Metro market assessment

# change target metro and peers here
# target_cbsa <- "19740"
target_cbsa <- "31140"
# peer_cbsa <- c("35620", "16980", "31080", "47900")
peer_cbsa <- c("13820","16740","17140","18140","24660","26900","28140","34980", "36540", "32820")
# peer_cbsa <- c("12420", "33460", "38060", "38900", "41740", "41620", "42660")

# get counties
target_co <- (county_cbsa_st %>%
  filter(cbsa_code == target_cbsa) )[["stco_code"]]

peer_co <- (county_cbsa_st %>%
  filter(cbsa_code %in% peer_cbsa) )[["stco_code"]]

# filter datasets
cbsa_output <- cbsa_all %>%
  filter(cbsa_code %in% c(target_cbsa, peer_cbsa)) %>%
  left_join(county_cbsa_st[c("cbsa_code", "cbsa_emp")], by = "cbsa_code") %>%
  unique()

# make it a shiny app!
# selectInput("target_cbsa", "", )

Dynamism

Tradable share of employment–metro and peers

emp <- read_csv("../../_metro_data_warehouse/data_raw/CBP EMP/efsy_cbp_2016.csv")

emp %>%
  mutate(stco_code = paste0(str_pad(fipstate, 2, "left", "0"), str_pad(fipscty,3,"left",0))) %>%
  filter(stco_code %in% c(target_co, peer_co)) %>%
  left_join(metro.data::naics6_traded, by = c("naics" = "code_naics6_2017")) %>%
  filter(!is.na(traded.naics6)) %>%
  left_join(county_cbsa_st[c("stco_code", "cbsa_code", "cbsa_name")]) %>%
  group_by(cbsa_code, cbsa_name,traded.naics6 ) %>%
  summarise(emp = sum(emp)) %>%
  pivot_wider(names_from = "traded.naics6", values_from = "emp") %>%
  mutate(pct_traded = Traded/(Traded+Local)) %>%
  knitr::kable()

cbsa_code	cbsa_name	Local	Traded	pct_traded
13820	Birmingham-Hoover, AL	312514	119670	0.2768960
16740	Charlotte-Concord-Gastonia, NC-SC	679092	348580	0.3391938
17140	Cincinnati, OH-KY-IN	591205	336021	0.3623939
18140	Columbus, OH	562781	295833	0.3445471
24660	Greensboro-High Point, NC	198770	117291	0.3711024
26900	Indianapolis-Carmel-Anderson, IN	575627	303785	0.3454410
28140	Kansas City, MO-KS	601578	326737	0.3519678
31140	Louisville/Jefferson County, KY-IN	367578	194084	0.3455530
32820	Memphis, TN-MS-AR	365153	168990	0.3163760
34980	Nashville-Davidson–Murfreesboro–Franklin, TN	535153	291923	0.3529579
36540	Omaha-Council Bluffs, NE-IA	266871	148215	0.3570706

Change in share of employment in Advanced Industries–metro and peers

emp06 <- read_csv("../../_metro_data_warehouse/data_raw/CBP EMP/efsy_cbp_2006.csv")

pct_ai <- function(df)df%>%
  mutate(stco_code = paste0(str_pad(fipstate, 2, "left", "0"), str_pad(fipscty,3,"left",0))) %>%
  filter(stco_code %in% c(target_co, peer_co)) %>%
  mutate(naics = gsub("\\/", "", naics))%>%
  filter(str_length(naics)== 4)%>%
  left_join(metro.data::naics4_ai, by = c("naics" = "naics4_code")) %>%
  mutate(is.ai = ifelse(is.na(naics4_aitype), NA, 1))%>%
  left_join(county_cbsa_st[c("stco_code", "cbsa_code", "cbsa_name")]) %>%
  group_by(cbsa_code, cbsa_name,is.ai) %>%
  summarise(emp = sum(emp)) %>%
  pivot_wider(names_from = "is.ai", values_from = "emp") %>%
  mutate(pct_ai = `1`/(`1`+`NA`)) %>%
  select(cbsa_code, cbsa_name, pct_ai)

pct_ai(emp06)%>%
  rename(pct_ai_06 = pct_ai)%>%
  left_join(pct_ai(emp), by = c("cbsa_code", "cbsa_name")) %>%
  knitr::kable()

