Visibility Graph Analysis
library(alcyon)
galleryMap <- st_read(
system.file(
"extdata", "testdata", "gallery",
"gallery_lines.mif",
package = "alcyon"
),
geometry_column = 1L, quiet = TRUE
)
pointMap <- makeVGAPointMap(
galleryMap,
fillX = 3.01,
fillY = 6.7,
gridSize = 0.06
)
plot(pointMap["Connectivity"])
![](data:image/png;base64,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)
linkedPointMap <- linkCoords(pointMap, 1.74, 6.7, 5.05, 5.24)
vgaMap <- allToAllTraverse(
pointMap,
traversalType = TraversalType$Metric,
radii = -1,
radiusTraversalType = TraversalType$None
)
plot(vgaMap["Metric Mean Shortest-Path Angle"])
![](data:image/png;base64,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)
vgaMap <- allToAllTraverse(
vgaMap,
traversalType = TraversalType$Angular,
radii = -1,
radiusTraversalType = TraversalType$None
)
plot(vgaMap["Angular Mean Depth"])
![](data:image/png;base64,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)
vgaMap <- allToAllTraverse(
vgaMap,
traversalType = TraversalType$Topological,
radii = -1,
radiusTraversalType = TraversalType$None
)
plot(vgaMap["Visual Integration [HH]"])
![](data:image/png;base64,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)
vgaMap <- vgaThroughVision(vgaMap)
plot(vgaMap["Through vision"])
![](data:image/png;base64,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)
vgaMap <- vgaVisualLocal(vgaMap, FALSE)
plot(vgaMap["Visual Control"])
![](data:image/png;base64,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)
boundaryMap <- as(galleryMap[, vector()], "ShapeMap")
vgaMap <- vgaIsovist(vgaMap, boundaryMap)
plot(vgaMap["Isovist Area"])
![](data:image/png;base64,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)
vgaMap <- oneToAllTraverse(
vgaMap,
traversalType = TraversalType$Metric,
fromX = 3.01,
fromY = 6.7
)
plot(vgaMap["Metric Step Shortest-Path Angle"])
![](data:image/png;base64,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)
vgaMap <- oneToAllTraverse(
vgaMap,
traversalType = TraversalType$Angular,
fromX = 3.01,
fromY = 6.7
)
plot(vgaMap["Angular Step Depth"])
![](data:image/png;base64,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)
vgaMap <- oneToAllTraverse(
vgaMap,
traversalType = TraversalType$Topological,
fromX = 3.01,
fromY = 6.7
)
plot(vgaMap["Visual Step Depth"])
![](data:image/png;base64,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)
vgaMap <- oneToOneTraverse(
vgaMap,
traversalType = TraversalType$Topological,
fromX = 4.86,
fromY = 5.25,
toX = 1.27,
toY = 7.60
)
nuv <- length(unique(unlist(vgaMap["Visual Shortest Path"])))
plot(vgaMap["Visual Shortest Path"],
breaks = "equal",
nbreaks = nuv,
col = c("lightgray", depthmap.axmanesque.colour(nuv - 2))
)
![](data:image/png;base64,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)
vgaMap <- oneToOneTraverse(
vgaMap,
traversalType = TraversalType$Topological,
fromX = 4.86,
fromY = 5.25,
toX = 1.27,
toY = 7.60
)
nuv <- length(unique(unlist(vgaMap["Visual Shortest Path Visual Zone"])))
plot(vgaMap["Visual Shortest Path Visual Zone"],
breaks = "equal",
nbreaks = nuv,
col = c("lightgray", depthmap.axmanesque.colour(nuv - 2))
)
![](data:image/png;base64,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)
vgaMap <- oneToOneTraverse(
vgaMap,
traversalType = TraversalType$Metric,
fromX = 4.86,
fromY = 5.25,
toX = 1.27,
toY = 7.60
)
nuv <- length(unique(unlist(vgaMap["Metric Shortest Path"])))
plot(vgaMap["Metric Shortest Path"],
breaks = "equal",
nbreaks = nuv,
col = c("lightgray", depthmap.axmanesque.colour(nuv - 2))
)
![](data:image/png;base64,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)
vgaMap <- oneToOneTraverse(
vgaMap,
traversalType = TraversalType$Angular,
fromX = 4.86,
fromY = 5.25,
toX = 1.27,
toY = 7.60
)
nuv <- length(unique(unlist(vgaMap["Angular Shortest Path"])))
plot(vgaMap["Angular Shortest Path"],
breaks = "equal",
nbreaks = nuv,
col = c("lightgray", depthmap.axmanesque.colour(nuv - 2))
)
![](data:image/png;base64,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)
vgaMap <- oneToOneTraverse(
vgaMap,
traversalType = TraversalType$Angular,
fromX = 4.86,
fromY = 5.25,
toX = 1.27,
toY = 7.60
)
nuv <- length(unique(unlist(vgaMap["Angular Shortest Path Metric Zone"])))
plot(vgaMap["Angular Shortest Path Metric Zone"],
breaks = "equal",
nbreaks = nuv,
col = c("lightgray", depthmap.classic.colour(nuv - 2))
)
![](data:image/png;base64,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)