Library

# Turn off scientific notation
options(scipen=999)

Functions

# Load packages
library(here)        # relative file paths for reproducibility
library(tidyverse)   # data wrangling
library(stringi)     # string data wrangling
library(tigris)      # US census TIGER/Line shapefiles
library(ggplot2)     # data visualization
library(cowplot)     # data visualization plotting
library(gridExtra)   # grid for data visualizations
library(biscale)     # bivariate mapping
library(kableExtra)  # table formatting
library(scales)      # palette and number formatting
library(cluster)     # clustering algorithms
library(factoextra)  # clustering algorithms & visualization
library(moments)     # skewness and kurtosis testing

import::here( "fips_census_regions",
              "load_svi_data",
              "merge_svi_data",
              "census_division",
              "flag_summarize",
              "summarize_county_nmtc",
              "summarize_county_lihtc",
              "elbow_plot",
             # notice the use of here::here() that points to the .R file
             # where all these R objects are created
             .from = here::here("analysis/project_data_steps_Jazzy.R"),
             .character_only = TRUE)

census_division

## [1] "Pacific Division"

Data

Load Data

# Load SVI data sets
svi_2010 <- readRDS(here::here("data/raw/Census_Data_SVI/svi_2010_trt10.rds"))
svi_2020 <- readRDS(here::here("data/raw/Census_Data_SVI/svi_2020_trt10.rds"))

# Load mapping data sets
svi_county_map2010 <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_county_svi_flags10.rds")))

svi_county_map2020 <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_county_svi_flags20.rds")))

divisional_st_sf <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_st_sf.rds")))

# Load NMTC & LIHTC Tract Eligibility Data
orig_nmtc <- readxl::read_excel(here::here("data/raw/NMTC_LIHTC_tracts/nmtc_2011-2015_lic_110217.xlsx"), sheet="NMTC LICs 2011-2015 ACS")

high_migration_nmtc <- readxl::read_excel(here::here("data/raw/NMTC_LIHTC_tracts/nmtc_2011-2015_lic_110217.xlsx"), sheet="High migration tracts", skip=1)

nmtc_awards_data <- readxl::read_excel(here::here("data/raw/NMTC_LIHTC_tracts/NMTC_Public_Data_Release_includes_FY_2021_Data_final.xlsx"), sheet = "Projects 2 - Data Set PUBLISH.P")

lihtc_eligible <- readxl::read_excel(here::here("data/raw/NMTC_LIHTC_tracts/qct_data_2010_2011_2012.xlsx"))

lihtc_projects <- read.csv(here::here("data/raw/NMTC_LIHTC_tracts/lihtcpub/LIHTCPUB.csv"))

# National 2010 Data
svi_2010_national <- load_svi_data(svi_2010, percentile=.75)
svi_2010_national %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

# Divisional 2010 Data
svi_2010_divisional <- load_svi_data(svi_2010, rank_by = "divisional", location = census_division, percentile=.75)
svi_2010_divisional %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

# National 2020 Data
svi_2020_national <- load_svi_data(svi_2020, percentile=.75)
svi_2020_national %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

# Divisional 2020 Data
svi_2020_divisional <- load_svi_data(svi_2020, rank_by = "divisional", location =  census_division, percentile=.75)
svi_2020_divisional %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

Merge 2010 and 2020 Data

# Find tracts with divisional data in both 2010 and 2020
svi_divisional <- merge_svi_data(svi_2010_divisional, svi_2020_divisional)
svi_divisional %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

# Find tracts with divisional data in both 2010 and 2020
svi_national <- merge_svi_data(svi_2010_national, svi_2020_national)
svi_national %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

NMTC Data Wrangling

orig_nmtc_df <- orig_nmtc %>% 
  rename("GEOID10" = "2010 Census Tract Number FIPS code. GEOID",
         "nmtc_eligibility_orig" = "Does Census Tract Qualify For NMTC Low-Income Community (LIC) on Poverty or Income Criteria?")

orig_nmtc_df %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID10 OMB Metro/Non-metro Designation, July 2015 (OMB 15-01) nmtc_eligibility_orig Census Tract Poverty Rate % (2011-2015 ACS) Does Census Tract Qualify on Poverty Criteria\>=20%? Census Tract Percent of Benchmarked Median Family Income (%) 2011-2015 ACS Does Census Tract Qualify on Median Family Income Criteria\<=80%? Census Tract Unemployment Rate (%) 2011-2015 County Code State Abbreviation State Name County Name Census Tract Unemployment to National Unemployment Ratio Is Tract Unemployment to National Unemployment Ratio \>1.5? Population for whom poverty status is determined 2011-2015 ACS
01001020100 Metropolitan No 8.1 No 122.930646878856 No 5.4 01001 AL Alabama Autauga 0.6506024096385542 No 1948
01001020200 Metropolitan Yes 25.5 Yes 82.402258244451573 No 13.3 01001 AL Alabama Autauga 1.6024096385542168 Yes 1983
01001020300 Metropolitan No 12.7 No 94.261422220719723 No 6.2 01001 AL Alabama Autauga 0.74698795180722888 No 2968
01001020400 Metropolitan No 2.1 No 116.82358310373388 No 10.8 01001 AL Alabama Autauga 1.3012048192771084 No 4423
01001020500 Metropolitan No 11.4 No 127.74293876033198 No 4.2 01001 AL Alabama Autauga 0.50602409638554213 No 10563
01001020600 Metropolitan No 14.4 No 111.98255607579317 No 10.9 01001 AL Alabama Autauga 1.3132530120481927 No 3851 
high_migration_nmtc_df <- high_migration_nmtc %>% rename("GEOID10" = "2010 Census Tract Number FIPS code GEOID")

high_migration_nmtc_df %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID10 20-year County population loss 1990-2010 census % Median Family Income (MFI) / Area Income 2011-2015 (between 80%-85% MFI)
01087231601 -0.1394416 82.06754
05039970300 -0.1558144 84.78236
08017960600 -0.2340426 84.36239
17067953800 -0.1061620 80.36788
17067954200 -0.1061620 84.48551
17067954300 -0.1061620 84.44497 
# See original doesn't have high migration tracts coded as eligible
orig_nmtc_df %>% filter(GEOID10 == "01087231601") %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID10 OMB Metro/Non-metro Designation, July 2015 (OMB 15-01) nmtc_eligibility_orig Census Tract Poverty Rate % (2011-2015 ACS) Does Census Tract Qualify on Poverty Criteria\>=20%? Census Tract Percent of Benchmarked Median Family Income (%) 2011-2015 ACS Does Census Tract Qualify on Median Family Income Criteria\<=80%? Census Tract Unemployment Rate (%) 2011-2015 County Code State Abbreviation State Name County Name Census Tract Unemployment to National Unemployment Ratio Is Tract Unemployment to National Unemployment Ratio \>1.5? Population for whom poverty status is determined 2011-2015 ACS
01087231601 Non-Metropolitan No 16.2 No 82.067544858242542 No 11.3 01087 AL Alabama Macon 1.3614457831325302 No 888 
# Add column to label tracts as high migration
high_migration_nmtc_df <- high_migration_nmtc_df %>% mutate(high_migration = "Yes")

# Join to original column
orig_nmtc_df <- left_join(orig_nmtc_df, high_migration_nmtc_df, join_by(GEOID10 == GEOID10))

# Update eligibility column with coalesce()
nmtc_df <- orig_nmtc_df %>% 
  mutate(nmtc_eligibility = coalesce(high_migration, nmtc_eligibility_orig))

nmtc_df %>% filter(GEOID10 == "01087231601") %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID10 OMB Metro/Non-metro Designation, July 2015 (OMB 15-01) nmtc_eligibility_orig Census Tract Poverty Rate % (2011-2015 ACS) Does Census Tract Qualify on Poverty Criteria\>=20%? Census Tract Percent of Benchmarked Median Family Income (%) 2011-2015 ACS Does Census Tract Qualify on Median Family Income Criteria\<=80%? Census Tract Unemployment Rate (%) 2011-2015 County Code State Abbreviation State Name County Name Census Tract Unemployment to National Unemployment Ratio Is Tract Unemployment to National Unemployment Ratio \>1.5? Population for whom poverty status is determined 2011-2015 ACS 20-year County population loss 1990-2010 census % Median Family Income (MFI) / Area Income 2011-2015 (between 80%-85% MFI) high_migration nmtc_eligibility
01087231601 Non-Metropolitan No 16.2 No 82.067544858242542 No 11.3 01087 AL Alabama Macon 1.3614457831325302 No 888 -0.1394416 82.06754 Yes Yes 
nmtc_eligible <- nmtc_df %>% 
  select(GEOID10, nmtc_eligibility, `County Code`, `County Name`, `State Abbreviation`, `State Name`) %>% 
  filter(tolower(nmtc_eligibility) == "yes")

nmtc_eligible %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID10 nmtc_eligibility County Code County Name State Abbreviation State Name
01001020200 Yes 01001 Autauga AL Alabama
01001020700 Yes 01001 Autauga AL Alabama
01001021100 Yes 01001 Autauga AL Alabama
01003010200 Yes 01003 Baldwin AL Alabama
01003010500 Yes 01003 Baldwin AL Alabama
01003010600 Yes 01003 Baldwin AL Alabama 
# Save just tract ID and eligibility
nmtc_eligible_df <- nmtc_eligible %>% select(GEOID10, nmtc_eligibility)
nmtc_eligible_df %>% head()

