Robin Lovelace

lot

library(stats19)
crashes = get_stats19(year = 1979:2024, type = "crashes")

pak::pak("ropensci/stats19")

type -p curl >/dev/null || (sudo apt update && sudo apt install curl -y)
curl -fsSL https://cli.github.com/packages/githubcli-archive-keyring.gpg | sudo dd of=/usr/share/keyrings/githubcli-archive-keyring.gpg \
&& sudo chmod go+r /usr/share/keyrings/githubcli-archive-keyring.gpg \
&& echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/githubcli-archive-keyring.gpg] https://cli.github.com/packages stable main" | sudo tee /etc/apt/sources.list.d/github-cli.list > /dev/null \
&& sudo apt update \
&& sudo apt install gh -y
# Log-in to GitHub from the command line
gh auth login
gh repo clone Robinlovelace/install-geocomp-ubuntu
cd install-geocomp-ubuntu
less README.md
git config --global user.email "test@tilia.org.uk"
git config --global user.name "trachelium"
sudo apt update
sudo apt upgrade
sudo apt install -y --no-install-recommends software-properties-common dirmngr
# Add the keys
wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | sudo tee -a /etc/apt/trusted.gpg.d/cran_ubuntu_key.asc
# add the R 4.0 repo from CRAN -- adjust 'focal' to 'groovy' or 'bionic' as needed
sudo add-apt-repository "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/"
# You'll be asked to choose your geographical area
sudo apt install -y r-base r-base-core r-recommended r-base-dev
# Install PPA with pre-compiled packages
sudo add-apt-repository ppa:c2d4u.team/c2d4u4.0+
# My own selection among 5000...
sudo apt install -y r-cran-rgl r-cran-rjags r-cran-snow r-cran-ggplot2 r-cran-igraph r-cran-lme4 r-cran-rjava r-cran-devtools r-cran-roxygen2 r-cran-rjava
sudo apt install r-cran-tidyverse
sudo apt install r-cran-sf r-cran-tmap r-cran-osmextract r-cran-mapview
# System deps for cartography
sudo apt install -y libgdal-dev libproj-dev libgeos-dev libudunits2-dev libv8-dev libnode-dev libcairo2-dev libnetcdf-dev
sudo apt install -y libglu1-mesa-dev freeglut3-dev mesa-common-dev
# Extra packages for image manipulation
sudo apt install -y libmagick++-dev libjq-dev libv8-dev libprotobuf-dev protobuf-compiler libsodium-dev imagemagick libgit2-dev
# rspatial
sudo apt install r-cran-sf r-cran-terra r-cran-mapedit r-cran-tmap r-cran-mapdeck r-cran-shinyjs
Rscript -e 'install.packages("languageserver")'
wget https://download1.rstudio.org/electron/jammy/amd64/rstudio-2023.03.0-386-amd64.deb
sudo dpkg -i rstudio*
rm -v rstudio*
rstudio
rstudioapi::openProject("~/Download/install-geocomp-ubuntu/")
git status
git commit -am 'Update instructions with info on using RStudio'
wget -qO- https://packages.microsoft.com/keys/microsoft.asc | gpg --dearmor > packages.microsoft.gpg
sudo install -o root -g root -m 644 packages.microsoft.gpg /etc/apt/trusted.gpg.d/
rm packages.microsoft.gpg
sudo add-apt-repository "deb [arch=amd64] https://packages.microsoft.com/repos/vscode stable main"
sudo apt update
sudo apt-get install code
code --install-extension ms-vscode-remote.remote-containers
code --install-extension quarto.quarto
code --install-extension reditorsupport.r
code --install-extension github.copilot
wget https://github.com/quarto-dev/quarto-cli/releases/download/v1.3.313/quarto-1.3.313-linux-amd64.deb -O /tmp/quarto.deb
sudo dpkg -i /tmp/quarto.deb
Rscript -e "remotes::install_github('quarto-dev/quarto-r')"
sudo apt-get install \
ca-certificates \
curl \
gnupg \
lsb-release
sudo mkdir -p /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
# Use the following command to set up the repository:
echo \
"deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
# Install Docker Engine
# Update the apt package index, and install the latest version of Docker Engine, containerd, and Docker Compose, or go to the next step to install a specific version:
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io docker-compose-plugin
sudo groupadd docker
# Add your user to the docker group:
sudo usermod -aG docker $USER
# Log out and log back in so that your group membership is re-evaluated.
# If you’re running Linux in a virtual machine, it may be necessary to restart the virtual machine for changes to take effect.
# You can also run the following command to activate the changes to groups:
newgrp docker
# Verify that you can run docker commands without sudo.
docker run hello-world
# If you initially ran Docker CLI commands using sudo before adding your user to the docker group, you may see the following error:
# WARNING: Error loading config file: /home/user/.docker/config.json -
# stat /home/user/.docker/config.json: permission denied
# This error indicates that the permission settings for the ~/.docker/ directory are incorrect, due to having used the sudo command earlier.
# To fix this problem, either remove the ~/.docker/ directory (it’s recreated automatically, but any custom settings are lost), or change its ownership and permissions using the following commands:
sudo chown "$USER":"$USER" /home/"$USER"/.docker -R
sudo chmod g+rwx "$HOME/.docker" -R
gh repo clone geocompx/geocompy
code geocompy
quarto preview
sudo apt install software-properties-common
sudo add-apt-repository ppa:appimagelauncher-team/stable
sudo apt update
sudo apt install appimagelauncher
sudo curl -o /usr/share/keyrings/syncthing-archive-keyring.gpg https://syncthing.net/release-key.gpg
# Add the "stable" channel to your APT sources:
echo "deb [signed-by=/usr/share/keyrings/syncthing-archive-keyring.gpg] https://apt.syncthing.net/ syncthing stable" | sudo tee /etc/apt/sources.list.d/syncthing.list
# Add the "candidate" channel to your APT sources:
echo "deb [signed-by=/usr/share/keyrings/syncthing-archive-keyring.gpg] https://apt.syncthing.net/ syncthing candidate" | sudo tee /etc/apt/sources.list.d/syncthing.list
# Update and install syncthing:
sudo apt-get update
sudo apt-get install syncthing
blogdown::shortcode_html("tweet", "1166628966410719232")

