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Network visualization that constrains nodes within a shapefile polygon
Output shapefile for the igraph network in RConvert a shapefile from polygons to points?Coloring a Shapefile Polygons in MatlabR: Plotting points from a .gdb file over polygons in a shapefileShapefile with multiple shapes but only one polygonHow to get polygons of a shapefile containing centroids of polygons from another shapefile in R?Close a spatial line into a polygon using a shapefileHow to loop through polygons in a shapefile in r?R visNetwork + igraph weighted network visualization with visEdgesFilter shapefile polygons by area
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
I'm trying to create a network visualization of contact tracing data, but constraining groups of nodes by GIS coordinates or loosely within a shape file polygon.
Ideally this would be within ggplot2 framework so I can integrate with other mapping, but if there are igraph or other solutions I'd be happy as well.
Example contact network from the ggnetwork vignette distributing the nodes using force-directed algorithm.
library(network)
library(sna)
library(ggnetwork)
n <- network(rgraph(10, tprob = 0.2), directed = FALSE)
n %v% "family" <- sample(letters[1:3], 10, replace = TRUE)
n %v% "importance" <- sample(1:3, 10, replace = TRUE)
e <- network.edgecount(n)
set.edge.attribute(n, "type", sample(letters[24:26], e, replace = TRUE))
set.edge.attribute(n, "day", sample(1:3, e, replace = TRUE))
ggplot(n, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(color = "black") +
geom_nodes(color = "black", size = 8) +
geom_nodetext(aes(color = family, label = LETTERS[ vertex.names ]),
fontface = "bold") +
theme_blank()
Looking under the hood a bit:
ggnetwork(n)
x y family importance na.x vertex.names xend yend day
1 0.9716994 0.2514118 b 3 FALSE 1 0.9716994 0.2514118 NA
2 0.2287786 0.3320298 a 3 FALSE 2 0.2287786 0.3320298 NA
3 1.0000000 0.6549319 c 3 FALSE 3 1.0000000 0.6549319 NA
4 0.6364260 0.6691008 b 2 FALSE 4 0.6364260 0.6691008 NA
5 0.5913509 0.2442173 b 3 FALSE 5 0.5913509 0.2442173 NA
6 0.2744755 0.7079372 c 2 FALSE 6 0.2744755 0.7079372 NA
7 0.4569772 1.0000000 a 3 FALSE 7 0.4569772 1.0000000 NA
8 0.0000000 0.5340600 a 2 FALSE 8 0.0000000 0.5340600 NA
9 0.8101132 0.0000000 b 1 FALSE 9 0.8101132 0.0000000 NA
10 0.8281340 0.3730087 c 2 FALSE 10 0.8281340 0.3730087 NA
17 0.0000000 0.5340600 a 2 FALSE 8 0.2287786 0.3320298 1
21 0.0000000 0.5340600 a 2 FALSE 8 0.2744755 0.7079372 2
41 0.2744755 0.7079372 c 2 FALSE 6 0.2287786 0.3320298 3
51 0.2744755 0.7079372 c 2 FALSE 6 0.6364260 0.6691008 3
61 0.4569772 1.0000000 a 3 FALSE 7 0.6364260 0.6691008 2
71 0.4569772 1.0000000 a 3 FALSE 7 0.2744755 0.7079372 2
81 0.5913509 0.2442173 b 3 FALSE 5 0.9716994 0.2514118 3
91 0.5913509 0.2442173 b 3 FALSE 5 0.2287786 0.3320298 3
101 0.5913509 0.2442173 b 3 FALSE 5 0.6364260 0.6691008 3
11 0.6364260 0.6691008 b 2 FALSE 4 1.0000000 0.6549319 3
12 0.8101132 0.0000000 b 1 FALSE 9 0.5913509 0.2442173 2
13 0.8101132 0.0000000 b 1 FALSE 9 0.9716994 0.2514118 2
14 0.8281340 0.3730087 c 2 FALSE 10 0.9716994 0.2514118 3
15 0.8281340 0.3730087 c 2 FALSE 10 0.6364260 0.6691008 2
16 0.8281340 0.3730087 c 2 FALSE 10 0.8101132 0.0000000 3
18 1.0000000 0.6549319 c 3 FALSE 3 0.9716994 0.2514118 2
na.y type
1 NA <NA>
2 NA <NA>
3 NA <NA>
4 NA <NA>
5 NA <NA>
6 NA <NA>
7 NA <NA>
8 NA <NA>
9 NA <NA>
10 NA <NA>
17 FALSE z
21 FALSE z
41 FALSE z
51 FALSE y
61 FALSE y
71 FALSE y
81 FALSE y
91 FALSE y
101 FALSE x
11 FALSE x
12 FALSE z
13 FALSE z
14 FALSE z
15 FALSE y
16 FALSE z
18 FALSE z
I imagine that I could randomize the x/y pairs within each polygon (e.g. splancs::csr or sp::spsample), but obviously the output would be a mess of lines and points.