## # A tibble: 6 × 2
##   GEOID10     nmtc_eligibility
##   <chr>       <chr>           
## 1 01001020200 Yes             
## 2 01001020700 Yes             
## 3 01001021100 Yes             
## 4 01003010200 Yes             
## 5 01003010500 Yes             
## 6 01003010600 Yes

nmtc_awards <- nmtc_awards_data %>% 
  mutate(`2010 Census Tract` = str_pad(`2010 Census Tract`, 11, "left", pad=0)) %>%
  rename("GEOID10" =`2010 Census Tract`)

nmtc_awards %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
Project ID GEOID10 Metro/Non-Metro, 2010 Census Origination Year Community Development Entity (CDE) Name Project QLICI Amount Estimated Total Project Cost City State Zip Code QALICB Type Multi-CDE Multi-Tract Project
AK0001 02070000100 Non-Metropolitan 2008 Alaska Growth Capital BIDCO, Inc.  300000 300000 Aleknagik Alaska 99555 NRE NO NO
AK0002 02020001000 Metropolitan 2006 Alaska Growth Capital BIDCO, Inc.  1008750 1345000 Anchorage Alaska 99501 NRE NO NO
AK0003 02020000600 Metropolitan 2006 HEDC New Markets, Inc 5061506 8694457 Anchorage Alaska 99508 NRE NO NO
AK0004 02020001000 Metropolitan 2006 Alaska Growth Capital BIDCO, Inc.  187500 250000 Anchorage Alaska 99501 NRE NO NO
AK0006 02020001802 Metropolitan 2006 Alaska Growth Capital BIDCO, Inc.  750000 1180000 Anchorage Alaska 99507 NRE NO NO
AK0007 02020001900 Metropolitan 2006 Alaska Growth Capital BIDCO, Inc.  127500 150000 Anchorage Alaska 99503 NRE NO NO 
# Create character zip_code column:
nmtc_awards <- nmtc_awards %>% 
  mutate(zip_code = str_pad(`Zip Code`, 5, "left", pad=0))

nmtc_awards %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
Project ID GEOID10 Metro/Non-Metro, 2010 Census Origination Year Community Development Entity (CDE) Name Project QLICI Amount Estimated Total Project Cost City State Zip Code QALICB Type Multi-CDE Multi-Tract Project zip_code
AK0001 02070000100 Non-Metropolitan 2008 Alaska Growth Capital BIDCO, Inc.  300000 300000 Aleknagik Alaska 99555 NRE NO NO 99555
AK0002 02020001000 Metropolitan 2006 Alaska Growth Capital BIDCO, Inc.  1008750 1345000 Anchorage Alaska 99501 NRE NO NO 99501
AK0003 02020000600 Metropolitan 2006 HEDC New Markets, Inc 5061506 8694457 Anchorage Alaska 99508 NRE NO NO 99508
AK0004 02020001000 Metropolitan 2006 Alaska Growth Capital BIDCO, Inc.  187500 250000 Anchorage Alaska 99501 NRE NO NO 99501
AK0006 02020001802 Metropolitan 2006 Alaska Growth Capital BIDCO, Inc.  750000 1180000 Anchorage Alaska 99507 NRE NO NO 99507
AK0007 02020001900 Metropolitan 2006 Alaska Growth Capital BIDCO, Inc.  127500 150000 Anchorage Alaska 99503 NRE NO NO 99503 
# View tracts
nmtc_awards_pre2010 <- nmtc_awards %>% 
  filter(`Origination Year` <= 2010) %>% 
  count(GEOID10) %>% 
  rename("pre10_nmtc_project_cnt" = "n")

nmtc_awards_dollars_pre2010 <- nmtc_awards %>% 
  filter(`Origination Year` <= 2010) %>% 
  group_by(GEOID10) %>% 
  summarise(pre10_nmtc_dollars = sum(`Project QLICI Amount`, na.rm = TRUE))

nmtc_awards_pre2010 <- left_join(nmtc_awards_pre2010, 
                                 nmtc_awards_dollars_pre2010, 
                                 join_by(GEOID10 == GEOID10))

nmtc_awards_pre2010$pre10_nmtc_dollars_formatted <- scales::dollar_format()(nmtc_awards_pre2010$pre10_nmtc_dollars)

nmtc_awards_pre2010 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID10 pre10_nmtc_project_cnt pre10_nmtc_dollars pre10_nmtc_dollars_formatted
01059973500 1 5000000 \$5,000,000
01069041400 1 2500000 \$2,500,000
01073001902 1 14400000 \$14,400,000
01073002700 1 1000000 \$1,000,000
01073004200 1 5908129 \$5,908,129
01073004500 3 37950000 \$37,950,000 
nmtc_awards_post2010 <- nmtc_awards %>% 
  filter(`Origination Year` > 2010 & `Origination Year` <= 2020) %>% 
  count(GEOID10) %>% 
  rename("post10_nmtc_project_cnt" = "n")

nmtc_awards_dollars_post2010 <- nmtc_awards %>% 
  filter(`Origination Year` > 2010 & `Origination Year` <= 2020) %>% 
  group_by(GEOID10) %>% 
  summarise(post10_nmtc_dollars = sum(`Project QLICI Amount`, na.rm = TRUE))

nmtc_awards_post2010 <- left_join(nmtc_awards_post2010, 
                                  nmtc_awards_dollars_post2010, 
                                  join_by(GEOID10 == GEOID10))

nmtc_awards_post2010$post10_nmtc_dollars_formatted <- scales::dollar_format()(nmtc_awards_post2010$post10_nmtc_dollars)

nmtc_awards_post2010 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID10 post10_nmtc_project_cnt post10_nmtc_dollars post10_nmtc_dollars_formatted
0. 3 24200000 \$24,200,000
01003010200 1 408000 \$408,000
01003010300 1 9880000 \$9,880,000
01003010600 1 8000000 \$8,000,000
01003010904 1 22460000 \$22,460,000
01003011501 6 37147460 \$37,147,460 
# Divisional data
svi_divisional_nmtc_eligible <- left_join(svi_divisional, nmtc_eligible_df, join_by("GEOID_2010_trt" == "GEOID10")) %>% filter(tolower(nmtc_eligibility) == "yes")

