| Title: | Fast and Memory Efficient Spatial Interaction Models |
| Version: | 0.2 |
| Author: | Ivann Schlosser 
[aut, cre] |
| Maintainer: | Ivann Schlosser <ivann.schlosser.19@ucl.ac.uk> |
| Description: | Building on top of the 'RcppArmadillo' linear algebra functionalities to do fast spatial interaction models in the context of urban analytics, geography, transport modelling. It uses the Newton root search algorithm to determine the optimal cost exponent and can run country level models with thousands of origins and destinations. It aims at implementing an easy approach based on matrices, that can originate from various routing and processing steps earlier in an workflow. Currently, the simplest form of production, destination and doubly constrained models are implemented. Schlosser et al. (2023) <doi:10.48550/arXiv.2309.02112>. |
| Date: | 2025-08-29 |
| License: | MIT + file LICENSE |
| URL: | https://ischlo.github.io/cppSim/, https://github.com/ischlo/cppSim |
| Depends: | R (≥ 3.6) |
| Imports: | Rcpp |
| Suggests: | data.table, foreach, knitr, rlist, rmarkdown, cli, sf, testthat (≥ 3.0.0) |
| LinkingTo: | Rcpp, RcppArmadillo |
| VignetteBuilder: | knitr |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| LazyDataCompression: | bzip2 |
| RoxygenNote: | 7.3.1 |
| SystemRequirements: | Quarto command line tools (https://github.com/quarto-dev/quarto-cli). |
| NeedsCompilation: | yes |
| Packaged: | 2025-08-29 14:44:01 UTC; cenv1069 |
| Repository: | CRAN |
| Date/Publication: | 2025-09-03 21:00:14 UTC |
Calibrating the balancing factors
Description
Function to calibrate the A and B coefficients of the gravity model through
Function to check availability of OPENMP to run in parallel. If openmp is found, this function returns TRUE
Description
Function to check availability of OPENMP to run in parallel. If openmp is found, this function returns TRUE
Usage
cpp_found_openmp()
distance_test
Description
distance_test
Usage
distance_test
Format
distance_test
A 983x983 matrix of distances between MSOAs in London. Computed using the London road network from OpenStreetMap and the cppRouting package.
Source
Ivann Schlosser, 2022
flows_london
Description
flows_london
Usage
flows_london
Format
flows_london
A data.table with flows information
Source
ONS, Office for National Statistics, 2011
flows_test
Description
flows_test
Usage
flows_test
Format
flows_test
A matrix of size 983x983 containing flows of users using walking or cycling as the main method of commute.
Source
UK Census, 2011
london_msoa
Description
london_msoa
Usage
london_msoa
Format
london_msoa
A data.table with London MSOA, their centroids and geometries
Source
ONS, Office for National Statistics, 2011
Running doubly constrained model
Description
This function is the C++ implementation of run_model, it will run a doubly constrained model
Usage
run_model(flows, distance, beta = 0.25)
Arguments
flows |
A integer matrix of Origin-Destination flows. |
distance |
a distance matrix between origins and destinations, provide distance in km. |
beta |
Exponent to use when calculating the cost function. |
Value
A list containing an integer matrix with predicted values.
Examples
data(flows_test)
data(distance_test)
model_test <- run_model(flows_test,distance_test)
Run model
Description
This function is the C++ implementation of run_model, it will run a model
Arguments
flows |
A integer matrix of Origin-Destination flows. |
distance |
a distance matrix between origins and destinations. |
beta |
Exponent to use when calculating the cost function. |
Running a singly constrained model
Description
This function is the C++ implementation of run_model, it will run a singly constrained model there must be a match in the dimensions, when running a production constrained model, any(dim(distance) == length(flows)) must be TRUE if no values for weight are provided, a vector with ones is used
Usage
run_model_single(flows, distance, weight = NULL, beta = 0.25)
Arguments
flows |
A vector of either origin (production constrained) or destination (attraction constrained) flows. |
distance |
a distance matrix between origins and destinations, provide distance in km. |
weight |
a vector of weights for the unconstrained part of the model. |
beta |
Exponent to use when calculating the cost function, default .25. |
Value
A list containing a matrix with predicted values.
Examples
data(flows_test)
data(distance_test)
flows_test <- apply(flows_test,MARGIN = 1,FUN = sum)
model_test <- run_model_single(flows_test,distance_test)
Running a whole simulation of a doubly constrained gravity model
Description
this script takes flows data, distance matrix, and a reference beta parameter and finds the optimal beta value for the model, runs it, and returns the result and the beta of best fit.
currently only the exp value is accepted for the cost_fun parameter.
Usage
simulation(flows_matrix, dist_matrix, beta_offset = 0.25)
Arguments
flows_matrix |
a integer matrix of flows |
dist_matrix |
a distance matrix containing numeric values in kilometers |
beta_offset |
an offset from 0 from which to start looking for the best fit value. |
Value
creates a folder based on the run_name parameter to which images and files are written. The file run_name_best_fit.rds contain the matrices with values from the model , and the quality of fit values for the beta values.
Examples
data(flows_test)
data(distance_test)
model <- simulation(flows_test,distance_test)