Map Pre-processing for Special Constraints"

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
set.seed(5118)

In redistricting analysis, it is often useful or necessary to analyze only a portion of a map, or hold some districts fixed while others are re-simulated. Other analyses might require a status-quo-type constraint that encourages simulated districts to be close to a reference plan.

All of these requirements may be achieved through map pre-processing, and this vignette shows how to use redist to do so, using the Metropolitan King County Council districts as an example.

The Map

King County is the most populous county in the state of Washington, and contains the city of Seattle. The nine members of the county council are elected from single-member districts which are redrawn every decade. According to the county charter, the districts should be "drawn to produce districts with compact and contiguous territory, composed of economic and geographic units and approximately equal in population," and should follow municipality lines as much as possible.

The precinct data are available online, and contain population, presidential vote, city, and existing district information.

library(dplyr)
library(ggplot2)
library(scales)
library(patchwork)

library(redist)

data_url = "https://github.com/alarm-redist/redist-data/raw/main/data/king_county.rds"
data_path = tempfile()
download.file(data_url, data_path)
king_shp = readRDS(data_path)
print(king_shp)
n_city = length(unique(king_shp$city)) - 1
tot_area = as.numeric(sum(sf::st_area(king_shp)))
tot_pop = sum(king_shp$pop)
unincorp_area = as.numeric(sum(sf::st_area(king_shp[king_shp$city == "UNINCORP", ])))
unincorp_pop = sum(king_shp$pop[king_shp$city == "UNINCORP"])

There are r n_city incorporated cities in King County, which together cover r percent(1-unincorp_area/tot_area) of the population and r percent(1-unincorp_area/tot_area) of the area of the county. The remainder is "unincorporated King County". The county contains a significant amount of water, too, which complicates the drawing of districts; Vashon Island in the southwest part of the county is not connected to the rest of the county by land.

We'll start by looking at some maps of the county and the districts.

areas = as.numeric(units::set_units(sf::st_area(king_shp), mi^2))
pop_plot = ggplot(king_shp, aes(fill=pop/areas)) +
    geom_sf(size=0) +
    scale_fill_viridis_c(trans="sqrt", labels=comma, limits=c(0, 25e3), oob=squish) +
    labs(title="Population Density") +
    theme_void() + theme(legend.position="bottom")
water_plot = ggplot(king_shp, aes(fill=pct_water)) +
    geom_sf(size=0) +
    scale_fill_viridis_c(labels=percent) +
    labs(title="Water") +
    theme_void() + theme(legend.position="bottom")
water_plot + pop_plot
cities = summarize(group_by(king_shp, city, distr), .groups="drop")
districts = summarize(group_by(king_shp, distr))
ggplot(cities) +
    geom_sf(aes(fill=city, alpha=city!="UNINCORP"), color="#444444") +
    geom_sf(color="black", size=1, data=districts, fill="#00000000") +
    geom_sf_text(aes(label=distr), size=10, fontface="bold", color="#000000aa",
                 data=districts) +
    scale_alpha_manual(values=c(0, 0.8)) +
    guides(fill="none", alpha="none") +
    labs(title="Cities with Council District Overlays") + 
    theme_void()

Creating the redist_map object

The first step one in a redist analysis is creating the redist_map object, which stores the basic parameters of the redistricting problem. Among these parameters is the desired level of population parity. Here, we'll compute the parity of the current set of districts, and ensure that all of our simulations do no worse.

existing_parity = redist.parity(king_shp$distr, king_shp$pop)
king = redist_map(king_shp, existing_plan=distr, pop_tol=existing_parity)
print(king)

This redist_map object contains an adjacency graph for the county. We can explore this graph, and zoom in on the city of Seattle, using plot().

plot(king, adj=T, centroids=F, zoom_to=(city == "SEA"))

Subsetting

Often, we wish to restrict our analysis to a part of a map or only a few of the districts. This is supported in redist using the filter() function from dplyr. The package's version of filter() will automatically update the adjacency graph, the number of districts, and the relevant population bounds.

Specific districts

Suppose we wanted to study districts 2, 4, and 8, which cover most of Seattle.

filter(king, distr %in% c(2, 4, 8))

Looking at the information in the header, and comparing it to the original king object, we see that the number of districts has been updated from 9 to 3, and the population tolerances have been updated from 214,506 ± 1.663% to 213,049 - 0.9909% and 213,049 + 2.358%. Not visible but equally important are the edits to the adjacency graph to reflect the new geometry of the map.

Dealing with water and islands

Another way we might want to subset would be to cut out the precincts which are just water, so that districts won't unnecessarily cross bodies of water. Of course, we'll have to ensure that Vashon Island is still connected to the mainland by at least one precinct. We'll start by subsetting to the water precincts and plotting labels.

plot(filter(king, pct_water >= 0.99, pop == 0)) + geom_sf_text(aes(label=id))

We see that by removing all water precincts except WVPS34 and WVSP34, we can maintain a connection between the island and the mainland (incidentally, the state ferry connecting the island to Seattle runs through these precincts).

water_prec = filter(king, pct_water >= 0.99, pop == 0) %>% pull(id)
water_prec = setdiff(water_prec, c("WVPS34", "WVSP34"))
king_land = filter(king, !(id %in% water_prec))
plot(king_land)

Zooming in again to view the adjacency graph in the city of Seattle, we see that the graph has been appropriately edited to remove the water precincts.

plot(king_land, adj=T, centroids=F, zoom_to=(city == "SEA"))

Merging

Often, we want to merge some units together to form larger units, either to visualize or analyze at a coarser scale, or to ensure that the merged units are treated as one "block" in any redistricting algorithm. Merging units is a part of most map preprocessing workflows, and in redist is carried out by the merge_by() function, which works like a combination of the group_by() and summarize() verbs of dplyr.