cbsa_code	cbsa_name	pct_ai_06	pct_ai
13820	Birmingham-Hoover, AL	0.0881043	0.0873591
16740	Charlotte-Concord-Gastonia, NC-SC	0.0881569	0.0898580
17140	Cincinnati, OH-KY-IN	0.0968568	0.0914242
18140	Columbus, OH	0.0921319	0.0832840
24660	Greensboro-High Point, NC	0.0741018	0.0803745
26900	Indianapolis-Carmel-Anderson, IN	0.1042892	0.1035331
28140	Kansas City, MO-KS	0.1006752	0.1176457
31140	Louisville/Jefferson County, KY-IN	0.0864373	0.0957105
32820	Memphis, TN-MS-AR	0.0524400	0.0553771
34980	Nashville-Davidson–Murfreesboro–Franklin, TN	0.0954992	0.0777788
36540	Omaha-Council Bluffs, NE-IA	0.0702365	0.0735086

R&D per 1,000 workers–metro and peers

Annual average R&D expenditure at higher education institutions per 1000 workers, 2012 - 2016 (plots can be generated for other tables as well, if needed)

cbsa_output %>%
  select(cbsa_code, cbsa_name, rd_total, cbsa_emp) %>%
  mutate(rd_per_1000worker = rd_total/cbsa_emp * 1000/5) %>%
  knitr::kable()

cbsa_code	cbsa_name	rd_total	cbsa_emp	rd_per_1000worker
13820	Birmingham-Hoover, AL Metro Area	2195530.9	436077	1006.9464
16740	Charlotte-Concord-Gastonia, NC-SC Metro Area	196464.6	1058398	37.1249
17140	Cincinnati, OH-KY-IN Metro Area	2104942.5	946308	444.8747
18140	Columbus, OH Metro Area	3708114.7	874951	847.6165
24660	Greensboro-High Point, NC Metro Area	265721.8	323805	164.1246
26900	Indianapolis-Carmel-Anderson, IN Metro Area	1020124.2	911388	223.8617
28140	Kansas City, MO-KS Metro Area	1512807.6	954263	317.0630
31140	Louisville/Jefferson County, KY-IN Metro Area	918709.5	577816	317.9938
32820	Memphis, TN-MS-AR Metro Area	559642.9	539734	207.3773
34980	Nashville-Davidson–Murfreesboro–Franklin, TN Metro Area	3187781.5	855849	744.9402
36540	Omaha-Council Bluffs, NE-IA Metro Area	828706.9	419890	394.7257

quick_bar(cbsa_output, "rd_total",HL = target_cbsa, wt = T, scale = 200)

Do we have rankings for UofL by research focus similar to UAB stats?

Annual R&D expenditure at UofL, thousand US dollar

load("univ_rd_detail/univ_rd_18.rda")

univ_rd_18 %>%
  filter(stco_code %in% target_co) %>%
  select(year, univ_name, field_broad, field_detail, total_rd)%>%
  unique()%>%
  filter(field_detail == "Total") %>%
  pivot_wider(names_from = "year", values_from = "total_rd") %>%
  DT::datatable()

Startups launched at universities–metro and peers

Number of start-ups launched at universities, 2006 - 2017

cbsa_output %>%
  select(cbsa_code, cbsa_name, tot_st) %>%
  knitr::kable()

	cbsa_code	cbsa_name	tot_st
1	13820	Birmingham-Hoover, AL Metro Area	6
7	16740	Charlotte-Concord-Gastonia, NC-SC Metro Area	4
18	17140	Cincinnati, OH-KY-IN Metro Area	5
34	18140	Columbus, OH Metro Area	9
44	24660	Greensboro-High Point, NC Metro Area	0
47	26900	Indianapolis-Carmel-Anderson, IN Metro Area	NA
58	28140	Kansas City, MO-KS Metro Area	0
72	31140	Louisville/Jefferson County, KY-IN Metro Area	5
82	32820	Memphis, TN-MS-AR Metro Area	0
90	34980	Nashville-Davidson–Murfreesboro–Franklin, TN Metro Area	12
103	36540	Omaha-Council Bluffs, NE-IA Metro Area	0

