Package 'geomander'

Description

A compilation of tools to complete common tasks for studying gerrymandering. This focuses on the geographic tool side of common problems, such as linking different levels of spatial units or estimating how to break up units. Functions exist for creating redistricting-focused data for the US.

Package Content

Index of help topics:

add_edge                Add Edges to an Adjacency List
adjacency               Build Adjacency List
alarm_states            List Available States from ALARM Data
baf_to_vtd              Estimate Plans from a Block Assignment File to
                        Voting Districts
block2prec              Aggregate Block Table by Matches
block2prec_by_county    Aggregate Block Table by Matches and County
check_contiguity        Check Contiguity by Group
check_polygon_contiguity
                        Check Polygon Contiguity
checkerboard            Checkerboard
checkerboard_adj        Checkerboard Adjacency
clean_vest              Clean VEST Names
compare_adjacencies     Compare Adjacency Lists
count_connections       Count Times Precincts are Connected
create_block_table      Create Block Level Data
create_tract_table      Create Tract Level Data
dra2r                   DRA to R
estimate_down           Estimate Down Levels
estimate_up             Estimate Up Levels
geo_estimate_down       Estimate Down Geography Levels
geo_estimate_up         Estimate Up Geography Levels
geo_filter              Filter to Intersecting Pieces
geo_match               Match Across Geographic Layers
geo_plot                Plots a Shape with Row Numbers as Text
geo_plot_group          Create Plots of Shapes by Group with Connected
                        Components Colored
geo_sort                Sort Precincts
geo_trim                Trim Away Small Pieces
geomander-package       Geographic Tools for Studying Gerrymandering
geos_centerish          Get the kind of center of each shape
geos_circle_center      Get the centroid of the maximum inscribed
                        circle
get_alarm               Get ALARM Dataset
get_dra                 Get Dave's Redistricting App Dataset
get_heda                Get Harvard Election Data Archive ("HEDA")
                        Dataset
get_lewis               Get historical United States Congressional
                        District Shapefiles
get_rpvnearme           Get Racially Polarized Voting Dataset from RPV
                        Near Me
get_vest                Get Voting and Election Science Team ("VEST")
                        Dataset
global_gearys           Compute Global Geary's C
global_morans           Compute Global Moran's I
gstar_i                 Compute Standardized Getis Ord G*i
heda_states             List Available States from HEDA Dataverse
local_gearys            Compute Local Geary's C
local_morans            Compute Local Moran's I
nrcsd                   nrcsd
orange                  orange
precincts               precincts
r2dra                   R to DRA
regionalize             Estimate Regions by Geographic Features
rockland                rockland
seam_adj                Filter Adjacency to Edges Along Border
seam_geom               Filter Shape to Geographies Along Border
seam_rip                Remove Edges along a Boundary
seam_sew                Suggest Edges to Connect Two Sides of a Border
split_precinct          Split a Precinct
st_centerish            Get the kind of center of each shape
st_circle_center        Get the centroid of the maximum inscribed
                        circle
subtract_edge           Subtract Edges from an Adjacency List
suggest_component_connection
                        Suggest Connections for Disconnected Groups
suggest_neighbors       Suggest Neighbors for Lonely Precincts
towns                   towns
va18sub                 va18sub
va_blocks               va_blocks
va_vtd                  va_vtd
vest_states             List Available States from VEST Dataverse

Maintainer

Christopher T. Kenny <[email protected]>

Author(s)

Christopher T. Kenny [aut, cre] (<https://orcid.org/0000-0002-9386-6860>), Cory McCartan [ctb] (<https://orcid.org/0000-0002-6251-669X>)

Description

Add Edges to an Adjacency List

Usage

add_edge(adj, v1, v2, ids = NULL, zero = TRUE)

Arguments

Value

adjacency list.