svi_divisional_nmtc_eligible %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt FIPS_st FIPS_county FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 nmtc_eligibility
02013000100 02 013 000100 AK Alaska Aleutians East Borough 4 West Region 9 Pacific Division 3703 474 267 1212 3695 32.80108 0.7570 1 111 3163 3.509327 0.08691 0 25 158 15.82278 0.01337 0 17 109 15.59633 0.02605 0 42 267 15.73034 0.004754 0 1082 3017 35.863441 0.85420 1 2060 3112 66.195373 0.99990 1 127 3.429652 0.042400 0 315 8.506616 0.03961 0 182 2849 6.388206 0.077750 0 50 165 30.30303 0.8835 1 1070 3617 29.5825270 0.93700 1 3492 3703 94.30192 0.9141 1 474 8 1.687764 0.29250 0 42 8.8607595 0.8128 1 7 267 2.6217228 0.4003 0 77 267 28.8389513 0.96850 1 2969 3703 80.1782339 0.9940 1 2.702764 0.5611 3 1.980260 0.23800 2 0.9141 0.9047 1 3.46810 0.8902 3 9.065224 0.6397 9 3389 1199 988 698 3379 20.65700 0.5925 0 86 2414 3.562552 0.2665 0 67 607 11.037891 0.01803 0 74 381 19.42257 0.04067 0 141 988 14.27126 0.006988 0 354 2646 13.378685 0.61070 0 1345 3384 39.745863 0.99970 1 381 11.2422544 0.31390 0 443 13.07170 0.0988 0 339 2941.000 11.526692 0.386000 0 135 593.00 22.765599 0.7920 1 334 3276 10.1953602 0.72620 0 2939 3389.000 86.72175 0.8110 1 1199 38 3.169308 0.3474 0 69 5.754796 0.7806 1 30 988 3.0364372 0.36010 0 220 988.000 22.267207 0.9527 1 1035 3389 30.539982 0.9843 1 2.476388 0.4947 1 2.316900 0.37850 1 0.8110 0.8038 1 3.42510 0.8683 3 9.029388 0.6419 6 Yes
02016000100 02 016 000100 AK Alaska Aleutians West Census Area 4 West Region 9 Pacific Division 1774 1056 166 328 1231 26.64500 0.6553 0 15 1370 1.094890 0.01369 0 25 95 26.31579 0.09653 0 16 71 22.53521 0.05099 0 41 166 24.69880 0.029080 0 207 1330 15.563910 0.58390 0 484 973 49.743063 0.99520 1 53 2.987599 0.031800 0 182 10.259301 0.05188 0 147 747 19.678715 0.864200 1 19 96 19.79167 0.6606 0 79 1718 4.5983702 0.46890 0 1154 1774 65.05073 0.6522 0 1056 22 2.083333 0.31610 0 0 0.0000000 0.2497 0 10 166 6.0240964 0.6154 0 84 166 50.6024096 0.99320 1 1324 1774 74.6335964 0.9935 1 2.277170 0.4443 1 2.077380 0.27780 1 0.6522 0.6454 0 3.16790 0.7874 2 8.174650 0.5311 4 950 694 199 218 719 30.31989 0.7848 1 15 560 2.678571 0.1560 0 11 117 9.401709 0.01305 0 14 82 17.07317 0.03088 0 25 199 12.56281 0.003541 0 48 681 7.048458 0.37250 0 238 721 33.009709 0.99890 1 116 12.2105263 0.37310 0 195 20.52632 0.4153 0 113 526.000 21.482890 0.893100 1 31 98.00 31.632653 0.9318 1 17 900 1.8888889 0.29830 0 713 950.000 75.05263 0.6900 0 694 17 2.449568 0.3163 0 0 0.000000 0.2466 0 7 199 3.5175879 0.39980 0 68 199.000 34.170854 0.9826 1 274 950 28.842105 0.9832 1 2.315741 0.4476 2 2.911600 0.70420 2 0.6900 0.6839 0 2.92850 0.6794 2 8.845841 0.6188 6 Yes
02020000300 02 020 000300 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 6308 1834 1707 1137 5839 19.47251 0.4988 0 59 1024 5.761719 0.26830 0 11 11 100.00000 0.99780 1 609 1696 35.90802 0.17490 0 620 1707 36.32103 0.215100 0 85 2458 3.458096 0.12670 0 125 4961 2.519653 0.02643 0 0 0.000000 0.003301 0 2744 43.500317 0.99640 1 54 2007 2.690583 0.007821 0 301 1635 18.40979 0.6168 0 11 5308 0.2072344 0.06620 0 2167 6308 34.35320 0.3715 0 1834 24 1.308615 0.27080 0 0 0.0000000 0.2497 0 10 1707 0.5858231 0.1573 0 10 1707 0.5858231 0.07765 0 469 6308 7.4350032 0.9359 1 1.135330 0.1355 0 1.690522 0.13070 1 0.3715 0.3677 0 1.69135 0.1520 1 4.888702 0.1113 2 8256 1834 1731 1603 6583 24.35060 0.6772 0 95 1105 8.597285 0.8029 1 7 16 43.750000 0.91050 1 1127 1715 65.71429 0.88900 1 1134 1731 65.51127 0.985700 1 148 3181 4.652625 0.23830 0 80 5243 1.525844 0.08775 0 119 1.4413760 0.00975 0 3086 37.37888 0.9880 1 193 2171.088 8.889551 0.188800 0 136 1429.97 9.510687 0.3216 0 0 7040 0.0000000 0.02391 0 3808 8256.294 46.12239 0.4209 0 1834 127 6.924755 0.4701 0 0 0.000000 0.2466 0 13 1731 0.7510110 0.12710 0 179 1731.395 10.338487 0.7913 1 1673 8256 20.264050 0.9768 1 2.791850 0.5891 2 1.532060 0.07776 1 0.4209 0.4172 0 2.61190 0.5330 2 7.356710 0.4139 5 Yes
02020000400 02 020 000400 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 5991 1360 1246 628 4602 13.64624 0.3404 0 117 924 12.662338 0.81630 1 0 12 0.00000 0.00240 0 761 1234 61.66937 0.78730 1 761 1246 61.07544 0.929600 1 24 1995 1.203008 0.03078 0 55 4075 1.349693 0.01061 0 0 0.000000 0.003301 0 2117 35.336338 0.93430 1 86 1820 4.725275 0.029420 0 138 1246 11.07544 0.3314 0 14 5099 0.2745636 0.07606 0 1539 5991 25.68853 0.2688 0 1360 0 0.000000 0.09395 0 10 0.7352941 0.5653 0 38 1246 3.0497592 0.4365 0 21 1246 1.6853933 0.19700 0 1389 5991 23.1847772 0.9762 1 2.127690 0.4021 2 1.374481 0.05613 1 0.2688 0.2660 0 2.26895 0.3836 1 6.039921 0.2480 4 5090 1440 1377 657 4243 15.48433 0.4416 0 82 1435 5.714286 0.5455 0 0 0 NaN NA NA 912 1377 66.23094 0.89700 1 912 1377 66.23094 0.987300 1 28 1928 1.452282 0.05471 0 82 3349 2.448492 0.16300 0 12 0.2357564 0.00585 0 1446 28.40864 0.8460 1 68 1902.717 3.573837 0.008563 0 56 1032.00 5.426357 0.1342 0 9 4411 0.2040354 0.06983 0 2444 5089.955 48.01614 0.4425 0 1440 38 2.638889 0.3255 0 0 0.000000 0.2466 0 7 1377 0.5083515 0.09514 0 92 1377.000 6.681191 0.6436 0 820 5090 16.110020 0.9730 1 2.192110 0.4140 1 1.064443 0.02264 1 0.4425 0.4386 0 2.28384 0.3878 1 5.982893 0.2198 3 Yes
02020000500 02 020 000500 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 1872 979 956 384 1872 20.51282 0.5238 0 30 957 3.134796 0.06633 0 56 149 37.58389 0.39630 0 321 807 39.77695 0.23920 0 377 956 39.43515 0.311800 0 190 1139 16.681299 0.60890 0 314 2109 14.888573 0.48950 0 221 11.805556 0.574800 0 434 23.183761 0.43040 0 307 1475 20.813559 0.894900 1 91 385 23.63636 0.7603 1 129 1793 7.1946458 0.58420 0 1048 1872 55.98291 0.5787 0 979 578 59.039837 0.95260 1 0 0.0000000 0.2497 0 22 956 2.3012552 0.3729 0 78 956 8.1589958 0.68640 0 0 1872 0.0000000 0.3743 0 2.000330 0.3676 0 3.244600 0.82880 2 0.5787 0.5727 0 2.63590 0.5502 1 8.459530 0.5669 3 2039 1074 985 624 2039 30.60324 0.7906 1 119 1125 10.577778 0.8901 1 42 138 30.434783 0.56020 0 361 847 42.62102 0.32940 0 403 985 40.91371 0.614800 0 61 1468 4.155313 0.20970 0 350 1966 17.802645 0.95510 1 200 9.8087298 0.22920 0 322 15.79205 0.1707 0 233 1644.283 14.170309 0.581400 0 143 338.00 42.307692 0.9859 1 48 1920 2.5000000 0.35480 0 1060 2039.045 51.98512 0.4840 0 1074 642 59.776536 0.9485 1 0 0.000000 0.2466 0 39 985 3.9593909 0.43720 0 230 985.000 23.350254 0.9573 1 0 2039 0.000000 0.1370 0 3.460300 0.7607 3 2.322000 0.38140 1 0.4840 0.4797 0 2.72660 0.5866 2 8.992900 0.6375 6 Yes
02020000600 02 020 000600 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 6502 2547 2297 2979 6488 45.91554 0.9023 1 405 2951 13.724161 0.85840 1 125 420 29.76190 0.16330 0 940 1877 50.07991 0.48980 0 1065 2297 46.36482 0.564500 0 1084 3392 31.957547 0.82070 1 1929 6823 28.272021 0.86800 1 301 4.629345 0.088050 0 2216 34.081821 0.90820 1 1084 4580 23.668122 0.942300 1 710 1495 47.49164 0.9914 1 479 5750 8.3304348 0.62260 0 4671 6502 71.83943 0.7062 0 2547 512 20.102081 0.74490 0 24 0.9422850 0.5880 0 384 2297 16.7174576 0.8552 1 514 2297 22.3770135 0.94220 1 52 6502 0.7997539 0.7682 1 4.013900 0.8673 4 3.552550 0.92110 3 0.7062 0.6989 0 3.89850 0.9653 3 12.171150 0.9496 10 7641 2942 2510 3841 7418 51.77946 0.9712 1 278 2450 11.346939 0.9134 1 273 771 35.408560 0.73590 0 943 1739 54.22657 0.63530 0 1216 2510 48.44622 0.817700 1 562 4063 13.832144 0.62240 0 702 7423 9.457093 0.74250 0 614 8.0355974 0.13620 0 3131 40.97631 0.9967 1 994 4292.000 23.159366 0.926700 1 685 1527.00 44.859201 0.9910 1 222 6659 3.3338339 0.42160 0 5706 7641.000 74.67609 0.6868 0 2942 331 11.250850 0.5797 0 33 1.121686 0.6182 0 284 2510 11.3147410 0.76010 1 564 2510.000 22.470119 0.9540 1 204 7641 2.669808 0.8654 1 4.067200 0.8945 3 3.472200 0.91650 3 0.6868 0.6807 0 3.77740 0.9460 3 12.003600 0.9504 9 Yes 
# National data
svi_national_nmtc_eligible <- left_join(svi_national, nmtc_eligible_df, join_by("GEOID_2010_trt" == "GEOID10")) %>% filter(tolower(nmtc_eligibility) == "yes")

