SPEW Framework for Generating Synthetic Ecosystems


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Documentation for package ‘spew’ version 1.3.0

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A B C D E F G H I M O P R S T U V W

spew-package spew: an R package for generating synthetic ecosystems

-- A --

add_characteristic Add a characteristic to an existing population
add_char_demo Add characteristic by matching on demographics
align_pums Match the pums variables with marginal totals
allocate_count Re-allocate excess counts to other locations
assign_place Assign a place to a person
assign_place_coords Assign a place with long/lat coords to a synthetic population
assign_schools Assign schools to a synthetic population.
assign_schools_inner Function which assigns schools
assign_weights Assign weights for ipf-based sampling
assign_workplaces Assign an ESRI workplace to synthetic population
assign_workplaces_inner Function which assigns workplaces

-- B --

base_map_theme The base map theme for SPEW

-- C --

calc_dists Calculate distance b/w cont table row and pums
call_spew Wrapper for reading, formatting, and writing SPEW ecosystems
cap_default How to weight the capacities of of school.
ccount Adjust number of households
checkDF Check if df is in the right format
check_logfile Check to see if a SPEW log-file is complete
check_path Check the path to output to run diags
check_place_ids Check the Place ID's match
check_pop_table Check the pop_table has all the necessary components
check_puma_ids Check the puma id's match
check_pums Check that the pums has all the required components
check_shapefile Check the shapefile has the necessary components
check_var_names Check to see if variable names are in SPEW outputs
clean_names Remove whitespace, capitals, and non ASCII
combine_counts Combine two rows of a pop_table into one
create_column Parse a SPEW Log-file to into an appropriate column

-- D --

delaware Input data for Keny County, Delaware
demo_sample Sample extra characteristics from char pums and add them to the pop df

-- E --

euclidean_dist Get the euclidean distance between two points (x1, y1) and (x2, y2)
extract_st_co_tr Extract the state, county, and tract ID from a string
extrapolate_probs_to_pums Take unique probabilites for table and spread them to rest of PUMS
extrapolate_probs_to_pums_joint Take unique probabilites for table and spread them to rest of PUMS

-- F --

fill_cont_table Fill marginal contingency with ipf
fips_to_name Translate FIPS number to place name
format_data Format data before entering make

-- G --

get_base_map Get the base map for plotting
get_centers Get the center longitude and latitude for each region
get_coords_scaled Getting a plotting data frame
get_data_group Extract data-group from location name
get_dfs Get the dataframes from SPEW summary output
get_dists Get the distances between the schools and the people.
get_dist_mat Get the distance matrix
get_envs Gather the unique assignments for the region
get_filenames Get the filenames of the SPEW output, separated by the level
get_header Extract the header from a population
get_level Obtain the correct level for ipums data
get_pop_totals Get the population totals from a summarized SPEW region
get_rows Extract rows with a certain character
get_shapefile_indices Obtain the shapefile indices corresponding to the pop table
get_targets Obtain the target marginals for IPF
get_total_time Extract the total run-time from a SPEW log-file
get_weight_dists Weight place assignment probabilities

-- H --

haversine Get the haversine distance between two points (x1, y1) and (x2, y2) scaled between 0 and 1.
haversine_dist Get the haversine distance between two points (x1, y1) and (x2, y2) scaled between 0 and 1.

-- I --

impute_missing_vals Impute Missing Values in a data frame

-- M --

make_ipf_obj Set up for creating a set of marginal information for IPF sampling
make_mm_obj Make moment matching object
merge_reduce Wrapper function for merge

-- O --

organize_summaries Organize the summaries into a more palatable format

-- P --

people_to_households Convert a population count to household count
plot_agents Plot the agents of synthetic ecosystem
plot_bds Plot the boundaries of the synthetic ecosystem
plot_characteristic_proportions Plot characteristic summary output from summarize_top_spew_region
plot_env Plot the environments of the synthetic ecosystem
plot_interior Plot the interior of the synthetic ecosystem
plot_labs Add the labels and the theme to the plot
plot_pop_totals Plot characteristic summary output from summarize_top_spew_region as totals
plot_region Plot SPEW region
plot_roads Plot the roads of the synthetic ecosystem
plot_syneco Plot Synthetic Ecosystem
print_region_list Write out information on each region

-- R --

read_data Read SPEW input data from files
read_marginals Read in the marginals population characteristic totals
read_moments Read in the R data object for moment matching
read_pop_table Read in the population counts
read_roads Read in road lines shapefiles
read_shapespatial_to_ogr Read in shapefile using readOGR
remove_count Remove a row from the pop_table
remove_excess Remove comma's, accents, etc. from name
remove_holes Remove holes from an object of class Polygon
remove_words Remove excess words
replace_word Replace an existing word

-- S --

sample_households Sample appropriate indices from household PUMS
sample_ipf Sample households PUMS accoording to IPF
sample_locations Generic sampling locations function
sample_locations_roads Sample coordinates from roads
sample_locations_uniform Sample from a particular polygon shapefile
sample_mm Sample households PUMS according to MM
sample_people Sample from the individual person PUMS data frame
sample_uniform Sample households uniformly
sample_with_cont Sample from pums
samp_roads Sample the locations from a SpatialLines object
solve_mm_for_joint Do the Moment Matching solving for joint distribution
solve_mm_for_var Do the MM solving for an individual variable
solve_mm_weights Weight the records of the PUMS so the averages in mm_df will be obtained
spew SPEW algorithm to generate synthetic ecosystems
spewlog_to_df Convert a SPEW Logfile into a data-frame
spew_mc Run SPEW in Parallel with a Multicore backend
spew_mpi Run SPEW in Parallel with an MPI backend
spew_place Generate synthetic ecosystem for single place
spew_seq Run SPEW Sequentially
spew_sock Run SPEW in Parallel with a SOCK backend
standardize_pop_table Make sure pop_table has the appropriate columns
subset_pums Align pums with marginal totals
subset_schools Subset the schools to that of the county
subset_shapes_roads Subset the shapefile and road lines to proper roads within specified tract
summarize_environment Return the unique environment assignments in a region
summarize_features Summarize individual features of a region
summarize_spew Summarize a SPEW region
summarize_spew_out Summarize spew output
summarize_spew_region Summarize a singular region from spew output
summarize_syneco Summarize synthetic ecosystem for SPEW console output
summarize_top_region Summarize the region in a more human-readable format

-- T --

tartanville Input data for Tartanville

-- U --

update_freqs Update frequencies to match # of households
uruguay Input data for Uruguay
us Table of US states and counties
us_pums_sf An example marginal distribution table

-- V --

verify_column Verify the column is the correct size

-- W --

weight_dists Weight school assignment probabilities
weight_dists2 Weight school assignment probabilities
weight_dists_C Weight school assignment probabilities by capacity only
weight_dists_D Weight school assignment probabilities, distance only
write_data Output our final synthetic populations as csv's
write_pop_table Write out the final, formatted table
write_schools Write school environment
write_workplaces Write workplaces environment