Is it possible to use a force directed algorithm to distribute groups of nodes algorithmically within certain constraints of coordinates?
r gis igraph visnetwork ggnetwork
asked Mar 26 at 22:59
micturalgiamicturalgia
1001 gold badge2 silver badges11 bronze badges
add a comment |
I'm trying to create a network visualization of contact tracing data, but constraining groups of nodes by GIS coordinates or loosely within a shape file polygon.
Ideally this would be within ggplot2 framework so I can integrate with other mapping, but if there are igraph or other solutions I'd be happy as well.
Example contact network from the ggnetwork vignette distributing the nodes using force-directed algorithm.
library(network)
library(sna)
library(ggnetwork)
n <- network(rgraph(10, tprob = 0.2), directed = FALSE)
n %v% "family" <- sample(letters[1:3], 10, replace = TRUE)
n %v% "importance" <- sample(1:3, 10, replace = TRUE)
e <- network.edgecount(n)
set.edge.attribute(n, "type", sample(letters[24:26], e, replace = TRUE))
set.edge.attribute(n, "day", sample(1:3, e, replace = TRUE))
ggplot(n, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(color = "black") +
geom_nodes(color = "black", size = 8) +
geom_nodetext(aes(color = family, label = LETTERS[ vertex.names ]),
fontface = "bold") +
theme_blank()
Looking under the hood a bit:
ggnetwork(n)
x y family importance na.x vertex.names xend yend day
1 0.9716994 0.2514118 b 3 FALSE 1 0.9716994 0.2514118 NA
2 0.2287786 0.3320298 a 3 FALSE 2 0.2287786 0.3320298 NA
3 1.0000000 0.6549319 c 3 FALSE 3 1.0000000 0.6549319 NA
4 0.6364260 0.6691008 b 2 FALSE 4 0.6364260 0.6691008 NA
5 0.5913509 0.2442173 b 3 FALSE 5 0.5913509 0.2442173 NA
6 0.2744755 0.7079372 c 2 FALSE 6 0.2744755 0.7079372 NA
7 0.4569772 1.0000000 a 3 FALSE 7 0.4569772 1.0000000 NA
8 0.0000000 0.5340600 a 2 FALSE 8 0.0000000 0.5340600 NA
9 0.8101132 0.0000000 b 1 FALSE 9 0.8101132 0.0000000 NA
10 0.8281340 0.3730087 c 2 FALSE 10 0.8281340 0.3730087 NA
17 0.0000000 0.5340600 a 2 FALSE 8 0.2287786 0.3320298 1
21 0.0000000 0.5340600 a 2 FALSE 8 0.2744755 0.7079372 2
41 0.2744755 0.7079372 c 2 FALSE 6 0.2287786 0.3320298 3
51 0.2744755 0.7079372 c 2 FALSE 6 0.6364260 0.6691008 3
61 0.4569772 1.0000000 a 3 FALSE 7 0.6364260 0.6691008 2
71 0.4569772 1.0000000 a 3 FALSE 7 0.2744755 0.7079372 2
81 0.5913509 0.2442173 b 3 FALSE 5 0.9716994 0.2514118 3
91 0.5913509 0.2442173 b 3 FALSE 5 0.2287786 0.3320298 3
101 0.5913509 0.2442173 b 3 FALSE 5 0.6364260 0.6691008 3
11 0.6364260 0.6691008 b 2 FALSE 4 1.0000000 0.6549319 3
12 0.8101132 0.0000000 b 1 FALSE 9 0.5913509 0.2442173 2
13 0.8101132 0.0000000 b 1 FALSE 9 0.9716994 0.2514118 2
14 0.8281340 0.3730087 c 2 FALSE 10 0.9716994 0.2514118 3
15 0.8281340 0.3730087 c 2 FALSE 10 0.6364260 0.6691008 2
16 0.8281340 0.3730087 c 2 FALSE 10 0.8101132 0.0000000 3
18 1.0000000 0.6549319 c 3 FALSE 3 0.9716994 0.2514118 2
na.y type
1 NA <NA>
2 NA <NA>
3 NA <NA>
4 NA <NA>
5 NA <NA>
6 NA <NA>
7 NA <NA>
8 NA <NA>
9 NA <NA>
10 NA <NA>
17 FALSE z
21 FALSE z
41 FALSE z
51 FALSE y
61 FALSE y
71 FALSE y
81 FALSE y
91 FALSE y
101 FALSE x
11 FALSE x
12 FALSE z
13 FALSE z
14 FALSE z
15 FALSE y
16 FALSE z
18 FALSE z
I imagine that I could randomize the x/y pairs within each polygon (e.g. splancs::csr or sp::spsample), but obviously the output would be a mess of lines and points.
