Use clusters in spatial conditions

Spatial conditions are a core part of Biodiverse

Most people seem to focus on using single cells for their analysis and trying to find the ideal cell size.  This is missing much of the benefit of spatial analyses.  You are not constrained to using single cells in isolation.  

You can analyse regions around each focal location (processing group) using geometric shapes like circles.  Varying the size of the window gives an understanding of the spatial scale of the patterns (the operational scale).  However, there is no need to be geometric - you can use arbitrarily complex spatial conditions based on polygon features, proximity and/or matching text.  See for example Laffan and Crisp (2003) and Laity et al. (2015).  

You can also use cluster (and region grower) analyses to define your spatial windows.  These allow you to let the data define the regions, with the calculations then applied giving you more understanding of the groupings that have been identified.  Care needs to be taken with interpretation due to the risk of circularity, but that's not unusual.  And sometimes you just want to understand something about the assemblage that falls under a node (branch).  You might also be interested in the environmental properties associated with a cluster.

One issue with the cluster approach is that it can be difficult to use the branches in a spatial condition for a different analysis.  Consider the case where one wants to spatially partition a randomisation so labels are kept within their associated clusters (for a given cluster cutoff).  You could export the clusters to shapefile format, extract the relevant features to a new shapefile, and then use that in a new spatial condition.  But that's a lot of work and not easy for people less familiar with geoprocessing and GIS.  

From version 4 you can access the set of groups under a cluster analysis and use that to define spatial conditions (actually it is in the 3.99_003 development version).  This can use any of the current cutting methods, so you can slice by distance from the tips, depth, or number of clusters from the root using the sp_points_in_same_cluster condition.  You can also select individual branches (nodes) by name (sp_point_in_cluster).   

Some snippets are below that can be copied into your spatial conditions windows.  No screenshots this time, but I can add a new post of that is needed.  

Note that the cluster analysis being referred to must be in the same basedata.  

## sp_points_in_same_cluster examples

#  Try to use the highest four clusters from the root.
#  Note that the next highest number will be used
#  if four is not possible, e.g. there might be five
#  siblings below the root.  Fewer will be returned
#  if the tree has insufficient tips.
sp_points_in_same_cluster (
  output       => "some_cluster_output",
  num_clusters => 4,
)

#  Cut the tree at a distance of 0.25 from the tips
sp_points_in_same_cluster (
  output          => "some_cluster_output",
  target_distance => 0.25,
)

#  Cut the tree at a depth of 3 from the root.
#  The root is depth 1.
sp_points_in_same_cluster (
  output          => "some_cluster_output",
  target_distance => 3,
  group_by_depth  => 1,
)

#  Select four clusters below a specified node
sp_points_in_same_cluster (
  output       => "some_cluster_output",
  num_clusters => 4,
  from_node    => '118___',  #  use the node's name
)

#  target_distance is ignored if num_clusters is set

#  so this is the same as the first example
sp_points_in_same_cluster (
  output          => "some_cluster_output",
  num_clusters    => 4,
  target_distance => 0.25,
)


## sp_point_in_cluster examples

#  This will select any element that is a terminal in the cluster output
#  It is useful when the cluster analysis was run under
#  a definition query to reduce the number of elements clustered,

#  and you want the same set of elements.
sp_point_in_cluster (
  output       => "some_cluster_output",
)

#  Now specify a cluster within the output
sp_point_in_cluster (
  output       => "some_cluster_output",
  from_node    => '118___',  #  use the node's name
)

#  Specify an element to check instead of the current
#  processing element.
sp_point_in_cluster (
  output       => "some_cluster_output",
  from_node    => '118___',  #  use the node's name
  element      => '123:456', #  specify an element to check
)

 

Shawn Laffan

02-May-2022

For more details about Biodiverse, see http://shawnlaffan.github.io/biodiverse/  

To see what else Biodiverse has been used for, see https://github.com/shawnlaffan/biodiverse/wiki/PublicationsList 

You can also join the Biodiverse-users mailing list at https://groups.google.com/group/Biodiverse-users