svi_national_nmtc_eligible %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt FIPS_st FIPS_county FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 nmtc_eligibility
01001020200 01 001 020200 AL Alabama Autauga County 3 South Region 6 East South Central Division 2020 816 730 495 1992 24.84940 0.5954 0 68 834 8.153477 0.57540 0 49 439 11.16173 0.02067 0 105 291 36.08247 0.30190 0 154 730 21.09589 0.09312 0 339 1265 26.79842 0.8392 1 313 2012 15.55666 0.6000 0 204 10.09901 0.3419 0 597 29.55446 0.8192 1 359 1515 23.69637 0.8791 1 132 456 28.947368 0.8351 1 15 1890 0.7936508 0.40130 0 1243 2020 61.53465 0.7781 1 816 0 0.0000000 0.1224 0 34 4.1666667 0.6664 0 13 730 1.780822 0.5406 0 115 730 15.753425 0.8382 1 0 2020 0.0000 0.3640 0 2.70312 0.5665 1 3.27660 0.8614 3 0.7781 0.7709 1 2.5316 0.5047 1 9.28942 0.6832 6 1757 720 573 384 1511 25.41363 0.6427 0 29 717 4.044630 0.4132 0 33 392 8.418367 0.03542 0 116 181 64.08840 0.9086 1 149 573 26.00349 0.40410 0 139 1313 10.58644 0.5601 0 91 1533 5.936073 0.4343 0 284 16.163916 0.5169 0 325 18.49744 0.2851 0 164 1208.000 13.57616 0.4127 0 42 359.0000 11.699164 0.3998 0 0 1651 0.0000000 0.09479 0 1116 1757.000 63.51736 0.7591 1 720 3 0.4166667 0.2470 0 5 0.6944444 0.5106 0 9 573 1.5706806 0.4688 0 57 573.000 9.947644 0.7317 0 212 1757 12.0660216 0.9549 1 2.45440 0.4888 0 1.70929 0.1025 0 0.7591 0.7527 1 2.9130 0.6862 1 7.83579 0.4802 2 Yes
01001020700 01 001 020700 AL Alabama Autauga County 3 South Region 6 East South Central Division 2664 1254 1139 710 2664 26.65165 0.6328 0 29 1310 2.213741 0.05255 0 134 710 18.87324 0.13890 0 187 429 43.58974 0.47090 0 321 1139 28.18262 0.28130 0 396 1852 21.38229 0.7478 0 345 2878 11.98749 0.4459 0 389 14.60210 0.6417 0 599 22.48499 0.4007 0 510 2168 23.52399 0.8752 1 228 712 32.022472 0.8712 1 0 2480 0.0000000 0.09298 0 694 2664 26.05105 0.5138 0 1254 8 0.6379585 0.2931 0 460 36.6826156 0.9714 1 0 1139 0.000000 0.1238 0 125 1139 10.974539 0.7477 0 0 2664 0.0000 0.3640 0 2.16035 0.4069 0 2.88178 0.6997 2 0.5138 0.5090 0 2.5000 0.4882 1 8.05593 0.5185 3 3562 1313 1248 1370 3528 38.83220 0.8512 1 128 1562 8.194622 0.7935 1 168 844 19.905213 0.44510 0 237 404 58.66337 0.8359 1 405 1248 32.45192 0.60420 0 396 2211 17.91045 0.7857 1 444 3547 12.517620 0.7758 1 355 9.966311 0.1800 0 954 26.78271 0.7923 1 629 2593.000 24.25762 0.8730 1 171 797.0000 21.455458 0.7186 0 0 3211 0.0000000 0.09479 0 1009 3562.000 28.32678 0.4668 0 1313 14 1.0662605 0.3165 0 443 33.7395278 0.9663 1 73 1248 5.8493590 0.8211 1 17 1248.000 1.362180 0.1554 0 112 3562 3.1443010 0.8514 1 3.81040 0.8569 4 2.65869 0.5847 2 0.4668 0.4629 0 3.1107 0.7714 3 10.04659 0.7851 9 Yes
01001021100 01 001 021100 AL Alabama Autauga County 3 South Region 6 East South Central Division 3298 1502 1323 860 3298 26.07641 0.6211 0 297 1605 18.504673 0.94340 1 250 1016 24.60630 0.32070 0 74 307 24.10423 0.11920 0 324 1323 24.48980 0.17380 0 710 2231 31.82429 0.8976 1 654 3565 18.34502 0.7018 0 411 12.46210 0.5001 0 738 22.37720 0.3934 0 936 2861 32.71583 0.9807 1 138 825 16.727273 0.5715 0 9 3155 0.2852615 0.25010 0 1979 3298 60.00606 0.7703 1 1502 14 0.9320905 0.3234 0 659 43.8748336 0.9849 1 44 1323 3.325775 0.7062 0 137 1323 10.355253 0.7313 0 0 3298 0.0000 0.3640 0 3.33770 0.7351 2 2.69580 0.6028 1 0.7703 0.7631 1 3.1098 0.7827 1 9.91360 0.7557 5 3499 1825 1462 1760 3499 50.30009 0.9396 1 42 966 4.347826 0.4539 0 426 1274 33.437991 0.85200 1 52 188 27.65957 0.1824 0 478 1462 32.69494 0.61110 0 422 2488 16.96141 0.7638 1 497 3499 14.204058 0.8246 1 853 24.378394 0.8688 1 808 23.09231 0.5829 0 908 2691.100 33.74084 0.9808 1 179 811.6985 22.052524 0.7323 0 8 3248 0.2463054 0.26220 0 1986 3498.713 56.76373 0.7175 0 1825 29 1.5890411 0.3551 0 576 31.5616438 0.9594 1 88 1462 6.0191518 0.8269 1 148 1461.993 10.123166 0.7364 0 38 3499 1.0860246 0.7013 0 3.59300 0.8073 3 3.42700 0.9156 2 0.7175 0.7114 0 3.5791 0.9216 2 11.31660 0.9150 7 Yes
