Description of functions
1. Preparing flow data sets:
1.1 General functions
You can use long “L” or
matrix “M” [n*n] flow dataset formats.
– flowtabmat() is to transform “L” to “M” formats, also
to build an empty square matrix from spatial codes.
– flowcarre() is to square a matrix.
– flowjointure() is to performs a spatial join between a
flow dataset and a spatial features layer or an external matrix.
– flowstructmat() fixes an unpreviously codes shift in
the flow dataset “M” format. If necessary this function is to be used
with flowjointure and flowtabmat.
1.2. Flow computation:
– flowtype() is to compute several types of flow from an
asymmetric matrix:
x= flux for remaining initial flow
(Fij)
x= transpose for reverse flow value (Fji)
x= bivolum for bilateral volum, as gross flow (FSij)
x= bibal for bilateral balance, as net flow (FBij)
x=
biasym for asymetry of bilateral flow (FAij)
x=
bimin for minimum of bilateral flow (minFij)
x=
bimax for maximum of bilateral flow (maxFij)
x=
birange for bilateral flow range (rangeFij)
x=
bidisym for bilateral disymetry as (FDij)
– flowplaces() is to compute several types of flow
places oriented from an asymmetric:
ie. as a dataframe that
describes the flows from Origin / destination point of view
x=
ini for the number of incoming links (as in-degree)
x= outi for the number of outcoming links (as out-degree)
x= degi for the total number of links (as in and out
degrees)
x= intra for total intra zonal interaction
(if main diagonal is not empty
x= Dj for the total
flows received by (j) place
x= voli for the total
volume of flow per place
x= bali for the net balance
of flow per place
x= asyi for the asymetry of flow
per place
x= allflowplaces for computing all the
above indicators
1.3. Flow reduction:
– flowlowup() is to extracts the upper or the lower
triangular part of a matrix - preferably for symmetrical matrixes.
x= up for the part above the main diagonal
x=
low for the part below the main diagonal
– flowreduct() is to reduce the flow dataset regarding
another matrix, e.g. distances travelled.
metric is the metric of the distance matrix :
-
metric= continuous (e.g. for kilometers)
- metric=
ordinal (e.g. for k contiguity)
If the metric is continuous (e.g for filtering flows by
kilometric distances travelled), use:
d.criteria is for selecting the minimum or the maximum
distance criteria
- d.criteria= dmin for keeping only
flows up to a dmin criterion in km
- d.criteria=
dmax for selecting values less than a dmax
criterion in km
d is the value of the selected dmin or
dmax criteria.
Notice that these arguments can be used as a filter criterion in
flowmap().
See Cartograflow_distance and Cartograflow_ordinal_distance Vignettes
for examples.
URL: https://github.com/fbahoken/cartogRaflow/tree/master/vignettes
2. Flows filtering:
2.1. Filtering from flow concentration analysis
Flow concentration analysis:
– flowgini() performs a Gini’s concentration analysis of
the flow features, by computing Gini coefficient and plotting
interactive Lorenz curve.
To be use before flowanalysis()
See Cartograflow_concentration Vignette for example.
URL: https://github.com/fbahoken/cartogRaflow/tree/master/vignettes
Flow filtering according to a concentration
criterion:
– flowanalysis() computes filters criterions based
on:
- argument
critflow is to filter the flows according to
their significativity (% of total of flow information) ;
- argument
critlink is to filter the flows according to
their density (% of total features)
These arguments can be used as filter criterion in
flowmap().
See Cartograflow_concentration Vignette for example.
URL: https://github.com/fbahoken/cartogRaflow/tree/master/vignettes
2.2. Spatial / territorial filtering of flows
Flow filtering based on a continuous distance
criterion
– flowdist() computes a continous distance
matrix from spatial features (area or points). The result is a matrix of
the distances travelled between ODs, with flows filtered or not.
See Cartograflow_distance Vignette for example.
URL: https://github.com/fbahoken/cartogRaflow/tree/master/vignettes
Flow filtering based on an ordinal distance / neighbourhood
criterion:
– flowcontig() compute an ordinal distance
matrix from spatial features (area). The result is a matrix of adjacency
or k-contiguity of the ODs.
background is the areal spatial features ;
- code` is the spatial features codes ;
k is to enter the number (k:1,2,…,k) of the contiguity
matrix to be constructed : if (k=1), ODs places are adjacent, then the
flow have to cross only 1 boundary, else (k=k) ODs places are distant
from n borders ;
algo is the algorithm to use for ordinal distance
calculation (also Default is “automatic” for “Dijkstra’s”) ;
Notice that the function automatically returns the maximum (k) number
of the spatial layer.
