Package 'pct'

Description

Cycle route desire lines for Leeds

Examples

# see data-raw folder for generation code
desire_lines_leeds

Description

Downloads and processes data on where people live in England and Wales. See geoportal.statistics.gov.uk.

Usage

get_centroids_ew()

Description

This function generates "desire lines" from census 2011 data. By default gets all desire lines from census in region, but can get the top n.

Usage

get_desire_lines(region = NULL, n = NULL, omit_intrazonal = FALSE)

Arguments

Examples

if(curl::has_internet()) {
desire_lines = get_desire_lines("wight")
plot(desire_lines)
intra_zonal = desire_lines$geo_code1 == desire_lines$geo_code2
plot(desire_lines[intra_zonal, ])
}

Description

This function downloads a .csv file representing movement between MSOA zones in England and Wales. By default it returns national data, but region can be set to subset the output to a specific local authority or region.

Usage

get_od(
  region = NULL,
  n = NULL,
  type = "within",
  omit_intrazonal = FALSE,
  base_url = paste0("https://s3-eu-west-1.amazonaws.com/",
    "statistics.digitalresources.jisc.ac.uk", "/dkan/files/FLOW/"),
  filename = "wu03ew_v2",
  u = NULL
)

Arguments

Details

OD datasets available include wu03uk_v3 and others listed on the Wicid website.

Examples

get_od("wight", n = 3)
get_od()
get_od(filename = "wu03uk_v3")
u = "https://www.nomisweb.co.uk/output/census/2011/wf02ew_oa.zip"
# get_od(u = u)

Description

This function gets data generated for the Propensity to Cycle Tool project and returns objects in the modern sf class.

Usage

get_pct(
  base_url = "https://github.com/npct/pct-outputs-regional-notR/raw/master",
  purpose = "commute",
  geography = "lsoa",
  region = NULL,
  layer = NULL,
  extension = ".geojson",
  national = FALSE
)

Arguments

Examples

## Not run: 
rf = get_pct(region = "isle-of-wight", layer = "rf")
names(rf)[1:20]
vars_to_plot = 10:13
plot(rf[vars_to_plot])
z = get_pct(region = "isle-of-wight", layer = "z")
rf = get_pct(region = "west-yorkshire", layer = "rf")
z_all = get_pct(layer = "z", national = TRUE)

## End(Not run)

Description

Wrapper around ⁠[get_pct()]⁠ that gets centroid data from the PCT.

Usage

get_pct_centroids(
  region = NULL,
  purpose = "commute",
  geography = "lsoa",
  extension = ".geojson"
)

Arguments

Examples

## Not run: 
# don't test to reduce build times
c = get_pct_centroids("isle-of-wight")
plot(c)

## End(Not run)

Description

Wrapper around ⁠[get_pct()]⁠ that gets l (lines) data from the PCT.

Usage

get_pct_lines(
  region = NULL,
  purpose = "commute",
  geography = "lsoa",
  extension = ".geojson"
)

Arguments

Examples

## Not run: 
# don't test to reduce build times
l = get_pct_lines("isle-of-wight")
plot(l)

## End(Not run)

Description

Wrapper around ⁠[get_pct()]⁠ that gets route road network data from the PCT.

Usage

get_pct_rnet(
  region = NULL,
  purpose = "commute",
  geography = "lsoa",
  extension = ".geojson"
)

Arguments

Examples

## Not run: 
# don't test to reduce build times
rnet =  get_pct_rnet("isle-of-wight")
plot(rnet)

## End(Not run)

Description

Wrapper around ⁠[get_pct()]⁠ that gets rf data from the PCT.

Usage

get_pct_routes_fast(
  region = NULL,
  purpose = "commute",
  geography = "lsoa",
  extension = ".geojson"
)

Arguments

Examples

## Not run: 
# don't test to reduce build times
rf = get_pct_routes_fast("isle-of-wight")
plot(rf)

## End(Not run)

Description

Wrapper around ⁠[get_pct()]⁠ that gets rq data from the PCT.

Usage

get_pct_routes_quiet(
  region = NULL,
  purpose = "commute",
  geography = "lsoa",
  extension = ".geojson"
)

Arguments

Examples

## Not run: 
# don't test to reduce build times
rq =  get_pct_routes_quiet("isle-of-wight")
plot(rq)

## End(Not run)

Description

Wrapper around ⁠[get_pct()]⁠ that gets zone data from the PCT.

