Degree (In-degree and Out-degree) of nodes

Description

A function that returns degrees of nodes from provided “nodelist” and “edgelist”; while in-degrees and out-degrees are returned if edges are directed).

Usage

Degree.list(nodelist, edgelist, Directed=F)

Arguments

Value

Lists of different contents are returned for undirected and directed edges respectively: For undirected graph:

For directed type:

Note

This function returns differently for undirected and directed networks, where both In-degrees and Out-degrees are calculated for a directed network, and only degrees are returned for an undirected networks.

Author(s)

Binbin Lu binbinlu@whu.edu.cn

Orientate all the edges in a given edgelist

Description

A function to orientate each edge according to the given vector.

Usage

Directed(edgelist, direction.v=rep(0,length(edgelist[,1])), eadf=NULL)

Arguments

Details

Within a road network, some road segments might be one-way while the rest are double-way. This suitation makes it complex to define directed or undirected edges in a graph. This function is to orientate each edge according to the given vector, “direction.v”: 1 (TRUE) indicates one-way, while 0 (FALSE) represents double-way. All the double-way (undirected) edges in the given “edgelist” are redefined as two directed edges (e.g. a double-way (undirected) edge(nid1, nid2) is redefined as two one-way (directed) edges, (nid1, nid2) and (nid2, nid1)). The one-way (directed) edges are left in their directed forms. In other words, all the edges returned by this function will be directed, and used to be construct a directed graph.

Value

A list consisted of:

Author(s)

Binbin Lu binbinlu@whu.edu.cn

Estevan road network from OpenStreetMap (SpatialLinesDataFrame)

Description

This data set is a part of Estevan road network downloaded from OpenStreetMap, and a version with topology errors corrected is also incorporated.

Usage

data(ERN_OSM)
data(ERN_OSM_correct)

Format

Two “SpatialLinesDataFrame” objects named “ERN_OSM.nt” and “ERN_OSM_cor.nt”.

Source

https://www.openstreetmap.org/#map=14/49.1388/-102.9961

References

OpenStreetMap. 2014. Estevan road network. ed. O. community.

London house price and hedonic data (SpatialPointsDataFrame)

Description

This data set is sampled from a house price data set provided by the Nationwide Building Society of the UK, and combined with various hedonic variables.

Usage

data(LNHP)

Format

A “SpatialPointsDataFrame” object named “LN.prop”.

Details

In this data set, attributes are interpreted as follows:

PURCHASE: sale price in GBP

FLOORSZ: floor size of the property in square metres;

BATH2: 1 if the property has 2 or more bathrooms, 0 otherwise;

BEDS2: 1 if the property has 2 or more bedrooms, 0 otherwise;

CENTHEAT: 1 if the property has central heating, 0 otherwise;

GARAGE1: 1 if the property has one or more garages, 0 otherwise;

BLDPWW1: 1 if the property was built prior to 1914, 0 otherwise;

BLDINTW: is 1 if the property was built between 1914 and 1939, 0 otherwise;

BLD60S: 1 if the property was built between 1960 and 1969, 0 otherwise;

BLD70S: 1 if the property was built between 1970 and 1979, 0 otherwise;

BLD80S: 1 if the property was built between 1980 and 1989, 0 otherwise;

BLD90S: 1 if the property was built between 1990 and 2000, 0 otherwise;

TYPEDETCH: 1 if the property is detached (i.e. it is stand-alone), 0 otherwise;

TYPETRRD: 1 if the property is in a terrace of similar houses, 0 otherwise;

TYPEFLAT: 1 if the property is a flat or apartment, 0 otherwise;

PROF: percentage of the workforce in professional or managerial occupations in the census enumeration district in which the house is located.

UNEMPLOY: percentage of unemployment in the census enumeration district in which the house is located.

X: X coordinate.

Y: Y coordinat.

References

Lu, B., M. Charlton, P. Harris & A. S. Fotheringham (2014) Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data. International Journal of Geographical Information Science, 28, 660-681.

London Road network data (SpatialLinesDataFrame)

Description

The London Road network data set is produced by the UK Ordnance Survey (OS) in 2001.

