v.percolate - GRASS GIS manual

NAME

v.percolate - Continuum percolation analysis

KEYWORDS

SYNOPSIS

v.percolate

v.percolate --help

v.percolate [-e] input=name [layer=string] id=string type=string[,string,...] output=string [min=float] [inc=float] [max=float] [interval=integer] [keep=string] [--help] [--verbose] [--quiet] [--ui]

Flags:

-e: Terminate once all points are connected in one group
--help: Print usage summary
--verbose: Verbose module output
--quiet: Quiet module output
--ui: Force launching GUI dialog

Parameters:

input=name [required]: Name of existing vector map; Or data source for direct OGR access
layer=string: Layer number or name; Vector features can have category values in different layers. This number determines which layer to use. When used with direct OGR access this is the layer name.; Default: 1
id=string [required]: Name of field in input map which contains ID; Vector features can have category values in different layers. This number determines which layer to use. When used with direct OGR access this is the layer name.; Default:
type=string[,string,...] [required]: Feature type (point only); Input feature type; Options: point; Default: point
output=string [required]: Root name for output plain text CSV file
min=float: Minimum distance threshold for analysis; Default: 0.0
inc=float: Amount by which distance threshold is incremented between minthresh and maxthresh; Default: 0.0
max=float: Maximum distance threshold for analysis; Default: 0.0
interval=integer: Choose interval output. E.g. interval 10 will produce output for every tenth node-pair assigned cluster membership. Zero disables; Default: 0
keep=string: Rule for deciding which cluster to keep: oldest or biggest; Options: oldest, biggest; Default: oldest

DESCRIPTION
NOTES
REFERENCES
SEE ALSO
AUTHORS

DESCRIPTION

v.percolate implements continuum percolation analysis. It identifies clusters of point locations at multiple threshold distances and outputs various statistics into plain text CSV files. See notes for the difference between v.percolate and v.cluster.

For each input point in an input vector map v.percolate outputs the following information at each threshdold distance:

Cat: Cat value.
<fieldname>: The ID of the point in a chosen field in the input vector map.
X: X coordinate (easting).
Y: Y coordinate (northing).
Membership: Cluster membership (cluster ID).
FirstChange: Iteration at which the point first joined a cluster.
LastChange: Iteration at which the point most recently joined a new cluster.
NChanges: Number of changes of cluster membership.
FirstDistance: Distance at which the point first joined a cluster.
LastDistance: Distance at which the point most recently joined a new cluster.
MaxConCoeff: Maximum connection coefficient obtained.
LastGroupConnected: The cluster ID of the most recently connected cluster (the point itself may not have changed clusters)
LastDistanceConnection: Distance at which the most recently connected cluster joined (the point itself may not have changed clusters))

For each cluster formed or already in existence at each threshold distance v.percolate outputs:

Cluster: The cluster ID.
Birth: Iteration at which the cluster was formed.
BirthDist: Distance at which the cluster was formed.
Death: Iteration at which the cluster was absorbed into another cluster and so ceased to exist as an independent entity.
DeathDist: Distance at which the cluster was absorbed into another cluster and so ceased to exist as an independent entity.
Longevity: Number of iterations during which the cluster existed as an independent entity.
MaxSize: The number of points in the cluster just before it was absorbed into another cluster.
Wins: The number of occasions when this cluster continued to exist after joining with another cluster because, depending on the rule chosen, it was either the larger cluster or the older cluster.

In addition to identifying clusters, v.percolate also computes an experimental Connection Coefficient for each point location. This numerical value is intended to capture a property roughly analogous to Betweeness Centrality in network analysis. The Connection Coefficient is smaller if a point location joins 2 small clusters, or 1 large and 1 small cluster, and greater if it joins 2 large clusters.

By default, the series of distance thresholds at which the above statistics will be reported is determined by setting min, inc and max. v.percolate will never proceed beyond the maximum distance threshold, but it may cease to provide output before that distance is reached if the -e flag is set to force termination once all input points are connected in one cluster.

If interval is set to a positive non-zero value then v.percolate no longer outputs statistics at fixed distance thresholds. Instead, it outputs statistics for every Nth node-pair that is joined in a cluster, where N is the value given as the interval. In general this is less useful than the default behaviour, but it has application for certain purposes.

The value of keep determines what happens when two clusters, each of 2 or more points, are to be joined. The choice is between absorbing the more recently formed cluster into the older cluster, or absorbing the smaller cluster into the large cluster. Setting keep to 'oldest' makes it possible to track the gradual growth of one large super-cluster, but that is not necessarily most appropriate if the location of the first cluster is of no real significance.

NOTES

v.cluster already provides several methods for partitioning a set of points into clusters and will be more appropriate for most purposes.

v.percolate has a very specific purpose, which is to facilitate continuum percolation analysis of point locations, as for example described in Arcaute et al. 2016. The emphasis of this form of analysis is less on finding optimal partitioning of points into clusters of certain sizes and more on observing discontinuities in cluster growth for the purpose of identifying 'natural' sales of interaction. Thus v.percolate automates the reasonably efficient production and recording of clusters at multiple threshold distances. For example, on a 2018 mid-range laptop computer v.percolate requires around 100 seconds user time to find clusters in 10,513 points (55,256,328 pairwise relationships) at 128 different distance thresholds. Since the results will almost certainly be subject to further analysis in other software, such as R, a range of information (as described above) is output into plain text CSV files.

Note that v.percolate offers only one method of clustering, which is based purely on threshold distance: if 2 points are closer than the threshold distance then they are joined in a cluster. This method will return the same clusters as the DBSCAN method if one relaxes the latter's requirement for clusters to include a minimum number of points. As a result, clusters created using v.percolate can be joined together by long strings of points, each with only 2 neighbours within the given threshold difference, a situation which DBSCAN avoids.

REFERENCES

Arcaute, E., C. Molinero, E. Hatna, R. Murcio, C. Vargas-Ruiz, P. Masucci and M. Batty (2016). 'Regions and Cities in Britain through Hierarchical Percolation'. ArXiv:1504.08318v2 [Physics.Soc-Ph]. https://arxiv.org/abs/1504.08318.
Arcaute, E., S. Brookes, T. Brown, M. Lake and A. Reynolds (in prep). 'Case studies in percolation analysis: the distribution of English settlement in the 11th and 19th centuries compared'. For submission to Journal of Archaeological Science.
Lake, M, T. Brown and S. Maddison (2018). 'Percolation robustness and the deep history of regionality'. Presentation to Connected Past, Oxford.

AUTHORS

Theo Brown, UCL Institute of Archaeology / Helyx Secure Information Systems, UK

Mark Lake, UCL Institute of Archaeology, University College London, UK

SOURCE CODE

Available at: v.percolate source code (history)

Latest change: Monday Nov 11 18:04:48 2024 in commit: 59e289fdb093de6dd98d5827973e41128196887d