INC 5000 high growth companies–metro and peers

Number of Inc.5000 high growth companies per 1M workers, 2011 - 2017

cbsa_output %>%
  select(cbsa_code, cbsa_name, i5hgc_density) %>%
  knitr::kable()

	cbsa_code	cbsa_name	i5hgc_density
1	13820	Birmingham-Hoover, AL Metro Area	78.0
7	16740	Charlotte-Concord-Gastonia, NC-SC Metro Area	114.4
18	17140	Cincinnati, OH-KY-IN Metro Area	104.9
34	18140	Columbus, OH Metro Area	117.2
44	24660	Greensboro-High Point, NC Metro Area	66.3
47	26900	Indianapolis-Carmel-Anderson, IN Metro Area	111.4
58	28140	Kansas City, MO-KS Metro Area	99.3
72	31140	Louisville/Jefferson County, KY-IN Metro Area	88.3
82	32820	Memphis, TN-MS-AR Metro Area	46.7
90	34980	Nashville-Davidson–Murfreesboro–Franklin, TN Metro Area	152.5
103	36540	Omaha-Council Bluffs, NE-IA Metro Area	96.7

Startups per 1,000 workers–metro and peers

load("../crunchbase/cb-result/data/cb_cbsa_index.rda")

index %>%
  filter(cbsa_code %in% c(target_cbsa, peer_cbsa))%>%
  select(cbsa_code, cbsa_name, msa_total, SCI = div) %>%
  unique()%>%
  left_join(cbsa_output[c("cbsa_code", "cbsa_emp")], by = "cbsa_code") %>%
  mutate(startup_per_million = msa_total/cbsa_emp*1000000)%>%
  knitr::kable()

cbsa_code	cbsa_name	msa_total	SCI	cbsa_emp	startup_per_million
31140	Louisville/Jefferson County, KY-IN	5	24.88571	577816	8.653274
34980	Nashville-Davidson–Murfreesboro–Franklin, TN	317	72.66204	855849	370.392441
28140	Kansas City, MO-KS	166	60.14050	954263	173.956236
18140	Columbus, OH	255	65.20317	874951	291.444892
32820	Memphis, TN-MS-AR	52	41.88420	539734	96.343755
13820	Birmingham-Hoover, AL	42	36.08807	436077	96.313266
16740	Charlotte-Concord-Gastonia, NC-SC	197	59.92288	1058398	186.130359
36540	Omaha-Council Bluffs, NE-IA	19	26.96706	419890	45.249946
17140	Cincinnati, OH-KY-IN	205	64.96580	946308	216.631372
26900	Indianapolis-Carmel-Anderson, IN	170	52.80212	911388	186.528679

Skills

% of population 25+ with AA/BA–metro and peers

cbsa_output %>%
  select(cbsa_code, cbsa_name, pct_edu_associate, pct_edu_baplus)%>%
  knitr::kable()

	cbsa_code	cbsa_name	pct_edu_associate	pct_edu_baplus
1	13820	Birmingham-Hoover, AL Metro Area	0.084	0.296
7	16740	Charlotte-Concord-Gastonia, NC-SC Metro Area	0.090	0.346
18	17140	Cincinnati, OH-KY-IN Metro Area	0.084	0.330
34	18140	Columbus, OH Metro Area	0.075	0.361
44	24660	Greensboro-High Point, NC Metro Area	0.088	0.289
47	26900	Indianapolis-Carmel-Anderson, IN Metro Area	0.078	0.339
58	28140	Kansas City, MO-KS Metro Area	0.076	0.365
72	31140	Louisville/Jefferson County, KY-IN Metro Area	0.084	0.286
82	32820	Memphis, TN-MS-AR Metro Area	0.070	0.277
90	34980	Nashville-Davidson–Murfreesboro–Franklin, TN Metro Area	0.073	0.348
103	36540	Omaha-Council Bluffs, NE-IA Metro Area	0.089	0.358

Employment-to-population ratio 18-64–metro and peers

cbsa_output %>% 
  select(cbsa_code, cbsa_name, epratio_total)%>%
  knitr::kable()

	cbsa_code	cbsa_name	epratio_total
1	13820	Birmingham-Hoover, AL Metro Area	0.569
7	16740	Charlotte-Concord-Gastonia, NC-SC Metro Area	0.630
18	17140	Cincinnati, OH-KY-IN Metro Area	0.625
34	18140	Columbus, OH Metro Area	0.642
44	24660	Greensboro-High Point, NC Metro Area	0.584
47	26900	Indianapolis-Carmel-Anderson, IN Metro Area	0.636
58	28140	Kansas City, MO-KS Metro Area	0.648
72	31140	Louisville/Jefferson County, KY-IN Metro Area	0.615
82	32820	Memphis, TN-MS-AR Metro Area	0.591
90	34980	Nashville-Davidson–Murfreesboro–Franklin, TN Metro Area	0.650
103	36540	Omaha-Council Bluffs, NE-IA Metro Area	0.675