Examples

data(towns)
adj <- adjacency(towns)

add_edge(adj, 2, 3)
add_edge(adj, "West Haverstraw", "Stony Point", towns$MUNI)

Description

This mimics redist's redist.adjacency using GEOS to create the patterns, rather than sf. This is faster than that version, but forces projections.

Usage

adjacency(shp, epsg = 3857)

Arguments

Value

list with nrow(shp) entries

Examples

data(precincts)
adj <- adjacency(precincts)

Description

List Available States from ALARM Data

Usage

alarm_states()

Value

character abbreviations for states

Examples

## Not run: 
# relies on internet availability and interactivity on some systems
alarm_states()

## End(Not run)

Description

District lines are often provided at the census block level, but analyses often occur at the voting district level. This provides a simple way to estimate the block level to the voting district level.

Usage

baf_to_vtd(baf, plan_name, GEOID = "GEOID", year = 2020)

Arguments

Details

If a voting district is split between blocks, this currently uses the most common district.

Value

a tibble with a vtd-level assignment file

Examples

# Not guaranteed to reach download from redistrict2020.org
## Not run: 
# download and read baf ----
url <- paste0('https://github.com/PlanScore/Redistrict2020/', 
              'raw/main/files/DE-2021-01/DE_SLDU_bef.zip')
tf <- tempfile('.zip')
utils::download.file(url, tf)
utils::unzip(tf, exdir = dirname(tf))
baf <- readr::read_csv(
  file = paste0(dirname(tf), '/DE_SLDU_bef.csv'),
  col_types = 'ci'
)
names(baf) <- c('GEOID', 'ssd_20')

# convert to vtd level ----
baf_to_vtd(baf = baf, plan_name = 'ssd_20', 'GEOID')

## End(Not run)

Description

Aggregates block table values up to a higher level, normally precincts, hence the name block2prec.

Usage

block2prec(block_table, matches, geometry = FALSE)

Arguments

Value

dataframe with length(unique(matches)) rows

Examples

set.seed(1)
data(rockland)
rockland$id <- sample(1:2, nrow(rockland), TRUE)
block2prec(rockland, rockland$id)

Description

Performs the same type of operation as block2prec, but subsets a precinct geometry based on a County fips column. This helps get around the problem that county geometries often have borders that follow rivers and lead to funny shaped blocks. This guarantees that every block is matched to a precinct which is in the same county.

Usage

block2prec_by_county(block_table, precinct, precinct_county_fips, epsg = 3857)

Arguments

Value

dataframe with nrow(precinct) rows

Examples

## Not run: 
# Need Census API
data(towns)
towns$fips <- '087'
block <- create_block_table('NY', 'Rockland')
block2prec_by_county(block, towns, 'fips')

## End(Not run)

Description

Identify contiguous sets of units and numbers each set. Can be extended to repeat the procedure within a subgeography.

Usage

check_contiguity(adj, group)

cct(adj, group)

ccm(adj, group)

Arguments

Details

Given a zero-indexed adjacency list and an array of group identifiers, this returns a tibble which identifies the connected components. The three columns are group for the inputted group, group_number which uniquely identifies each group as a positive integer, and component which identifies the connected component number for each corresponding entry of adjacency and group. If everything is connected within the group, then each element of component will be 1. Otherwise, the largest component is given the value 1, the next largest 2, and so on.

If nothing is provided to group, it will default to a vector of ones, checking if the adjacency graph is connected.

cct() is shorthand for creating a table of the component values. If everything is connected within each group, it returns a value of 1. In general, it returns a frequency table of components.

ccm() is shorthand for getting the maximum component value. It returns the maximum number of components that a group is broken into. This returns 1 if each group is connected. #'

Value

tibble with contiguity indicators. Each row is the units of adj. Columns include

• group Values of the inputted group argument. If group is not specified, then all values will be 1.

• component A number for each contiguous set of units within a group. If all units within a group are contiguous, all values are 1. If there are two sets, each discontiguous with the other, the larger one will be numbered 1 and the smaller one will be numbered as 2.