01003010200 01 003 010200 AL Alabama Baldwin County 3 South Region 6 East South Central Division 2612 1220 1074 338 2605 12.97505 0.2907 0 44 1193 3.688181 0.14720 0 172 928 18.53448 0.13090 0 31 146 21.23288 0.09299 0 203 1074 18.90130 0.05657 0 455 1872 24.30556 0.8016 1 456 2730 16.70330 0.6445 0 401 15.35222 0.6847 0 563 21.55436 0.3406 0 410 2038 20.11776 0.7755 1 64 779 8.215661 0.2181 0 0 2510 0.0000000 0.09298 0 329 2612 12.59571 0.3113 0 1220 38 3.1147541 0.4648 0 385 31.5573770 0.9545 1 20 1074 1.862197 0.5509 0 43 1074 4.003724 0.4088 0 0 2612 0.0000 0.3640 0 1.94057 0.3398 1 2.11188 0.2802 1 0.3113 0.3084 0 2.7430 0.6129 1 7.10675 0.3771 3 2928 1312 1176 884 2928 30.19126 0.7334 0 29 1459 1.987663 0.1356 0 71 830 8.554217 0.03726 0 134 346 38.72832 0.3964 0 205 1176 17.43197 0.12010 0 294 2052 14.32749 0.6940 0 219 2925 7.487179 0.5423 0 556 18.989071 0.6705 0 699 23.87295 0.6339 0 489 2226.455 21.96317 0.8122 1 191 783.8820 24.365914 0.7799 1 0 2710 0.0000000 0.09479 0 398 2927.519 13.59513 0.2511 0 1312 13 0.9908537 0.3111 0 400 30.4878049 0.9557 1 6 1176 0.5102041 0.2590 0 81 1176.202 6.886570 0.6115 0 7 2928 0.2390710 0.4961 0 2.22540 0.4183 0 2.99129 0.7634 2 0.2511 0.2490 0 2.6334 0.5496 1 8.10119 0.5207 3 Yes
01003010500 01 003 010500 AL Alabama Baldwin County 3 South Region 6 East South Central Division 4230 1779 1425 498 3443 14.46413 0.3337 0 166 1625 10.215385 0.71790 0 151 1069 14.12535 0.04638 0 196 356 55.05618 0.73830 0 347 1425 24.35088 0.17010 0 707 2945 24.00679 0.7967 1 528 4001 13.19670 0.5005 0 619 14.63357 0.6436 0 790 18.67612 0.1937 0 536 3096 17.31266 0.6572 0 165 920 17.934783 0.6102 0 20 4021 0.4973887 0.32320 0 754 4230 17.82506 0.4023 0 1779 97 5.4525014 0.5525 0 8 0.4496908 0.4600 0 63 1425 4.421053 0.7762 1 90 1425 6.315790 0.5691 0 787 4230 18.6052 0.9649 1 2.51890 0.5121 1 2.42790 0.4539 0 0.4023 0.3986 0 3.3227 0.8628 2 8.67180 0.6054 3 5877 1975 1836 820 5244 15.63692 0.3902 0 90 2583 3.484321 0.3361 0 159 1345 11.821561 0.10530 0 139 491 28.30957 0.1924 0 298 1836 16.23094 0.09053 0 570 4248 13.41808 0.6669 0 353 5247 6.727654 0.4924 0 1109 18.870172 0.6645 0 1144 19.46571 0.3411 0 717 4102.545 17.47696 0.6332 0 103 1286.1180 8.008596 0.2341 0 0 5639 0.0000000 0.09479 0 868 5877.481 14.76823 0.2709 0 1975 26 1.3164557 0.3359 0 45 2.2784810 0.6271 0 9 1836 0.4901961 0.2540 0 116 1835.798 6.318779 0.5811 0 633 5877 10.7708014 0.9507 1 1.97613 0.3410 0 1.96769 0.1961 0 0.2709 0.2686 0 2.7488 0.6077 1 6.96352 0.3406 1 Yes
01003010600 01 003 010600 AL Alabama Baldwin County 3 South Region 6 East South Central Division 3724 1440 1147 1973 3724 52.98067 0.9342 1 142 1439 9.867964 0.69680 0 235 688 34.15698 0.62950 0 187 459 40.74074 0.40290 0 422 1147 36.79163 0.55150 0 497 1876 26.49254 0.8354 1 511 3661 13.95794 0.5334 0 246 6.60580 0.1481 0 1256 33.72718 0.9305 1 496 2522 19.66693 0.7587 1 274 838 32.696897 0.8779 1 32 3479 0.9198045 0.42810 0 2606 3724 69.97852 0.8184 1 1440 21 1.4583333 0.3683 0 321 22.2916667 0.9036 1 97 1147 8.456844 0.8956 1 167 1147 14.559721 0.8209 1 0 3724 0.0000 0.3640 0 3.55130 0.7859 2 3.14330 0.8145 3 0.8184 0.8108 1 3.3524 0.8725 3 10.86540 0.8550 9 4115 1534 1268 1676 3997 41.93145 0.8814 1 294 1809 16.252073 0.9674 1 341 814 41.891892 0.94320 1 204 454 44.93392 0.5438 0 545 1268 42.98107 0.83620 1 624 2425 25.73196 0.9002 1 994 4115 24.155529 0.9602 1 642 15.601458 0.4841 0 1126 27.36331 0.8175 1 568 2989.000 19.00301 0.7045 0 212 715.0000 29.650350 0.8592 1 56 3825 1.4640523 0.53120 0 2715 4115.000 65.97813 0.7732 1 1534 0 0.0000000 0.1079 0 529 34.4850065 0.9685 1 101 1268 7.9652997 0.8795 1 89 1268.000 7.018927 0.6184 0 17 4115 0.4131227 0.5707 0 4.54540 0.9754 5 3.39650 0.9081 2 0.7732 0.7667 1 3.1450 0.7858 2 11.86010 0.9520 10 Yes 
# Join divisional data to nmtc_awards_pre2010, set count to 0 if no data
svi_divisional_nmtc_eligible <- 
  left_join(svi_divisional_nmtc_eligible, nmtc_awards_pre2010, join_by("GEOID_2010_trt" == "GEOID10")) %>%
  mutate(pre10_nmtc_project_cnt = if_else(is.na(pre10_nmtc_project_cnt), 0, pre10_nmtc_project_cnt)) %>%
    mutate(pre10_nmtc_dollars = if_else(is.na(pre10_nmtc_dollars), 0, pre10_nmtc_dollars)) %>%
    mutate(pre10_nmtc_dollars_formatted = if_else(is.na(pre10_nmtc_dollars_formatted), "$0", pre10_nmtc_dollars_formatted))