Prevalence of health problems–metro and peers

library(RSocrata)
token <- Sys.getenv("RSocrata_token")

cities <- paste0(gsub("\\,.+","",cbsa_output$cbsa_name_short), collapse = "','")

cityhealth <- read.socrata(paste0("https://chronicdata.cdc.gov/resource/csmm-fdhi.csv?category=Health Outcomes&$where=cityname in",
                                  "('",cities,"')"),token)

# peer comparison
cityhealth %>% 
  filter(measureid %in% c("MHLTH", "PHLTH")) %>%
  filter(geographiclevel == "City") %>%
  filter(datavaluetypeid == "AgeAdjPrv") %>%
  select(cityname, measure, data_value, year) %>%
  group_by(cityname, measure)%>%
  summarise(data_value = mean(data_value))%>%
  pivot_wider(names_from = "measure", values_from = "data_value") %>%
  knitr::kable()

cityname	Mental health not good for >=14 days among adults aged >=18 Years	Physical health not good for >=14 days among adults aged >=18 Years
Birmingham	16.80	17.10
Charlotte	12.50	12.10
Cincinnati	16.00	15.20
Columbus	14.65	13.40
Indianapolis	14.80	14.00
Kansas City	14.55	14.55
Louisville	14.90	15.10
Memphis	15.80	16.10
Nashville	13.80	13.00

Struggling families? (I think this may not be available?)–metro and peers

not available

Share of workers who are low-wage–metro and peers

cbsa_low_wage_worker %>%
  filter(cbsa_code %in% c(target_cbsa, peer_cbsa)) %>%
  filter(population %in% c("Low-wage workers","Workers"))%>%
  select(cbsa_code, cbsa_name, population, total)%>%
  pivot_wider(names_from = "population", values_from = "total") %>%
  mutate(pct_low_wage = `Low-wage workers`/`Workers`) %>%
  knitr::kable()

cbsa_code	cbsa_name	Workers	Low-wage workers	pct_low_wage
13820	Birmingham-Hoover, AL	427390	168857	0.3950888
16740	Charlotte-Concord-Gastonia, NC-SC	985032	410718	0.4169590
17140	Cincinnati, OH-KY-IN	862747	309114	0.3582904
18140	Columbus, OH	797816	299778	0.3757483
24660	Greensboro-High Point, NC	304540	134668	0.4422014
26900	Indianapolis-Carmel-Anderson, IN	811297	322005	0.3969015
28140	Kansas City, MO-KS	877608	338260	0.3854340
31140	Louisville/Jefferson County, KY-IN	508070	203787	0.4011002
32820	Memphis, TN-MS-AR	483660	212111	0.4385539
34980	Nashville-Davidson–Murfreesboro–Franklin, TN	784841	337988	0.4306452
36540	Omaha-Council Bluffs, NE-IA	413998	162415	0.3923087

Share of population out-of-work–metro and peers

Note: metro aggregates only include core counties, as listed

names <- co_oow %>%
  filter(cbsa_code %in% c(target_cbsa, peer_cbsa)) %>%
  select(cbsa_code, cbsa_name, pl_name) %>%
  unique()%>%
  group_by(cbsa_code) %>%
  summarise(cbsa_name = last(cbsa_name),
            core_counties = paste0(pl_name, collapse = ", "))


co_oow %>%
  filter(cbsa_code %in% c(target_cbsa, peer_cbsa))%>%
  filter(population %in% c("Out-of-work population","Universe population")) %>%
  select(stco_code, age, cbsa_code, cbsa_name, pl_name, population, total) %>%
  group_by(cbsa_code, population, age) %>%
  summarise(total = sum(total))%>%
  pivot_wider(names_from = "population", values_from = "total") %>%
  mutate(pct_oow = `Out-of-work population`/`Universe population`) %>%
  select(-contains("population")) %>%
  pivot_wider(names_from = "age", values_from = "pct_oow")%>%
  left_join(names, by = "cbsa_code")%>%
  knitr::kable()