Examples

data(checkerboard)
adj <- adjacency(checkerboard)
# These each indicate the graph is connected.
check_contiguity(adj) # all contiguous
# If there are two discontiguous groups, there will be 2 values of `component`
cct(adj)
ccm(adj)

Description

Cast shp to component polygons, build the adjacency, and check the contiguity. Avoids issues where a precinct is actually a multipolygon

Usage

check_polygon_contiguity(shp, group, epsg = 3857)

Arguments

Value

tibble with a column for each of inputted group, created group number, and the identified connected component number

Examples

data(checkerboard)
check_polygon_contiguity(checkerboard, i)

Description

This data set contains 64 squares in an 8x8 grid, like a checkerboard.

Usage

data("checkerboard")

Format

An sf dataframe with 64 observations

Examples

data('checkerboard')

Description

This data contains a zero indexed adjacency list for the checkerboard dataset.

Usage

data("checkerboard_adj")

Format

A list with 64 entries

Examples

data('checkerboard_adj')

Description

Clean VEST Names

Usage

clean_vest(data)

Arguments

Value

data with cleaned names

Examples

data(va18sub)
va <- clean_vest(va18sub)

Description

Compare Adjacency Lists

Usage

compare_adjacencies(adj1, adj2, shp, zero = TRUE)

Arguments

Value

tibble with row indices to compare, and optionally columns which describe the DE-9IM relationship between differences.

Examples

data(towns)
rook <- adjacency(towns)
sf_rook <- lapply(sf::st_relate(towns, pattern = 'F***1****'), function(x) {
  x - 1L
})
compare_adjacencies(rook, sf_rook, zero = FALSE)

Description

Count Times Precincts are Connected

Usage

count_connections(dm, normalize = FALSE)

Arguments

Value

matrix with the number of connections between precincts

Examples

set.seed(1)
dm <- matrix(sample(1:2, size = 100, TRUE), 10)
count_connections(dm)

Description

Creates a block level dataset, using the decennial census information, with the standard redistricting variables.

Usage

create_block_table(
  state,
  county = NULL,
  geometry = TRUE,
  year = 2020,
  mem = FALSE,
  epsg = 3857
)

Arguments

Value

dataframe with data for each block in the selected region. Data includes 2 sets of columns for each race or ethnicity category: population (pop) and voting age population (vap)

Examples

## Not run: 
# uses the Census API
create_block_table(state = 'NY', county = 'Rockland', geometry = FALSE)

## End(Not run)

Description

Create Tract Level Data

Usage

create_tract_table(
  state,
  county,
  geometry = TRUE,
  year = 2019,
  mem = FALSE,
  epsg = 3857
)

Arguments

Value

dataframe with data for each tract in the selected region. Data includes 3 sets of columns for each race or ethnicity category: population (pop), voting age population (vap), and citizen voting age population (cvap)

Examples

## Not run: 
# Relies on Census Bureau API
tract <- create_tract_table('NY', 'Rockland', year = 2018)

## End(Not run)

Description

Creates a block or precinct level dataset from DRA csv output.

Usage

dra2r(dra, state, precincts, epsg = 3857)

Arguments

Value

sf dataframe either at the block level or precinct level

Examples

## Not run: 
# Needs Census Bureau API
# dra_utah_test is available at https://bit.ly/3c6UDKk
blocklevel <- dra2r('dra_utah_test.csv', state = 'UT')

## End(Not run)

Description

Non-geographic partner function to geo_estimate_down. Allows users to estimate down without the costly matching operation if they've already matched.

