Package 'intensitynet'

Title: Intensity Analysis of Spatial Point Patterns on Complex Networks
Description: Tools to analyze point patterns in space occurring over planar network structures derived from graph-related intensity measures for undirected, directed, and mixed networks. This package is based on the following research: Eckardt and Mateu (2018) <doi:10.1080/10618600.2017.1391695>. Eckardt and Mateu (2021) <doi:10.1007/s11749-020-00720-4>.
Authors: Pol Llagostera [aut, cre], Matthias Eckardt [aut]
Maintainer: Pol Llagostera <[email protected]>
License: GPL-3
Version: 1.4.0
Built: 2024-10-30 03:45:28 UTC
Source: https://github.com/cran/intensitynet

Help Index

Retrieve an intensitynet object focused on a given area

Description

Get the intensitynet object delimited by the given window

Usage

ApplyWindow(obj, x_coords, y_coords)

## S3 method for class 'intensitynet'
ApplyWindow(obj, x_coords, y_coords)

Arguments

obj

intensitynet object
x_coords

vector containing the x coordinate limits of the window
y_coords

vector containing the y coordinate limits of the window

Value

intensitynet object delimited by the window (sub-part of the original)

Examples

data("und_intnet_chicago")
sub_intnet_chicago <- ApplyWindow(und_intnet_chicago, 
                                  x_coords = c(300, 900), 
                                  y_coords = c(500, 1000))

Checks if events are related to the intensitynet object

Description

Checks if events are related to the intensitynet object

Usage

AreEventsRelated(obj)

## S3 method for class 'intensitynet'
AreEventsRelated(obj)

Arguments

obj

Intensitynet object

Value

TRUE if related, FALSE otherwise

Examples

data("und_intnet_chicago")
AreEventsRelated(und_intnet_chicago)

Calculates the distances between all pairs of nodes from the given network

Description

Calculates the distances between all pairs of nodes from the given network

Usage

## S3 method for class 'netTools'
CalculateDistancesMtx(obj)

Arguments

obj

netTools object -> list(): with the node coordinates 'x' and 'y'

Value

distances matrix

This data is an intensitynet object containing a directed network. The base data used is from Chicago, extracted from the spatstat package.

Description

This data is an intensitynet object containing a directed network. The base data used is from Chicago, extracted from the spatstat package.

Usage

dir_intnet_chicago

Format

An object of class intensitynetDir (inherits from intensitynet) of length 6.

Source

https://rdrr.io/cran/spatstat.data/man/chicago.html

Calculate all the edge intensities of the graph.

Description

Calculate all the edge intensities of the graph. It's more fast than using iteratively the function EdgeIntensity for all edges.

Usage

## S3 method for class 'intensitynet'
EdgeIntensitiesAndProportions(obj)

Arguments

obj

intensitynet object

Value

intensitynet class object where the graph contains all the edge intensities as an attribute

Given two nodes, calculates its edge intensity

Description

If not calculated, calculates the intensity of the edge with nodes; node_id1, node_id2. If the edge already contains an intensity, the function gives it directly without re-calculation.

Usage

## S3 method for class 'intensitynet'
EdgeIntensity(obj, node_id1, node_id2)

Arguments

obj

intensitynet object
node_id1

First node ID of the edge
node_id2

Second node ID of the edge

Value

Intensity of the edge

Plot heatmaps of a network

Description

This function uses internally the package 'ggplot2' to plot heatmaps of a network

Usage

## S3 method for class 'netTools'
GeoreferencedGgplot2(obj, ...)

Arguments

obj

netTools object -> list( intnet: intensitynet object, data_df: dataframe( xcoord: x coordinates of the nodes, ycoord: y coordinates of the nodes, value: vector values to plot ), net_vertices: chosen vertices to plot the heatmap (or its related edges in case to plot the edge heatmap), net_edges chosen edges to plot the heatmap, can be either the edge id's or its node endpoints (e.j. c(1,2, 2,3, 7,8)), heat_type: data which the heatmap will refer, mode: ('moran', 'getis', 'v_intensity', 'e_intensity' or mark), show_events: boolean to show or not the events as orange squares, alpha optional argument to set the transparency of the events (show_events = TRUE). The range is from 0.1 (transparent) to 1 (opaque). Default: alpha = 1 )
...

