https://ranges-support.anatrack.com/w/index.php?title=Location_Analysis&feed=atom&action=historyLocation Analysis - Revision history2024-03-29T01:30:27ZRevision history for this page on the wikiMediaWiki 1.23.2https://ranges-support.anatrack.com/w/index.php?title=Location_Analysis&diff=350&oldid=prevRobertKenward at 17:40, 19 November 20142014-11-19T17:40:43Z<p></p>
<table class='diff diff-contentalign-left'>
<col class='diff-marker' />
<col class='diff-content' />
<col class='diff-marker' />
<col class='diff-content' />
<tr style='vertical-align: top;'>
<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 17:40, 19 November 2014</td>
</tr><tr><td colspan="2" class="diff-lineno">Line 51:</td>
<td colspan="2" class="diff-lineno">Line 51:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Midline Analyses (Interlocation, Linear Ranges and Clusters)==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Midline Analyses (Interlocation, Linear Ranges and Clusters)==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Midline analyses assess the distance between locations using the [[Midline Analysis#How does Ranges calculate midline distances ?|route along a user-defined line]], rather than the shortest route between them as in all other analyses. This was developed principally for fish in rivers but could be useful for riparian species or other situations where movements are restricted to a simple network. The analyses require a [[Midline Analysis#Creating a midline file|midline file]] along which all distances will be measured, this needs to be a Ranges vector line file formatted correctly. [[Midline Analysis#Midline inter-location measures|Midline inter-location]] measures distances and speeds along the line. [[Midline Analysis#Midline linear ranges|Midline linear ranges]] finds the maximum linear extents from all of the locations in a range, and produces a linear range which is a subsection of the input midline file. [[Midline Analysis#Midline cluster analysis|Midline clusters]] was developed as a potential means of estimating the home range areas of fish in rivers using the same methodology as for cluster analysis, but with all distances calculated along the midline rather than the shortest route between locations. Publications using midline techniques include [[Bibliography|Hodder et al. (2007]]) and Knight et al. (<del class="diffchange diffchange-inline">[[Bibliography|</del>2009]]).</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Midline analyses assess the distance between locations using the [[Midline Analysis#How does Ranges calculate midline distances ?|route along a user-defined line]], rather than the shortest route between them as in all other analyses. This was developed principally for fish in rivers but could be useful for riparian species or other situations where movements are restricted to a simple network. The analyses require a [[Midline Analysis#Creating a midline file|midline file]] along which all distances will be measured, this needs to be a Ranges vector line file formatted correctly. [[Midline Analysis#Midline inter-location measures|Midline inter-location]] measures distances and speeds along the line. [[Midline Analysis#Midline linear ranges|Midline linear ranges]] finds the maximum linear extents from all of the locations in a range, and produces a linear range which is a subsection of the input midline file. [[Midline Analysis#Midline cluster analysis|Midline clusters]] was developed as a potential means of estimating the home range areas of fish in rivers using the same methodology as for cluster analysis, but with all distances calculated along the midline rather than the shortest route between locations. Publications using midline techniques include [[Bibliography|Hodder et al. (2007]]) and <ins class="diffchange diffchange-inline"> [[Bibliography|</ins>Knight et al. (2009]]).</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[Midline Analysis|Midline Analysis]].</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[Midline Analysis|Midline Analysis]].</div></td></tr>
</table>RobertKenwardhttps://ranges-support.anatrack.com/w/index.php?title=Location_Analysis&diff=349&oldid=prevRobertKenward at 17:39, 19 November 20142014-11-19T17:39:12Z<p></p>
<table class='diff diff-contentalign-left'>
<col class='diff-marker' />
<col class='diff-content' />
<col class='diff-marker' />
<col class='diff-content' />
<tr style='vertical-align: top;'>
<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 17:39, 19 November 2014</td>
</tr><tr><td colspan="2" class="diff-lineno">Line 51:</td>