Is it possible to use a force directed algorithm to distribute groups of nodes algorithmically within certain constraints of coordinates?
r gis igraph visnetwork ggnetwork
asked Mar 26 at 22:59
micturalgiamicturalgia
1001 gold badge2 silver badges11 bronze badges
It is comparatively easy to add arbitrary boundaries to a force directed layout (just don't move a node that attempts to "escape" its "frame"). In fact, Fruchterman and Reingold, the authors of what is now commonly referred to as the "spring layout" algorithm, explicitly discuss this point in their original paper. However, this will almost certainly require a custom implementation of the FR algorithm (or any other force directed layout algorithm).
– Paul Brodersen
Apr 1 at 14:58
add a comment |
I'm trying to create a network visualization of contact tracing data, but constraining groups of nodes by GIS coordinates or loosely within a shape file polygon.
Ideally this would be within ggplot2 framework so I can integrate with other mapping, but if there are igraph or other solutions I'd be happy as well.
Example contact network from the ggnetwork vignette distributing the nodes using force-directed algorithm.
library(network)
library(sna)
library(ggnetwork)
n <- network(rgraph(10, tprob = 0.2), directed = FALSE)
n %v% "family" <- sample(letters[1:3], 10, replace = TRUE)
n %v% "importance" <- sample(1:3, 10, replace = TRUE)
e <- network.edgecount(n)
set.edge.attribute(n, "type", sample(letters[24:26], e, replace = TRUE))
set.edge.attribute(n, "day", sample(1:3, e, replace = TRUE))
ggplot(n, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(color = "black") +
geom_nodes(color = "black", size = 8) +
geom_nodetext(aes(color = family, label = LETTERS[ vertex.names ]),
fontface = "bold") +
theme_blank()
Looking under the hood a bit:
ggnetwork(n)
x y family importance na.x vertex.names xend yend day
1 0.9716994 0.2514118 b 3 FALSE 1 0.9716994 0.2514118 NA
2 0.2287786 0.3320298 a 3 FALSE 2 0.2287786 0.3320298 NA
3 1.0000000 0.6549319 c 3 FALSE 3 1.0000000 0.6549319 NA
4 0.6364260 0.6691008 b 2 FALSE 4 0.6364260 0.6691008 NA
5 0.5913509 0.2442173 b 3 FALSE 5 0.5913509 0.2442173 NA
6 0.2744755 0.7079372 c 2 FALSE 6 0.2744755 0.7079372 NA
7 0.4569772 1.0000000 a 3 FALSE 7 0.4569772 1.0000000 NA
8 0.0000000 0.5340600 a 2 FALSE 8 0.0000000 0.5340600 NA
9 0.8101132 0.0000000 b 1 FALSE 9 0.8101132 0.0000000 NA
10 0.8281340 0.3730087 c 2 FALSE 10 0.8281340 0.3730087 NA
17 0.0000000 0.5340600 a 2 FALSE 8 0.2287786 0.3320298 1
21 0.0000000 0.5340600 a 2 FALSE 8 0.2744755 0.7079372 2
41 0.2744755 0.7079372 c 2 FALSE 6 0.2287786 0.3320298 3
51 0.2744755 0.7079372 c 2 FALSE 6 0.6364260 0.6691008 3
61 0.4569772 1.0000000 a 3 FALSE 7 0.6364260 0.6691008 2
71 0.4569772 1.0000000 a 3 FALSE 7 0.2744755 0.7079372 2
81 0.5913509 0.2442173 b 3 FALSE 5 0.9716994 0.2514118 3
91 0.5913509 0.2442173 b 3 FALSE 5 0.2287786 0.3320298 3
101 0.5913509 0.2442173 b 3 FALSE 5 0.6364260 0.6691008 3
11 0.6364260 0.6691008 b 2 FALSE 4 1.0000000 0.6549319 3
12 0.8101132 0.0000000 b 1 FALSE 9 0.5913509 0.2442173 2
13 0.8101132 0.0000000 b 1 FALSE 9 0.9716994 0.2514118 2
14 0.8281340 0.3730087 c 2 FALSE 10 0.9716994 0.2514118 3
15 0.8281340 0.3730087 c 2 FALSE 10 0.6364260 0.6691008 2
16 0.8281340 0.3730087 c 2 FALSE 10 0.8101132 0.0000000 3
18 1.0000000 0.6549319 c 3 FALSE 3 0.9716994 0.2514118 2
na.y type
1 NA <NA>
2 NA <NA>
3 NA <NA>
4 NA <NA>
5 NA <NA>
6 NA <NA>
7 NA <NA>
8 NA <NA>
9 NA <NA>
10 NA <NA>
17 FALSE z
21 FALSE z
41 FALSE z
51 FALSE y
61 FALSE y
71 FALSE y
81 FALSE y
91 FALSE y
101 FALSE x
11 FALSE x
12 FALSE z
13 FALSE z
14 FALSE z
15 FALSE y
16 FALSE z
18 FALSE z
I imagine that I could randomize the x/y pairs within each polygon (e.g. splancs::csr or sp::spsample), but obviously the output would be a mess of lines and points.