For example, we could merge our King County data by city.

merge_by(king_land, city)

Under the hood, merge_by() does several things. First, it groups the shapefile by the provided key or keys (here, city). By default it also groups by existing districts, so that the merged units will still follow district boundaries. Then for each remaining column, merge_by() tries to automatically summarize it. Most numeric columns are summed (but columns with percentage values are averaged), and most character columns are collapsed into summary variables. You can read more about the details of this process in the documentation. Finally, merge_by() makes the appropriate edits to the adjacency graph.

Merging geographic shapefile units can be computationally intensive, and so by default merge_by() drops the geometry before merging. This is OK for analysis purposes, since all of the relevant adjacency information is still encoded in the graph. After analysis, you can use the pullback() method to un-merge objects and restore plotting capability. However, should you want to preserve the geometry through merging, you can simply set drop_geom=FALSE.

king_merged = merge_by(king_land, city, drop_geom=FALSE)
plot(king_merged, adj=T)

We will see more uses of merge_by() in the sections below.

Freezing

Sometimes, rather than completely remove a portion of a map, we want to freeze it in place, so that all of the units in that portion stay together in the same district. The reasons for doing this might vary, but include enforcing a county or administrative boundary split constraint, aiding in setting up Voting Rights Act constraints, or preparing a map to be optimized according to a set of criteria.

In the context of King County Council seats, we might want to implement the requirement that districts follow municipal lines by ensuring that any sampled redistricting plans not split any municipalities which are not split by the existing plan. That way, the number of split municipalities in the set of sampled plans will be guaranteed to not exceed the number of existing splits.

In redist, freezing is accomplished by using the freeze() and merge_by() functions. The former takes in a description of the units which should be frozen, and groups them into contiguous chunks of frozen units, returning an indexing vector that uniquely identifies each group. Then merge_by() merges these groups together. We can use the redist.splits() function to count split municipalities, and the is_county_split() function to identify split municipalities.

cat(redist.splits(king_land$distr, king_land$city), "split cities\n")

king_land %>%
    mutate(is_unsplit = !is_county_split(distr, city)) %>%
plot(is_unsplit)

king_unsplit = king_land %>%
    mutate(unsplit_id = freeze(!is_county_split(distr, city))) %>%
    merge_by(unsplit_id, city, collapse_chr=FALSE)
print(king_unsplit)

The plot above shows which cities will be frozen together so that they cannot be split. Notice that we merge by not just unsplit_id but also city, so that adjacent unsplit cities are not merged together. We also set collapse_chr=FALSE to drop the id and precinct columns, which become slightly unwieldy after a large merge.

To see this in action, we'll sample 100 redistricting plans using redist_smc() on this partially frozen map. We'll use pullback() to then reconstruct the plan output of redist_smc() so that it is congruous with the original geometry object, king_land.

plans = redist_smc(king_unsplit, 100, silent=T)
print(plans)
print(pullback(plans))
redist.plot.plans(pullback(plans), draws=1:4, shp=king_land)

Notice how the Merged from another map... line disappears and the number of map units changes after using pullback(). Notice also that each of the sampled plans completely preserves the municipalities which were frozen with freeze() and merge_by().

District Cores

A common requirement in redistricting is that districts after redistricting resemble the original districts, or "preserve the cores" of previous districts, to ensure relative continuity of representation. The redist package operationalizes this idea by explicitly constructing the cores of a set of districts with make_cores(), and merging them together with merge_by().

The idea for constructing district cores is to work inwards from district boundaries. First, we merge each district completely. Then, we un-freeze the precincts which lie along district boundaries. Then, we un-freeze the precincts which were unfrozen in the previous step. We repeat this process a user-specified number of times, leaving only the central "cores" of each district frozen. When it comes time to simulate, these cores will be assigned a district as a unit, preserving representation for all the people living in the core.

The make_cores() function takes a boundary parameter which counts the number of these steps; boundary=1 corresponds to un-freezing the precincts along the boundary only. This is often sufficient for a moderate-to-strong status quo constraint.

For example, if we set boundary=1 in King county, 82% of the population lives inside a district core, while with boundary=2 that number drops to 58%.

pop_inside_cores = function(boundary) {
    king_land %>%
        mutate(core = make_cores(boundary=boundary)) %>%
        as_tibble() %>%
        group_by(core) %>%
        filter(n() > 2) %>% # filter to cores only
        pull(pop) %>% sum
}
pop_inside_cores(1) / sum(king_land$pop)
pop_inside_cores(2) / sum(king_land$pop)

Here, we'll use boundary=1. We can see how large areas inside each district are merged together after applying merge_by() to the generated cores.

king_cores = king_land %>%
    mutate(core = make_cores(boundary=1)) %>%
    merge_by(core, drop_geom=F)
plot(king_cores)

If we sample redistrict plans from this modified map, we observe that the simulated plans generally follow the same location and shape as the original plan. Sampling is also faster, since there are fewer units in the map.

plans = redist_smc(king_cores, 100, silent=T)
redist.plot.plans(plans, draws=1:4, shp=king_cores)


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redist documentation built on June 16, 2022, 9:05 a.m.