Changes between Initial Version and Version 1 of AnimoveWishlist


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Timestamp:
Oct 8, 2010, 5:10:55 PM (9 years ago)
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cavallini@…
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  • AnimoveWishlist

    v1 v1  
     1= !AniMove Wish List =
     2
     3'''This is a list of metrics that can be useful. Feel free to comment, and add your own.'''
     4
     5Here we try to summarize what is necessary to have a full set of home range analyses in AniMove. Many of the following analyses are already available, check AnimoveHowto.
     6
     7== Summary Statistics ==
     8
     9''Number of bearings:'' Total number of bearings (angles) per dataset
     10
     11''Mean bearing:'' Mean bearing per dataset (azimuth)
     12
     13''R Concentration of angles:'' The concentration of angles 1-r is the "circular variance"
     14
     15''Angular deviation:'' The angular equivalent of  linear standard deviation
     16
     17''Rayleigh's z for angles:'' The z value for Rayleigh's test for significant angles
     18
     19''Duration of Study:'' Total number of days per dataset
     20
     21''Primary axis angle:'' The angle which the primary axis is offset from the X axis (90 to -90)
     22
     23''Cramer-von Mises:'' Test statistic for Complete Spatial Randomness (CSR)(see Cramer-von Mises)
     24
     25''CM heterogeneity p:'' The probability value for rejecting the null hypothesis of CSR using the Cramer-von Mises
     26
     27''Nearest-Neighbor R:'' The nearest neighbor test statistic (see Nearest-Neighbor)
     28
     29''Nearest-Neighbor z:'' The z value of R
     30
     31''Nearest-Neighbor p:'' The probability value for rejecting the null hypothesis of CSR using Nearest-Neighbor R
     32
     33== Alpha Hull ==
     34
     35Code for generating alpha hulls. This example generates hulls for the dataset "Richards Pipit" which consists of two columns,
     36the first being the x coordinate, and the second being the y coordinate for each bird observation. Heuristic rules in this
     37version are 2, 3 and 4 times the average triangle edge length.
     38
     39Method proposed by '''Burgman, M.A. & Fox, J.C.''' (2003). Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning. Animal Conservation 6: 19- 28.
     40
     41== k-Nearest Neighbors Convex Hull ==
     42
     43A k-NNCH covering for constructing UDs Given a set of specified
     44points the method begins by constructing the convex hull associated with each
     45point and its (k-1) nearest neighbors. We refer to the area covered by the
     46union of all these convex hulls as a k-NNCH covering. We then order the hulls
     47from the smallest to the largest. By progressively taking the union of these
     48from the smallest upwards, until x% of points are included (with some rounding
     49error), we construct the areas whose boundaries represent the x% isolpleth of
     50the densest set of points in our k-NNCH covering. Clément Calenge has written the R code (now included in Adehabitat) to determine the k-nearest-neighbor convex hull following the method by '''Getz, W.M. & Wilmers, C.C.''' (2004). //A local nearest-neighbor convex-hull construction of home ranges and utilization distributions//. Ecography 27: 489-505.[http://www.cnr.berkeley.edu/%7Egetz/Reprints04/Getz&WilmersEcography04.pdf PDF].
     51
     52== MCP Sample Size Bootstrap ==
     53
     54Bootstraps the points in a dataset given the user- selected parameters to test the effects of sample size on mcp area.
     55
     56== Nearest Neighbor Analysis Test For Complete Spatial Randomness ==
     57
     58The Nearest Neighbor Analysis program tests for complete spatial
     59randomness using a selected graphic or polygon feature from a polygon
     60shapefile. Returns a message box displaying the area of the chosen
     61study site and the z and r values.\\
     62This program references the FunNNDCSR, which implements the Clark and
     63Evans (1954, Ecology 35. pp445-453) algorithm and allows for either
     64points beyond the boundary to be used for correcting edge effects or
     65uses the correction of Donnely (1978, Holder (eds) Simulation methods
     66in archaeology. pp.91-95). This allows considerable flexibility. If the
     67population has been completely sampled, e.g. animal locations from
     68radio tracking or tag returns, then choose FALSE for correction and
     69ignore the boundary boolean (i.e. set it to TRUE or FALSE as it won't
     70matter). The program defaults to using the boundary correction for
     71anlaysis. If you have not sampled the complete population then select
     72either the edge correction or the boundary correction (if you have
     73sample points beyond the boundary). It checks to see if the sample size
     74is too small (from Donnely 1978) for the normal distribution.
