County-level population change in Wisconsin

Which counties have gained the most population between 2020 and 2024? Which ones shrunk?

The US Census Bureau recently released the latest population estimates. The estimates are available at the national, state, county, and local level. In this post, we’ll take a quick look at which Wisconsin counties had the largest population gains and which ones shrunk.

One-year change: 2023 to 2024

Looking just at the two most most recent years, Dane County saw the largest absolute and relative increase in population: From 2023 to 2024, the counties population grew by 1.5%, or over 8,600 people. The only other county with a 1.5% population was Menominee. But Menominee is Wisconsin least populous county, and so a 1.5% increase is just 65 people! For absolute increases, Waukesha and Milwaukee counties come in second and third. Especially for Milwaukee County this is an exciting turnaround, after many years of losing population.

Fourteen of the Wisconsin’s 72 counties saw their populations shrink, but the losses were modest: Adams County saw the highest absolute and relative loses, with -0.8%, or 176 fewer people living in the county.

Show code

library(tidycensus)
library(tidyverse)
library(tmap)
library(sf)
library(gt)
wi_pop_2024 <- get_estimates(geography = "county", 
                             variables = "POPESTIMATE", 
                             state = "WI", 
                             geometry = TRUE, 
                             vintage = 2024,
                             year = 2024)

wi_pop_2023 <- get_estimates(geography = "county", 
                             variables = "POPESTIMATE", 
                             state = "WI", 
                             geometry = TRUE, 
                             vintage = 2024,
                             year = 2023)
wi_pop_23_24 <- rbind(wi_pop_2023, wi_pop_2024)

Show code

wi_pop_23_24 <- wi_pop_23_24 |> 
  st_drop_geometry() |> 
  pivot_wider(names_from = "year", values_from = "value", 
              names_prefix = "pop_",
              id_cols = "NAME") 

q <- wi_pop_2024 |> 
  select(NAME, geometry) |> 
  left_join(wi_pop_23_24, by = "NAME") |> 
  mutate(pop_change_abs = pop_2024 - pop_2023,
         pop_change_pct = (pop_2024 - pop_2023)/pop_2023) |> 
  tm_shape() +
  tm_polygons(fill = "pop_change_abs",
              fill.scale = tm_scale_intervals(
                n = 5,
                style = "pretty"
              ),
              fill.legend = tm_legend(
                title = "Population change",
                position = c("top", "right"))) +
  tm_title(" ") +
  tm_credits("Data: US Census Bureau Population Estimates\nVisualization: Harald Kliems", 
             position = c("RIGHT", "BOTTOM"),
             just = "right")


p <- wi_pop_2024 |> 
  select(NAME, geometry) |> 
  left_join(wi_pop_23_24, by = "NAME") |> 
  mutate(pop_change_abs = pop_2024 - pop_2023) |> 
  mutate(pop_change_pct = round((pop_change_abs/pop_2023)*100, 1)) |> 
  tm_shape() +
  tm_polygons(fill = "pop_change_pct",
              fill.legend = tm_legend(
                title = "relative change (%)",
                position = c("top", "right")
              ))+
  tm_title("Wisconsin 1-year county population change 2023-2024") 

tmap_arrange(p,q)

Four-year change: 2024 to 2024

If we compare the latest estimates compare with the 2020 estimates, the picture at the very top doesn’t change: Dane County again leads the pack, with a population growth of over 25,000 people, or 4.6%. For absolute growth, Waukesha County is second again, but its 2.3% relative growth doesn’t even get it into the top 10. Thirteen counties lost population since 2020, none more than Milwaukee County. Almost 14,000 fewer people, or 1.5% lived in the county compared to 2020. It will be interesting to see if the one-year turnaround from 2023 to 2024 signals a larger trend!

Show code

wi_pop_2020 <- get_estimates(geography = "county", 
                             variables = "POPESTIMATE", 
                             state = "WI", 
                             geometry = TRUE, 
                             vintage = 2024,
                             year = 2020)

wi_pop_20_24 <- rbind(wi_pop_2020, wi_pop_2024)