<td colspan="2" class="diff-lineno">Line 51:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Midline Analyses (Interlocation, Linear Ranges and Clusters)==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Midline Analyses (Interlocation, Linear Ranges and Clusters)==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Midline analyses assess the distance between locations using the [[Midline Analysis#How does Ranges calculate midline distances ?|route along a user-defined line]], rather than the shortest route between them as in all other analyses. This was developed principally for fish in rivers but could be useful for riparian species or other situations where movements are restricted to a simple network. The analyses require a [[Midline Analysis#Creating a midline file|midline file]] along which all distances will be measured, this needs to be a Ranges vector line file formatted correctly. [[Midline Analysis#Midline inter-location measures|Midline inter-location]] measures distances and speeds along the line. [[Midline Analysis#Midline linear ranges|Midline linear ranges]] finds the maximum linear extents from all of the locations in a range, and produces a linear range which is a subsection of the input midline file. [[Midline Analysis#Midline cluster analysis|Midline clusters]] was developed as a potential means of estimating the home range areas of fish in rivers using the same methodology as for cluster analysis, but with all distances calculated along the midline rather than the shortest route between locations. Publications using midline techniques include Hodder et al. (<del class="diffchange diffchange-inline">[[Bibliography|</del>2007]]) and Knight et al. ([[Bibliography|2009]]).</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Midline analyses assess the distance between locations using the [[Midline Analysis#How does Ranges calculate midline distances ?|route along a user-defined line]], rather than the shortest route between them as in all other analyses. This was developed principally for fish in rivers but could be useful for riparian species or other situations where movements are restricted to a simple network. The analyses require a [[Midline Analysis#Creating a midline file|midline file]] along which all distances will be measured, this needs to be a Ranges vector line file formatted correctly. [[Midline Analysis#Midline inter-location measures|Midline inter-location]] measures distances and speeds along the line. [[Midline Analysis#Midline linear ranges|Midline linear ranges]] finds the maximum linear extents from all of the locations in a range, and produces a linear range which is a subsection of the input midline file. [[Midline Analysis#Midline cluster analysis|Midline clusters]] was developed as a potential means of estimating the home range areas of fish in rivers using the same methodology as for cluster analysis, but with all distances calculated along the midline rather than the shortest route between locations. Publications using midline techniques include <ins class="diffchange diffchange-inline">[[Bibliography|</ins>Hodder et al. (2007]]) and Knight et al. ([[Bibliography|2009]]).</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[Midline Analysis|Midline Analysis]].</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[Midline Analysis|Midline Analysis]].</div></td></tr>
</table>RobertKenwardhttps://ranges-support.anatrack.com/w/index.php?title=Location_Analysis&diff=341&oldid=prevRobertKenward at 15:39, 19 November 20142014-11-19T15:39:26Z<p></p>
<table class='diff diff-contentalign-left'>
<col class='diff-marker' />
<col class='diff-content' />
<col class='diff-marker' />
<col class='diff-content' />
<tr style='vertical-align: top;'>
<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 15:39, 19 November 2014</td>
</tr><tr><td colspan="2" class="diff-lineno">Line 1:</td>
<td colspan="2" class="diff-lineno">Line 1:</td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>This panel provides a number of analyses of location data, which are used either to provide simple [[#Inter-location Measures|inter-location measures]], including dispersal detection, or to estimate home ranges. A wide variety of different methods of range analysis have been published. Ranges implements those that have been used to produce refereed publications beyond a first description, including [[#Convex Polygons|convex polygons]] (sometimes called minimum <del class="diffchange diffchange-inline">area </del>convex polygons, MCPs <del class="diffchange diffchange-inline">or MAPs</del>), [[#Concave Polygons|concave polygons]] (with restricted edges), polygons in [[#Clusters|clusters]], [[#Ellipses|ellipses]] and contouring from [[#Harmonic Means|harmonic means]] and other density [[#Kernels|kernels]]. Grid cells may be plotted by selecting options within [[#Concave Polygons|concave polygons]].  </div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>This panel provides a number of analyses of location data, which are used either to provide simple [[#Inter-location Measures|inter-location measures]], including dispersal detection, or to estimate home ranges. A wide variety of different methods of range analysis have been published. Ranges implements those that have been used to produce refereed publications beyond a first description, including [[#Convex Polygons|convex polygons]] (sometimes called minimum convex polygons, MCPs), [[#Concave Polygons|concave polygons]] (with restricted edges), polygons in [[#Clusters|clusters]], [[#Ellipses|ellipses]] and contouring from [[#Harmonic Means|harmonic means]] and other density [[#Kernels|kernels]]. Grid cells may be plotted by selecting options within [[#Concave Polygons|concave polygons]].  </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>The pros and cons of these different techniques are discussed in more detail in the [[Review Of Home Range Analyses|Review Of Home Range Analyses]] and for a more comprehensive recent review, see “A Manual for Wildlife Radio Tagging” ([[Bibliography|Kenward 2001]]) and Kenward et al. ([[Bibliography|2001]]).  </div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>The pros and cons of these different techniques are discussed in more detail in the [[Review Of Home Range Analyses|Review Of Home Range Analyses]] and for a more comprehensive recent review, see “A Manual for Wildlife Radio Tagging” ([[Bibliography|Kenward 2001]]) and Kenward et al. ([[Bibliography|2001]]).  </div></td></tr>
<tr><td colspan="2" class="diff-lineno">Line 51:</td>
<td colspan="2" class="diff-lineno">Line 51:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Midline Analyses (Interlocation, Linear Ranges and Clusters)==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Midline Analyses (Interlocation, Linear Ranges and Clusters)==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Midline analyses assess the distance between locations using the [[Midline Analysis#How does Ranges calculate midline distances ?|route along a user-defined line]], rather than the shortest route between them as in all other analyses. This was developed principally for fish in rivers but could be useful for riparian species or other situations where movements are restricted to a simple network. The analyses require a [[Midline Analysis#Creating a midline file|midline file]] along which all distances will be measured, this needs to be a Ranges vector line file formatted correctly. [[Midline Analysis#Midline inter-location measures|Midline inter-location]] measures distances and speeds along the line. [[Midline Analysis#Midline linear ranges|Midline linear ranges]] finds the maximum linear extents from all of the locations in a range, and produces a linear range which is a subsection of the input midline file. [[Midline Analysis#Midline cluster analysis|Midline clusters]] <del class="diffchange diffchange-inline">were </del>developed as a potential means of estimating the home range areas of fish in rivers using the same methodology as for cluster analysis, but with all distances calculated along the midline rather than the shortest route between locations<del class="diffchange diffchange-inline">, initial results have not been very encouraging</del>.  </div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Midline analyses assess the distance between locations using the [[Midline Analysis#How does Ranges calculate midline distances ?|route along a user-defined line]], rather than the shortest route between them as in all other analyses. This was developed principally for fish in rivers but could be useful for riparian species or other situations where movements are restricted to a simple network. The analyses require a [[Midline Analysis#Creating a midline file|midline file]] along which all distances will be measured, this needs to be a Ranges vector line file formatted correctly. [[Midline Analysis#Midline inter-location measures|Midline inter-location]] measures distances and speeds along the line. [[Midline Analysis#Midline linear ranges|Midline linear ranges]] finds the maximum linear extents from all of the locations in a range, and produces a linear range which is a subsection of the input midline file. [[Midline Analysis#Midline cluster analysis|Midline clusters]] <ins class="diffchange diffchange-inline">was </ins>developed as a potential means of estimating the home range areas of fish in rivers using the same methodology as for cluster analysis, but with all distances calculated along the midline rather than the shortest route between locations<ins class="diffchange diffchange-inline">. Publications using midline techniques include Hodder et al. ([[Bibliography|2007]]) and Knight et al. ([[Bibliography|2009]])</ins>.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[Midline Analysis|Midline Analysis]].</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[Midline Analysis|Midline Analysis]].</div></td></tr>
</table>RobertKenwardhttps://ranges-support.anatrack.com/w/index.php?title=Location_Analysis&diff=330&oldid=prevRobertKenward at 14:35, 19 November 20142014-11-19T14:35:37Z<p></p>
<table class='diff diff-contentalign-left'>
<col class='diff-marker' />
<col class='diff-content' />
<col class='diff-marker' />
<col class='diff-content' />
<tr style='vertical-align: top;'>
<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 14:35, 19 November 2014</td>
</tr><tr><td colspan="2" class="diff-lineno">Line 33:</td>
