ds_points: ds_points

In dabrowskia/dspace: Dividing Space - Data Driven Segementation of Spatial Data

Description

Creates a vector of community assignment based on neighboring points. It is based on a topological network structure where points represent nodes and the edges are the degree of similarity between those nodes. Communities are created using fast greedy algorithm that maximizes their modularity.

Usage

ds_points(
  x,
  k = 2,
  data = -grep(names(x), pattern = "^geom"),
  similarity.measure = "euclidean",
  style = "B",
  n.neigh = 8,
  plot = TRUE,
  explain = TRUE
)

Arguments

x	point or polygon shapefile data;
k	number of communities;
data	attributes of the spatial data frame to calculate similarity or distance measure;
similarity.measure	Character or function to declare distance method transformed into similarity measure. If method is character, method must be "mahalanobis" or "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". If method is one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski", see dist for details, because this function is used to compute the distance. If method is set to "mahalanobis", the mahalanobis distance is computed between neighbor points. If method is a function, this function is used to compute the distance.
style	style can take values “W”, “B”, “C”, “U”, “minmax” and “S” (see spdep::nb2listw)
n.neigh	number of neighbors considered in the k-nearest neighbor algorithm that builds topology network
plot	should the neighborhood be plotted
explain	logical. If TRUE a machine learning (randomForest using 5 fold cross validation) model is being constructed based on the data provided for regionalization. The accuracy of this model explains how much of the regionalization can be attributed to the data and how much to the spatial distribution.

Value

vector of numbers representing regions to which each element belongs to

dabrowskia/dspace documentation built on July 3, 2020, 8:47 p.m.