See Cartograflow_distance_ordinal Vignette for example.
3. Flow mapping
– flowmap() is to plot flows as segments or arrows, by
acting on the following arguments:
filter is to filter or not flow’s information or
features
threshold is used to set the filtering level of the
flows when filter=“True”
taille is the value of the width of the flow feature
a.head is the arrow head parameter (in, out, in and
out)
a.length is the length of the edges of the arrow head
(in inches)
a.angle is the angle from the shaft of the arrow to the
edge of the arrow head
a.col is the arrow’s color
plota is to add spatial features as map background to
the flows’s plot
add is to allow to overlay flow features on external
spatial features background
1. Load datasets
Flow dataset
# Load Statistical information
tabflow<-read.csv2("./data/MOBPRO_ETP.csv", header=TRUE, sep=";",stringsAsFactors=FALSE,
encoding="UTF-8", dec=".",check.names=FALSE)
## 'data.frame': 121 obs. of 4 variables:
## $ i : chr "T1" "T1" "T1" "T1" ...
## $ j : chr "T1" "T10" "T11" "T12" ...
## $ Fij : num 291058 8297 3889 17064 12163 ...
## $ count: num 351 43 13 77 52 55 134 63 53 14 ...
Select variable and change matrix format
# Selecting useful variables for changing format
tabflow<-tabflow %>% select(i,j,Fij)
# From list (L) to matrix (M) format
matflow <-flowtabmat(tabflow,matlist="M")
head(matflow[1:4,1:4])
## T1 T10 T11 T12
## T1 291058 8297 3889 17064
## T10 73743 19501 11707 4931
## T11 22408 9359 12108 6084
## T12 68625 1906 7269 46515
## [1] 12 12
Geographical dataset
2. Flow types computing
Compute bilateral flows types : eg. volum, balance, bilateral
maximum and all types
# Bilateral volum (gross) FSij:
tabflow_vol<-flowtype(tabflow, format="L", origin="i", destination="j", fij="Fij", x= "bivolum" )
# Matrix format (M= : matflow_vol<-flowtype(matflow, format="M", "bivolum")
# Bilateral balance (net ) FBij:
tabflow_net<-flowtype(tabflow, format="L", origin="i", destination="j", fij="Fij", x="bibal")
# Bilateral maximum (maxFij):
tabflow_max<-flowtype(tabflow, format="L", origin="i", destination="j", fij="Fij", x="bimax")
# Compute all types of bilateral flows, in one 11 columns
tabflow_all<-flowtype(tabflow,format="L", origin="i", destination="j", fij="Fij", x="alltypes")
head(tabflow_all)
## i j Fij Fji FSij FBij FAij minFij maxFij rangeFij FDij
## 1 T1 T1 291058 291058 582116 0 0.0000000 291058 291058 0 0.0000000
## 2 T1 T10 8297 73743 82040 65446 0.7977328 8297 73743 65446 0.7977328
## 3 T1 T11 3889 22408 26297 18519 0.7042248 3889 22408 18519 0.7042248
## 4 T1 T12 17064 68625 85689 51561 0.6017225 17064 68625 51561 0.6017225
## 5 T1 T2 12163 47427 59590 35264 0.5917771 12163 47427 35264 0.5917771
## 6 T1 T3 32682 45772 78454 13090 0.1668494 32682 45772 13090 0.1668494
3. Direct flow mapping
3.1. Plot all origin-destination without any filtering
criterion The result will reveal a graphic complexity
(“spaghetti-effect”“)
Plot links
library(sf)
map<-st_read("./data/MGP_TER.shp")
## Reading layer `MGP_TER' from data source
## `/tmp/RtmpDMwbzy/Rbuild120857ab09da8/cartograflow/vignettes/data/MGP_TER.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 12 features and 14 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 637297.4 ymin: 6838629 xmax: 671752.1 ymax: 6879246
## Projected CRS: Lambert_Conformal_Conic
# Add and overlay spatial background
par(bg = "NA")
# Graphic parameters
par(mar=c(0,0,1,0))
extent <- c(2800000, 1340000, 6400000, 4800000)
resolution<-150
plot(st_geometry(map), col = NA, border=NA, bg="#dfe6e1")
plot(st_geometry(map), col = "light grey", add=TRUE)