download.file("https://www.openstreetmap.org/trace/3046619/data", "track1.gpx")
sf::st_layers("track1.gpx")
track = sf::read_sf("track1.gpx", layer = "tracks")
mapview::mapview(track)

library(stplanr)

remotes::install_github("ropensci/stplanr", ref = "refactor-overline")

library(stplanr)
routes_fast_sf$value = 1
sl = routes_fast_sf[2:3, ]
sl$value = c(2, 5)
overline_sf2 = function(sl, attrib) {
 attrib = "value"
 attrib1 = paste0(attrib, ".1")
 sl_intersection = sf::st_intersection(sl[1, attrib], sl[2, attrib])
 sl_intersection[[attrib]] = sl_intersection[[attrib]] + sl_intersection[[attrib1]]
 sl_intersection[[attrib1]] = NULL
 sl_seg = sf::st_difference(sl[attrib], sf::st_geometry(sl_intersection))
 rnet = rbind(sl_intersection, sl_seg)
 return(rnet)
}

rnet = overline_sf(sl, "value")
plot(rnet["value"], lwd = rnet$value)
plot(sl["value"], lwd = sl$value, col = sf::sf.colors(2, alpha = 0.5))

sl3 = routes_fast_sf[4, ]
rnet = overline_sf2(rnet, sl)
plot(rnet$geometry, lwd = rnet$value)
plot(sl3, col = "red", add = TRUE)

attrib = "value"
rnet3 = rbind(rnet, sl3[attrib])
plot(rnet3$geometry, lwd = rnet3$value)

sl1_3 = as(routes_fast_sf[2:4, ], "Spatial")
rnet3_sp = overline(sl1_3, attrib = "value")
plot(rnet3_sp, lwd = rnet3_sp$value)

sl4_5 = routes_fast_sf[5:6, ]
plot(rnet3$geometry, lwd = rnet3$value)
plot(sl4_5$geometry, col = "red", add = TRUE)

relations = sf::st_relate(sl4_5, rnet3)
relations
unique(as.vector(relations))

relate_rnet_3 = sf::st_relate(rnet, sl3, pattern = "1F1F00102")
relate_rnet_3
any(lengths(relate_rnet_3))

sl4 = sl4_5[1, ]
relate_rnet_4 = sf::st_relate(rnet3, sl4, pattern = "1F1F00102")
relate_rnet_4
any(lengths(relate_rnet_4))

