travel_data_template: Generalized template for travel data
In COVID-19-Mobility-Data-Network/mobility: Tools for Analyzing Human Mobility Data

Description Usage Format Author(s) Examples

This template data frame provides a general structure for travel data that integrates with data synthesis and modeling functions. Stays (individuals reported as not traveling outside home location) are to be included in this data frame, where origin and destination are the same. Note that models fitted and then extrapolated using other data assume that the same method for defining population size is used throughout. Either dates or time span must be filled.

1	travel_data_template

a data frame with empty columns and generalized column names

date_start: date: beginning of the time interval for the trip count
date_stop: date: end of the time interval for the trip count
date_span: integer: time span in days
indiv_id: integer: unique individual identifier
indiv_age: numeric: age of participant
indiv_sex: logical: gender of perticipant
indiv_type: factor: if individual participants belong to different groups
orig_adm0: character: name of highest administration level of origin location (Country)
orig_adm1: character: name of administration level 1 of origin location (e.g. Division, State)
orig_adm2: character: name of administration level 2 of origin location (e.g. District, County)
orig_adm3: character: name of administration level 3 of origin location (e.g. Sub-district, Province)
orig_adm4: character: name of administration level 4 of origin location (e.g. City, Municipality)
orig_adm5: character: name of administration level 5 of origin location (e.g. Town, Village, Community, Ward)
orig_type: character: administrative type for the origin location (e.g. sub-district, community vs town, or urban vs rural)
orig_x: numeric: longitude of origin location centroid in decimal degrees (centroid of smallest admin unit)
orig_y: numeric: latitude of origin location centroid in decimal degrees (centroid of smallest admin unit)
orig_pop: numeric: population size of lowest administrative unit for origin location
dest_adm0: character: name of highest administration level of destination location (Country)
dest_adm1: character: name of administration level 1 of destination location (e.g. Division, State)
dest_adm2: character: name of administration level 2 of destination location (e.g. District, County)
dest_adm3: character: name of administration level 3 of destination location (e.g. Sub-district, Province)
dest_adm4: character: name of administration level 4 of destination location (e.g. City, Municipality)
dest_adm5: character: name of administration level 5 of destination location (e.g. Town, Village, Community, Ward)
dest_type: character: administrative type for the destination location (e.g. sub-district, community vs town, or urban vs rural)
dest_x: numeric: longitude of destination location in decimal degrees (centroid of smallest admin unit)
dest_y: numeric: latitude of destination location centroid in decimal degrees (centroid of smallest admin unit)
dest_pop: numeric: population size of lowest administrative unit for destination location
trips: numeric: total number of observed trips made from origin to destination during time span

John Giles

#--------------------------------
# Travel among locations
#--------------------------------

trip <- travel_data_template
n <- 3 # Add some observations
trip[1:n,] <- NA

# Time span of travel survey
trip$date_start <- as.Date("2020-01-01")
trip$date_stop <- trip$date_start + 30
trip$date_span <- difftime(trip$date_stop, trip$date_start, units='days')

# Participant info
trip$indiv_id <- sample(1:100, n)
trip$indiv_age <- round(runif(n, 5, 80))
trip$indiv_sex <- rbinom(n, 1, 0.5)

# Origin info
trip$orig_adm0 <- 'A'
trip$orig_adm1 <- 'A'
trip$orig_adm2 <- 'A'
trip$orig_adm3 <- LETTERS[1:n]
trip$orig_type <- 'Sub-district' # Type of admin unit for lowest admin level
trip$orig_x <- rnorm(n, 100, 5)
trip$orig_y <- rnorm(n, 20, 2)
trip$orig_pop <- rpois(n, 10000)

# Destination info
trip$dest_adm0 <- 'A'
trip$dest_adm1 <- 'A'
trip$dest_adm2 <- 'B'
trip$dest_adm3 <- LETTERS[(n+1):(n*2)]
trip$dest_type <- 'Sub-district' # Type of admin unit for lowest admin level
trip$dest_x <- rnorm(n, 100, 5)
trip$dest_y <- rnorm(n, 20, 2)
trip$dest_pop <- rpois(n, 5000)

# Number of reported trips
trip$trips <- rpois(n, 10)

head(trip)



#-----------------------
# Stays in home location
#-----------------------

stay <- travel_data_template
n <- 3 # add some observations
stay[1:n,] <- NA

# Time span of travel survey
stay$date_start <- as.Date("2020-01-01")
stay$date_stop <- stay$date_start + 30
stay$date_span <- difftime(trip$date_stop, trip$date_start, units='days')

# Participant info
stay$indiv_id <- sample(100:200, n)
stay$indiv_age <- round(runif(n, 5, 80))
stay$indiv_sex <- rbinom(n, 1, 0.5)

# Origin info
stay$orig_adm0 <- stay$dest_adm0 <- 'A'
stay$orig_adm1 <- stay$dest_adm1 <- 'A'
stay$orig_adm2 <- stay$dest_adm2 <- 'A'
stay$orig_adm3 <- stay$dest_adm3 <- LETTERS[1:n]
stay$orig_type <- stay$dest_type <- 'Sub-district'
stay$orig_x <- stay$dest_x <- rnorm(n, 100, 5)
stay$orig_y <- stay$dest_y <- rnorm(n, 20, 2)
stay$orig_pop <- stay$dest_pop <- rpois(n, 10000)

stay$trips <- NA

head(stay)

# Combine
survey_data <- dplyr::full_join(trip, stay)
head(survey_data)



#----------------------------------------
# Dataset with which to extrapolate model
#----------------------------------------

pred <- travel_data_template
n <- 6 # Add some observations
pred[1:n,] <- NA

# Time span of the interval over which to extrapolate the fitted model
pred$date_span <- as.difftime(7, units='days')

# Origin info
pred$orig_adm0 <- 'A'
pred$orig_adm1 <- 'A'
pred$orig_adm2 <- LETTERS[1:n]
pred$orig_type <- 'District' # Type of admin unit for lowest admin level
pred$orig_x <- rnorm(n, 100, 5)
pred$orig_y <- rnorm(n, 20, 2)
pred$orig_pop <- rpois(n, 1e+05)

# Number of reported trips (unobserved for extrapolation data)
trip$trips <- NA

head(pred)