Usage

data(LNNT)

Format

A “SpatialLinesDataFrame” object named “LN.nt”.

Details

In this data set, roads are categorized into four types (nt_RoadTyp):

Mo: motorway

a: A-road

b: B-road

Mi: minor road

Speed limit for each road segment is also incoporated in the attribute “Speed” (Transport for London, 2011)

References

Lu, B., M. Charlton, P. Harris & A. S. Fotheringham (2014) Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data. International Journal of Geographical Information Science, 28, 660-681.

Transport for London, (2011) Intelligent Speed Adaptation [online]. http://www.tfl.gov.uk/corporate/projectsandschemes/7893.aspx#For_developers. Available from: http://www.tfl.gov.uk/corporate/projectsandschemes/7893.aspx#For_developers.

Simplify multiple edges in a network

Description

A function to simplfy multiple-edge into one representative edge, where a multiple-edge structure refers to a set of edges sharing with the same pair of nodes.

Usage

ME.simplification(nodelist, edgelist, eadf=NULL, ea.prop=NULL, Directed=F, 
                  DegreeL=NULL, InDegreeL=NULL, OutDegreeL=NULL,Nexception=NULL,
                  Eexception=NULL)

Arguments

Value

Two types of list returned for undirected and directed edges, respectively:

For “undirected” edges:

For “directed” network:

Note

Edges are recognised as a structure of multiple-edge when they share the same pair of nodes. Note that the order of the nodes should be also the same when edges are directed.

Author(s)

Binbin Lu binbinlu@whu.edu.cn

See Also

SL.extraction, PN.amalgamation

Return the coordinates of nodes in a “nodelist”

Description

A function that returns the coordinates of nodes in a two-column matrix (X,Y) from a “nodelist”

Usage

Nodes.coordinates(nodelist)

Arguments

Value

Author(s)

Binbin Lu binbinlu@whu.edu.cn

Ontario road network data (SpatialLinesDataFrame)

Description

This data set is a part of Ontario road network data set downloaded from Ontario Ministry of Natural Resources.

Usage

data(ORN)

Format

A “SpatialLinesDataFrame” object named “ORN.nt”.

Source

https://www.geographynetwork.ca/website/orn/viewer.htm

References

Peterborough, ON: Ontario Ministry of Natural Resources, Ontario Road Network. Available: https://www.geographynetwork.ca/website/orn/viewer.htm, 2006, (Accessed Dec. 4, 2009)

Amalgamate edges connected by a pseudo-node

Description

A function to amalgamate edges connected by a pseudo-node.

Usage

PN.amalgamation(nodelist, edgelist, eadf=NULL, ea.prop=NULL, Directed=F, 
                DegreeL=NULL, InDegreeL=NULL, OutDegreeL=NULL,Nexception=NULL,
                Eexception=NULL)

Arguments

Value

Two types of list returned for undirected and directed edges, respectively:

For “undirected” edges:

For “directed” network:

Note

Node in a directed network is recognised a a pseudo-node with in-degree and out-degree equalling to 1; while it could be a pseudo-node with degree equalling to 2 in a undirected network.

Author(s)

Binbin Lu binbinlu@whu.edu.cn

See Also

SL.extraction, ME.simplification

Examples

data(ORN)
rtNEL<-readshpnw(ORN.nt, ELComputed=TRUE)
res.sl<-SL.extraction(rtNEL[[2]],rtNEL[[3]])
res.me<-ME.simplification(res.sl[[1]],res.sl[[2]],DegreeL=res.sl[[4]]) 
res.pn<-PN.amalgamation(res.me[[1]],res.me[[2]],DegreeL=res.me[[4]])
ptcoords<-Nodes.coordinates(res.pn[[1]])
#plot(ORN.nt)
#points(ptcoords, col="green")
#plot(ORN.nt)
#points(Nodes.coordinates(rtNEL[[2]]), col="red")

A collection of functions for redefining attributes of edited edges

Description

A function to provide a collection of functions to redefine attributes of edited edges in the functions PN.amalgamation and ME.simplification.

Usage

Redef.functions(v, typ=1)

Arguments

Note

It is easy to include more specific methods for attribute redefinition via incorporating the corresponding functions. In other words, you can define more methods by numbering them with 5, 6, etc.