sel_overlaps = lengths(relate_rnet_4) > 0
rnet_overlaps = rnet3[sel_overlaps, ]
rnet3_tmp = rnet3[!sel_overlaps, ]

nrow(rnet_overlaps)

rnet_overlaps4 = overline_sf2(rbind(rnet_overlaps, sl4[attrib]))

rnet = rbind(rnet3_tmp, rnet_overlaps4)
plot(rnet$geometry, lwd = rnet$value)

stplanr::overline_sf

stplanr::overline_sf2

stplanr::overlaps
stplanr::to_linestring

r6 = routes_fast_sf[2:6, ]
rnet6 = overline_sf(r6, attrib = "value")
plot(rnet6)

bench::mark(check = FALSE,
 overline = stplanr::overline(r6, "value"),
 overline_sf = stplanr::overline_sf(r6, "value"),
 overline2 = stplanr::overline2(r6, "value")
)

library(stplanr)
sl_sp = routes_fast[2:3, ]
sl_sp@data = data.frame(value = c(2, 5))
plot(sl_sp, lwd = 9, col = "yellow")
rnet_sp = overline(sl_sp, attr = "value")
plot(rnet_sp, add = TRUE, lwd = rnet_sp$value)

library(stplanr)
sl = routes_fast_sf[2:3, ]
sl$value = c(2, 5)

sl_intersection = sf::st_intersection(sl[1, ], sl[2, ])
plot(sl$geometry, lwd = 9, col = sf::sf.colors(2, alpha = 0.5))
plot(sl_intersection, add = TRUE)

sl_intersection$value = sum(sl_intersection$value, sl_intersection$value.1)

sl_seg1 = sf::st_difference(sl[1, ], sl_intersection)
sl_seg2 = sf::st_difference(sl[2, ], sl_intersection)
plot(sl$geometry, lwd = 9, col = sf::sf.colors(2, alpha = 0.5))
plot(sl_seg1, add = TRUE)
plot(sl_seg2, add = TRUE)

rnet = rbind(sl_seg1, sl_seg2, sl_intersection)

attrib = "value"
attrib1 = paste0(attrib, ".1")
sl_intersection = sf::st_intersection(sl[1, attrib], sl[2, attrib])
sl_intersection[[attrib]] = sl_intersection[[attrib]] + sl_intersection[[attrib1]]
sl_intersection[[attrib1]] = NULL

sl_seg = sf::st_difference(sl[attrib], sf::st_geometry(sl_intersection))
rnet = rbind(sl_intersection, sl_seg)

plot(rnet, lwd = rnet$value)

overline_sf2 = function(sl, attrib) {
 attrib = "value"
 attrib1 = paste0(attrib, ".1")
 sl_intersection = sf::st_intersection(sl[1, attrib], sl[2, attrib])
 sl_intersection[[attrib]] = sl_intersection[[attrib]] + sl_intersection[[attrib1]]
 sl_intersection[[attrib1]] = NULL
 sl_seg = sf::st_difference(sl[attrib], sf::st_geometry(sl_intersection))
 rnet = rbind(sl_intersection, sl_seg)
 return(rnet)
}

bench::mark(check = F,
 overline.sp = overline(sl_sp, attrib = "value"),
 overline.sf = overline(sl, attrib = "value"),
 overline_sf2 = overline_sf2(sl, attrib = "value")
 )

u_pct = "https://github.com/npct/pct-data/raw/master/isle-of-wight/l.Rds"
if(!file.exists("l.Rds"))
 download.file(u_pct, "l.Rds")
l = readRDS("l.Rds")
library(sp)
plot(l)

## Warning in CPL_crs_from_input(x): GDAL Message 1: +init=epsg:XXXX syntax is
## deprecated. It might return a CRS with a non-EPSG compliant axis order.

sel_walk = l$foot > 9
l_walk = l[sel_walk,]
plot(l)
plot(l_walk, add = T, col = "red", lwd = 3)

library(dplyr) # for next slide...

l_walk1 = l %>% filter(All > 10) # fails

library(sf)

## Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE

l_sf = st_as_sf(l)
class(l_sf)

## [1] "sf" "data.frame"

plot(l_sf[6:15])

## Warning: plotting the first 9 out of 10 attributes; use max.plot = 10 to plot
## all

l_walk2 = l_sf %>%
 filter(foot > 9)
plot(l_sf[6])
plot(l_walk2, add = T, lwd = 3)

