cul_dist: cul_dist
In CRC1266-A2/moin: Modelling Interactions

Description Usage Arguments Details Value Author(s) Examples

Cultural distance matrix

1 2	cul_dist(nodes_x, nodes_y, nodes_id, features, type_col, pre_size = 1, method)

`nodes_x`	a vector containing metric x coordinates of nodes
`nodes_y`	a vector containing metric y coordinates of nodes
`nodes_id`	a vector containing ID for nodes
`features`	a data.frame containing metric x and y coordinates of features, and feature type. Coordinates are expected to be the first two columns.
`type_col`	a character string naming the columname containing feature types.
`pre_size`	numeric, amount of letters, e.g. characters before typenumbers
`method`	character string, the distance measure to be used ("euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski")

Creates a cultural distance matrix by aggregating all features to the nodes by means of a Voronoi tesselation. xxxxxxx. The n dimensional distance matrix of the nodes and their assigned features will be calculated with euclidean distances. The bidirectional matrix will be returned and can be used as network weight.

bidirectional cultural distance matrix

Franziska Faupel <franziska-faupel@gmx.de>

set.seed(1234)

cul_dist(nodes_x=sample(3433806:3581396, 10, replace = TRUE),
               nodes_y=sample(5286004:5484972, 10, replace = TRUE), 
               nodes_id=c(1:10), 
             features=data.frame(x=sample(3433806:3581396, 100, replace = TRUE),
                   y=sample(5286004:5484972, 100, replace = TRUE),
                   type=paste0("B", c(rep(1, 5), rep(2,15), sample(11:19, 20, replace = TRUE), 
                   sample(111:119, 30, replace = TRUE), sample(1111:1115, 30, replace = TRUE)))
                   ), 
             type_col="type" , pre_size=1, method = "euclidean")