extra arguments for the ggplot

Plot the given network using its node coordinates

Description

Plot the given network using its node coordinates

Usage

## S3 method for class 'netTools'
GeoreferencedPlot(obj, ...)

Arguments

obj

netTools object -> list( intnet: intensitynet object, vertex_labels: list of labels for the vertices, edge_labels: list of labels for the edges, xy_axes: boolean to show or not the x and y axes, enable_grid: boolean to draw or not a background grid, show_events: boolean to show or not the events as orange squares, show_events option to show the events as orange squares, FALSE by default, alpha optional argument to set the transparency of the events (show_events = TRUE). The range is from 0.1 (transparent) to 1 (opaque). Default: alpha = 1, path: vector with the nodes of the path to be highlighted. Default NULL)
...

extra arguments for the plot

Gives the event correction value related to the intensitynet object

Description

Gives the event correction value related to the intensitynet object

Usage

GetEventCorrection(obj)

## S3 method for class 'intensitynet'
GetEventCorrection(obj)

Arguments

obj

intensitynet object

Value

integer, event correction value

Examples

data("und_intnet_chicago")
GetEventCorrection(und_intnet_chicago)

Gives the events related to the intensitynet object

Description

Returns a matrix containing the events information, i.e. coordinates and categories

Usage

GetEvents(obj)

## S3 method for class 'intensitynet'
GetEvents(obj)

Arguments

obj

intensitynet object

Value

matrix containing the event information

Examples

data("und_intnet_chicago")
GetEvents(und_intnet_chicago)

Gives the graph related to the intensitynet object

Description

Returns the 'igraph' class network related to the intensitynet object

Usage

GetGraph(obj)

## S3 method for class 'intensitynet'
GetGraph(obj)

Arguments

obj

intensitynet object

Value

igraph class object

Examples

data("und_intnet_chicago")
GetGraph(und_intnet_chicago)

Gives the type of graph related to the intensitynet object

Description

Gives the type of graph related to the intensitynet object

Usage

GetGraphType(obj)

## S3 method for class 'intensitynet'
GetGraphType(obj)

Arguments

obj

intensitynet object

Value

graph type in characters

Examples

data("und_intnet_chicago")
GetGraphType(und_intnet_chicago)

Creates an igraph network with the given data

Description

Creates an igraph network with the given data

Set igraph network node coordinates as its attributes

Usage

## S3 method for class 'netTools'
InitGraph(obj)

## S3 method for class 'netTools'
SetNetCoords(obj)

Arguments

obj

netTools object -> list(graph: igraph, list(): with the node coordinates 'x' and 'y')

Value

igraph network

igraph network with the given coordinates as the attributes of the nodes

Constructor of the class intensitynet.

Description

This constructor creates an intensitynet object using an adjacency matrix, the coordinates of the nodes and the coordinates of the events.

Usage

intensitynet(
  adjacency_mtx,
  node_coords,
  event_data,
  graph_type = c("undirected", "directed", "mixed"),
  event_correction = 5
)

Arguments

adjacency_mtx

Network adjacency matrix
node_coords

Nodes latitude and longitude matrix (coordinates)
event_data

DataFrame with event latitude and longitude coordinates (mandatory columns) and optional attributes related to the events
graph_type

Network type: 'undirected' (default), 'directed' or 'mixed'
event_correction

Value that determines how far can be an event to be considered part of a segment (default 5). This value highly depends on the given coordinate system

Value

intensitynet class object containing: graph = <igraph>, events = <matrix>, graph_type = c('directed', 'undirected', 'mixed'), distances = <matrix>, event_correction = <integer>, events_related = <boolean>

Examples

library(spatstat)
data(chicago)
chicago_df <- as.data.frame(chicago[["data"]]) # Get as dataframe the data from Chicago

# Get the adjacency matrix. One way is to create an igraph object from the edge coordinates.
edges <- cbind(chicago[["domain"]][["from"]], chicago[["domain"]][["to"]])
chicago_net <- igraph::graph_from_edgelist(edges)

# And then use the igraph function 'as_adjacency_matrix'
chicago_adj_mtx <- as.matrix(igraph::as_adjacency_matrix(chicago_net))
chicago_node_coords <- data.frame(xcoord = chicago[["domain"]][["vertices"]][["x"]], 
                                 ycoord = chicago[["domain"]][["vertices"]][["y"]])
                                  
# Create the intensitynet object, in this case will be undirected 
intnet_chicago <- intensitynet(chicago_adj_mtx, 
                               node_coords = chicago_node_coords, 
                               event_data = chicago_df)

Is this class object intensitynet?

