Package index • blvim

Creating spatial interaction models

Functions for creating spatial interaction models and extracting their parameters.

static_blvim(): Compute flows between origin and destination locations
blvim(): Compute an equilibrium solution of the Harris and Wilson model
blvim_gradient_control(): Control parameters for the blvim gradient algorithms
blvim_initialise(): Specify how initial attractivenesses are generated
costs(): Extract the cost matrix used to compute a model
inverse_cost(): Extract the inverse cost scale parameter used to compute a model
return_to_scale(): Extract the return to scale parameter used to compute a model
sim_conversion(): Return the conversion factors between attractivenesses and incoming flows
sim_is_bipartite(): Report whether a spatial interaction model is bipartite
location_names() `location_names<-`(): Names of origin and destination locations in a spatial interaction model
location_positions() `location_positions<-`(): Positions of origin and destination locations in a spatial interaction model
destination_flow(): Compute the incoming flow at each destination location
destination_names() `destination_names<-`(): Names of destination locations in a spatial interaction model
destination_positions() `destination_positions<-`(): Positions of destination locations in a spatial interaction model
origin_names() `origin_names<-`(): Names of origin locations in a spatial interaction model
origin_positions() `origin_positions<-`(): Positions of origin locations in a spatial interaction model

Using spatial interaction models

Functions for getting properties of spatial interaction models.

attractiveness(): Extract the attractivenesses from a spatial interaction model object
initial_attractiveness(): Extract the initial attractivenesses from a spatial interaction model object
destination_flow(): Compute the incoming flow at each destination location
diversity(): Compute the diversity of the destination locations in a spatial interaction model
flows(): Extract the flow matrix from a spatial interaction model object
flows_df(): Extract the flow matrix from a spatial interaction model object in data frame format
production(): Extract the production constraints from a spatial interaction model object
sim_potential(): Compute the potential of a spatial interaction model
sim_residual_jacobian(): Compute the Jacobian of the residual map G

Monitoring the construction of spatial interaction models

Functions for getting diagnostics of the algorithms used to build the models.

sim_converged(): Report whether the construction of a spatial interaction model converged
sim_diagnostic(): Return a full diagnostic of the algorithm used to build a spatial interaction model
sim_history(): Return the iteration history of the algorithm used to build a spatial interaction model
summary(<blvim_history>) print(<summary_blvim_history>): Summary of the iteration history of blvim
sim_iterations(): Return the number of iterations used to produce a spatial interaction model
sim_logs(): Extract summaries of individual algorithm runs
summary(<blvim_logs>) print(<summary_blvim_logs>): Summary of the runs of a multiple start blvim
sim_runs(): Extract spatial interaction models produced by individual runs

Non-bipartite models

Functions that apply only to non-bipartite models.

is_terminal(): Report whether locations are terminals
nd_graph(): Compute the Nystuen and Dacey graph for a spatial interaction model
terminals(): Compute terminals for a spatial interaction model

Multiple models

Functions to create and use multiple spatial interaction models using a grid of parameters.

costs(<sim_list>): Extract the common cost matrix from a collection of spatial interaction models
grid_attractiveness(): Extract all the attractivenesses from a collection of spatial interaction models
grid_autoplot(): Create a complete ggplot for spatial interaction models in a data frame
grid_blvim(): Compute a collection of Boltzmann-Lotka-Volterra model solutions
grid_destination_flow(): Extract all the destination flows from a collection of spatial interaction models
grid_diversity(): Compute diversities for a collection of spatial interaction models
grid_is_terminal(): Extract all terminal statuses from a collection of spatial interaction models
grid_sim_converged(): Report the convergence statuses of a collection of spatial interaction models
grid_sim_history(): Return the iteration histories of a collection of spatial interaction models
grid_sim_iterations(): Return the numbers of iterations used to produce a collection of spatial interaction models
grid_sim_potential(): Compute the potentials of a collection of spatial interaction models
grid_var_autoplot(): Create a complete variability plot for spatial interaction models in a data frame
median(<sim_list>): Compute the "median" of a collection of spatial interaction models
quantile(<sim_list>): Compute quantiles of the flows in a collection of spatial interaction models
sim_df(): Create a spatial interaction models data frame from a collection of interaction models
`$<-`(<sim_df>) `[[<-`(<sim_df>) `[`(<sim_df>) `[<-`(<sim_df>): Extract or replace parts of a SIM data frame
`names<-`(<sim_df>): Set the column names of a SIM data frame
sim_column(): Get the collection of spatial interaction models from a SIM data frame
sim_distance(): Compute all pairwise distances between the spatial interaction models in a collection
sim_list(): Create a sim_list object from a list of spatial interaction models
c(<sim_list>): Combine multiple sim_list objects into a single one
as.data.frame(<sim_list>): Coerce a collection of spatial interaction models to a data frame
summary(<sim_list>) print(<summary_sim_list>): Summary of a collection of spatial interaction models

Graphical representations

Functions that provide graphical representations of spatial interaction models.

autoplot(<blvim_history>): Plot the iteration history of blvim
autoplot(<blvim_logs>): Plot the runs of a multiple start blvim
autoplot(<sim>): Create a complete ggplot for a spatial interaction model
autoplot(<sim_df>): Create a complete ggplot for a spatial interaction models data frame
autoplot(<sim_list>): Create a complete variability plot for a collection of spatial interaction models
fortify(<sim>): Turn a spatial interaction model into a data frame
fortify(<sim_list>): Turn a collection of spatial interaction models into a data frame
grid_autoplot(): Create a complete ggplot for spatial interaction models in a data frame
grid_var_autoplot(): Create a complete variability plot for spatial interaction models in a data frame

Helper functions

sample_weighted_simplex(): Sample points from a weighted simplex
blvim_simplex_initialise(): Generate initial attractivenesses from the weighted simplex

Data sets

french_cities: French cities
french_cities_distances french_cities_times: Distances and travel times between French cities
french_departments: French departments
french_regions: French regions