cbsa_code	18-24	25-64	cbsa_name	core_counties
13820	0.1733161	0.1283212	Birmingham-Hoover, AL	Jefferson County, AL, NA
16740	0.1558372	0.1239217	Charlotte-Concord-Gastonia, NC-SC	Mecklenburg County, NC, NA
17140	0.1478477	0.1300298	Cincinnati, OH-KY-IN	Hamilton County, OH, NA
18140	0.1103143	0.1161686	Columbus, OH	Franklin County, OH, NA
26900	0.1738600	0.1454038	Indianapolis-Carmel-Anderson, IN	Indianapolis, IN, NA
28140	0.1639243	0.1059291	Kansas City, MO-KS	Jackson County, MO, Johnson County, KS, NA
31140	0.1331677	0.1186111	Louisville/Jefferson County, KY-IN	Louisville/Jefferson County, KY, NA
32820	0.2271379	0.1522109	Memphis, TN-MS-AR	Shelby County, TN, NA
34980	0.1434097	0.1179778	Nashville-Davidson–Murfreesboro–Franklin, TN	Nashville-Davidson, TN, NA
36540	NA	0.0994130	NA	Douglas County, NE

Digital skills demand, 2002/2016–metro

	cbsa_code	cbsa_name	cbsa_pct_high_02	cbsa_pct_medium_02	cbsa_pct_low_02	cbsa_pct_high_16	cbsa_pct_medium_16	cbsa_pct_low_16
1	13820	Birmingham-Hoover, AL Metro Area	0.0447100	0.4019414	0.5533486	0.2213629	0.5019834	0.2766537
7	16740	Charlotte-Concord-Gastonia, NC-SC Metro Area	0.0393641	0.3869759	0.5736600	0.2411426	0.4735609	0.2852965
18	17140	Cincinnati, OH-KY-IN Metro Area	0.0448693	0.3812387	0.5738921	0.2285413	0.4799647	0.2914940
34	18140	Columbus, OH Metro Area	0.0573894	0.3825038	0.5601068	0.2483362	0.4617268	0.2899370
44	24660	Greensboro-High Point, NC Metro Area	0.0419281	0.3782357	0.5798363	0.2043621	0.4620994	0.3335386
47	26900	Indianapolis-Carmel-Anderson, IN Metro Area	0.0474312	0.3844814	0.5680874	0.2162944	0.4660410	0.3176646
58	28140	Kansas City, MO-KS Metro Area	0.0600598	0.3875261	0.5524141	0.2577715	0.4674781	0.2747504
72	31140	Louisville/Jefferson County, KY-IN Metro Area	0.0432075	0.3834751	0.5733174	0.2015948	0.4749915	0.3234137
82	32820	Memphis, TN-MS-AR Metro Area	0.0354234	0.3712143	0.5933623	0.1951220	0.4507223	0.3541557
90	34980	Nashville-Davidson–Murfreesboro–Franklin, TN Metro Area	0.0449740	0.3749311	0.5800949	0.2387505	0.4739482	0.2873012
103	36540	Omaha-Council Bluffs, NE-IA Metro Area	0.0640518	0.3803509	0.5555973	0.2418920	0.4655395	0.2925685

Access

Job density change by block group?–metro

we don’t have block group data in the warehouse yet - can ask Joanne

Geographic of prosperity trend (UVA data)–metro + MW/Southeastern trendlines

http://statchatva.org/changing-shape-of-american-cities/

Prevalence of health problems by census tract?–metro

Only available for core city

tract_health <- cityhealth %>%
  filter(measureid %in% c("MHLTH", "PHLTH")) %>%
  filter(geographiclevel == "Census Tract") %>%
  mutate(stcotr_code = str_pad(tractfips, 11, "left","0"))%>%
  select(year, stcotr_code, measureid, data_value) %>%
  pivot_wider(names_from = "measureid", values_from = "data_value")

map_health <- map %>%
  mutate(stco_code = str_sub(GEOID,1,5)) %>%
  left_join(tract_health, by = c("GEOID" = "stcotr_code"))

tm_shape(map_health %>%
  filter(stco_code %in% target_co) %>%
  filter(!is.na(year)))+
  tm_polygons(c("MHLTH", "PHLTH"), alpha = 0.7)+
  tm_facets(nrow = 1, sync = T)