# View table
svi_divisional_nmtc_eligible %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt FIPS_st FIPS_county FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 nmtc_eligibility pre10_nmtc_project_cnt pre10_nmtc_dollars pre10_nmtc_dollars_formatted
02013000100 02 013 000100 AK Alaska Aleutians East Borough 4 West Region 9 Pacific Division 3703 474 267 1212 3695 32.80108 0.7570 1 111 3163 3.509327 0.08691 0 25 158 15.82278 0.01337 0 17 109 15.59633 0.02605 0 42 267 15.73034 0.004754 0 1082 3017 35.863441 0.85420 1 2060 3112 66.195373 0.99990 1 127 3.429652 0.042400 0 315 8.506616 0.03961 0 182 2849 6.388206 0.077750 0 50 165 30.30303 0.8835 1 1070 3617 29.5825270 0.93700 1 3492 3703 94.30192 0.9141 1 474 8 1.687764 0.29250 0 42 8.8607595 0.8128 1 7 267 2.6217228 0.4003 0 77 267 28.8389513 0.96850 1 2969 3703 80.1782339 0.9940 1 2.702764 0.5611 3 1.980260 0.23800 2 0.9141 0.9047 1 3.46810 0.8902 3 9.065224 0.6397 9 3389 1199 988 698 3379 20.65700 0.5925 0 86 2414 3.562552 0.2665 0 67 607 11.037891 0.01803 0 74 381 19.42257 0.04067 0 141 988 14.27126 0.006988 0 354 2646 13.378685 0.61070 0 1345 3384 39.745863 0.99970 1 381 11.2422544 0.31390 0 443 13.07170 0.0988 0 339 2941.000 11.526692 0.386000 0 135 593.00 22.765599 0.7920 1 334 3276 10.1953602 0.72620 0 2939 3389.000 86.72175 0.8110 1 1199 38 3.169308 0.3474 0 69 5.754796 0.7806 1 30 988 3.0364372 0.36010 0 220 988.000 22.267207 0.9527 1 1035 3389 30.539982 0.9843 1 2.476388 0.4947 1 2.316900 0.37850 1 0.8110 0.8038 1 3.42510 0.8683 3 9.029388 0.6419 6 Yes 0 0 \$0
02016000100 02 016 000100 AK Alaska Aleutians West Census Area 4 West Region 9 Pacific Division 1774 1056 166 328 1231 26.64500 0.6553 0 15 1370 1.094890 0.01369 0 25 95 26.31579 0.09653 0 16 71 22.53521 0.05099 0 41 166 24.69880 0.029080 0 207 1330 15.563910 0.58390 0 484 973 49.743063 0.99520 1 53 2.987599 0.031800 0 182 10.259301 0.05188 0 147 747 19.678715 0.864200 1 19 96 19.79167 0.6606 0 79 1718 4.5983702 0.46890 0 1154 1774 65.05073 0.6522 0 1056 22 2.083333 0.31610 0 0 0.0000000 0.2497 0 10 166 6.0240964 0.6154 0 84 166 50.6024096 0.99320 1 1324 1774 74.6335964 0.9935 1 2.277170 0.4443 1 2.077380 0.27780 1 0.6522 0.6454 0 3.16790 0.7874 2 8.174650 0.5311 4 950 694 199 218 719 30.31989 0.7848 1 15 560 2.678571 0.1560 0 11 117 9.401709 0.01305 0 14 82 17.07317 0.03088 0 25 199 12.56281 0.003541 0 48 681 7.048458 0.37250 0 238 721 33.009709 0.99890 1 116 12.2105263 0.37310 0 195 20.52632 0.4153 0 113 526.000 21.482890 0.893100 1 31 98.00 31.632653 0.9318 1 17 900 1.8888889 0.29830 0 713 950.000 75.05263 0.6900 0 694 17 2.449568 0.3163 0 0 0.000000 0.2466 0 7 199 3.5175879 0.39980 0 68 199.000 34.170854 0.9826 1 274 950 28.842105 0.9832 1 2.315741 0.4476 2 2.911600 0.70420 2 0.6900 0.6839 0 2.92850 0.6794 2 8.845841 0.6188 6 Yes 0 0 \$0
02020000300 02 020 000300 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 6308 1834 1707 1137 5839 19.47251 0.4988 0 59 1024 5.761719 0.26830 0 11 11 100.00000 0.99780 1 609 1696 35.90802 0.17490 0 620 1707 36.32103 0.215100 0 85 2458 3.458096 0.12670 0 125 4961 2.519653 0.02643 0 0 0.000000 0.003301 0 2744 43.500317 0.99640 1 54 2007 2.690583 0.007821 0 301 1635 18.40979 0.6168 0 11 5308 0.2072344 0.06620 0 2167 6308 34.35320 0.3715 0 1834 24 1.308615 0.27080 0 0 0.0000000 0.2497 0 10 1707 0.5858231 0.1573 0 10 1707 0.5858231 0.07765 0 469 6308 7.4350032 0.9359 1 1.135330 0.1355 0 1.690522 0.13070 1 0.3715 0.3677 0 1.69135 0.1520 1 4.888702 0.1113 2 8256 1834 1731 1603 6583 24.35060 0.6772 0 95 1105 8.597285 0.8029 1 7 16 43.750000 0.91050 1 1127 1715 65.71429 0.88900 1 1134 1731 65.51127 0.985700 1 148 3181 4.652625 0.23830 0 80 5243 1.525844 0.08775 0 119 1.4413760 0.00975 0 3086 37.37888 0.9880 1 193 2171.088 8.889551 0.188800 0 136 1429.97 9.510687 0.3216 0 0 7040 0.0000000 0.02391 0 3808 8256.294 46.12239 0.4209 0 1834 127 6.924755 0.4701 0 0 0.000000 0.2466 0 13 1731 0.7510110 0.12710 0 179 1731.395 10.338487 0.7913 1 1673 8256 20.264050 0.9768 1 2.791850 0.5891 2 1.532060 0.07776 1 0.4209 0.4172 0 2.61190 0.5330 2 7.356710 0.4139 5 Yes 0 0 \$0
02020000400 02 020 000400 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 5991 1360 1246 628 4602 13.64624 0.3404 0 117 924 12.662338 0.81630 1 0 12 0.00000 0.00240 0 761 1234 61.66937 0.78730 1 761 1246 61.07544 0.929600 1 24 1995 1.203008 0.03078 0 55 4075 1.349693 0.01061 0 0 0.000000 0.003301 0 2117 35.336338 0.93430 1 86 1820 4.725275 0.029420 0 138 1246 11.07544 0.3314 0 14 5099 0.2745636 0.07606 0 1539 5991 25.68853 0.2688 0 1360 0 0.000000 0.09395 0 10 0.7352941 0.5653 0 38 1246 3.0497592 0.4365 0 21 1246 1.6853933 0.19700 0 1389 5991 23.1847772 0.9762 1 2.127690 0.4021 2 1.374481 0.05613 1 0.2688 0.2660 0 2.26895 0.3836 1 6.039921 0.2480 4 5090 1440 1377 657 4243 15.48433 0.4416 0 82 1435 5.714286 0.5455 0 0 0 NaN NA NA 912 1377 66.23094 0.89700 1 912 1377 66.23094 0.987300 1 28 1928 1.452282 0.05471 0 82 3349 2.448492 0.16300 0 12 0.2357564 0.00585 0 1446 28.40864 0.8460 1 68 1902.717 3.573837 0.008563 0 56 1032.00 5.426357 0.1342 0 9 4411 0.2040354 0.06983 0 2444 5089.955 48.01614 0.4425 0 1440 38 2.638889 0.3255 0 0 0.000000 0.2466 0 7 1377 0.5083515 0.09514 0 92 1377.000 6.681191 0.6436 0 820 5090 16.110020 0.9730 1 2.192110 0.4140 1 1.064443 0.02264 1 0.4425 0.4386 0 2.28384 0.3878 1 5.982893 0.2198 3 Yes 0 0 \$0
02020000500 02 020 000500 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 1872 979 956 384 1872 20.51282 0.5238 0 30 957 3.134796 0.06633 0 56 149 37.58389 0.39630 0 321 807 39.77695 0.23920 0 377 956 39.43515 0.311800 0 190 1139 16.681299 0.60890 0 314 2109 14.888573 0.48950 0 221 11.805556 0.574800 0 434 23.183761 0.43040 0 307 1475 20.813559 0.894900 1 91 385 23.63636 0.7603 1 129 1793 7.1946458 0.58420 0 1048 1872 55.98291 0.5787 0 979 578 59.039837 0.95260 1 0 0.0000000 0.2497 0 22 956 2.3012552 0.3729 0 78 956 8.1589958 0.68640 0 0 1872 0.0000000 0.3743 0 2.000330 0.3676 0 3.244600 0.82880 2 0.5787 0.5727 0 2.63590 0.5502 1 8.459530 0.5669 3 2039 1074 985 624 2039 30.60324 0.7906 1 119 1125 10.577778 0.8901 1 42 138 30.434783 0.56020 0 361 847 42.62102 0.32940 0 403 985 40.91371 0.614800 0 61 1468 4.155313 0.20970 0 350 1966 17.802645 0.95510 1 200 9.8087298 0.22920 0 322 15.79205 0.1707 0 233 1644.283 14.170309 0.581400 0 143 338.00 42.307692 0.9859 1 48 1920 2.5000000 0.35480 0 1060 2039.045 51.98512 0.4840 0 1074 642 59.776536 0.9485 1 0 0.000000 0.2466 0 39 985 3.9593909 0.43720 0 230 985.000 23.350254 0.9573 1 0 2039 0.000000 0.1370 0 3.460300 0.7607 3 2.322000 0.38140 1 0.4840 0.4797 0 2.72660 0.5866 2 8.992900 0.6375 6 Yes 0 0 \$0
02020000600 02 020 000600 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 6502 2547 2297 2979 6488 45.91554 0.9023 1 405 2951 13.724161 0.85840 1 125 420 29.76190 0.16330 0 940 1877 50.07991 0.48980 0 1065 2297 46.36482 0.564500 0 1084 3392 31.957547 0.82070 1 1929 6823 28.272021 0.86800 1 301 4.629345 0.088050 0 2216 34.081821 0.90820 1 1084 4580 23.668122 0.942300 1 710 1495 47.49164 0.9914 1 479 5750 8.3304348 0.62260 0 4671 6502 71.83943 0.7062 0 2547 512 20.102081 0.74490 0 24 0.9422850 0.5880 0 384 2297 16.7174576 0.8552 1 514 2297 22.3770135 0.94220 1 52 6502 0.7997539 0.7682 1 4.013900 0.8673 4 3.552550 0.92110 3 0.7062 0.6989 0 3.89850 0.9653 3 12.171150 0.9496 10 7641 2942 2510 3841 7418 51.77946 0.9712 1 278 2450 11.346939 0.9134 1 273 771 35.408560 0.73590 0 943 1739 54.22657 0.63530 0 1216 2510 48.44622 0.817700 1 562 4063 13.832144 0.62240 0 702 7423 9.457093 0.74250 0 614 8.0355974 0.13620 0 3131 40.97631 0.9967 1 994 4292.000 23.159366 0.926700 1 685 1527.00 44.859201 0.9910 1 222 6659 3.3338339 0.42160 0 5706 7641.000 74.67609 0.6868 0 2942 331 11.250850 0.5797 0 33 1.121686 0.6182 0 284 2510 11.3147410 0.76010 1 564 2510.000 22.470119 0.9540 1 204 7641 2.669808 0.8654 1 4.067200 0.8945 3 3.472200 0.91650 3 0.6868 0.6807 0 3.77740 0.9460 3 12.003600 0.9504 9 Yes 5 11493716 \$11,493,716 
# Join national data to nmtc_awards_pre2010, set count to 0 if no data
svi_national_nmtc_eligible <- 
  left_join(svi_national_nmtc_eligible, nmtc_awards_pre2010, join_by("GEOID_2010_trt" == "GEOID10")) %>%
  mutate(pre10_nmtc_project_cnt = if_else(is.na(pre10_nmtc_project_cnt), 0, pre10_nmtc_project_cnt)) %>%
    mutate(pre10_nmtc_dollars = if_else(is.na(pre10_nmtc_dollars), 0, pre10_nmtc_dollars))%>%
    mutate(pre10_nmtc_dollars_formatted = if_else(is.na(pre10_nmtc_dollars_formatted), "$0", pre10_nmtc_dollars_formatted))