Is it possible to use a force directed algorithm to distribute groups of nodes algorithmically within certain constraints of coordinates?
r gis igraph visnetwork ggnetwork
asked Mar 26 at 22:59
micturalgiamicturalgia
1001 gold badge2 silver badges11 bronze badges
I'm trying to create a network visualization of contact tracing data, but constraining groups of nodes by GIS coordinates or loosely within a shape file polygon.
Ideally this would be within ggplot2 framework so I can integrate with other mapping, but if there are igraph or other solutions I'd be happy as well.
Example contact network from the ggnetwork vignette distributing the nodes using force-directed algorithm.
library(network)
library(sna)
library(ggnetwork)
n <- network(rgraph(10, tprob = 0.2), directed = FALSE)
n %v% "family" <- sample(letters[1:3], 10, replace = TRUE)
n %v% "importance" <- sample(1:3, 10, replace = TRUE)
e <- network.edgecount(n)
set.edge.attribute(n, "type", sample(letters[24:26], e, replace = TRUE))
set.edge.attribute(n, "day", sample(1:3, e, replace = TRUE))
ggplot(n, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(color = "black") +
geom_nodes(color = "black", size = 8) +
geom_nodetext(aes(color = family, label = LETTERS[ vertex.names ]),
fontface = "bold") +
theme_blank()
Looking under the hood a bit:
ggnetwork(n)
x y family importance na.x vertex.names xend yend day
1 0.9716994 0.2514118 b 3 FALSE 1 0.9716994 0.2514118 NA
2 0.2287786 0.3320298 a 3 FALSE 2 0.2287786 0.3320298 NA
3 1.0000000 0.6549319 c 3 FALSE 3 1.0000000 0.6549319 NA
4 0.6364260 0.6691008 b 2 FALSE 4 0.6364260 0.6691008 NA
5 0.5913509 0.2442173 b 3 FALSE 5 0.5913509 0.2442173 NA
6 0.2744755 0.7079372 c 2 FALSE 6 0.2744755 0.7079372 NA
7 0.4569772 1.0000000 a 3 FALSE 7 0.4569772 1.0000000 NA
8 0.0000000 0.5340600 a 2 FALSE 8 0.0000000 0.5340600 NA
9 0.8101132 0.0000000 b 1 FALSE 9 0.8101132 0.0000000 NA
10 0.8281340 0.3730087 c 2 FALSE 10 0.8281340 0.3730087 NA
17 0.0000000 0.5340600 a 2 FALSE 8 0.2287786 0.3320298 1
21 0.0000000 0.5340600 a 2 FALSE 8 0.2744755 0.7079372 2
41 0.2744755 0.7079372 c 2 FALSE 6 0.2287786 0.3320298 3
51 0.2744755 0.7079372 c 2 FALSE 6 0.6364260 0.6691008 3
61 0.4569772 1.0000000 a 3 FALSE 7 0.6364260 0.6691008 2
71 0.4569772 1.0000000 a 3 FALSE 7 0.2744755 0.7079372 2
81 0.5913509 0.2442173 b 3 FALSE 5 0.9716994 0.2514118 3
91 0.5913509 0.2442173 b 3 FALSE 5 0.2287786 0.3320298 3
101 0.5913509 0.2442173 b 3 FALSE 5 0.6364260 0.6691008 3
11 0.6364260 0.6691008 b 2 FALSE 4 1.0000000 0.6549319 3
12 0.8101132 0.0000000 b 1 FALSE 9 0.5913509 0.2442173 2
13 0.8101132 0.0000000 b 1 FALSE 9 0.9716994 0.2514118 2
14 0.8281340 0.3730087 c 2 FALSE 10 0.9716994 0.2514118 3
15 0.8281340 0.3730087 c 2 FALSE 10 0.6364260 0.6691008 2
16 0.8281340 0.3730087 c 2 FALSE 10 0.8101132 0.0000000 3
18 1.0000000 0.6549319 c 3 FALSE 3 0.9716994 0.2514118 2
na.y type
1 NA <NA>
2 NA <NA>
3 NA <NA>
4 NA <NA>
5 NA <NA>
6 NA <NA>
7 NA <NA>
8 NA <NA>
9 NA <NA>
10 NA <NA>
17 FALSE z
21 FALSE z
41 FALSE z
51 FALSE y
61 FALSE y
71 FALSE y
81 FALSE y
91 FALSE y
101 FALSE x
11 FALSE x
12 FALSE z
13 FALSE z
14 FALSE z
15 FALSE y
16 FALSE z
18 FALSE z
I imagine that I could randomize the x/y pairs within each polygon (e.g. splancs::csr or sp::spsample), but obviously the output would be a mess of lines and points.