## Warning in plot.sf(l_walk2, add = T, lwd = 3): ignoring all but the first
## attribute

l_sf$distsf = as.numeric(st_length(l_sf))
l_drive_short = l_sf %>%
 filter(distsf < 1000) %>%
 filter(car_driver > foot)

library(tmap)
tmap_mode("view")
qtm(l_drive_short)

plot(1:9)

install.packages("stplanr")

library(stplanr)
route = route_cyclestreet(from = "Leeds", to = "Cambridge")

library(leaflet)
leaflet() %>% addTiles() %>% addPolylines(data = route)

u = "http://geostat-course.org/system/files/data_0.zip"
download.file(u, "data_0.zip")
unzip("data_0.zip")

old = setwd("~/repos/geostat2016-rl/")
library(raster)

## Loading required package: sp

bbo = shapefile("data/bbo.shp")
buildings = shapefile("data/buildings.shp")
deaths = shapefile("data/deaths.shp")
b_pump = shapefile("data/b_pump.shp")
nb_pump = shapefile("data/nb_pump.shp")
plot(buildings)

setwd(old)

library(sp)
x <- c(4,7,3,8)
y <- c(9,6,12,11)
xy <- data.frame(x, y)
SpatialPoints(xy)

## class : SpatialPoints
## features : 4
## extent : 3, 8, 6, 12 (xmin, xmax, ymin, ymax)
## coord. ref. : NA

d = data.frame(v1 = 1:4, v2 = LETTERS[1:4])
spd = SpatialPointsDataFrame(coords = xy, data = d)
plot(spd)

library(raster)
r = raster(nc = 10, nr = 10)
values(r) = 1:ncell(r)
plot(r)

as.matrix(r)

## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 2 3 4 5 6 7 8 9 10
## [2,] 11 12 13 14 15 16 17 18 19 20
## [3,] 21 22 23 24 25 26 27 28 29 30
## [4,] 31 32 33 34 35 36 37 38 39 40
## [5,] 41 42 43 44 45 46 47 48 49 50
## [6,] 51 52 53 54 55 56 57 58 59 60
## [7,] 61 62 63 64 65 66 67 68 69 70
## [8,] 71 72 73 74 75 76 77 78 79 80
## [9,] 81 82 83 84 85 86 87 88 89 90
## [10,] 91 92 93 94 95 96 97 98 99 100

q = sqrt(r)
plot(q)

x = q + r
s = stack(r, q, x)
ss = s * r # r is recycled so each layer is multiplied by r
1:3 * 2 # here 2 is recycled

## [1] 2 4 6

library(dismo)
g = gmap("Albacete, Spain", scale = T, lonlat = T)

## Loading required namespace: XML

plot(g, interpolate = T)

dismo::geocode("Universidad Castilla la Mancha")

## originalPlace
## 1 Universidad Castilla la Mancha
## interpretedPlace longitude
## 1 Paseo Universidad, 13005 Ciudad Real, Cdad. Real, Spain -3.921711
## latitude xmin xmax ymin ymax uncertainty
## 1 38.99035 -3.922007 -3.919309 38.98919 38.99189 131

install.packages("units")

library(units)
m = with(ud_units, m)
s = with(ud_units, s)
km = with(ud_units, km)
h = with(ud_units, h)
x = 1:3 * m/s

from <- 'Leeds station, New Station Street, Leeds LS1 5DL, United Kingdom'
to <- 'LS2 9JT'
route_df <- route(from, to, structure = 'route', mode = 'walking')

qmap('Merrion Centre', zoom = 15) +
 geom_path(
 aes(x = lon, y = lat), colour = 'red', size = 1.5,
 data = route_df, lineend = 'round')

rquiet <- gLines2CyclePath(l = rlines, plan = "quietest")
plot(rquiet[1,]) # route from Leeds station to Leeds University (North - South)
plot(rquiet[2,]) # route from Leeds to Manchester!

setwd("../")
library(rgdal)
stations <- readOGR("data", "lnd-stns")
## OGR data source with driver: ESRI Shapefile
## Source: "data", layer: "lnd-stns"
## with 2532 features and 27 fields
## Feature type: wkbPoint with 2 dimensions
zones <- readOGR("data", "london_sport")
## OGR data source with driver: ESRI Shapefile
## Source: "data", layer: "london_sport"
## with 33 features and 4 fields
## Feature type: wkbPolygon with 2 dimensions