Number of jobs reachable by travel time (including public transit)–metro

Number of jobs reachable by 30 minutes auto and 30 minutes transit

cbsa_output %>% 
  select(cbsa_code, cbsa_name,auto_30, transit_30, ratio_30)%>%
  knitr::kable()

	cbsa_code	cbsa_name	auto_30	transit_30	ratio_30
1	13820	Birmingham-Hoover, AL Metro Area	268251	2713	98.87615
7	16740	Charlotte-Concord-Gastonia, NC-SC Metro Area	450125	7682	58.59477
18	17140	Cincinnati, OH-KY-IN Metro Area	494927	7080	69.90494
34	18140	Columbus, OH Metro Area	605435	10857	55.76448
44	24660	Greensboro-High Point, NC Metro Area	NA	NA	NA
47	26900	Indianapolis-Carmel-Anderson, IN Metro Area	556698	7491	74.31558
58	28140	Kansas City, MO-KS Metro Area	615321	6864	89.64467
72	31140	Louisville/Jefferson County, KY-IN Metro Area	423448	7263	58.30208
82	32820	Memphis, TN-MS-AR Metro Area	402266	NA	NA
90	34980	Nashville-Davidson–Murfreesboro–Franklin, TN Metro Area	307872	5380	57.22528
103	36540	Omaha-Council Bluffs, NE-IA Metro Area	NA	NA	NA

map: http://access.umn.edu/research/america/transit/2018/maps/index.html

Are the NCES data behind the proficiency map downloaded and generally available, or did you do those special for BHM?

You can download school level data from NCES. we aggregated school level data to various geography.

tract_school <- final %>%
  mutate(stco_code = str_sub(fips,1,5)) %>%
  filter(stco_code %in% target_co) %>%
  mutate(stcotr_code = str_sub(fips, 1,11)) %>%
  group_by(stcotr_code) %>%
  summarise(pct_pass = sum(ALL_TOTAL_passed_H)/sum(ALL_TOTAL_total))

map_school <- map %>%
  mutate(stco_code = str_sub(GEOID,1,5)) %>%
  left_join(tract_school, by = c("GEOID" = "stcotr_code"))


tm_shape(map_school%>%
  filter(str_sub(GEOID,1,5) %in% target_co) %>%
    mutate(lab = scales::percent(pct_pass)))+
  tm_polygons(col = "pct_pass", id = "lab", alpha = 0.7)

# map_school %>%
#   filter(str_sub(GEOID,1,5) %in% target_co)%>%
#   ggplot(aes(fill = pct_pass))+
#   scale_fill_distiller(type = "div", palette = "PiYG")+
#   geom_sf()+
#   coord_sf(crs = 26911)

Metro market assessment

Sifan

2020-02-28

Dynamism

R&D per 1,000 workers–metro and peers

Do we have rankings for UofL by research focus similar to UAB stats?

Startups launched at universities–metro and peers

INC 5000 high growth companies–metro and peers

Startups per 1,000 workers–metro and peers

Skills

% of population 25+ with AA/BA–metro and peers

Employment-to-population ratio 18-64–metro and peers

Prevalence of health problems–metro and peers

Struggling families? (I think this may not be available?)–metro and peers

Digital skills demand, 2002/2016–metro

Access

Job density change by block group?–metro

Geographic of prosperity trend (UVA data)–metro + MW/Southeastern trendlines

Prevalence of health problems by census tract?–metro

Number of jobs reachable by travel time (including public transit)–metro

Are the NCES data behind the proficiency map downloaded and generally available, or did you do those special for BHM?

Metro market assessment

Sifan

2020-02-28

Dynamism

Shift share (job change by industry above/below national average)–for metro

Tradable share of employment–metro and peers

Change in share of employment in Advanced Industries–metro and peers

R&D per 1,000 workers–metro and peers

Do we have rankings for UofL by research focus similar to UAB stats?

Startups launched at universities–metro and peers

INC 5000 high growth companies–metro and peers

Startups per 1,000 workers–metro and peers

Skills

% of population 25+ with AA/BA–metro and peers

Employment-to-population ratio 18-64–metro and peers

Prevalence of health problems–metro and peers

Struggling families? (I think this may not be available?)–metro and peers

Share of workers who are low-wage–metro and peers

Share of population out-of-work–metro and peers

Digital skills demand, 2002/2016–metro

Access

Job density change by block group?–metro

Geographic of prosperity trend (UVA data)–metro + MW/Southeastern trendlines

Prevalence of health problems by census tract?–metro

Number of jobs reachable by travel time (including public transit)–metro

Are the NCES data behind the proficiency map downloaded and generally available, or did you do those special for BHM?