# Flowmapping of all links
flowmap(tab=tabflow,
fij="Fij",
origin.f = "i",
destination.f = "j",
bkg = map,
code="EPT_NUM",
nodes.X="X",
nodes.Y = "Y",
filter=FALSE,
add=TRUE
)
library(cartography)
# Map cosmetics
layoutLayer(title = "All origin-destination for commuting in Greater Paris, 2017",
coltitle ="black",
author = "Cartograflow, 2020",
sources = "Data : INSEE, 2017 ; Basemap : APUR, RIATE, 2018.",
scale = 2,
tabtitle = FALSE,
frame = TRUE,
col = "grey"
)
# North arrow
north("topright")
3.2. Plot the above-average flows
library(sf)
map<-st_read("./data/MGP_TER.shp")
## Reading layer `MGP_TER' from data source
## `/tmp/RtmpDMwbzy/Rbuild120857ab09da8/cartograflow/vignettes/data/MGP_TER.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 12 features and 14 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 637297.4 ymin: 6838629 xmax: 671752.1 ymax: 6879246
## Projected CRS: Lambert_Conformal_Conic
# Add and overlay spatial background
par(bg = "NA")
# Graphic parameters
par(mar=c(0,0,1,0))
extent <- c(2800000, 1340000, 6400000, 4800000)
resolution<-150
plot(st_geometry(map), col = NA, border=NA, bg="#dfe6e1")
plot(st_geometry(map), col = "light grey", add=TRUE)
# Flow mapping above-average flows
flowmap(tab=tabflow,
fij="Fij",
origin.f = "i",
destination.f = "j",
bkg = map,
code="EPT_NUM",
nodes.X="X",
nodes.Y = "Y",
filter=TRUE,
threshold =(mean(tabflow$Fij)), #mean value is the level of threshold
taille=20,
a.head = 1,
a.length = 0.11,
a.angle = 30,
a.col="#138913",
add=TRUE)
# Map Legend
legendPropLines(pos="topleft",
title.txt="Commuters > 13220 ",
title.cex=0.8,
cex=0.5,
values.cex= 0.7,
var=c(mean(tabflow$Fij),max(tabflow$Fij)),
lwd=5,
frame = FALSE,
col="#138913",
values.rnd = 0
)
#Map cosmetic
layoutLayer(title = "Commuters up to above-average in Greater Paris",
coltitle ="black",
author = "Cartograflow, 2020",
sources = "Data : INSEE, 2017 ; Basemap : APUR, RIATE, 2018.",
scale = 2,
tabtitle = FALSE,
frame = TRUE,
col = "grey"
)
# North arrow
north("topright")
3.3. Plot the net flows of bilateral flows
#library(sf)
map<-st_read("./data/MGP_TER.shp")
## Reading layer `MGP_TER' from data source
## `/tmp/RtmpDMwbzy/Rbuild120857ab09da8/cartograflow/vignettes/data/MGP_TER.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 12 features and 14 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 637297.4 ymin: 6838629 xmax: 671752.1 ymax: 6879246
## Projected CRS: Lambert_Conformal_Conic
# Net matrix reduction
tabflow_net <- tabflow_net %>% filter(.data$FBij>=0)
# Net matrix thresholding
Q80<-quantile(tabflow_net$FBij,0.95)
# Add and overlay spatial background
par(bg = "NA")
# Graphic parameters
par(mar=c(0,0,1,0))
extent <- c(2800000, 1340000, 6400000, 4800000)
resolution<-150
plot(st_geometry(map), col = NA, border=NA, bg="#dfe6e1")
plot(st_geometry(map), col = "light grey", add=TRUE)
# Flow mapping above-average flows
flowmap(tab=tabflow_net,
fij="FBij",
origin.f = "i",
destination.f = "j",
bkg = map,
code="EPT_NUM",
nodes.X="X",
nodes.Y = "Y",
filter=TRUE,
threshold = Q80,
taille=12,
a.head = 1,
a.length = 0.11,
a.angle = 30,
a.col="#4e8ef5",
add=TRUE)
# Map Legend
legendPropLines(pos="topleft",
title.txt="Commuters > 5722 ",
title.cex=0.8,
cex=0.5,
values.cex= 0.7,
var=c(Q80,max(tabflow_net$FBij)),
lwd=12,
frame = FALSE,
col="#4e8ef5",
values.rnd = 0
)
#Map cosmetic
layoutLayer(title = "Net commuters in Greater Paris (20% strongest)",
coltitle ="black",
author = "Cartograflow, 2020",
sources = "Data : INSEE, 2017 ; Basemap : APUR, RIATE, 2018.",
scale = 2,
tabtitle = FALSE,
frame = TRUE,
col = "grey"
)
# North arrow
north("topright")
