Z
table.
The fun.CircularPtStats which this program
calls Circular Point Statistics has the following input and output structure:
RETURNS: CircleStatsList =
TheNmbrBearings,Bearings,Distances,theMeanBearing,TheXMean, TheYMean,r,s,z}
The number of bearings, The List of
Bearings, The List of Distances,
The
Mean Bearing, The Arithmetic mean of X, the arithmetic mean of Y,
the
angular intensity, The angular dispersion, and Rayleigh's Statistic
INPUT:
TheFTab = Self.Get(0) 'The Ftab of
the location points
TheSet
= Self.Get(1) 'The Selection BitMap of the locations
ThePrj
= Self.Get(2) 'The projection of the location points
Does a series of circular spatial
statistics on X,Y points representing animal locations or other travel path
variables
Requires
an active point theme.
Requires
compiled script Fun.CircularPtStats
Outputs: Mean bearing, Number of
Bearings with a length > 1,
List of Bearings, List of Distances, s, z.
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Creates
a polyline file using either the arithmetic mean, harmonic mean, or another
theme to calculate the centers. Use the area and perimeter
update tool to add the length measurements to the attribute table in the
view distance units. This program is useful for building distance relationships
between either the arithmetic (A) or harmonic mean (B) or other point (C) or
polygon themes (D). It builds the lines up to the the closest objects using the
center of that object. This is useful for developing test of habitat
relationships that are not as sensitive to locational error or polygon
mismapping as are point on polygon methods.
A)
Calculated with the arithmetic
mean
B) Calculated with the harmonic mean
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C)
Calculated with a point
theme
D) Calculated with a polygon theme
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Description:
The program first prompts for the type of centers to use. If the user selects
themes the program creates spider diagrams based on the distances between
the CLOSEST points (or center of shapes) contained in two themes. If
the user selects Arithmetic or Harmonic Mean the program creates the spider
diagram from just that one mean point. The Output is a new (spider) theme
with distances stored in the FTAB.
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Takes the selected point theme and classifies it based on the selected field of a polygon theme. It adds a field to the point attribute table with the classification information. This works on the selected records or prompts the user if none are selected to use the entire point theme.
Creates a histogram based on a selected field of a theme using its legend classification
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Enter the
percentage of features to select. |
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Enter the percentage of points to be removed. Select
the method of removal (only harmonic mean in v. 1.0). The Harmonic mean method
(Dixon & Chapman 1980) removes points by their largest harmonic mean
value. The program recalculates values after each removal as suggested
by White & Garrot (1990) e.g.
In this outlier removal process, 5% of the outlying points were removed, and 95%
(shown in yellow) become selected for further analysis |
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The outlier removal can be
used to create MCPs containing different percentages of the total locations.
In this example the 100% (black, partially covered by magenta), the 95%
(magenta) and the 50% (red) are shown. These were built by
sequentially unselecting a percentage of outliers using this menu choice
and then building a polygon using the location statistics
menu choice (view graphics option). After each selection all the points
were reselected (or unselect all the points).
Inputs: A selected point theme, the percentage of points to remove and whether they should be removed with or without replacement.
Outputs: A selection of points with
the outliers removed
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Adds geographical coordinates of a point or the
centroid of a polygon to the theme's attribute table. Coordinate values are based on the current
projection of the view.
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Creates a random point theme using a selected record of a polygon theme or polygon graphic as the boundary. User selects the polygon record or graphic, then specifies number of points, distance from boundary, and distance from other points. In
this example, 100 points were chosen using 1 meter distances. |
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Calculates distance from selected items in one theme to items of another theme This script will prompt the user for two point themes in the active view: theFromTheme = the point theme containing the selected points that you wish to calculate the distance FROM. theToTheme = the point theme containing points that you wish to calculate the distance TO. The user will also be prompted for an identifying field in the FROM theme. The value of this field will be used to name to distance field in the TO theme. A distance field will be added to the TO theme for each point selected in the FROM theme. This distance field will be populated with the distance between the selected From point and the To point. If the view is projected, the distance will be returned in distance units.
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Summarizes the attributes of the selected theme based on a dialog input box.
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Sorts the attributes of the selected shapefile theme based on the chosen attribute. This script sorts a shapefile to ascending order using Avenue's dictionary data type to store original bitmap values indexed by sort field (unique) values. This does a permanent sort of the data. Sorting by date is required for several functions in movement. If you wish to keep the original sorting use the add.
Requires: A
shapefile with attributes and an unique key.
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V. 2.0 VIEW MENU ADDITIONS
(Currently Under Construction)
MOVEMENT MENU
Animates the
movement path at a given speed. Requires a polyline file of movement paths.