Usage

estimate_down(wts, value, group)

Arguments

Value

numeric vector with each value split by weight

Examples

library(dplyr)
set.seed(1)
data(checkerboard)
counties <- checkerboard |>
  group_by(id <= 32) |>
  summarize(geometry = sf::st_union(geometry)) |>
  mutate(pop = c(100, 200))
matches <- geo_match(checkerboard, counties)
estimate_down(wts = rep(1, nrow(checkerboard)), value = counties$pop, group = matches)

Description

Non-geographic partner function to geo_estimate_up. Allows users to aggregate up without the costly matching operation if they've already matched.

Usage

estimate_up(value, group)

Arguments

Value

numeric vector with each value aggregated by group

Examples

library(dplyr)
set.seed(1)
data(checkerboard)
counties <- checkerboard |>
  group_by(id <= 32) |>
  summarize(geometry = sf::st_union(geometry)) |>
  mutate(pop = c(100, 200))
matches <- geo_match(checkerboard, counties)
estimate_up(value = checkerboard$i, group = matches)

Description

Simple method for estimating data down to a lower level. This is most often useful for getting election data down from a precinct level to a block level in the case that a state or other jurisdiction split precincts when creating districts. Geographic partner to estimate_down.

Usage

geo_estimate_down(from, to, wts, value, method = "center", epsg = 3857)

Arguments

Value

numeric vector with each value split by weight

Examples

library(dplyr)
set.seed(1)
data(checkerboard)
counties <- checkerboard |>
  group_by(id <= 32) |>
  summarize(geometry = sf::st_union(geometry)) |>
  mutate(pop = c(100, 200))
geo_estimate_down(from = counties, to = checkerboard, value = counties$pop)

Description

Simple method for aggregating data up to a higher level This is most often useful for getting population data from a block level up to a precinct level. Geographic partner to estimate_up.

Usage

geo_estimate_up(from, to, value, method = "center", epsg = 3857)

Arguments

Value

numeric vector with each value aggregated by group

Examples

library(dplyr)
set.seed(1)
data(checkerboard)
counties <- checkerboard |>
  group_by(id <= 32) |>
  summarize(geometry = sf::st_union(geometry)) |>
  mutate(pop = c(100, 200))
geo_estimate_up(from = checkerboard, to = counties, value = checkerboard$i)

Description

Filter to Intersecting Pieces

Usage

geo_filter(from, to, bool = FALSE, epsg = 3857)

Arguments

Value

sf data frame or logical vector if bool == TRUE

Examples

## Not run: 
# Needs Census Bureau API
data(towns)
block <- create_block_table('NY', 'Rockland')
geo_filter(block, towns)

## End(Not run)

data(towns)
data(rockland)
sub <- geo_filter(rockland, towns)

Description

Match Across Geographic Layers

Usage

geo_match(
  from,
  to,
  method = "center",
  by = NULL,
  tiebreaker = TRUE,
  epsg = 3857
)

Arguments

Details

Methods are as follows:

• centroid: matches each element of from to the to entry that the geographic centroid intersects

• center: very similar to centroid, but it matches an arbitrary center point within from if the centroid of from is outside the bounds of from. (This happens for non-convex shapes only).

• point: matches each element of from to the to entry that the "point on surface" intersects.

• circle: matches each element of from to the to entry that the centroid of the maximum inscribed circle intersects

• area: matches each element of from to the to element which has the largest area overlap

Value

Integer Vector of matches length(to) with values in 1:nrow(from)

Examples

library(dplyr)
data(checkerboard)
counties <- sf::st_as_sf(as.data.frame(rbind(
  sf::st_union(checkerboard |> filter(i < 4)),
  sf::st_union(checkerboard |> filter(i >= 4))
)))

geo_match(from = checkerboard, to = counties)
geo_match(from = checkerboard, to = counties, method = 'area')

Description

One liner to plot a shape with row numbers

Usage

geo_plot(shp)

Arguments

Value

ggplot

Examples

data(checkerboard)
geo_plot(checkerboard)

Description

Create Plots of Shapes by Group with Connected Components Colored

Usage

geo_plot_group(shp, adj, group, save = FALSE, path = "")

Arguments

Value

list of ggplots

Examples

library(dplyr)
data('checkerboard')
data('checkerboard_adj')

checkerboard <- checkerboard |> mutate(discont = as.integer(j == 5 | j == 6))

p <- geo_plot_group(checkerboard, checkerboard_adj, checkerboard$discont)

p[[1]]
p[[2]]

Description

Reorders precincts by distance from the NW corner of the bounding box.