Show code

wi_pop_20_24 <- wi_pop_20_24 |> 
  st_drop_geometry() |> 
  pivot_wider(names_from = "year", values_from = "value", 
              names_prefix = "pop_",
              id_cols = "NAME") 

q <- wi_pop_2024 |> 
  select(NAME, geometry) |> 
  left_join(wi_pop_20_24, by = "NAME") |> 
  mutate(pop_change_abs = pop_2024 - pop_2020,
         pop_change_pct = (pop_2024 - pop_2020)/pop_2020) |> 
  tm_shape() +
  tm_polygons(fill = "pop_change_abs",
              fill.legend = tm_legend(
                title = "absolute change",
                position = c("top", "right"))) +
  tm_title(" ") +
  tm_credits("Data: US Census Bureau Population Estimates\nVisualization: Harald Kliems", 
             position = c("RIGHT", "BOTTOM"),
             just = "right")


p <- wi_pop_2024 |> 
  select(NAME, geometry) |> 
  left_join(wi_pop_20_24, by = "NAME") |> 
  mutate(pop_change_abs = pop_2024 - pop_2020) |> 
  mutate(pop_change_pct = round((pop_change_abs/pop_2020)*100, 1)) |> 
  tm_shape() +
  tm_polygons(fill = "pop_change_pct",
              fill.scale = tm_scale_intervals(
                n = 6,
                style = "pretty"
              ),
              fill.legend = tm_legend(
                title = "relative change (%)",
                position = c("top", "right")
              ))+
  tm_title("Wisconsin county population change 2020-2024") 

tmap_arrange(p,q)

Here are all the numbers in a big table.

Show code

wi_pop_20_24 |> 
  left_join(wi_pop_23_24 |> select(NAME, pop_2023)) |> 
  mutate(NAME = str_remove(NAME, "County, Wisconsin"),
         chg_20_24_abs = pop_2024 - pop_2020,
         chg_20_24_pct = chg_20_24_abs / pop_2020,
         chg_23_24_abs = pop_2024 - pop_2023,
         chg_23_24_pct = chg_23_24_abs / pop_2023) |> 
  arrange(desc(chg_20_24_pct)) |> 
    gt() |>
  data_color(
    columns = c(chg_20_24_abs, 
                chg_20_24_pct,
                chg_23_24_abs,
                chg_23_24_pct),
    method = "numeric",
    palette = "BrBG"
  ) |>
  cols_label(
    NAME = "County",
    pop_2020 = "2020",
    pop_2023 = "2023",
    pop_2024 = "2024",
    chg_20_24_abs = "absolute",
    chg_23_24_abs = "absolute",
    chg_23_24_pct = "percent",
    chg_20_24_pct = "percent"
  ) |>
  tab_spanner(
    label = "Population estimate",
    columns = c(pop_2020, pop_2023, pop_2024)
  ) |>
    tab_spanner(
    label = "4-year change (2020-2024)",
    columns = c(chg_20_24_abs, chg_20_24_pct)
  ) |>
    tab_spanner(
      label = "1-year change (2023-2024)",
      columns = c(chg_23_24_abs, chg_23_24_pct)
    ) |> 
  fmt_percent(
    columns = ends_with("_pct"),
    scale_value = T, decimals = 1, force_sign = T
  ) |>
  fmt_integer(
    columns = c(
      pop_2020,
      pop_2023,
      pop_2024,
      chg_23_24_abs,
      chg_20_24_abs
    ),
    use_seps = T
  ) |> 
  tab_source_note(
    source_note = md(
      "Data: US Census Population Estimates, Vintage 2024"
    )
  )