Usage

get_pct_zones(
  region = NULL,
  purpose = "commute",
  geography = "lsoa",
  extension = ".geojson"
)

Arguments

Examples

## Not run: 
# don't test to reduce build times
z = get_pct_zones("isle-of-wight")
plot(z)

## End(Not run)

Description

Data downloaded 4th March 2019. According to Uber, the dataset is from: 1/1/2018 - 1/31/2018 (Every day, Daily Average)

Examples

# see data-raw folder for generation code
leeds_uber_sample

Description

And conversion into R-friendly versions

Examples

mode_names

Description

Model cycling levels as a function of explanatory variables

Usage

model_pcycle_pct_2020(pcycle, distance, gradient, weights)

Arguments

Examples

# l = get_pct_lines(region = "isle-of-wight")
# l = get_pct_lines(region = "cambridgeshire")
l = wight_lines_pct
pcycle = l$bicycle / l$all
pcycle_dutch = l$dutch_slc / l$all
m1 = model_pcycle_pct_2020(
  pcycle,
  distance = l$rf_dist_km,
  gradient = l$rf_avslope_perc - 0.78,
  weights = l$all
  )
m2 = model_pcycle_pct_2020(
  pcycle_dutch, distance = l$rf_dist_km,
  gradient = l$rf_avslope_perc - 0.78,
  weights = l$all
)
m3 = model_pcycle_pct_2020(
  pcycle_dutch, distance = l$rf_dist_km,
  gradient = l$rf_avslope_perc - 0.78,
  weights = rep(1, nrow(l))
)
m1
plot(l$rf_dist_km, pcycle, cex = l$all / 100, ylim = c(0, 0.5))
points(l$rf_dist_km, m1$fitted.values, col = "red")
points(l$rf_dist_km, m2$fitted.values, col = "blue")
points(l$rf_dist_km, pcycle_dutch, col = "green")
cor(l$dutch_slc, m2$fitted.values * l$all)^2 # 95% captured
# identical means:
mean(l$dutch_slc)
mean(m2$fitted.values * l$all)
pct_coefficients_2020 = c(
  alpha = -4.018 + 2.550,
  d1 = -0.6369 -0.08036,
  d2 = 1.988,
  d3 = 0.008775,
  h1 = -0.2555,
  i1 = 0.02006,
  i2 = -0.1234
)
pct_coefficients_2020
m2$coef
plot(pct_coefficients_2020, m2$coeff)
cor(pct_coefficients_2020, m2$coeff)^2
cor(pct_coefficients_2020, m3$coeff)^2 # explains 95%+ variability in params

Description

od_leeds contains the 100 most travelled work desire lines in Leeds, according to the 2011 Census.

Examples

# see data-raw folder for generation code
od_leeds

Description

See data-raw folder for generation code

Examples

pct_regions

Description

For matching pct_regions object with local authority names in England and Wales.

Examples

names(pct_regions_lookup)
head(pct_regions_lookup)

Description

Route network for Leeds

Examples

# see data-raw folder for generation code
rnet_leeds

Description

Fastest cycle routes for the desire_lines_leeds

Examples

# see data-raw folder for generation code
routes_fast_leeds

Description

See https://github.com/pedalea/pctSantiago folder for generation code

Examples

# u = "https://github.com/pedalea/pctSantiago/releases/download/0.0.1/od_agg_zone_sub.Rds"
# download.file(u, destfile = "od_agg_zone_sub.Rds")
# desire_lines = readRDS("od_agg_zone_sub.Rds")
santiago_zones

Description

See https://github.com/pedalea/pctSantiago folder for generation code

Examples

# u = "https://github.com/pedalea/pctSantiago/releases/download/0.0.1/santiago_od.Rds"
# download.file(u, destfile = "santiago_od.Rds", mode = "wb")
# santiago_od = readRDS("santiago_od.Rds")
santiago_od

Description

This data was obtained using code shown in the International application of the PCT methods vignette.

