This function estimates mobility flows using different distribution laws and models. As described in Lenormand et al. (2016), the function uses a two-step approach to generate mobility flows by separating the trip distribution law (gravity or intervening opportunities) from the modeling approach used to generate the flows based on this law.
run_law_model(
law = "Unif",
mass_origin,
mass_destination = mass_origin,
distance = NULL,
opportunity = NULL,
param = NULL,
model = "UM",
nb_trips = 1000,
out_trips = NULL,
in_trips = out_trips,
average = FALSE,
nbrep = 3,
maxiter = 50,
mindiff = 0.01,
write_proba = FALSE,
check_names = FALSE
)An object of class TDLM. A list of list of matrices containing for each
parameter value the nbrep simulated matrices and the matrix of
probabilities (called proba) if write_proba = TRUE. If
length(param) = 1 or law = "Rad" or law = "Unif" only a list of
matrices will be returned.
A character indicating which law to use (see Details).
A numeric vector representing the mass at the origin (i.e.
demand).
A numeric vector representing the mass at
the destination (i.e. attractiveness).
A squared matrix representing the distance between locations
(see Details).
A squared matrix representing the number of opportunities
between locations (see Details). Can be easily computed with
extract_opportunities().
A numeric vector or a single numeric value used to adjust
the importance of distance or opportunity associated with the chosen law.
Not necessary for the original radiation law or the uniform law (see
Details).
A character indicating which model to use.
A numeric value indicating the total number of trips. Must
be an integer if average = FALSE (see Details).
A numeric vector representing the number of outgoing
trips per location. Must be a vector of integers if average = FALSE
(see Details).
A numeric vector representing the number of incoming
trips per location. Must be a vector of integers if average = FALSE
(see Details).
A boolean indicating if the average mobility flow matrix
should be generated instead of the nbrep matrices based on random draws
(see Details).
An integer indicating the number of replications
associated with the model run. Note that nbrep = 1 if average = TRUE
(see Details).
An integer indicating the maximal number of iterations for
adjusting the Doubly Constrained Model (see Details).
A numeric strictly positive value indicating the
stopping criterion for adjusting the Doubly Constrained Model (see Details).
A boolean indicating if the estimation of the
probability to move from one location to another obtained with the
distribution law should be returned along with the flow estimations.
A boolean indicating whether the location IDs used as
matrix rownames and colnames should be checked for consistency
(see Note).
Maxime Lenormand (maxime.lenormand@inrae.fr)
First, we compute the matrix proba estimating the probability to observe a
trip from one location to another. This probability is based on the demand
(argument mass_origin) and the attractiveness (argument
mass_destination). Note that the population is typically used as a
surrogate for both quantities (this is why mass_destination = mass_origin
by default). It also depends on the distance between locations
(argument distance) OR the number of opportunities between locations
(argument opportunity) depending on the chosen law. Both the effect of the
distance and the number of opportunities can be adjusted with a parameter
(argument param) except for the original radiation law and the uniform law.
In this package we consider eight probabilistic laws described in details in Lenormand et al. (2016). Four gravity laws (Barthelemy, 2011), three intervening opportunity laws (Schneider, 1959; Simini et al., 2012; Yang et al., 2014) and a uniform law.
Gravity law with an exponential distance decay function
(law = "GravExp"). The arguments mass_origin, mass_destination
(optional), distance and param will be used.
Normalized gravity law with an exponential distance decay function
(law = "NGravExp"). The arguments mass_origin, mass_destination
(optional), distance and param will be used.
Gravity law with a power distance decay function
(law = "GravPow"). The arguments mass_origin, mass_destination
(optional), distance and param will be used.
Normalized gravity law with a power distance decay function
(law = "NGravPow"). The arguments mass_origin, mass_destination
(optional), distance and param will be used.
Schneider's intervening opportunities law (law = "Schneider"). The
arguments mass_origin, mass_destination (optional), opportunity and
param will be used.
Radiation law (law = "Rad"). The arguments mass_origin,
mass_destination (optional) and opportunity will be used.
Extended radiation law (law = "RadExt"). The arguments mass_origin,
mass_destination (optional), opportunity and param will be used.
Uniform law (law = "Unif"). The argument mass_origin will be used to
extract the number of locations.
Second, we propose four constrained models to generate the flows from these
distribution of probability as described in Lenromand et al. (2016).
These models respect different level of constraints. These constraints can
preserve the total number of trips (argument nb_trips) OR the number of
out-going trips (argument out_trips) AND/OR the number of in-coming
(argument in_trips) according to the model. The sum of out-going trips
should be equal to the sum of in-coming trips.
Unconstrained model (model = "UM"). Only nb_trips will be preserved
(arguments out_trips and in_trips will not be used).
Production constrained model (model = "PCM"). Only out_trips will be
preserved (arguments nb_trips and in_trips will not be used).
Attraction constrained model (model = "ACM"). Only in_trips will be
preserved (arguments nb_trips and out_trips will not be used).
Doubly constrained model (model = "DCM"). Both out_trips and
in_trips will be preserved (arguments nb_tripswill not be used). The
doubly constrained model is based on an Iterative Proportional Fitting
process (Deming & Stephan, 1940). The arguments maxiter (50 by
default) and mindiff (0.01 by default) can be used to tune the model.
mindiff is the minimal tolerated relative error between the
simulated and observed marginals. maxiter ensures that the algorithm stops
even if it has not converged toward the mindiff wanted value.
By default, when average = FALSE, nbrep matrices are generated from
proba with multinomial random draws that will take different forms
according to the model used. In this case, the models will deal with positive
integers as inputs and outputs. Nevertheless, it is also possible to generate
an average matrix based on a multinomial distribution (based on an infinite
number of drawings). In this case, the models' inputs can be either positive
integer or real numbers and the output (nbrep = 1 in this case) will be a
matrix of positive real numbers.
Barthelemy M (2011). Spatial Networks. Physics Reports 499, 1-101.
Deming WE & Stephan FF (1940) On a Least Squares Adjustment of a Sample Frequency Table When the Expected Marginal Totals Are Known. Annals of Mathematical Statistics 11, 427-444.
Lenormand M, Bassolas A, Ramasco JJ (2016) Systematic comparison of trip distribution laws and models. Journal of Transport Geography 51, 158-169.
Schneider M (1959) Gravity models and trip distribution theory. Papers of the regional science association 5, 51-58.
Simini F, González MC, Maritan A & Barabási A (2012) A universal model for mobility and migration patterns. Nature 484, 96-100.
Yang Y, Herrera C, Eagle N & González MC (2014) Limits of Predictability in Commuting Flows in the Absence of Data for Calibration. Scientific Reports 4, 5662.
For more details illustrated with a practical example, see the vignette: https://epivec.github.io/TDLM/articles/TDLM.html#run-functions.
Associated functions:
run_law(), run_model(), gof().
data(mass)
data(distance)
mi <- as.numeric(mass[, 1])
mj <- mi
N <- 1000
res <- run_law_model(law = "GravExp",
mass_origin = mi,
mass_destination = mj,
distance = distance,
opportunity = NULL,
param = 0.01,
model = "UM",
nb_trips = N,
out_trips = NULL,
in_trips = NULL,
average = TRUE,
nbrep = 2,
maxiter = 50,
mindiff = 0.01,
write_proba = FALSE,
check_names = FALSE)
print(res)
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