<td colspan="2" class="diff-lineno">Line 33:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Neighbour linkage or cluster analysis is particularly good for eliminating outliers and separating range cores. This can identify patchiness in range use, for instance where the study animal forages in several separate areas. Convex polygons are used to provide an outline around these cores, that are calculated by looking at the distances between all locations rather than the distances from one centre.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Neighbour linkage or cluster analysis is particularly good for eliminating outliers and separating range cores. This can identify patchiness in range use, for instance where the study animal forages in several separate areas. Convex polygons are used to provide an outline around these cores, that are calculated by looking at the distances between all locations rather than the distances from one centre.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[<del class="diffchange diffchange-inline">Cluster analysis, OREPs</del>|Neighbour Linkage]].</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[<ins class="diffchange diffchange-inline">Clusters</ins>|Neighbour Linkage]].</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Harmonic Mean Contours==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Harmonic Mean Contours==</div></td></tr>
</table>RobertKenwardhttps://ranges-support.anatrack.com/w/index.php?title=Location_Analysis&diff=329&oldid=prevRobertKenward at 14:34, 19 November 20142014-11-19T14:34:24Z<p></p>
<table class='diff diff-contentalign-left'>
<col class='diff-marker' />
<col class='diff-content' />
<col class='diff-marker' />
<col class='diff-content' />
<tr style='vertical-align: top;'>
<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 14:34, 19 November 2014</td>
</tr><tr><td colspan="2" class="diff-lineno">Line 33:</td>
<td colspan="2" class="diff-lineno">Line 33:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Neighbour linkage or cluster analysis is particularly good for eliminating outliers and separating range cores. This can identify patchiness in range use, for instance where the study animal forages in several separate areas. Convex polygons are used to provide an outline around these cores, that are calculated by looking at the distances between all locations rather than the distances from one centre.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Neighbour linkage or cluster analysis is particularly good for eliminating outliers and separating range cores. This can identify patchiness in range use, for instance where the study animal forages in several separate areas. Convex polygons are used to provide an outline around these cores, that are calculated by looking at the distances between all locations rather than the distances from one centre.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[<del class="diffchange diffchange-inline">Clusters</del>|Neighbour Linkage]].</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[<ins class="diffchange diffchange-inline">Cluster analysis, OREPs</ins>|Neighbour Linkage]].</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Harmonic Mean Contours==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Harmonic Mean Contours==</div></td></tr>
</table>RobertKenwardhttps://ranges-support.anatrack.com/w/index.php?title=Location_Analysis&diff=328&oldid=prevRobertKenward at 14:33, 19 November 20142014-11-19T14:33:25Z<p></p>
<table class='diff diff-contentalign-left'>
<col class='diff-marker' />
<col class='diff-content' />
<col class='diff-marker' />
<col class='diff-content' />
<tr style='vertical-align: top;'>
<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 14:33, 19 November 2014</td>
</tr><tr><td colspan="2" class="diff-lineno">Line 29:</td>
<td colspan="2" class="diff-lineno">Line 29:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[Ellipses|Ellipses]].</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[Ellipses|Ellipses]].</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>==Neighbour Linkage (<del class="diffchange diffchange-inline">Clusters</del>, <del class="diffchange diffchange-inline">OREP</del>)==</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>==Neighbour Linkage (<ins class="diffchange diffchange-inline">Cluster analysis</ins>, <ins class="diffchange diffchange-inline">OREPs</ins>)==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Neighbour linkage or cluster analysis is particularly good for eliminating outliers and separating range cores. This can identify patchiness in range use, for instance where the study animal forages in several separate areas. Convex polygons are used to provide an outline around these cores, that are calculated by looking at the distances between all locations rather than the distances from one centre.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Neighbour linkage or cluster analysis is particularly good for eliminating outliers and separating range cores. This can identify patchiness in range use, for instance where the study animal forages in several separate areas. Convex polygons are used to provide an outline around these cores, that are calculated by looking at the distances between all locations rather than the distances from one centre.</div></td></tr>
