fit_prob_travel: Estimate probability of travelling outside origin

In COVID-19-Mobility-Data-Network/mobility: Tools for Analyzing Human Mobility Data

Description

This function fits a hierarchical beta-binomial model that estimates the probability an individual travels outside their home location within the time period of the survey (tau). The model estimates both the overall population-level probability of travel (tau_pop) and the origin-level probability of travel (tau_i). Further this method is designed for sparse observations that typically result from data with high temporal resolution or travel survey data. When data are missing, unobserved routes of travel regress to the population mean.

Usage

fit_prob_travel(
  travel,
  total,
  n_chain = 2,
  n_burn = 1000,
  n_samp = 1000,
  n_thin = 1,
  DIC = FALSE,
  parallel = FALSE
)

Arguments

travel	named vector of number of people that reported travelling outside their home location
total	named vector of the total number of individuals in travel survey for each location
n_chain	number of MCMC sampling chains
n_burn	number of iterations to discard before sampling of chains begins (burn in)
n_samp	number of iterations to sample each chain
n_thin	interval to thin samples
DIC	logical indicating whether or not to calculate the Deviance Information Criterion (DIC)
parallel	logical indicating whether or not to run MCMC chains in parallel or sequentially (default = FALSE)

Value

An object of class mobility.model containing model information, data, and fitted model parameters

Author(s)

John Giles

See Also

Other model: check(), compare(), fit_jags(), mobility(), predict(), residuals(), summary()

Other travel probability: get_stay_data()

Examples

n_orig <- 20
n_missing <- 4
orig_id <- LETTERS[1:n_orig]

N <- rpois(n_orig, 100)    # population size of each origin
p <- rbeta(n_orig, 1, 2)   # probability of leaving origin

travel <- setNames(rbinom(n_orig, N, p), orig_id)
total <- setNames(N, orig_id)

miss <- sample(1:n_orig, n_missing) # missing observations
travel[miss] <- total[miss] <- NA

# Estimate probability of travel for each locations (missing locations regress to mean)
prob_trav <- fit_prob_travel(travel=travel, total=total)
prob_trav_smry <- summary(prob_trav, probs=c(0.025, 0.25, 0.75, 0.975))

library(ggplot2)
ggplot(data=prob_trav_smry) +
  geom_vline(aes(xintercept=mean[ which(is.na(travel))[1] ]), color='red', linetype=2) +
  geom_point(aes(x=mean, y=orig_id), size=2) +
  ggstance::geom_linerangeh(aes(y=orig_id, xmin=Q2.5, xmax=Q97.5)) +
  ggstance::geom_linerangeh(aes(y=orig_id, xmin=Q25, xmax=Q75), size=1) +
  xlab('Probability of travel outside origin') + ylab('Origin') +
  xlim(0,1) +
  theme_bw() + theme(axis.text.x=element_text(size=10),
                     axis.text.y=element_text(size=10),
                     axis.title.x=element_text(size=12, margin = margin(t = 15)),
                     axis.title.y=element_text(size=12, margin = margin(r = 15)),
                     panel.border = element_rect(colour = "black", fill=NA, size=1),
                     legend.position='right')

COVID-19-Mobility-Data-Network/mobility documentation built on Nov. 22, 2021, 12:17 a.m.