County	Population estimate			4-year change (2020-2024)		1-year change (2023-2024)
	2020	2023	2024	absolute	percent	absolute	percent
Dane	562,620	579,739	588,347	25,727	+4.6%	8,608	+1.5%
St. Croix	93,893	97,156	97,954	4,061	+4.3%	798	+0.8%
Lafayette	16,604	17,059	17,306	702	+4.2%	247	+1.4%
Sawyer	18,119	18,619	18,835	716	+4.0%	216	+1.2%
Bayfield	16,238	16,809	16,838	600	+3.7%	29	+0.2%
Forest	9,168	9,381	9,506	338	+3.7%	125	+1.3%
Vilas	23,097	23,914	23,948	851	+3.7%	34	+0.1%
Burnett	16,578	17,089	17,187	609	+3.7%	98	+0.6%
Adams	20,682	21,528	21,352	670	+3.2%	−176	−0.8%
Pepin	7,343	7,482	7,555	212	+2.9%	73	+1.0%
Eau Claire	105,877	108,058	108,830	2,953	+2.8%	772	+0.7%
Pierce	42,229	43,101	43,380	1,151	+2.7%	279	+0.6%
Ozaukee	91,627	93,520	93,956	2,329	+2.5%	436	+0.5%
Oconto	39,077	39,741	40,037	960	+2.5%	296	+0.7%
Florence	4,563	4,695	4,673	110	+2.4%	−22	−0.5%
Waukesha	407,518	414,013	417,029	9,511	+2.3%	3,016	+0.7%
Outagamie	190,973	193,828	195,390	4,417	+2.3%	1,562	+0.8%
Portage	70,412	71,461	72,040	1,628	+2.3%	579	+0.8%
Washburn	16,623	16,996	16,982	359	+2.2%	−14	−0.1%
Waushara	24,550	25,014	25,079	529	+2.2%	65	+0.3%
Calumet	52,478	53,303	53,602	1,124	+2.1%	299	+0.6%
Vernon	30,710	31,143	31,351	641	+2.1%	208	+0.7%
Brown	268,966	272,074	273,909	4,943	+1.8%	1,835	+0.7%
Green Lake	19,027	19,417	19,370	343	+1.8%	−47	−0.2%
Polk	45,029	45,845	45,831	802	+1.8%	−14	−0.0%
Iron	6,140	6,277	6,235	95	+1.5%	−42	−0.7%
Dunn	45,453	45,827	46,135	682	+1.5%	308	+0.7%
Chippewa	66,346	66,949	67,323	977	+1.5%	374	+0.6%
Washington	136,811	138,496	138,727	1,916	+1.4%	231	+0.2%
Door	30,119	30,566	30,512	393	+1.3%	−54	−0.2%
Taylor	19,938	20,123	20,167	229	+1.1%	44	+0.2%
Ashland	16,015	16,103	16,196	181	+1.1%	93	+0.6%
Buffalo	13,323	13,440	13,464	141	+1.1%	24	+0.2%
Marinette	41,900	42,113	42,343	443	+1.1%	230	+0.5%
Sauk	65,793	66,143	66,486	693	+1.1%	343	+0.5%
Shawano	40,869	41,148	41,299	430	+1.1%	151	+0.4%
Marquette	15,580	15,851	15,743	163	+1.0%	−108	−0.7%
Rock	163,761	164,443	165,461	1,700	+1.0%	1,018	+0.6%
Iowa	23,718	23,881	23,963	245	+1.0%	82	+0.3%
Kewaunee	20,563	20,733	20,751	188	+0.9%	18	+0.1%
Winnebago	171,796	172,105	173,307	1,511	+0.9%	1,202	+0.7%
Oneida	37,877	38,189	38,175	298	+0.8%	−14	−0.0%
Marathon	138,114	138,838	139,091	977	+0.7%	253	+0.2%
Walworth	105,310	106,008	106,029	719	+0.7%	21	+0.0%
Grant	51,979	51,894	52,330	351	+0.7%	436	+0.8%
Menominee	4,258	4,221	4,286	28	+0.7%	65	+1.5%
Clark	34,629	34,792	34,801	172	+0.5%	9	+0.0%
Green	37,033	36,934	37,183	150	+0.4%	249	+0.7%
Price	14,036	14,098	14,087	51	+0.4%	−11	−0.1%
Langlade	19,466	19,435	19,535	69	+0.4%	100	+0.5%
Racine	198,065	197,407	198,651	586	+0.3%	1,244	+0.6%
Sheboygan	118,058	117,995	118,331	273	+0.2%	336	+0.3%
Barron	46,723	46,922	46,810	87	+0.2%	−112	−0.2%
Monroe	46,299	46,193	46,370	71	+0.2%	177	+0.4%
Manitowoc	81,412	81,325	81,513	101	+0.1%	188	+0.2%
Lincoln	28,429	28,430	28,461	32	+0.1%	31	+0.1%
Fond du Lac	104,156	104,135	104,269	113	+0.1%	134	+0.1%
La Crosse	120,943	120,717	121,060	117	+0.1%	343	+0.3%
Jefferson	86,208	85,971	86,245	37	+0.0%	274	+0.3%
Trempealeau	30,813	30,939	30,801	−12	−0.0%	−138	−0.4%
Rusk	14,181	14,252	14,168	−13	−0.1%	−84	−0.6%
Douglas	44,348	44,252	44,276	−72	−0.2%	24	+0.1%
Kenosha	169,202	167,867	168,754	−448	−0.3%	887	+0.5%
Jackson	21,095	20,938	21,027	−68	−0.3%	89	+0.4%
Wood	74,189	74,037	73,943	−246	−0.3%	−94	−0.1%
Crawford	16,084	15,991	16,008	−76	−0.5%	17	+0.1%
Columbia	58,520	57,949	58,113	−407	−0.7%	164	+0.3%
Juneau	26,780	26,557	26,590	−190	−0.7%	33	+0.1%
Dodge	89,344	88,501	88,635	−709	−0.8%	134	+0.2%
Richland	17,283	17,251	17,123	−160	−0.9%	−128	−0.7%
Waupaca	51,806	51,324	51,171	−635	−1.2%	−153	−0.3%
Milwaukee	938,609	921,860	924,740	−13,869	−1.5%	2,880	+0.3%
Data: US Census Population Estimates, Vintage 2024