</table>RobertKenwardhttps://ranges-support.anatrack.com/w/index.php?title=Location_Analysis&diff=327&oldid=prevRobertKenward at 14:32, 19 November 20142014-11-19T14:32:14Z<p></p>
<table class='diff diff-contentalign-left'>
<col class='diff-marker' />
<col class='diff-content' />
<col class='diff-marker' />
<col class='diff-content' />
<tr style='vertical-align: top;'>
<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 14:32, 19 November 2014</td>
</tr><tr><td colspan="2" class="diff-lineno">Line 29:</td>
<td colspan="2" class="diff-lineno">Line 29:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[Ellipses|Ellipses]].</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[Ellipses|Ellipses]].</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>==Neighbour Linkage==</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>==Neighbour Linkage <ins class="diffchange diffchange-inline">(Clusters, OREP)</ins>==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Neighbour linkage or cluster analysis is particularly good for eliminating outliers and separating range cores. This can identify patchiness in range use, for instance where the study animal forages in several separate areas. Convex polygons are used to provide an outline around these cores, that are calculated by looking at the distances between all locations rather than the distances from one centre.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Neighbour linkage or cluster analysis is particularly good for eliminating outliers and separating range cores. This can identify patchiness in range use, for instance where the study animal forages in several separate areas. Convex polygons are used to provide an outline around these cores, that are calculated by looking at the distances between all locations rather than the distances from one centre.</div></td></tr>
</table>RobertKenwardhttps://ranges-support.anatrack.com/w/index.php?title=Location_Analysis&diff=323&oldid=prevRobertKenward: /* Kernel Contours */2014-11-19T13:45:38Z<p><span dir="auto"><span class="autocomment">Kernel Contours</span></span></p>
<table class='diff diff-contentalign-left'>
<col class='diff-marker' />
<col class='diff-content' />
<col class='diff-marker' />
<col class='diff-content' />
<tr style='vertical-align: top;'>
<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 13:45, 19 November 2014</td>
</tr><tr><td colspan="2" class="diff-lineno">Line 43:</td>
<td colspan="2" class="diff-lineno">Line 43:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Kernel Contours==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Kernel Contours==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Strictly speaking, kernel analyses include the harmonic mean approach. Worton (1989) introduced the concept of using a function with a negative exponential term for distances of locations from intersections of the estimation matrix. This is a mathematically robust kernel that produces more consistent results than harmonic mean contouring, but is more sensitive to outlying locations</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Strictly speaking, kernel analyses include the harmonic mean approach. Worton (<ins class="diffchange diffchange-inline">[[Bibliography|</ins>1989<ins class="diffchange diffchange-inline">]]</ins>) introduced the concept of using a function with a negative exponential term for distances of locations from intersections of the estimation matrix. This is a mathematically robust kernel that produces more consistent results than harmonic mean contouring, but is more sensitive to outlying locations</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Location densities at matrix intersections are derived using a bivariate normal kernel estimator. This is less matrix-dependent than the harmonic mean function, so there is no need to centre locations between intersections or have very large matrices: the result is very similar for 20x20 and 100x100 grids. With relatively low smoothing, stable size estimates can be obtained with 15-20 locations.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Location densities at matrix intersections are derived using a bivariate normal kernel estimator. This is less matrix-dependent than the harmonic mean function, so there is no need to centre locations between intersections or have very large matrices: the result is very similar for 20x20 and 100x100 grids. With relatively low smoothing, stable size estimates can be obtained with 15-20 locations.</div></td></tr>
</table>RobertKenwardhttps://ranges-support.anatrack.com/w/index.php?title=Location_Analysis&diff=322&oldid=prevRobertKenward: /* Harmonic Mean Contours */2014-11-19T13:44:22Z<p><span dir="auto"><span class="autocomment">Harmonic Mean Contours</span></span></p>
<table class='diff diff-contentalign-left'>
<col class='diff-marker' />
<col class='diff-content' />
<col class='diff-marker' />
<col class='diff-content' />
<tr style='vertical-align: top;'>