Usage

geo_sort(shp, epsg = 3857)

Arguments

Value

sf dataframe

Examples

data(checkerboard)
geo_sort(checkerboard)

Description

Trim Away Small Pieces

Usage

geo_trim(from, to, thresh = 0.01, bool = FALSE, epsg = 3857)

Arguments

Value

sf data frame or logical vector if bool=TRUE

Examples

## Not run: 
# Needs Census Bureau API
data(towns)
block <- create_block_table('NY', 'Rockland')
geo_trim(block, towns, thresh = 0.05)

## End(Not run)

data(towns)
data(rockland)
sub <- geo_filter(rockland, towns)
rem <- geo_trim(sub, towns, thresh = 0.05)

Description

Returns points within the shape, near the center. Uses the centroid if that's in the shape, or point on surface if not.

Usage

geos_centerish(shp, epsg = 3857)

Arguments

Value

A geos geometry list

Examples

data(towns)
geos_centerish(towns)

Description

Returns the centroid of the largest inscribed circle for each shape

Usage

geos_circle_center(shp, tolerance = 0.01, epsg = 3857)

Arguments

Value

A geos geometry list

Examples

data(towns)
geos_circle_center(towns)

Description

Gets a dataset from the Algorithm-Assisted Redistricting Methodology Project. The current supported data is the 2020 retabulations of the VEST data, which can be downloaded with get_vest.

Usage

get_alarm(state, year = 2020, geometry = TRUE, epsg = 3857)

Arguments

Details

See the full available data at https://github.com/alarm-redist/census-2020.

Value

tibble with election data and optional geometry

Examples

ak <- get_alarm('AK', geometry = FALSE)

Description

Gets a dataset from Dave's Redistricting App.

Usage

get_dra(state, year = 2020, geometry = TRUE, clean_names = TRUE, epsg = 3857)

Arguments

Details

See the full available data at https://github.com/dra2020/vtd_data.

Value

tibble with election data and optional geometry

Examples

ak <- get_dra('AK', geometry = FALSE)

Description

Get Harvard Election Data Archive ("HEDA") Dataset

Usage

get_heda(state, path = tempdir(), epsg = 3857, ...)

Arguments

Value

sf tibble

Examples

shp <- get_heda('ND')

Description

Data sourced from the United States Congressional District Shapefiles, primarily hosted at https://cdmaps.polisci.ucla.edu/. Files are fetched through the GitHub repository at https://github.com/JeffreyBLewis/congressional-district-boundaries.

Usage

get_lewis(state, congress)

Arguments

Value

a sf tibble of the congressional district boundaries

References

Jeffrey B. Lewis, Brandon DeVine, Lincoln Pitcher, and Kenneth C. Martis. (2013) Digital Boundary Definitions of United States Congressional Districts, 1789-2012. [Data file and code book]. Retrieved from https://cdmaps.polisci.ucla.edu on [date of download].

Examples

get_lewis(state = 'NM', congress = 111)

Description

Get Racially Polarized Voting Dataset from RPV Near Me

Usage

get_rpvnearme(state, version = c(1, 2))

Arguments

Value

a tibble of precinct-level estimates of votes (party) by race

Examples

get_rpvnearme('DE')

Description

Get Voting and Election Science Team ("VEST") Dataset

Usage

get_vest(state, year, path = tempdir(), clean_names = TRUE, epsg = 3857, ...)