Examples

library(sf)
names(santiago_routes_cs)
head(santiago_routes_cs)
plot(santiago_routes_cs)

Description

See https://github.com/pedalea/pctSantiago folder for generation code

Examples

# u = "https://github.com/pedalea/pctSantiago/releases/download/0.0.1/z_centre.Rds"
# download.file(u, destfile = "z_centre.Rds", mode = "wb")
# santiago_zones = readRDS("z_centre.Rds")
santiago_zones

Description

This function implements the uptake model described in the original Propensity to Cycle Tool paper (Lovelace et al. 2017): https://doi.org/10.5198/jtlu.2016.862

Usage

uptake_pct_godutch(
  distance,
  gradient,
  alpha = -3.959 + 2.523,
  d1 = -0.5963 - 0.07626,
  d2 = 1.866,
  d3 = 0.00805,
  h1 = -0.271,
  i1 = 0.009394,
  i2 = -0.05135,
  verbose = FALSE
)

Arguments

Details

See uptake_pct_govtarget().

Examples

# https://www.jtlu.org/index.php/jtlu/article/download/862/1381/4359
# Equation 1B:
distance = 15
gradient = 2
logit = -3.959 + 2.523 +
  ((-0.5963 - 0.07626) * distance) +
  (1.866 * sqrt(distance)) +
  (0.008050 * distance^2) +
  (-0.2710 * gradient) +
  (0.009394 * distance * gradient) +
  (-0.05135 * sqrt(distance) * gradient)
logit
# Result: -3.144098

pcycle = exp(logit) / (1 + exp(logit))
# Result: 0.04132445
boot::inv.logit(logit)
uptake_pct_godutch(distance, gradient,
  alpha = -3.959 + 2.523, d1 = -0.5963 - 0.07626,
  d2 = 1.866, d3 = 0.008050, h1 = -0.2710, i1 = 0.009394, i2 = -0.05135
)
# these are the default values
uptake_pct_godutch(distance, gradient)
l = routes_fast_leeds
pcycle_scenario = uptake_pct_godutch(l$length, l$av_incline)
plot(l$length, pcycle_scenario)

Description

Uptake model that takes distance and hilliness and returns a percentage of trips that could be made by cycling along a desire line under scenarios of change. Source: appendix of pct paper, hosted at: www.jtlu.org which states that: "To estimate cycling potential,the Propensity to Cycle Tool (PCT) was designed to use the best available geographically disaggregated data sources on travel patterns."

Usage

uptake_pct_govtarget(
  distance,
  gradient,
  alpha = -3.959,
  d1 = -0.5963,
  d2 = 1.866,
  d3 = 0.00805,
  h1 = -0.271,
  i1 = 0.009394,
  i2 = -0.05135,
  verbose = FALSE
)

uptake_pct_govtarget_2020(
  distance,
  gradient,
  alpha = -4.018,
  d1 = -0.6369,
  d2 = 1.988,
  d3 = 0.008775,
  h1 = -0.2555,
  h2 = -0.78,
  i1 = 0.02006,
  i2 = -0.1234,
  verbose = FALSE
)

uptake_pct_godutch_2020(
  distance,
  gradient,
  alpha = -4.018 + 2.55,
  d1 = -0.6369 - 0.08036,
  d2 = 1.988,
  d3 = 0.008775,
  h1 = -0.2555,
  h2 = -0.78,
  i1 = 0.02006,
  i2 = -0.1234,
  verbose = FALSE
)

uptake_pct_ebike_2020(
  distance,
  gradient,
  alpha = -4.018 + 2.55,
  d1 = -0.6369 - 0.08036 + 0.05509,
  d2 = 1.988,
  d3 = 0.008775 - 0.000295,
  h1 = -0.2555 + 0.1812,
  h2 = -0.78,
  i1 = 0.02006,
  i2 = -0.1234,
  verbose = FALSE
)

uptake_pct_govtarget_school2(
  distance,
  gradient,
  alpha = -7.178,
  d1 = -1.87,
  d2 = 5.961,
  h1 = -0.529,
  h2 = -0.63,
  verbose = FALSE
)

uptake_pct_godutch_school2(
  distance,
  gradient,
  alpha = -7.178 + 3.574,
  d1 = -1.87 + 0.3438,
  d2 = 5.961,
  h1 = -0.529,
  h2 = -0.63,
  verbose = FALSE
)

Arguments

Details

The functional form of the cycling uptake model used in the PCT is as follows: (Source: npct.github.io)

logit (pcycle) = -3.959 +   # alpha
  (-0.5963 * distance) +    # d1
  (1.866 * distancesqrt) +  # d2
  (0.008050 * distancesq) + # d3
  (-0.2710 * gradient) +    # h1
  (0.009394 * distance * gradient) +  # i1
  (-0.05135 * distancesqrt *gradient) # i2

pcycle = exp ([logit (pcycle)]) / (1 + (exp([logit(pcycle)])

uptake_pct_govtarget_2020() and uptake_pct_godutch_2020() approximate the uptake models used in the updated 2020 release of the PCT results.