<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 13:44, 19 November 2014</td>
</tr><tr><td colspan="2" class="diff-lineno">Line 37:</td>
<td colspan="2" class="diff-lineno">Line 37:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Harmonic Mean Contours==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Harmonic Mean Contours==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The harmonic mean model <del class="diffchange diffchange-inline">of Dixon & Chapman </del>([[Bibliography|1980]]) estimates the location density distribution (equivalent to the probability of encountering the animal) at intersections of an estimation matrix. The density function used is the reciprocal of the mean inverse distances of all the locations from each intersection. Contours containing a specified percentage of actual locations or estimated location density are then interpolated across the matrix. Harmonic mean contours are sensitive to intersection spacing unless the analysis centres locations between intersections or has at least two intersections per unit of tracking resolution, but provide contours that are least sensitive to outlying locations and most precise in fitting core locations.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The harmonic mean model ([[Bibliography|<ins class="diffchange diffchange-inline">Dixon & Chapman </ins>1980]]) estimates the location density distribution (equivalent to the probability of encountering the animal) at intersections of an estimation matrix. The density function used is the reciprocal of the mean inverse distances of all the locations from each intersection. Contours containing a specified percentage of actual locations or estimated location density are then interpolated across the matrix. Harmonic mean contours are sensitive to intersection spacing unless the analysis centres locations between intersections or has at least two intersections per unit of tracking resolution, but provide contours that are least sensitive to outlying locations and most precise in fitting core locations.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[Harmonic Mean Contours|Harmonic Mean Contours]].</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[Harmonic Mean Contours|Harmonic Mean Contours]].</div></td></tr>
</table>RobertKenwardhttps://ranges-support.anatrack.com/w/index.php?title=Location_Analysis&diff=321&oldid=prevRobertKenward: /* Harmonic Mean Contours */2014-11-19T13:43:27Z<p><span dir="auto"><span class="autocomment">Harmonic Mean Contours</span></span></p>
<table class='diff diff-contentalign-left'>
<col class='diff-marker' />
<col class='diff-content' />
<col class='diff-marker' />
<col class='diff-content' />
<tr style='vertical-align: top;'>
<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 13:43, 19 November 2014</td>
</tr><tr><td colspan="2" class="diff-lineno">Line 37:</td>
<td colspan="2" class="diff-lineno">Line 37:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Harmonic Mean Contours==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Harmonic Mean Contours==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The harmonic mean model of Dixon & Chapman (1980) estimates the location density distribution (equivalent to the probability of encountering the animal) at intersections of an estimation matrix. The density function used is the reciprocal of the mean inverse distances of all the locations from each intersection. Contours containing a specified percentage of actual locations or estimated location density are then interpolated across the matrix. Harmonic mean contours are sensitive to intersection spacing unless the analysis centres locations between intersections or has at least two intersections per unit of tracking resolution, but provide contours that are least sensitive to outlying locations and most precise in fitting core locations.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The harmonic mean model of Dixon & Chapman (<ins class="diffchange diffchange-inline">[[Bibliography|</ins>1980<ins class="diffchange diffchange-inline">]]</ins>) estimates the location density distribution (equivalent to the probability of encountering the animal) at intersections of an estimation matrix. The density function used is the reciprocal of the mean inverse distances of all the locations from each intersection. Contours containing a specified percentage of actual locations or estimated location density are then interpolated across the matrix. Harmonic mean contours are sensitive to intersection spacing unless the analysis centres locations between intersections or has at least two intersections per unit of tracking resolution, but provide contours that are least sensitive to outlying locations and most precise in fitting core locations.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[Harmonic Mean Contours|Harmonic Mean Contours]].</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>More on this topic can be found here: [[Harmonic Mean Contours|Harmonic Mean Contours]].</div></td></tr>
</table>RobertKenward