     75
     76== Cramer-Von Mises Test for Complete Spatial Randomness ==
     77
     78//Description//: Works from a menu on either a selected point theme and a
     79selected rectangle graphic or from a rectangle in a shape file. This
     80function is only valid with a rectangular study plot. The wait cursor
     81will appear and then a messagebox will tell you the W value and how
     82many features were accounted for in the analysis. W values relate how
     83clustered or dispersed points are within the graphic rectangle or
     84rectangle theme you specified. A W value of greater than .3 indicates a
     85tendency towards a clumped (clustered) pattern. A W value of 0.3-.06
     86indicates a random distribution. An R value of less than .06 indicates
     87an organized (uniform) pattern. This statistical test
     88is insensitive to origin but highly sensitive to the upper right corner
     89of the study plot. Its primary usefulness is in detecting heterogeneity
     90that the nearest neighbor analysis (NNA) misses especially NNA base
     91purely on distance measurements. '''Zimmerman, D.L.''' 1993, A bivariate
     92Cramer- von Mises type of test for spatial randomness, Appl. Statist.
     93V. 42. pp. 43-54.
     94
     95== Circular Point Statistics ==
     96
     97Conducts circular point statistics and outputs a graphic representation
     98of the bearings. The red line = mean bearing, which is scaled
     99proportionately to the r value of the angles; r value = 0 to 1, (1=all
     100angles the same) multiplied by the longest line segment.\\
     101This implements circular statistics (Batschlet 1981) for the sequence
     102of points in a point coverage. Useful for determining the travel
     103directions from animal movement locations and the significance of
     104direction of travel.. This function requires that the data be ordered
     105in the sequence desired. The function works on the selected records (or
     106all if none are selected) which is useful for examining parts of the
     107movement path. The program will not tell the probability level of
     108rejecting the null hypothesis of directed movement but will give the Z
     109value and sample size which can be looked up in a Z table.
     110
     111== Site Fidelity Test ==
     112
     113Creates random angles and uses distances between existing sequential points to determine walk points.\\
     114Requires an active point theme. Prompts the user to select one of the
     115following starting points for the simulation: First Point, Last Point,
     116Arithmatic Mean, and Harmonic Mean (Defaults to the First Point).\\
     117The simulation compares the observed pattern with a user selectable
     118number of random walks. This uses a Monte Carlo simulation and
     119parameters from the original data to determine if the observed movement
     120pattern has more site fidelity than should occur randomly, is a random
     121pattern or is overly dispersed. It is suggested that a 100 simulations
     122be run first and if the data is close to chosen probability level break
     123point then run it with a 1000 simulations which will more accurately
     124reflect the random walk distribution. Outputs a table with 3 fields,
     125Replicate, R2 and Linearity. Outputs a polyline theme containing
     126polylines of random walks. The polyline attribute table and the R2
     127table are linked by Replicate and LinkID. If replicates < 100,
     128outputs a chart showing the r2 sorted in ascending order. To identify
     129which r2 belongs with which polyline, select the record from the output
     130table and view the selected polyline in the view. However,as the chart
     131is dynamically linked to the table, selecting individual records will
     132modify the charts color scheme. To save the original chart, add it to a
     133static layout before viewing individual records in the r2 table.The O
     134replicate in the output table and chart is the observed data the others
     135are replicates from simulation runs. Works on selected point records or
     136if none selected the entire table.A single selected polygon or polyline
     137may be use to limit the extent of the random walks. Progress bar tells
     138the progress through the simulation loop.