Arguments

Value

sf tibble

Examples

## Not run: 
# Requires Dataverse API
shp <- get_vest('CO', 2020)

## End(Not run)

Description

Computes the Global Geary's Contiguity statistic. Can produce spatial weights from an adjacency or sf data frame, in which case the spatial_mat is a contiguity matrix. Users can also provide a spatial_mat argument directly.

Usage

global_gearys(shp, adj, wts, spatial_mat, epsg = 3857)

Arguments

Value

double

Examples

library(dplyr)
data('checkerboard')
checkerboard <- checkerboard |> mutate(m = as.numeric((id + i) %% 2 == 0))
global_gearys(shp = checkerboard, wts = checkerboard$m)

Description

Computes the Global Moran's I statistic and expectation. Can produce spatial weights from an adjacency or sf data frame, in which case the spatial_mat is a contiguity matrix. Users can also provide a spatial_mat argument directly.

Usage

global_morans(shp, adj, wts, spatial_mat, epsg = 3857)

Arguments

Value

list

Examples

library(dplyr)
data('checkerboard')
checkerboard <- checkerboard |> mutate(m = as.numeric((id + i) %% 2 == 0))
global_morans(shp = checkerboard, wts = checkerboard$m)

Description

Returns the Getis Ord G*i in standardized form.

Usage

gstar_i(shp, adj, wts, spatial_mat, epsg = 3857)

Arguments

Value

vector of G*i scores

Examples

library(dplyr)
data('checkerboard')
checkerboard <- checkerboard |> mutate(m = as.numeric((id + i) %% 2 == 0))
gstar_i(shp = checkerboard, wts = checkerboard$m)

Description

List Available States from HEDA Dataverse

Usage

heda_states()

Value

character abbreviations for states

Examples

heda_states()

Description

Compute Local Geary's C

Usage

local_gearys(shp, adj, wts, spatial_mat, epsg = 3857)

Arguments

Value

numeric vector

Examples

library(dplyr)
data('checkerboard')
checkerboard <- checkerboard |> mutate(m = as.numeric((id + i) %% 2 == 0))
local_gearys(shp = checkerboard, wts = checkerboard$m)

Description

Compute Local Moran's I

Usage

local_morans(shp, adj, wts, spatial_mat, epsg = 3857)

Arguments

Value

tibble

Examples

library(dplyr)
data('checkerboard')
checkerboard <- checkerboard |> mutate(m = as.numeric((id + i) %% 2 == 0))
local_morans(shp = checkerboard, wts = checkerboard$m)

Description

The data contains the North Rockland Central School District.

Usage

data('nrcsd')

Format

An sf dataframe with 1 observation

Examples

data('nrcsd')

Description

This data contains the blocks for Orange County NY, with geographies simplified to allow for better examples.

Usage

data("orange")

Format

An sf dataframe with 10034 observations

Details

It can be recreated with: orange <- create_block_table('NY', 'Orange') orange <- rmapshaper::ms_simplify(orange, keep_shapes = TRUE)

Examples

data('orange')

Description

This data contains the election districts (or precincts) for Rockland County NY, with geographies simplified to allow for better examples.

Usage

data("precincts")

Format

An sf dataframe with 278 observations

References

https://www.rocklandgis.com/portal/apps/sites/#/data/datasets/2d91f9db816c48318848ad66eb1a18e9

Examples

data('precincts')

Description

Project a plan at the precinct level down to blocks into a format that can be used with DRA. Projecting down to blocks can take a lot of time for larger states.

Usage

r2dra(precincts, plan, state, path, epsg = 3857)

Arguments

Value

tibble with columns Id, as used by DRA, identical to GEOID in census terms and District.