M <- matrix(1:10, ncol = 5)
M[2, 3:5]
## [1] 6 8 10

stations <- spTransform(stations, CRS(proj4string(zones))) # transform CRS
plot(zones)
points(stations)

stations_subset <- stations[zones, ]

plot(zones)
points(stations_subset)

b <- bbox(zones)
b[1, ] <- (b[1, ] - mean(b[1, ])) * 0.5 + mean(b[1, ])
b[2, ] <- (b[2, ] - mean(b[2, ])) * 0.5 + mean(b[2, ])
b <- bbox(t(b))
plot(zones, xlim = b[1, ], ylim = b[2, ])

library(raster)
library(rgeos)
## rgeos version: 0.3-5, (SVN revision 447)
## GEOS runtime version: 3.4.2-CAPI-1.8.2 r3921
## Polygon checking: TRUE
gClip <- function(shp, bb){
if(class(bb) == "matrix") b_poly <- as(extent(as.vector(t(bb))), "SpatialPolygons")
else b_poly <- as(extent(bb), "SpatialPolygons")
gIntersection(shp, b_poly, byid = T)
}
zones_clipped <- gClip(zones, b)
## Warning: spgeom1 and spgeom2 have different proj4 strings
plot(zones_clipped)

westminster <- zones[grep("West", zones$name),]
zones_clipped_w <- gClip(zones, westminster)
## Warning: spgeom1 and spgeom2 have different proj4 strings
plot(zones_clipped_w); plot(westminster, col = "red", add = T)