Author(s)

Binbin Lu binbinlu@whu.edu.cn

Extract self-loops form given network

Description

A function to extract self-loops away in given network.

Usage

SL.extraction(nodelist, edgelist, eadf=NULL,Directed=F, DegreeL=NULL, 
              InDegreeL=NULL, OutDegreeL=NULL,Nexception=NULL,Eexception=NULL)

Arguments

Value

Two types of list returned for undirected and directed edges, respectively:

For “undirected” edges:

For “directed” network:

Note

An edge is recognised as a structure of self-loop when it starts and ends at the same point (location).

Author(s)

Binbin Lu binbinlu@whu.edu.cn

See Also

PN.amalgamation, ME.simplification

A designed structure to denote edges from a spatial network

Description

“edgelist” is an interchange structure of edges from a spatial network into an “igraph” object. It is a three-column matrix, of which each row is designed as [EdgeID,NodeID(from),NodeID(to)].

Details

Both “nodelist” and “edgelist” are interchange structures of nodes and edges extracted from a spatial network in a “SpatialLines” or “SpatialLinesDataFrame” object. They are always concerned together, and returned by the function readshpnw as initial results for the following steps.

Note

If the parameter “Detailed” specified in readshpnw is TRUE, all the endpoints of polyines will be extracted as nodes, then the converted graph will have the same spatial details with the transformed “SpatialLines” or “SpatialLinesDataFrame” object. To retrieve the original attributes in the “SpatialLinesDataFrame” object, the original edge ID is also kept and the row structure will be [EdgeID,eid,NodeID(from),NodeID(to)], in which EdgeID refers to the new edge id while eid represents the original edge ID.

Author(s)

Binbin Lu binbinlu@whu.edu.cn

Produce an “igraph” object

Description

A function to produce an “igraph” object with the “nodelist” and “edgelist”, which could be returned by the function readshpnw.

Usage

nel2igraph(nodelist, edgelist, weight = NULL, eadf = NULL, Directed = FALSE)

Arguments

Details

1. The weighting vector, “weight”, will be used as default for any weigted calculations with edges in the “igraph” object.

2. The coordinate of each node is attached as attributes “X” and “Y”, which could be retrived via the function “get.vertex.attribute” from the package igraph.

Value

Author(s)

Binbin Lu binbinlu@whu.edu.cn

Examples

data(ORN)
rtNEL<-readshpnw(ORN.nt, ELComputed=TRUE)
#Add the edge length as the weight for graph
igr<-nel2igraph(rtNEL[[2]],rtNEL[[3]],weight=rtNEL[[4]])
plot(igr, vertex.label=NA, vertex.size=2,vertex.size2=2)
#plot(ORN.nt)

A designed structure to denote nodes from a spatial network

Description

“nodelist” is an interchange structure of nodes from a spatial network into an “igraph” object. It is a two-column data frame, of which each row is designed as [NodeID,coordinate(X,Y)].

Details

Both “nodelist” and “edgelist” are interchange structures of nodes and edges extracted from a spatial network in a “SpatialLines” or “SpatialLinesDataFrame” object. They are always concerned together, and returned by the function readshpnw as initial results for the following steps.

Author(s)

Binbin Lu binbinlu@whu.edu.cn

Check the connectivity of a given network

Description

A function to check the connectivity of a given network.

Usage

nt.connect(nt)

Arguments

Details

In this function, all the nodes are traversed, and classified dynamically via a rule, that any pair of different nodes fall into a same category if they could be reached from each other. A map will be plotted spontaneously with differet categories (i.e. self-connected parts) in distinct colors. Finally, the self-connected part with the most nodes will be returned as a new “SpatialLinesDataFrame” object.

Author(s)

Binbin Lu binbinlu@whu.edu.cn

Integrate a point data set into a network

Description

A function to integrate an individual point data set into a network under different rules (see details below).