Use the point to polyline function to create one.
NOT IN V 2.0
BETA.
Calculates the degree of interaction between two movement paths without regard
to time.
NOT IN V 2.0
BETA.
Calculates the degree of simultaneous interaction between two movement paths.
Conducts a
DelaunayTriangulation of a point theme.
Conducts a
Dirichlet Tesselation of a point theme.
NOT IN
V.2.0 BETA.
Conducts a habitat analysis using compositional analysis.
NOT IN V.
2.0 BETA.
Conducts habitat analysis based on availability determined by possible travel
lengths.
Creates a
random point theme in the user specified graphic or polygon shape theme.
Calculate Successive Distances
Calculates the
distances between points and adds them to the table.
HOME RANGE MENU
Conducts batch processing
of any of the home range function and does point statistics.
Recalculate area, length,
and perimeter of selected items
This function will recalculate and update the area, length, and perimeter (if present) of selected items using the current projection and distance units of the view.
Description: Calculates area and perimeter
for polygon themes and length for line themes. If the View has been projected
the calculations are in projected meters. Otherwise the calculations are in
'native' map units. Modify the script to provide calculation in the current
report units of
the
View. The script processes the list of active themes to calculate area and
perimeter, or length, depending on the theme type.
The script will add the fields: Area and
Perimeter to polygon themes, Length to line themes if they do not exist. If the
fields exist their values will be recalculated. Re-run the script if you change
the projection of the view.
Create a point
buffer shapefile with specified buffer distance
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Creates a new polygon
shapefile based on specified distance units from selected records in a point
theme. |
Creates a point buffer shape file.
The user is asked to specify which, if any, of the point themes attributes will
be associated
with
the buffers. Like the 'Convert to Shapefile...' selection, this script
will create buffer features for each point of the specified theme.
If
no points have been selected, the new theme will contain a buffer feature
corresponding to each point of the point theme. If a subset is selected,
buffer features will be created only for the selected points. If the view has
been projected, the user will be prompted for buffer units
(m,ft,mi). Whether the view has been projected or not, the new theme will
appear as circles around the points as expected. You will note
that if the view was projected, and you unproject the view or add the theme to
a new view, the buffers appear to be ovals. That is becauseof the difference
in projection. It is, however, a geometrically correct buffer.
Removes ALL graphics from the view. This can be quite useful in this extension which can create a lot of graphic objects.
Display latitude/longitude and UTM coordinates
Select the XY button and click anywhere on the view.
Coordinate values in
latitude/longitude (DD and DM) and UTM will be displayed at the bottom of the
ArcView screen.
Hold down the Ctrl key and the
XY button to copy the X coordinate values to the clipboard.
Hold the Alt key and the XY
button to copy the Y coordinate values to the clipboard.
Degree of clustering or dispersion of points in a specified rectangle
Once you click on the button, you will be
asked for the study area shapefile and the study points shapefile. The
wait cursor will appear and then a messagebox will tell you the R value and how
many features were
accounted
for in the analysis. R values relate how clustered or dispersed points
are within the polygon theme you specified. An R value of less
than 1 indicates a tendency towards a clumped (clustered) pattern. An R value
of 1 indicates a random distribution. An R value of greater than 1 indicates an
organized (uniform) pattern. This script also applies a simple test of
significance for deviation from randomness, using the standard error of the
expected difference. This script uses code from the FUN.NNDCSR.
Displays
the movement paths of an animal
First, set the graphic symbols, colors, sizes and attribute field to display by running the set movement path variables function from the menu.
Description: Script runs from a tool and
works on an active polyline theme. Script creates a sequential series of
graphic lines which mimic underlying non-zero length polylines. Direction
of travel is indicated by a graphic dot at the end of the most recently created
graphic line. Complex polyline themes can be active but do not need to be
visible. Useful for travel path analysis.
Left click to move forward, shift key down
and left click to move backward. Select any non-zero length polyline to
start or start at the beginning by not selecting any polyline. Reset tool and
graphics by re-clicking on its tool button.
Record information is visible at the
status line or via the identify dialog box.
Generates a set
of random normal points at the user click with the 99 percentile being the
outer ring of the circle drawn by the user.
Displays field type information about a selected field
in a table
Select a field and choose
Field Properties from the movement menu.
Add Record Numbers
Adds unique
record numbers to the database either from the table order or the original data
(vtab) order.
Outputs selected records from an attribute table to a DBF table.
Creates a new field with the cumulative total from the selected field. Prompts the user to go either forward or backwards in tallying the cumulative field. If the field has already been cumulated then it will update the tally. This function is useful in travel path analysis.
Creates a histogram based on a selected
table field
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