u <- "https://github.com/Robinlovelace/lilacPlot/raw/master/F2.csv"
f <- read.csv(u)
knitr::kable(f[1:3, ])
library(ggplot2)
(p <- ggplot(f, aes(x = H, y = Allocation)) + geom_bar(color = "black", stat = "identity",
width = 1))
p + coord_polar()
w <- f$Value
pos <- 0.5 * (cumsum(w) + cumsum(c(0, w[-length(w)])))
(p <- ggplot(f, aes(x = pos)) + geom_bar(aes(y = Allocation), width = w, color = "black",
stat = "identity"))
p + coord_polar(theta = "x") + scale_x_continuous(labels = f$H, breaks = pos)
f$Deposit/f$Value
## [1] 0.09999573 0.09999759 0.10000314 0.09999837 0.10000120 0.10000000
## [7] 0.10000311 0.10000085 0.10000511 0.10000356 0.09999676 0.09999700
## [13] 0.09999812 0.10000511 0.10000085 0.10000240 0.10000000 0.10000694
## [19] 0.09999901 0.09999883
# add 10% in there
p <- ggplot(f)
p + geom_bar(aes(x = pos, y = Allocation), width = w, color = "black", stat = "identity") +
geom_bar(aes(x = pos, y = 0.1), width = w, color = "black", stat = "identity",
fill = "green") + coord_polar()
# make proportional to area
f$Allo <- sqrt(f$Allocation)
p <- ggplot(f)
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "black", stat = "identity") +
geom_bar(aes(x = pos, y = sqrt(0.1), width = w), color = "black", stat = "identity",
fill = "green") + coord_polar()
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
# add capital
capital <- (f$Captial + f$Deposit)/(f$Value) * f$Allocation
capital <- sqrt(capital)
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "black", stat = "identity") +
geom_bar(aes(x = pos, y = capital, width = w), color = "black", stat = "identity",
fill = "red") + geom_bar(aes(x = pos, y = sqrt(0.1), width = w), color = "black",
stat = "identity", fill = "green") + coord_polar() + scale_x_continuous(labels = f$H,
breaks = pos)
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
# add ablines
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40", stat = "identity",
fill = "lightgrey") + geom_bar(aes(x = pos, y = capital, width = w), color = "grey40",
stat = "identity", fill = "red") + geom_bar(aes(x = pos, y = sqrt(0.1),
width = w), color = "grey40", stat = "identity", fill = "green") + geom_abline(intercept = 1,
slope = 0, linetype = 2) + geom_abline(intercept = sqrt(1.1), slope = 0,
linetype = 3) + geom_abline(intercept = sqrt(0.9), slope = 0, linetype = 3)
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
# calculate vertical ablines of divisions
v1 <- 0.51 * f$Value[1]
v2 <- cumsum(f$Value)[17] + f$Value[18] * 0.31
v3 <- cumsum(f$Value)[17] + f$Value[18] * 0.64
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40", stat = "identity",
fill = "lightgrey") +
geom_vline(x = v1, linetype = 5, xintercept = 0) +
geom_vline(x = v2, linetype = 5, xintercept = 0) +
geom_vline(x = v3, linetype = 5, xintercept = 0) +
coord_polar()
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown parameters: x
## Warning: Ignoring unknown parameters: x
## Warning: Ignoring unknown parameters: x
# putting it all together
p <- ggplot(f)
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40", stat = "identity",
fill = "lightgrey") + geom_bar(aes(x = pos, y = capital, width = w), color = "grey40",
stat = "identity", fill = "red") + geom_bar(aes(x = pos, y = sqrt(0.1),
width = w), color = "grey40", stat = "identity", fill = "green") + geom_abline(intercept = 1,
slope = 0, linetype = 2) + geom_abline(intercept = sqrt(1.1), slope = 0,
linetype = 3) + geom_abline(intercept = sqrt(0.9), slope = 0, linetype = 3) +
geom_vline(x = v1, linetype = 5, xintercept = 0) +
geom_vline(x = v2, linetype = 5, xintercept = 0) +
geom_vline(x = v3, linetype = 5, xintercept = 0) +
coord_polar() + scale_x_continuous(labels = f$H, breaks = pos) +
theme_classic()
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown parameters: x
## Warning: Ignoring unknown parameters: x
## Warning: Ignoring unknown parameters: x
theme_infog <- theme_classic() + theme(axis.line = element_blank(), axis.title = element_blank(),
axis.ticks = element_blank(), axis.text.y = element_blank())
last_plot() + theme_infog
f$Cap.r <- f$Cap/mean(f$Cap) * 0.1 + 1.2
f$Cont.r <- f$Contribution/mean(f$Cap) * 0.1 + 1.2
f$Rep.r <- f$Cont.r + f$Repayments/mean(f$Cap) * 0.1
f$H <- c("a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n",
"o", "p", "q", "r", "s", "t")
p <- ggplot(f)
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40", stat = "identity",
fill = "lightgrey")
## Warning: Ignoring unknown aesthetics: width
# we need the axes to be bigger for starters - try 1.3 to 1.5
p + geom_bar(aes(x = pos, y = Cap.r, width = w), color = "grey40", stat = "identity",
fill = "white") + geom_bar(aes(x = pos, y = Rep.r, width = w), color = "grey40",
stat = "identity", fill = "grey80") + geom_bar(aes(x = pos, y = Cont.r,
width = w), color = "grey40", stat = "identity", fill = "grey30") + geom_bar(aes(x = pos,
y = 1.196, width = w), color = "white", stat = "identity", fill = "white")
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
last_plot() + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40",
stat = "identity", fill = "grey80") + geom_bar(aes(x = pos, y = capital,
width = w), color = "grey40", stat = "identity", fill = "grey30") + geom_bar(aes(x = pos,
y = sqrt(0.1), width = w), color = "grey40", stat = "identity", fill = "black") +
geom_abline(intercept = 1, slope = 0, linetype = 5) + geom_abline(intercept = sqrt(1.1),
slope = 0, linetype = 3) + geom_abline(intercept = sqrt(0.9), slope = 0,
linetype = 3) + coord_polar() + scale_x_continuous(labels = f$H, breaks = pos) +
theme_infog
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
p <- ggplot(f)
p + geom_bar(aes(x = pos, y = Allo, width = w), color = "grey40", stat = "identity",
fill = "grey80") + geom_bar(aes(x = pos, y = capital, width = w), color = "grey40",
stat = "identity", fill = "grey30") + geom_bar(aes(x = pos, y = sqrt(0.1),
width = w), color = "grey40", stat = "identity", fill = "black") + geom_abline(intercept = 1,
slope = 0, linetype = 5) + geom_abline(intercept = sqrt(1.1), slope = 0,
linetype = 3) + geom_abline(intercept = sqrt(0.9), slope = 0, linetype = 3) +
coord_polar() + scale_x_continuous(labels = f$H, breaks = pos) + theme_infog
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
## Warning: Ignoring unknown aesthetics: width
ggsave("just-inner.png", width = 7, height = 7, dpi = 800)