If the distance parameter is greater than 100, it is assumed that it is in m. If for some reason you want to model cycling uptake associated with trips with distances of less than 100 m, convert the distances to km first.

Examples

distance = 15
gradient = 2
logit_pcycle = -3.959 + # alpha
  (-0.5963 * distance) + # d1
  (1.866 * sqrt(distance)) + # d2
  (0.008050 * distance^2) + # d3
  (-0.2710 * gradient) + # h1
  (0.009394 * distance * gradient) + # i1
  (-0.05135 * sqrt(distance) * gradient) # i2
boot::inv.logit(logit_pcycle)
uptake_pct_govtarget(15, 2)
l = routes_fast_leeds
pcycle_scenario = uptake_pct_govtarget(l$length, l$av_incline)
pcycle_scenario_2020 = uptake_pct_govtarget_2020(l$length, l$av_incline)
plot(l$length, pcycle_scenario, ylim = c(0, 0.2))
points(l$length, pcycle_scenario_2020, col = "blue")

# compare with published PCT data:
## Not run: 
l_pct_2020 = get_pct_lines(region = "isle-of-wight")
# test for another region:
# l_pct_2020 = get_pct_lines(region = "west-yorkshire")
l_pct_2020$rf_avslope_perc[1:5]
l_pct_2020$rf_dist_km[1:5]
govtarget_slc = uptake_pct_govtarget(
  distance = l_pct_2020$rf_dist_km,
  gradient = l_pct_2020$rf_avslope_perc
) * l_pct_2020$all + l_pct_2020$bicycle
govtarget_slc_2020 = uptake_pct_govtarget_2020(
  distance = l_pct_2020$rf_dist_km,
  gradient = l_pct_2020$rf_avslope_perc
) * l_pct_2020$all + l_pct_2020$bicycle
mean(l_pct_2020$govtarget_slc)
mean(govtarget_slc)
mean(govtarget_slc_2020)
godutch_slc = uptake_pct_godutch(
  distance = l_pct_2020$rf_dist_km,
  gradient = l_pct_2020$rf_avslope_perc
) * l_pct_2020$all + l_pct_2020$bicycle
godutch_slc_2020 = uptake_pct_godutch_2020(
  distance = l_pct_2020$rf_dist_km,
  gradient = l_pct_2020$rf_avslope_perc
) * l_pct_2020$all + l_pct_2020$bicycle
mean(l_pct_2020$dutch_slc)
mean(godutch_slc)
mean(godutch_slc_2020)

## End(Not run)
# Take an origin destination (OD) pair between an LSOA centroid and a
# secondary school. In this OD pair, 30 secondary school children travel, of
# whom 3 currently cycle. The fastest route distance is 3.51 km and the
# gradient is 1.11%. The
# gradient as centred on Dutch hilliness levels is 1.11 – 0.63 = 0.48%.
# The observed number of cyclists is 2. ... Modelled baseline= 30 * .0558 = 1.8.
uptake_pct_govtarget_school2(3.51, 1.11)
# pcycle = exp ([logit (pcycle)])/(1 + (exp([logit(pcycle)]))).
# pcycle = exp(1.953)/(1 + exp(1.953)) = .8758, or 87.58%.
uptake_pct_godutch_school2(3.51, 1.11)

Description

This data was obtained using code shown in the introductory pct package vignette.

Examples

names(wight_lines_30)
plot(wight_lines_30)

Description

This data was obtained using code shown in the introductory pct package vignette.

Examples

names(wight_od)
head(wight_od)

Description

This data was obtained using code shown in the introductory pct package vignette.

Examples

library(sf)
names(wight_routes_30)
head(wight_routes_30)
plot(wight_routes_30)

Description

This data was obtained using code shown in the introductory pct package vignette.

Examples

library(sf)
names(wight_lines_30)
plot(wight_lines_30)

Description

Zones in Leeds

Examples

# see data-raw folder for generation code
zones_leeds