# View table
svi_national_nmtc_eligible %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt FIPS_st FIPS_county FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 nmtc_eligibility pre10_nmtc_project_cnt pre10_nmtc_dollars pre10_nmtc_dollars_formatted
01001020200 01 001 020200 AL Alabama Autauga County 3 South Region 6 East South Central Division 2020 816 730 495 1992 24.84940 0.5954 0 68 834 8.153477 0.57540 0 49 439 11.16173 0.02067 0 105 291 36.08247 0.30190 0 154 730 21.09589 0.09312 0 339 1265 26.79842 0.8392 1 313 2012 15.55666 0.6000 0 204 10.09901 0.3419 0 597 29.55446 0.8192 1 359 1515 23.69637 0.8791 1 132 456 28.947368 0.8351 1 15 1890 0.7936508 0.40130 0 1243 2020 61.53465 0.7781 1 816 0 0.0000000 0.1224 0 34 4.1666667 0.6664 0 13 730 1.780822 0.5406 0 115 730 15.753425 0.8382 1 0 2020 0.0000 0.3640 0 2.70312 0.5665 1 3.27660 0.8614 3 0.7781 0.7709 1 2.5316 0.5047 1 9.28942 0.6832 6 1757 720 573 384 1511 25.41363 0.6427 0 29 717 4.044630 0.4132 0 33 392 8.418367 0.03542 0 116 181 64.08840 0.9086 1 149 573 26.00349 0.40410 0 139 1313 10.58644 0.5601 0 91 1533 5.936073 0.4343 0 284 16.163916 0.5169 0 325 18.49744 0.2851 0 164 1208.000 13.57616 0.4127 0 42 359.0000 11.699164 0.3998 0 0 1651 0.0000000 0.09479 0 1116 1757.000 63.51736 0.7591 1 720 3 0.4166667 0.2470 0 5 0.6944444 0.5106 0 9 573 1.5706806 0.4688 0 57 573.000 9.947644 0.7317 0 212 1757 12.0660216 0.9549 1 2.45440 0.4888 0 1.70929 0.1025 0 0.7591 0.7527 1 2.9130 0.6862 1 7.83579 0.4802 2 Yes 0 0 \$0
01001020700 01 001 020700 AL Alabama Autauga County 3 South Region 6 East South Central Division 2664 1254 1139 710 2664 26.65165 0.6328 0 29 1310 2.213741 0.05255 0 134 710 18.87324 0.13890 0 187 429 43.58974 0.47090 0 321 1139 28.18262 0.28130 0 396 1852 21.38229 0.7478 0 345 2878 11.98749 0.4459 0 389 14.60210 0.6417 0 599 22.48499 0.4007 0 510 2168 23.52399 0.8752 1 228 712 32.022472 0.8712 1 0 2480 0.0000000 0.09298 0 694 2664 26.05105 0.5138 0 1254 8 0.6379585 0.2931 0 460 36.6826156 0.9714 1 0 1139 0.000000 0.1238 0 125 1139 10.974539 0.7477 0 0 2664 0.0000 0.3640 0 2.16035 0.4069 0 2.88178 0.6997 2 0.5138 0.5090 0 2.5000 0.4882 1 8.05593 0.5185 3 3562 1313 1248 1370 3528 38.83220 0.8512 1 128 1562 8.194622 0.7935 1 168 844 19.905213 0.44510 0 237 404 58.66337 0.8359 1 405 1248 32.45192 0.60420 0 396 2211 17.91045 0.7857 1 444 3547 12.517620 0.7758 1 355 9.966311 0.1800 0 954 26.78271 0.7923 1 629 2593.000 24.25762 0.8730 1 171 797.0000 21.455458 0.7186 0 0 3211 0.0000000 0.09479 0 1009 3562.000 28.32678 0.4668 0 1313 14 1.0662605 0.3165 0 443 33.7395278 0.9663 1 73 1248 5.8493590 0.8211 1 17 1248.000 1.362180 0.1554 0 112 3562 3.1443010 0.8514 1 3.81040 0.8569 4 2.65869 0.5847 2 0.4668 0.4629 0 3.1107 0.7714 3 10.04659 0.7851 9 Yes 0 0 \$0
01001021100 01 001 021100 AL Alabama Autauga County 3 South Region 6 East South Central Division 3298 1502 1323 860 3298 26.07641 0.6211 0 297 1605 18.504673 0.94340 1 250 1016 24.60630 0.32070 0 74 307 24.10423 0.11920 0 324 1323 24.48980 0.17380 0 710 2231 31.82429 0.8976 1 654 3565 18.34502 0.7018 0 411 12.46210 0.5001 0 738 22.37720 0.3934 0 936 2861 32.71583 0.9807 1 138 825 16.727273 0.5715 0 9 3155 0.2852615 0.25010 0 1979 3298 60.00606 0.7703 1 1502 14 0.9320905 0.3234 0 659 43.8748336 0.9849 1 44 1323 3.325775 0.7062 0 137 1323 10.355253 0.7313 0 0 3298 0.0000 0.3640 0 3.33770 0.7351 2 2.69580 0.6028 1 0.7703 0.7631 1 3.1098 0.7827 1 9.91360 0.7557 5 3499 1825 1462 1760 3499 50.30009 0.9396 1 42 966 4.347826 0.4539 0 426 1274 33.437991 0.85200 1 52 188 27.65957 0.1824 0 478 1462 32.69494 0.61110 0 422 2488 16.96141 0.7638 1 497 3499 14.204058 0.8246 1 853 24.378394 0.8688 1 808 23.09231 0.5829 0 908 2691.100 33.74084 0.9808 1 179 811.6985 22.052524 0.7323 0 8 3248 0.2463054 0.26220 0 1986 3498.713 56.76373 0.7175 0 1825 29 1.5890411 0.3551 0 576 31.5616438 0.9594 1 88 1462 6.0191518 0.8269 1 148 1461.993 10.123166 0.7364 0 38 3499 1.0860246 0.7013 0 3.59300 0.8073 3 3.42700 0.9156 2 0.7175 0.7114 0 3.5791 0.9216 2 11.31660 0.9150 7 Yes 0 0 \$0
01003010200 01 003 010200 AL Alabama Baldwin County 3 South Region 6 East South Central Division 2612 1220 1074 338 2605 12.97505 0.2907 0 44 1193 3.688181 0.14720 0 172 928 18.53448 0.13090 0 31 146 21.23288 0.09299 0 203 1074 18.90130 0.05657 0 455 1872 24.30556 0.8016 1 456 2730 16.70330 0.6445 0 401 15.35222 0.6847 0 563 21.55436 0.3406 0 410 2038 20.11776 0.7755 1 64 779 8.215661 0.2181 0 0 2510 0.0000000 0.09298 0 329 2612 12.59571 0.3113 0 1220 38 3.1147541 0.4648 0 385 31.5573770 0.9545 1 20 1074 1.862197 0.5509 0 43 1074 4.003724 0.4088 0 0 2612 0.0000 0.3640 0 1.94057 0.3398 1 2.11188 0.2802 1 0.3113 0.3084 0 2.7430 0.6129 1 7.10675 0.3771 3 2928 1312 1176 884 2928 30.19126 0.7334 0 29 1459 1.987663 0.1356 0 71 830 8.554217 0.03726 0 134 346 38.72832 0.3964 0 205 1176 17.43197 0.12010 0 294 2052 14.32749 0.6940 0 219 2925 7.487179 0.5423 0 556 18.989071 0.6705 0 699 23.87295 0.6339 0 489 2226.455 21.96317 0.8122 1 191 783.8820 24.365914 0.7799 1 0 2710 0.0000000 0.09479 0 398 2927.519 13.59513 0.2511 0 1312 13 0.9908537 0.3111 0 400 30.4878049 0.9557 1 6 1176 0.5102041 0.2590 0 81 1176.202 6.886570 0.6115 0 7 2928 0.2390710 0.4961 0 2.22540 0.4183 0 2.99129 0.7634 2 0.2511 0.2490 0 2.6334 0.5496 1 8.10119 0.5207 3 Yes 0 0 \$0
01003010500 01 003 010500 AL Alabama Baldwin County 3 South Region 6 East South Central Division 4230 1779 1425 498 3443 14.46413 0.3337 0 166 1625 10.215385 0.71790 0 151 1069 14.12535 0.04638 0 196 356 55.05618 0.73830 0 347 1425 24.35088 0.17010 0 707 2945 24.00679 0.7967 1 528 4001 13.19670 0.5005 0 619 14.63357 0.6436 0 790 18.67612 0.1937 0 536 3096 17.31266 0.6572 0 165 920 17.934783 0.6102 0 20 4021 0.4973887 0.32320 0 754 4230 17.82506 0.4023 0 1779 97 5.4525014 0.5525 0 8 0.4496908 0.4600 0 63 1425 4.421053 0.7762 1 90 1425 6.315790 0.5691 0 787 4230 18.6052 0.9649 1 2.51890 0.5121 1 2.42790 0.4539 0 0.4023 0.3986 0 3.3227 0.8628 2 8.67180 0.6054 3 5877 1975 1836 820 5244 15.63692 0.3902 0 90 2583 3.484321 0.3361 0 159 1345 11.821561 0.10530 0 139 491 28.30957 0.1924 0 298 1836 16.23094 0.09053 0 570 4248 13.41808 0.6669 0 353 5247 6.727654 0.4924 0 1109 18.870172 0.6645 0 1144 19.46571 0.3411 0 717 4102.545 17.47696 0.6332 0 103 1286.1180 8.008596 0.2341 0 0 5639 0.0000000 0.09479 0 868 5877.481 14.76823 0.2709 0 1975 26 1.3164557 0.3359 0 45 2.2784810 0.6271 0 9 1836 0.4901961 0.2540 0 116 1835.798 6.318779 0.5811 0 633 5877 10.7708014 0.9507 1 1.97613 0.3410 0 1.96769 0.1961 0 0.2709 0.2686 0 2.7488 0.6077 1 6.96352 0.3406 1 Yes 0 0 \$0
01003010600 01 003 010600 AL Alabama Baldwin County 3 South Region 6 East South Central Division 3724 1440 1147 1973 3724 52.98067 0.9342 1 142 1439 9.867964 0.69680 0 235 688 34.15698 0.62950 0 187 459 40.74074 0.40290 0 422 1147 36.79163 0.55150 0 497 1876 26.49254 0.8354 1 511 3661 13.95794 0.5334 0 246 6.60580 0.1481 0 1256 33.72718 0.9305 1 496 2522 19.66693 0.7587 1 274 838 32.696897 0.8779 1 32 3479 0.9198045 0.42810 0 2606 3724 69.97852 0.8184 1 1440 21 1.4583333 0.3683 0 321 22.2916667 0.9036 1 97 1147 8.456844 0.8956 1 167 1147 14.559721 0.8209 1 0 3724 0.0000 0.3640 0 3.55130 0.7859 2 3.14330 0.8145 3 0.8184 0.8108 1 3.3524 0.8725 3 10.86540 0.8550 9 4115 1534 1268 1676 3997 41.93145 0.8814 1 294 1809 16.252073 0.9674 1 341 814 41.891892 0.94320 1 204 454 44.93392 0.5438 0 545 1268 42.98107 0.83620 1 624 2425 25.73196 0.9002 1 994 4115 24.155529 0.9602 1 642 15.601458 0.4841 0 1126 27.36331 0.8175 1 568 2989.000 19.00301 0.7045 0 212 715.0000 29.650350 0.8592 1 56 3825 1.4640523 0.53120 0 2715 4115.000 65.97813 0.7732 1 1534 0 0.0000000 0.1079 0 529 34.4850065 0.9685 1 101 1268 7.9652997 0.8795 1 89 1268.000 7.018927 0.6184 0 17 4115 0.4131227 0.5707 0 4.54540 0.9754 5 3.39650 0.9081 2 0.7732 0.7667 1 3.1450 0.7858 2 11.86010 0.9520 10 Yes 0 0 \$0 
# See number of rows
svi_divisional_nmtc_eligible %>% nrow()