Description

Determine if the given object is from the class intensitynet

Usage

IsIntensitynet(obj)

Arguments

obj

The object which will be checked if it belongs to the intensitynet class

Value

boolean, 'TRUE' if the argument obj is a intensitynet object

Examples

data("und_intnet_chicago")
IsIntensitynet(und_intnet_chicago)

This data is an intensitynet object containing an mixed network. The base data used is from Chicago, extracted from the spatstat package.

Description

This data is an intensitynet object containing an mixed network. The base data used is from Chicago, extracted from the spatstat package.

Usage

mix_intnet_chicago

Format

An object of class intensitynetMix (inherits from intensitynet) of length 6.

Source

https://rdrr.io/cran/spatstat.data/man/chicago.html

Calculate dependence statistics on the network

Description

It allows to compute different dependence statistics on the network for the given vector and for neighborhoods of distinct order. Such statistics are; correlation, covariance, Moran’s I and Geary’s C.

Usage

NodeGeneralCorrelation(
  obj,
  dep_type,
  lag_max,
  intensity,
  partial_neighborhood = TRUE
)

## S3 method for class 'intensitynet'
NodeGeneralCorrelation(
  obj,
  dep_type = c("correlation", "covariance", "moran", "geary"),
  lag_max,
  intensity,
  partial_neighborhood = TRUE
)

Arguments

obj

intensitynet object
dep_type

'correlation', 'covariance', moran', 'geary'. The type of dependence statistic to be computed.
lag_max

Maximum geodesic lag at which to compute dependence
intensity

Vector containing the values to calculate the specified dependency in the network. Usually the node mean intensities.
partial_neighborhood

use partial neighborhood (TRUE) or cumulative (FALSE). TRUE by default

Value

A vector containing the dependence statistics (ascending from order 0).

Examples

data("und_intnet_chicago")
g <- und_intnet_chicago$graph
gen_corr <- NodeGeneralCorrelation(und_intnet_chicago, dep_type = 'correlation', lag_max = 2, 
                                   intensity = igraph::vertex_attr(g)$intensity)

Calculates the mean intensity of the given node (for directed networks)

Description

Given a node, calculates its mean intensities regarding in and out edges associated with the node.

Usage

## S3 method for class 'intensitynetDir'
MeanNodeIntensity(obj, node_id)

Arguments

obj

intensitynetDir object
node_id

ID of the node

Value

mean intensities of the given node for in and out edges

Calculates the mean intensity of the given node (for mixed networks)

Description

Given a node, calculates its mean intensities depending on the edges associated with the node, those intensities are: in, out (for directed edges), undirected and total intensity.

Usage

## S3 method for class 'intensitynetMix'
MeanNodeIntensity(obj, node_id)

Arguments

obj

intensitynetMix object
node_id

ID of the node

Value

mean intensities of the given node for undirected edges, in and out directed and total intensity.

Calculates the mean intensity of the given node (for undirected networks)

Description

Calculates the mean intensity of the given node (intensity of all the edges of the node/number of edges of the node)

Usage

## S3 method for class 'intensitynetUnd'
MeanNodeIntensity(obj, node_id)

Arguments

obj

intensitynetUnd object
node_id

ID of the node

Value

mean intensity of the given node

Calculates local correlations based on nodes

Description

Gives the node local Moran-I, Getis-Gstar or Geary-c correlations

Usage

NodeLocalCorrelation(obj, dep_type = "moran", intensity)

## S3 method for class 'intensitynet'
NodeLocalCorrelation(obj, dep_type = c("moran", "getis", "geary"), intensity)

Arguments

obj

intensitynet object
dep_type

'moran', 'getis' or 'geary'. Type of local correlation to be computed (Moran-i, Getis-Gstar, Geary-c), default = 'moran'.
intensity

vector containing the values to calculate the specified correlation for each node in the network.

