Nodal Regions and Flows

For practicals on networks and flows, we will use the R package flows dedicated to flows on networks

library(flows)
data(nav)
myflows <- prepflows(mat = nav, i = "i", j = "j", fij = "fij")
diag(myflows) <- 0

Select flows that represent at least 20% of the sum of outgoing flows for each urban area.

flowSel1 <- firstflows(mat = myflows/rowSums(myflows)*100, method = "xfirst",k = 20)

Then select the dominant flows (incoming flows criterion)

flowSel2 <- domflows(mat = myflows, w = colSums(myflows), k = 1)
flowSel <- myflows * flowSel1 * flowSel2
inflows <- data.frame(id = colnames(myflows), w = colSums(myflows))

and finally plot dominant flows map

opar <- par(mar = c(0,0,2,0))
sp::plot(GE, col = "#cceae7", border = NA)
plotMapDomFlows(mat = flowSel, spdf = UA, spdfid = "ID", w = inflows, wid = "id",wvar = "w", wcex = 0.05, add = TRUE,legend.flows.pos = "topright",legend.flows.title = "Nb. of commuters")
title("Dominant Flows of Commuters")

The code to get the background map is based on the GE object, defined in that package.

To go further on dominant flows read  Nystuen & Dacey (1961)

We will discuss in the last course, next week  two extensions that were not mentioned in the course. The first one is about congestion models. The second one is a nice application of flow to discuss sports issues in NBA (or NHL).

Traffic Flow of Kota Kinabalu (Malaysia)

For the second practicals of our course on networks and flows, we will study traffic flow of Kota Kinabalu (Malaysia), following several papers published by Noraini Abdullah and Ting Kien Hua, such as max flow min cut theorem to minimize traffic congestion in Kota Kinabalu, traffic congestion problem of road networks in Kota Kinabalu and application of the Shortest Path and Maximum Flow with Bottleneck in Traffic Flow of Kota Kinabalu.