Usage

points2network(ntdata,pointsxy,approach=1,ELComputed=FALSE,longlat=F,
               Detailed=F, ea.prop=NULL)

Arguments

Value

A list consisted of:

Author(s)

Binbin Lu binbinlu@whu.edu.cn

See Also

ptsinnt.view

Examples

## Not run: 
data(ORN)
pts<-spsample(ORN.nt, 100, type="random")
ptsxy<-coordinates(pts)[,1:2]
ptsxy<-cbind(ptsxy[,1]+0.008,ptsxy[,2]+0.008)
#Mapping each point to the nearest node in the network/graph
res<-points2network(ntdata=ORN.nt,pointsxy=ptsxy, approach=1)
ptsinnt.view(ntdata=rn, nodelist=res[[1]], pointsxy=ptsxy,
             CoorespondIDs=res[[3]])
#Mapping each point to the nearest point (add them as nodes if they are not) on 
#the network
res<-points2network(ntdata=ORN.nt,pointsxy=ptsxy, approach=2,ea.prop=rep(0,37))
ptsinnt.view(ntdata=ORN.nt, nodelist=res[[1]], pointsxy=ptsxy, CoorespondIDs=res[[3]]) 
#Add a new edge(Virtual edge) between each point and the nearest node
res<-points2network(ntdata=ORN.nt,pointsxy=ptsxy, approach=3,ea.prop=rep(0,37))
VElist<-res[[7]]
ptsinnt.view(ntdata=ORN.nt, nodelist=res[[1]], pointsxy=ptsxy, CoorespondIDs=res[[3]], 
             VElist=VElist)
#Add a new edge(Virtual edge) between each point and the nearest point
res<-points2network(ntdata=ORN.nt,pointsxy=ptsxy, approach=4,ea.prop=rep(0,37))
VElist<-res[[7]]
ptsinnt.view(ntdata=ORN.nt, nodelist=res[[1]], pointsxy=ptsxy, CoorespondIDs=res[[3]],
             VElist=VElist) 

## End(Not run)

Visualize the integration between a point data set and spatila network

Description

A specific function to visualizethe integration between a point data set and spatila network, i.e. results returned by the function points2network

Usage

ptsinnt.view(ntdata, nodelist, pointsxy, CoorespondIDs, VElist=NULL)

Arguments

Author(s)

Binbin Lu binbinlu@whu.edu.cn

See Also

points2network

Examples

data(ORN)
#pts<-spsample(ORN.nt, 100, type="random")
#ptsxy<-coordinates(pts)[,1:2]
#ptsxy<-cbind(ptsxy[,1]+0.008,ptsxy[,2]+0.008)
#Mapping each point to the nearest node in the network/graph
#res<-points2network(ntdata=ORN.nt,pointsxy=ptsxy, mapping.method=1)
#Visualize the results without virtual edges 
#ptsinnt.view(ntdata=ORN.nt, nodelist=res[[1]], pointsxy=ptsxy, 
#             CoorespondIDs=res[[3]])
#Visualize the results with virtual edges
#res<-points2network(ntdata=ORN.nt,pointsxy=ptsxy, mapping.method=3,
#                    ea.prop=rep(0,37))
#VElist<-res[[7]]
#ptsinnt.view(ntdata=ORN.nt, nodelist=res[[1]], pointsxy=ptsxy, 
#             CoorespondIDs=res[[3]], VElist=VElist)

Read a network from a “SpatialLines” or “SpatialLinesDataFrame” object

Description

A function to read a spatial network from a “SpatialLines” or “SpatialLinesDataFrame” object, and resolve it into a “nodelist” and “edgelist” for the following conversion.

Usage

readshpnw(ntdata, ELComputed=FALSE, longlat=FALSE, Detailed=FALSE, ea.prop=NULL)

Arguments

Details

This function plays the first step to convert a spatial network (in a “SpatialLines” or “SpatialLinesDataFrame” object) into an “igraph” object by returning a “nodelist” and “edgelist”.

Value

A list consisted of:

Author(s)

Binbin Lu binbinlu@whu.edu.cn

Examples

data(ORN)
rtNEL<-readshpnw(ORN.nt)
nl<-rtNEL[[2]]
el<-rtNEL[[3]]
#Compute edge length
rtNEL<-readshpnw(ORN.nt, ELComputed=TRUE)
edgelength<-rtNEL[[4]]
eadf<-rtNEL[[5]]