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Update paper based on @crvernon final comments
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80 changes: 42 additions & 38 deletions paper.bib
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@@ -1,10 +1,11 @@
@Article{Barbosa2018,
Title = {Human mobility: Models and applications},
Author = {Barbosa, H. and Barthelemy, M. and Ghoshal, G. and James, C. R. and Lenormand, M. and Louail, T. and Menezes, R. and Ramasco, J. J. and Simini, F. and Tomasini, M.},
Journal = {Physics Reports},
Year = {2018},
Pages = {1-74},
Volume = {734}
author = {Barbosa, H. and Barthelemy, M. and Ghoshal, G. and James, C. R. and Lenormand, M. and Louail, T. and Menezes, R. and Ramasco, J. J. and Simini, F. and Tomasini, M.},
journal = {Physics Reports},
title = {Human mobility: Models and applications},
year = {2018},
pages = {1-74},
volume = {734},
doi = {10.1016/j.physrep.2018.01.001},
}

@Article{Lenormand2016,
Expand All @@ -14,49 +15,51 @@ @Article{Lenormand2016
year = {2016},
pages = {158-169},
volume = {51},
doi = {10.1016/j.jtrangeo.2015.12.008},
}

@ARTICLE{Simini2012,
author = {Simini, F. and Gonz{\'{a}}lez, M. C. and Maritan, A. and Barabasi,
A.-L.},
title = {A universal model for mobility and migration patterns},
@Article{Simini2012,
author = {Simini, F. and Gonz{\'{a}}lez, M. C. and Maritan, A. and Barabasi, A.-L.},
journal = {Nature},
year = {2012},
volume = {484},
pages = {96-100}
title = {A universal model for mobility and migration patterns},
year = {2012},
pages = {96-100},
volume = {484},
doi = {10.1038/nature10856},
}

@ARTICLE{Yang2014,
author = {Yang, Y. and Herrera, C. and Eagle, N. and Gonz{\'{a}}lez, M. C.},
title = {Limits of Predictability in Commuting Flows in the Absence of Data
for Calibration},
@Article{Yang2014,
author = {Yang, Y. and Herrera, C. and Eagle, N. and Gonz{\'{a}}lez, M. C.},
journal = {Scientific Reports},
year = {2014},
volume = {4},
pages = {5662},
number = {5662}
title = {Limits of Predictability in Commuting Flows in the Absence of Data for Calibration},
year = {2014},
number = {5662},
pages = {5662},
volume = {4},
doi = {10.1038/srep05662},
}

@ARTICLE{Lenormand2012,
author = {Lenormand, M. and Huet, S. and Gargiulo, F. and Deffuant, G.},
title = {A {U}niversal {M}odel of {C}ommuting {N}etworks},
journal = {PLoS ONE},
year = {2012},
volume = {7},
pages = {e45985},
owner = {maxime.lenormand},
timestamp = {2012.03.23}
@Article{Lenormand2012,
author = {Lenormand, M. and Huet, S. and Gargiulo, F. and Deffuant, G.},
journal = {PLoS ONE},
title = {A {U}niversal {M}odel of {C}ommuting {N}etworks},
year = {2012},
pages = {e45985},
volume = {7},
doi = {10.1371/journal.pone.0045985},
owner = {maxime.lenormand},
timestamp = {2012.03.23},
}

@ARTICLE{Masucci2013,
author = {Masucci, A. and Serras, Joan and Johansson, Anders and Batty, Michael},
title = {Gravity versus radiation models: On the importance of scale and heterogeneity
in commuting flows},
@Article{Masucci2013,
author = {Masucci, A. and Serras, Joan and Johansson, Anders and Batty, Michael},
journal = {Phys. Rev. E},
year = {2013},
volume = {88},
pages = {022812},
issue = {2}
title = {Gravity versus radiation models: On the importance of scale and heterogeneity in commuting flows},
year = {2013},
pages = {022812},
volume = {88},
doi = {10.1103/physreve.88.022812},
issue = {2},
}

@Article{Wolwer2018,
Expand All @@ -67,6 +70,7 @@ @Article{Wolwer2018
number = {31},
pages = {1038},
volume = {3},
doi = {10.21105/joss.01038},
}

@Manual{Lovelace2023,
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24 changes: 12 additions & 12 deletions paper.md
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Expand Up @@ -7,7 +7,7 @@ tags:
- Commuting networks
- Gravity model
- Radiation model
date: "5 July 2023"
date: "25 July 2023"
output: pdf_document
authors:
- name: "Maxime Lenormand"
Expand All @@ -24,10 +24,11 @@ affiliations:
Spatial interaction models are widely used to estimate and explain spatial
interactions between geographical areas or locations. These models are usually
based on the characteristics of the locations and the way they are
spatially distributed. Interactions between locations can take several forms,
population movements, widely studied in geography, transportation research and
urban planning is one of them. The flows of individuals between locations is
usually represented by a trip table better known as Origin-Destination (OD)
spatially distributed. Several forms of interaction can occur between locations,
one of which is population movements. This particular type of interaction is
widely examined in the fields of geography, transportation research, and
urban planning. The flows of individuals between locations is
usually represented by a trip table better known as an Origin-Destination (OD)
matrix [@Lenormand2016;@Barbosa2018]. The estimation of OD matrices is part of
the four-step travel model in transportation research. It corresponds to the
second step, called trip distribution, the aim of which is to match the trip
Expand All @@ -37,16 +38,16 @@ framework.

In order to facilitate the use and comparison of trip distribution models, and
more generally spatial distribution models, we present **TDML** an R package
proposing a set of easy-to-use functions to rigorously and fairly compare
providing a set of easy-to-use functions to rigorously and fairly compare
trip distribution laws and models as described in @Lenormand2016.

# Statement of need

Trip distribution models are generally composed of two mechanisms, one mechanism
based on a 'law' to estimate the probability that an individual move from one
based on a 'law' to estimate the probability that an individual moves from one
location to another and a second mechanism based on a 'model' used to estimate
the number of individuals moving from one location to another. These two
mechanisms are rarely dissociated which could lead to methodological flaws when
mechanisms are rarely disentangled which could lead to methodological flaws when
comparing different laws and/or models [@Lenormand2012;@Simini2012;@Masucci2013;
@Yang2014]. This is particularly important when we compare the two historical
approaches - gravity and intervening opportunities - for the estimation of
Expand All @@ -69,16 +70,15 @@ of trip distribution laws more complicated for non-expert users. Furthermore,
observed and simulated OD matrices.

To overcome these limitations, the **TDML** R package is based on 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 from this law.
approach to generate mobility flows by separating the trip distribution law
from the modeling approach used to generate the flows from this law.

# Functionality

**TDLM** is available on [CRAN](https://cran.r-project.org/package=TDLM) and
[GitHub](https://github.com/EpiVec/TDLM/). The **TDLM**'s website includes a [tutorial](https://epivec.github.io/TDLM/articles/TDLM.html)
describing the functions of this package with an illustrative example based on
commuting data from US Kansas in 2000.
commuting data from Kansas in the United States in 2000.

**TDLM** features four main functions for generating OD matrices
based on a wide range of trip distribution laws and models and for evaluating
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