Value

a vector containing two values. The first value is a vector with the specified local correlations for each node. The second values is the given intensitynet class object but with the correlations added to the node attributes of its network.

Source

*"A Local Indicator of Multivariate SpatialAssociation: Extending Geary's c, Geographical Analysis" Luc Anselin (2018) <doi:10.1111/gean.12164>

Examples

## Not run: 
data("und_intnet_chicago")
g <- und_intnet_chicago$graph
data_moran <- NodeLocalCorrelation(und_intnet_chicago, 
                                   dep_type = 'moran', 
                                   intensity = igraph::vertex_attr(g)$intensity)
moran_i <- data_moran$correlation
intnet <- data_moran$intnet

## End(Not run)

Calculates the total weight of the given path

Description

Calculates the total weight of the given path

Usage

PathTotalWeight(obj, path_nodes, weight = NA)

## S3 method for class 'intensitynet'
PathTotalWeight(obj, path_nodes, weight = NA)

Arguments

obj

intensitynet object
path_nodes

vector containing the node ID's of the path
weight

an string specifying the type of weight to be computed. If no weight type is provided, the function will calculate the total amount of edges. Default NA.

Value

total weight of the path

Examples

data("und_intnet_chicago")
PathTotalWeight(und_intnet_chicago, c('V115', 'V123', 'V125', 'V134'), weight = 'intensity')

Plot intensitynet object

Description

Plot intensitynet object

Usage

## S3 method for class 'intensitynet'
plot(
  x,
  vertex_labels = "none",
  edge_labels = "none",
  xy_axes = TRUE,
  enable_grid = FALSE,
  show_events = FALSE,
  alpha = 1,
  path = NULL,
  ...
)

Arguments

x

intensitynet object
vertex_labels

list -> labels for the vertices
edge_labels

list -> labels for the edges
xy_axes

show the x and y axes
enable_grid

draw a background grid
show_events

option to show the events as orange squares, FALSE by default
alpha

optional argument to set the transparency of the events (show_events = TRUE). The range is from 0.1 (transparent) to 1 (opaque). Default: alpha = 1
path

vector with the nodes of the path to be highlighted. Default NULL
...

extra arguments for the plot

Value

No return value, same as graphics::plot.

Examples

data("und_intnet_chicago")
plot(und_intnet_chicago) # basic plot
plot(und_intnet_chicago, enable_grid = TRUE) # with grid
plot(und_intnet_chicago, xy_axes = FALSE) # without axes
plot(und_intnet_chicago, path = c("V1","V2","V24","V25","V26","V48")) # highlight a path

Given an intensitynet object, plot network heatmaps

Description

Plot the network correlations or intensities.

Usage

PlotHeatmap(
  obj,
  heat_type = "none",
  intensity_type = "none",
  net_vertices = NULL,
  net_edges = NULL,
  show_events = FALSE,
  alpha = 1,
  ...
)

## S3 method for class 'intensitynet'
PlotHeatmap(
  obj,
  heat_type = c("none", "moran", "geary", "v_intensity", "e_intensity"),
  intensity_type = c("none"),
  net_vertices = NULL,
  net_edges = NULL,
  show_events = FALSE,
  alpha = 1,
  ...
)

Arguments

obj

intensitynet object
heat_type

a string with the desired heatmap to be plotted, the options are; 'moran': Local Moran-i correlation (with 999 permutations), 'geary': Local Geary-c correlation. The correlations will use the indicated intensity type, 'v_intensity': vertex mean intensity, 'e_intensity': edge intensity, mark name: name of the mark (string) to plot its edge proportion, 'none': plain map.
intensity_type

name of the vertex intensity used to plot the heatmap for moran, geary and v_intensity options (of the heat_type argument). The options are; For undirected networks: 'intensity'. For directed networks: 'intensity_in' or 'intensity_out'. For mixed networks: 'intensity_in', 'intensity_out', 'intensity_und' or 'intensity_all'. If the intensity parameter is 'none', the function will use, if exist, the intensity (undirected) or intensity_in (directed) values from the network nodes. If the heat_type is 'e_intensity', this parameter will be skipped and plot the edge intensities instead.
net_vertices

chosen vertices to plot the heatmap (or its related edges in case to plot the edge heatmap)
net_edges

chosen edges to plot the heatmap, can be either the edge id's or its node endpoints (e.j. c(1,2, 2,3, 7,8))
show_events

option to show the events as orange squares, FALSE by default
alpha

optional argument to set the transparency of the events (show_events = TRUE). The range is from 0.1 (transparent) to 1 (opaque). Default: alpha = 1
...

