Zoning {GeoFIS} | R Documentation |
Class "Zoning"
Description
The main class to perform zoning
A complete use-case example is described in the vignette "Zoning with GeoFIS"
Active bindings
border
sp::SpatialPolygons object, The border used to limit the processed area, or
NULL
if the Convex Hull of data source is used
Only data points within the border polygon are processed
The default value isNULL
neighborhood
numeric value, The minimum edge length shared by two Voronoi polygons for being considered as neighbors
orNULL
if all contiguous Voronoi polygons are considered as neighbors
The default value isNULL
attribute_distance
list of Distance object (write-only), The functions used to compute the distance between two data points in the attribute space
The length of the list must be equal to the number of zonable attributes, the distance objects are treated in the order of zonable attributes
In case of a single attribute into the zonable dataset, the list is optional and a single Distance object can be provided
Allowed distance objects: EuclideanDistance, FuzzyDistance orNULL
if the attribute should not be used in the zoning process
The default value is a list of EuclideanDistance
See Zoning documentation main parameters univariate distancecombine_distance
Distance object (write-only), The function used to combine attribute distances in case of multivariate zoning
Allowed distance objects: EuclideanDistance or MinkowskiDistance
The default value is EuclideanDistance See Zoning documentation main parameters multivariate combinationzone_distance
Distance object (write-only), The function used to compute the distance between 2 zones
Allowed distance objects: MaximumDistance, MinimumDistance or MeanDistance
The default value is MaximumDistance
The pair of zones to be merged are those for which thezone_distance
is minimum.
See Zoning documentation main parameters between zone distancesmallest_zone
Smallest zone object (write-only), This criterion is used to determine the smallest size for a zone (number of points or area) to be kept in the final map
Allowed Smallest zone objects: ZoneSize or ZoneArea
The default value is ZoneSize with 1 point
Methods
Public methods
Method new()
Constructor, create a new instance of Zoning
Usage
Zoning$new(source, warn = TRUE)
Arguments
source
sp::SpatialPointsDataFrame or sp::SpatialMultiPointsDataFrame object, The data source
warn
logical value, Show warnings if TRUE, default value is TRUE
Method zonable_data()
Get the zonable data
Keep only the attributes that can be used in the zoning process, meaning numeric atributes, without missing values and with a range that is not limited to a unique value
The last condition is required by the min-max standardization process
Usage
Zoning$zonable_data()
Returns
sp::SpatialPointsDataFrame object
Method perform_voronoi()
Compute the Voronoi diagram
Usage
Zoning$perform_voronoi()
Method voronoi_map()
Get the Voronoi map
Usage
Zoning$voronoi_map()
Returns
sp::SpatialPolygons object
Method perform_neighborhood()
Identify adjacent polygons in the voronoi tesselation
Usage
Zoning$perform_neighborhood()
Method neighborhood_map()
Get the neighborhood map
Usage
Zoning$neighborhood_map()
Returns
sp::SpatialLinesDataFrame object
Method perform_zoning()
Perform the zoning
Usage
Zoning$perform_zoning()
Method map_size()
Get the number of maps with different number of zones available after perform zoning
Usage
Zoning$map_size()
Returns
integer value
Method map()
Get the map corresponding to a number of zones
Usage
Zoning$map(number_of_zones)
Arguments
number_of_zones
integer value, The number of zones in the map
Returns
sp::SpatialPolygonsDataFrame object
Method maps()
Get the maps corresponding to a number of zones
Usage
Zoning$maps(number_of_zones)
Arguments
number_of_zones
integer vector, The number of zones in each map
Returns
list of sp::SpatialPolygonsDataFrame object
References
Pedroso M, Taylor J, Tisseyre B, Charnomordic B, Guillaume S (2010). “A segmentation algorithm for the delineation of management zones.” Computer and Electronics in Agriculture, 70(1), 199-208. doi:10.1016/j.compag.2009.10.007.
Guillaume S, Charnomordic B, Loisel P (2013). “Fuzzy partitions: a way to integrate expert knowledge into distance calculations.” International Journal of Information Sciences, 245, 76-95. doi:10.1016/j.ins.2012.07.045.
Guillaume S, Charnomordic B (2013). “Fuzzy partition-based distance practical use and implementation.” In CFP12FUZ-USB ICN (ed.), IEEE International Conference on Fuzzy Systems, paper F-1136.