Examples

## Not run: 
# Needs Census Bureau API
cd <- tinytiger::tt_congressional_districts() |> filter(STATEFP == '49')
cnty <- tinytiger::tt_counties(state = 49)
matchedcty <- geo_match(from = cnty, to = cd)
# use counties as precincts and let the plan be their center match:
r2dra(cnty, matchedcty, 'UT', 'r2dra_ex.csv')

## End(Not run)

Description

This offers a basic method for dividing a shape into separate pieces

Usage

regionalize(shp, lines, adj = adjacency(shp), epsg = 3857)

Arguments

Value

integer vector of regions with nrow(shp) entries

Examples

data(towns)
# make some weird roadlike feature passing through the towns
lines <- sf::st_sfc(sf::st_linestring(sf::st_coordinates(sf::st_centroid(towns))),
  crs = sf::st_crs(towns)
)
regionalize(towns, lines)

Description

This data contains the blocks for Rockland County NY, with geographies simplified to allow for better examples.

Usage

data("rockland")

Format

An sf dataframe with 4764 observations

Details

It can be recreated with: rockland <- create_block_table('NY', 'Rockland') rockland <- rmapshaper::ms_simplify(rockland, keep_shapes = TRUE)

Examples

data('rockland')

Description

Filter Adjacency to Edges Along Border

Usage

seam_adj(adj, shp, admin, seam, epsg = 3857)

Arguments

Value

subset of adj

Examples

data('rockland')
data('orange')
data('nrcsd')

o_and_r <- rbind(orange, rockland)
o_and_r <- o_and_r |>
  geo_filter(nrcsd) |>
  geo_trim(nrcsd)
adj <- adjacency(o_and_r)

seam_adj(adj, shp = o_and_r, admin = 'county', seam = c('071', '087'))

Description

Filter Shape to Geographies Along Border

Usage

seam_geom(adj, shp, admin, seam, epsg = 3857)

Arguments

Value

subset of shp

Examples

data('rockland')
data('orange')
data('nrcsd')

o_and_r <- rbind(orange, rockland)
o_and_r <- o_and_r |>
  geo_filter(nrcsd) |>
  geo_trim(nrcsd)
adj <- adjacency(o_and_r)

seam_geom(adj, shp = o_and_r, admin = 'county', seam = c('071', '087'))

Description

Remove Edges along a Boundary

Usage

seam_rip(adj, shp, admin, seam, epsg = 3857)

Arguments

Value

adjacency list

Examples

data('rockland')
data('orange')
data('nrcsd')

o_and_r <- rbind(orange, rockland)
o_and_r <- o_and_r |>
  geo_filter(nrcsd) |>
  geo_trim(nrcsd)
adj <- adjacency(o_and_r)

seam_rip(adj, o_and_r, 'county', c('071', '087'))

Description

Suggest Edges to Connect Two Sides of a Border

Usage

seam_sew(shp, admin, seam, epsg = 3857)

Arguments

Value

tibble of edges connecting sides of a border

Examples

data('rockland')
data('orange')
data('nrcsd')

o_and_r <- rbind(orange, rockland)
o_and_r <- o_and_r |>
  geo_filter(nrcsd) |>
  geo_trim(nrcsd)
adj <- adjacency(o_and_r)

adds <- seam_sew(o_and_r, 'county', c('071', '087'))
adj <- adj |> add_edge(adds$v1, adds$v2)

Description

States often split a precinct when they create districts but rarely provide the geography for the split precinct. This allows you to split a precinct using a lower geography, typically blocks.

Usage

split_precinct(lower, precinct, split_by, lower_wt, split_by_id, epsg = 3857)

Arguments

Value

sf data frame with precinct split

Examples

library(sf)
data(checkerboard)
low <- checkerboard |> dplyr::slice(1:3, 9:11)
prec <- checkerboard |>
  dplyr::slice(1:3) |>
  dplyr::summarize(geometry = sf::st_union(geometry))
dists <- checkerboard |>
  dplyr::slice(1:3, 9:11) |>
  dplyr::mutate(dist = c(1, 2, 2, 1, 3, 3)) |>
  dplyr::group_by(dist) |>
  dplyr::summarize(geometry = sf::st_union(geometry))

split_precinct(low, prec, dists, split_by_id = 'dist')

Description

Returns points within the shape, near the center. Uses the centroid if that's in the shape, or point on surface if not.