extra arguments for the class ggplot

Value

The plot of the heatmap with class c("gg", "ggplot")

Examples

## Not run: 
data("und_intnet_chicago")
PlotHeatmap(und_intnet_chicago, heat_type='moran')

## End(Not run)

Plot the neighbors of a node including the closer events

Description

Plot the net and the events in the neighborhood area of the given node

Usage

PlotNeighborhood(obj, node_id, ...)

## S3 method for class 'intensitynet'
PlotNeighborhood(obj, node_id, ...)

Arguments

obj

intensitynet object
node_id

Id of the node which the plot will be focused
...

Extra arguments for plotting

Value

No return value, just plots the neighborhood and the events.

Examples

data("und_intnet_chicago")
PlotNeighborhood(und_intnet_chicago, node_id = 'V300')

Gives the distance between an event and the line formed by two nodes.

Description

Gives the distance between an event and the line (not segment) formed by two nodes.

Usage

## S3 method for class 'netTools'
PointToLine(obj)

Arguments

obj

netTools object -> list(p1:c(coordx, coordy), p2:c(coordx, coordy), e:c(coordx, coordy))

Value

the distance to the line

Gives the shortest distance between an event and the segment formed by two nodes.

Description

Gives the shortest distance between an event and the segment formed by two nodes.

Usage

PointToSegment_deprecated(obj)

Arguments

obj

netTools object -> list(p1:c(coordx, coordy), p2:c(coordx, coordy), e:c(coordx, coordy))

Value

distance to the segment

Gives the shortest distance between an event and a set of segments.

Description

Gives the shortest distance between an event and a set of segments.

Usage

PointToSegment(obj)

Arguments

obj

netTools object -> list(p1:matrix(coordx, coordy), p2:matrix(coordx, coordy), e:matrix(coordx, coordy))

Value

distance vector to each segment

Calculates intensity statistics for the given intensitynet object

Description

Calculates edgewise and mean nodewise intensities for the given intensitynet object and, for each edge, the proportions of all event covariates.

Usage

RelateEventsToNetwork(obj)

Arguments

obj

intensitynet object

Value

proper intensitynet object (Undirected, Directed, or Mixed) with a graph containing the nodewise intensity in the node attributes and the edgewise intensities and event covariate proportions as edge attributes.

Examples

data("und_intnet_chicago")
intnet_chicago <- RelateEventsToNetwork(und_intnet_chicago)

Calculates intensity statistics for the given intensitynet object

Description

Calculates edgewise and mean nodewise intensities for Directed networks and, for each edge, the proportions of all event covariates.

Usage

## S3 method for class 'intensitynetDir'
RelateEventsToNetwork(obj)

Arguments

obj

intensitynetDir object

Value

proper intensitynetDir object with a graph containing the nodewise intensity in the node attributes and the edgewise intensities and event covariate proportions as edge attributes.

Calculates intensity statistics for the given intensitynet object

Description

Calculates edgewise and mean nodewise intensities for Mixed networks and, for each edge, the proportions of all event covariates.

Usage

## S3 method for class 'intensitynetMix'
RelateEventsToNetwork(obj)

Arguments

obj

intensitynetMix object

Value

proper intensitynetMix object with a graph containing the nodewise intensity in the node attributes and the edgewise intensities and event covariate proportions as edge attributes.

Calculates intensity statistics for the given intensitynet object

Description

Calculates edgewise and mean nodewise intensities for Undirected networks and, for each edge, the proportions of all event covariates.