Is it possible to use a force directed algorithm to distribute groups of nodes algorithmically within certain constraints of coordinates?
r gis igraph visnetwork ggnetwork
r gis igraph visnetwork ggnetwork
asked Mar 26 at 22:59
micturalgiamicturalgia
1001 gold badge2 silver badges11 bronze badges
asked Mar 26 at 22:59
micturalgiamicturalgia
1001 gold badge2 silver badges11 bronze badges
edited Mar 27 at 1:05
micturalgia
asked Mar 26 at 22:59
micturalgiamicturalgia
1001 gold badge2 silver badges11 bronze badges
asked Mar 26 at 22:59
micturalgiamicturalgia
1001 gold badge2 silver badges11 bronze badges
asked Mar 26 at 22:59
micturalgiamicturalgia
1001 gold badge2 silver badges11 bronze badges
1001 gold badge2 silver badges11 bronze badges
It is comparatively easy to add arbitrary boundaries to a force directed layout (just don't move a node that attempts to "escape" its "frame"). In fact, Fruchterman and Reingold, the authors of what is now commonly referred to as the "spring layout" algorithm, explicitly discuss this point in their original paper. However, this will almost certainly require a custom implementation of the FR algorithm (or any other force directed layout algorithm).
– Paul Brodersen
Apr 1 at 14:58
add a comment |
It is comparatively easy to add arbitrary boundaries to a force directed layout (just don't move a node that attempts to "escape" its "frame"). In fact, Fruchterman and Reingold, the authors of what is now commonly referred to as the "spring layout" algorithm, explicitly discuss this point in their original paper. However, this will almost certainly require a custom implementation of the FR algorithm (or any other force directed layout algorithm).
– Paul Brodersen
Apr 1 at 14:58
It is comparatively easy to add arbitrary boundaries to a force directed layout (just don't move a node that attempts to "escape" its "frame"). In fact, Fruchterman and Reingold, the authors of what is now commonly referred to as the "spring layout" algorithm, explicitly discuss this point in their original paper. However, this will almost certainly require a custom implementation of the FR algorithm (or any other force directed layout algorithm).
– Paul Brodersen
Apr 1 at 14:58
It is comparatively easy to add arbitrary boundaries to a force directed layout (just don't move a node that attempts to "escape" its "frame"). In fact, Fruchterman and Reingold, the authors of what is now commonly referred to as the "spring layout" algorithm, explicitly discuss this point in their original paper. However, this will almost certainly require a custom implementation of the FR algorithm (or any other force directed layout algorithm).
– Paul Brodersen
Apr 1 at 14:58
add a comment |
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It is comparatively easy to add arbitrary boundaries to a force directed layout (just don't move a node that attempts to "escape" its "frame"). In fact, Fruchterman and Reingold, the authors of what is now commonly referred to as the "spring layout" algorithm, explicitly discuss this point in their original paper. However, this will almost certainly require a custom implementation of the FR algorithm (or any other force directed layout algorithm).
– Paul Brodersen
Apr 1 at 14:58