Usage

st_centerish(shp, epsg = 3857)

Arguments

Value

An sf dataframe where geometry is the center(ish) of each shape in shp

Examples

data(towns)
st_centerish(towns)

Description

Returns the centroid of the largest inscribed circle for each shape

Usage

st_circle_center(shp, tolerance = 0.01, epsg = 3857)

Arguments

Value

An sf dataframe where geometry is the circle center of each shape in shp

Examples

data(towns)
st_circle_center(towns)

Description

Subtract Edges from an Adjacency List

Usage

subtract_edge(adj, v1, v2, ids = NULL, zero = TRUE)

Arguments

Value

adjacency list.

Examples

data(towns)
adj <- adjacency(towns)

subtract_edge(adj, 2, 3)
subtract_edge(adj, "West Haverstraw", "Stony Point", towns$MUNI)

Description

Suggests nearest neighbors for connecting a disconnected group.

Usage

suggest_component_connection(shp, adj, group, epsg = 3857)

Arguments

Value

tibble with two columns of suggested rows of shp to connect in adj

Examples

library(dplyr)
data(checkerboard)
checkerboard <- checkerboard |> filter(i != 1, j != 1)
adj <- adjacency(checkerboard)
suggest_component_connection(checkerboard, adj)

Description

For precincts which have no adjacent precincts, this suggests the nearest precinct as a friend to add. This is useful for when a small number of precincts are disconnected from the remainder of the geography, such as an island.

Usage

suggest_neighbors(shp, adj, idx, neighbors = 1)

Arguments

Value

tibble with two columns of suggested rows of shp to connect in adj

Examples

library(dplyr)
data(va18sub)
va18sub <- va18sub |> filter(!VTDST %in% c('000516', '000510', '000505', '000518'))
adj <- adjacency(va18sub)
suggests <- suggest_neighbors(va18sub, adj)
adj <- adj |> add_edge(v1 = suggests$x, v2 = suggests$y)

Description

This data contains 7 town boundaries for the towns which overlap North Rockland School District in NY.

Usage

data("towns")

Format

An sf dataframe with 7 observations

References

https://www.rocklandgis.com/portal/apps/sites/#/data/items/746ec7870a0b4f46b168e07369e79a27

Examples

data('towns')

Description

This data contains the blocks Henrico County, VA with geographies simplified to allow for better examples.

Usage

data("va_blocks")

Format

An sf dataframe with 6354 observations

Details

blocks87 <- create_block_table(state = 'VA', county = '087') va_blocks <- rmapshaper::ms_simplify(va_blocks, keep_shapes = TRUE)

Examples

data('va_blocks')

Description

This data contains the blocks for Henrico County, VA with geographies simplified to allow for better examples.

Usage

data("va_blocks")

Format

An sf dataframe with 93 observations

Details

va_vtd <- tinytiger::tt_voting_districts(state = 'VA', county = '087', year = 2010) va_vtd <- rmapshaper::ms_simplify(va_vtd, keep_shapes = TRUE)

Examples

data('va_blocks')

Description

This data contains a 90 precinct subset of Virginia from the 2018 Senate race. Contains results for Henrico County

Usage

data("va18sub")

Format

An sf dataframe with 90 observations

References

Voting and Election Science Team, 2019, "va_2018.zip", 2 018 Precinct-Level Election Results, https://doi.org/10.7910/DVN/UBKYRU/FQDLOO, Harvard Dataverse, V4

Examples

data('va18sub')

Description

List Available States from VEST Dataverse

Usage

vest_states(year)

Arguments

Value

character abbreviations for states

Examples

## Not run: 
# Requires Dataverse API
vest_states(2020)

## End(Not run)