Session	Speaker	Title	link
Movement & transport	Rocio Joo	Navigating through the R packages for movement	http://www.user2019.fr/static/pres/t258615.pdf
Movement & transport	Mohammad Mehdi Moradi	Classes, methods and data analysis for trajectories	http://www.user2019.fr/static/pres/t251784.pdf
Movement & transport	Christine Thomas-Agnan	Modelling spatial flows with R	http://www.user2019.fr/static/pres/t256726.pdf
Movement & transport	Robin Lovelace	R for Transport Planning	https://www.robinlovelace.net/presentations/user2019-r-for-transport-planning.html

Attribute	GIS	GDS
Home disciplines	Geography	Geography, Computing, Statistics
Software focus	Graphic User Interface	Code
Reproduciblility	Minimal	Maximal

H	Value	Value.P	Allocation	Deposit	Captial	Debt	Cap	Contribution	Repayments
q	163827	0.065	0.979	16382	147445	0	2457.405	1287.24	0.00
a	165994	0.066	1.022	16599	5488	138847	2489.910	208.02	208.02
z	159425	0.063	0.933	15943	76632	63601	2391.375	995.46	995.46

Robin Lovelace

Example Page 1

Tip 1

Tip 2

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Tip 3

Tip 4

AUM2026: Applied Urban Modelling Symposium and Pre-conference Seminar

Abstract

10th Mobile Tartu Conference

Travel Planning Co-ordinator Event Leeds

stats19 v4.0.0: 45 Years of UK Road Crash Data, Unified

Unified schema

Parsing fixes

Missing values

Performance

New functions

Multi-year downloads

Feedback wanted

Acknowledgements

Links

Transport Committee Academic Engagement Event

Corenet: A flexible framework for generating and prioritising core cycle network designs

Outdoor lighting and active travel: A high-resolution analysis using satellite imagery and Strava data in Glasgow

spanishoddata: A package for accessing and working with Spanish Open Mobility Big Data

Delineating potential DRT operating areas: An origin–destination clustering approach

Modelling active travel with efficient, future-proof tools

Building Capacity for Data-Driven Urban Mobility

New tools for internationally-scalable future-proof transport planning

Route network simplification for transport planning

Data Science for Transport Planning: 2 day course

Note: tickets are now on sale! Get yours at store.leeds.ac.uk.

Future-Proof Transport Planning: Cities Workshop

Keynote Talk

Modelling of Traffic Collisions at Road Intersections in Cape Town, South Africa: A Bayesian Spatio-Temporal Approach

Integrating diverse data sources to support future-proof transport modelling

NPT Nominated for Most Innovative Project at Scottish Transport Awards 2025

New ways of working for a new generation of transport modellers

Seminar in St Andrews: Future-Proof Transport Planning

Introduction

Blurb

Inaugural Lecture: Data Science for Future-Proof Transport Planning

Data Driven 2025

Mapping, classifying, and integrating diverse street network datasets

Copenhagenize: examining a thesis on urban data science for sustainable mobility and developing bike/research networks

The city

References

Reproducible Methods for Modeling Combined Public Transport and Cycling Trips and Associated Benefits: Evidence from the biclaR Tool

Geocomputation with Python

Transport AI 2025

Looking back on 2024 and forward to 2025

Working at Active Travel England

Academic exploits

My year in open source

Community projects

Bike Fix Sunday

Chapeltown Cohousing

Oaty Cortado run club

Thoughts on 2025

flowcluster: Cluster origin-destination flow data

Tools for Future-Proof Transport Planning: Open Source, Open Access, Reproducible and Collaborative

Blurb

Pre-event write up

Cycle Route Uptake and Scenario Estimation (CRUSE): An Approach for Developing Strategic Cycle Network Planning Tools

Spatial Data Sciences across the Languages (SDSL) 2024

Packaging Code and Data for Reproducible Research: A Case Study of Journey Time Statistics

The Pathway to Professorship

My career path

The promotion process and portfolio

Key publications

Portfolio

Reflections on incentives, inspiration and future plans

Road User Video Evidence of Road Traffic Offences: Preliminary Analysis of Operation Snap Data and Suggestions for a Research Agenda

Mobile Tartu 2024

Tuesday 11th June

Wednesday 12th June

Thursday 13th June

Friday 14th June

Conclusion

Reproducible research and open tools for future-proof transport planning

The Active Travel Infrastructure Platform (ATIP)