Usage

## S3 method for class 'intensitynetUnd'
RelateEventsToNetwork(obj)

Arguments

obj

intensitynetUnd object

Value

proper intensitynetUnd object with a graph containing the nodewise intensity in the node attributes and the edgewise intensities and event covariate proportions as edge attributes.

Sets the given intensities as an edge attribute to the given igraph network

Description

Sets the given intensities as an edge attribute to the given igraph network

Usage

## S3 method for class 'netTools'
SetEdgeIntensity(obj)

Arguments

obj

netTools object -> list(graph: igraph, node_id1: node id, node_id2: node id, intensity: edge intensity)

Value

igraph network with the given intensities as attributes of the edges

Set attributes to the network edges or nodes

Description

Set attributes to the network edges or nodes

Usage

## S3 method for class 'intensitynet'
SetNetworkAttribute(obj, where, name, value)

Arguments

obj

intensitynet object
where

'vertex' or 'edge', where to set the attribute
name

name of the attribute
value

vector containing the data for the attribute

Value

intensitynet object containing the network with the added attributes

Sets the given intensities as a node attribute to the given igraph network

Description

Sets the given intensities as a node attribute to the given igraph network

Usage

## S3 method for class 'netTools'
SetNodeIntensity(obj)

Arguments

obj

netTools object -> list(graph: igraph, node_id: node id, intensity: node intensity)

Value

igraph network with the given intensities as attributes of the nodes

Given two nodes, gives its shortest distance based on the minimum amount of edges

Description

Calculates the shortest distance path between two nodes (based on the minimum amount of edges). The function also returns the total weight of the path, if the weight is not available, returns the number of edges.

Usage

## S3 method for class 'intensitynet'
ShortestNodeDistance(obj, node_id1, node_id2)

Arguments

obj

intensitynet object
node_id1

id of the starting node
node_id2

id of the end node

Value

distance of the path and the nodes of the path

Given two nodes, calculates the shortest path and its total weight

Description

Calculates the shortest path between two vertices (based on the minimum amount of edges) and calculates its total weight

Usage

ShortestPath(obj, node_id1, node_id2, weight = NA, mode = "all")

## S3 method for class 'intensitynet'
ShortestPath(obj, node_id1, node_id2, weight = NA, mode = "all")

Arguments

obj

intensitynet object
node_id1

starting node
node_id2

ending node
weight

an string, calculate the shortest path based on this type of weight. If no weight type is provided, the function will calculate the shortest path based on the minimum amount of edges. Default NA.
mode

Character 'in', 'out', 'all' (default). Gives whether the shortest paths to or from the given vertices should be calculated for directed graphs. If out then the shortest paths from the vertex, if in then to it will be considered. If all, the default, then the corresponding undirected graph will be used, ie. not directed paths are searched. This argument is ignored for undirected graphs.

Value

total weight of the shortest path and the path vertices with class igraph.vs

Examples

data("und_intnet_chicago")
ShortestPath(und_intnet_chicago, node_id1 = 'V1', node_id2 = 'V300', weight = 'intensity')

Summary of the intensitynet object

Description

Give information about the intensytinet object specific class (intensitynetUnd, intensitynetDir, or intensitynetMix), the network number of nodes, edges and events, the event correction value and, if the events had been related to the intensitynet object network.

Usage

## S3 method for class 'intensitynet'
summary(object, ...)

Arguments

object

Intensitynet object
...

Extra parameters for the summary function

Value

list containing the displayed information

Examples

data("und_intnet_chicago")
summary(und_intnet_chicago)

This data is an intensitynet object containing an undirected network. The base data used is from Chicago, extracted from the spatstat package.

Description

This data is an intensitynet object containing an undirected network. The base data used is from Chicago, extracted from the spatstat package.

Usage

und_intnet_chicago

Format

An object of class intensitynetUnd (inherits from intensitynet) of length 6.

Source

https://rdrr.io/cran/spatstat.data/man/chicago.html

Converts a directed adjacency matrix to undirected

Description

Creates a directed adjacency matrix from an Undirected one with random directions (in-out edges) but with the same connections between nodes.

Usage

## S3 method for class 'netTools'
Undirected2RandomDirectedAdjMtx(obj)

Arguments

obj

netTools object -> list(mtx: matrix)

Value

directed adjacency matrix with random directions