## [1] 4563

# See number of rows
svi_national_nmtc_eligible %>% nrow()

## [1] 30847

# Find count of NMTC projects after 2010
# Remove all tracts that do not have SVI flag counts for 2010
# Remove all tracts that do not have SVI flag counts for 2020
# Remove all tracts that had an NMTC project before 2010
svi_divisional_nmtc <- 
  left_join(svi_divisional_nmtc_eligible, nmtc_awards_post2010, join_by("GEOID_2010_trt" == "GEOID10")) %>%
  mutate(post10_nmtc_project_cnt = if_else(is.na(post10_nmtc_project_cnt), 0, post10_nmtc_project_cnt)) %>%
  mutate(post10_nmtc_dollars = if_else(is.na(post10_nmtc_dollars), 0, post10_nmtc_dollars))%>%
  mutate(post10_nmtc_dollars_formatted = if_else(is.na(post10_nmtc_dollars_formatted), "$0", post10_nmtc_dollars_formatted)) %>%
  mutate(nmtc_flag = if_else(post10_nmtc_project_cnt > 0, 1, 0)) %>% 
  filter(!is.na(F_TOTAL_10)) %>% 
  filter(!is.na(F_TOTAL_20)) %>% 
  filter(pre10_nmtc_project_cnt < 1)

svi_divisional_nmtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt FIPS_st FIPS_county FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 nmtc_eligibility pre10_nmtc_project_cnt pre10_nmtc_dollars pre10_nmtc_dollars_formatted post10_nmtc_project_cnt post10_nmtc_dollars post10_nmtc_dollars_formatted nmtc_flag
02013000100 02 013 000100 AK Alaska Aleutians East Borough 4 West Region 9 Pacific Division 3703 474 267 1212 3695 32.80108 0.7570 1 111 3163 3.509327 0.08691 0 25 158 15.82278 0.01337 0 17 109 15.59633 0.02605 0 42 267 15.73034 0.004754 0 1082 3017 35.863441 0.85420 1 2060 3112 66.195373 0.99990 1 127 3.429652 0.042400 0 315 8.506616 0.03961 0 182 2849 6.388206 0.077750 0 50 165 30.30303 0.8835 1 1070 3617 29.5825270 0.93700 1 3492 3703 94.30192 0.9141 1 474 8 1.687764 0.29250 0 42 8.8607595 0.8128 1 7 267 2.6217228 0.4003 0 77 267 28.8389513 0.96850 1 2969 3703 80.178234 0.9940 1 2.702764 0.5611 3 1.980260 0.23800 2 0.9141 0.9047 1 3.46810 0.8902 3 9.065224 0.6397 9 3389 1199 988 698 3379 20.65700 0.5925 0 86 2414 3.562552 0.2665 0 67 607 11.037891 0.01803 0 74 381 19.42257 0.04067 0 141 988 14.27126 0.006988 0 354 2646 13.378685 0.61070 0 1345 3384 39.745863 0.99970 1 381 11.2422544 0.31390 0 443 13.07170 0.0988 0 339 2941.000 11.526692 0.386000 0 135 593.00 22.765599 0.7920 1 334 3276 10.1953602 0.72620 0 2939 3389.000 86.72175 0.8110 1 1199 38 3.169308 0.3474 0 69 5.754796 0.7806 1 30 988 3.0364372 0.36010 0 220 988.000 22.267207 0.9527 1 1035 3389 30.539982 0.9843 1 2.476388 0.4947 1 2.316900 0.37850 1 0.8110 0.8038 1 3.42510 0.8683 3 9.029388 0.6419 6 Yes 0 0 \$0 1 15762500 \$15,762,500 1
02016000100 02 016 000100 AK Alaska Aleutians West Census Area 4 West Region 9 Pacific Division 1774 1056 166 328 1231 26.64500 0.6553 0 15 1370 1.094890 0.01369 0 25 95 26.31579 0.09653 0 16 71 22.53521 0.05099 0 41 166 24.69880 0.029080 0 207 1330 15.563910 0.58390 0 484 973 49.743063 0.99520 1 53 2.987599 0.031800 0 182 10.259301 0.05188 0 147 747 19.678715 0.864200 1 19 96 19.79167 0.6606 0 79 1718 4.5983702 0.46890 0 1154 1774 65.05073 0.6522 0 1056 22 2.083333 0.31610 0 0 0.0000000 0.2497 0 10 166 6.0240964 0.6154 0 84 166 50.6024096 0.99320 1 1324 1774 74.633596 0.9935 1 2.277170 0.4443 1 2.077380 0.27780 1 0.6522 0.6454 0 3.16790 0.7874 2 8.174650 0.5311 4 950 694 199 218 719 30.31989 0.7848 1 15 560 2.678571 0.1560 0 11 117 9.401709 0.01305 0 14 82 17.07317 0.03088 0 25 199 12.56281 0.003541 0 48 681 7.048458 0.37250 0 238 721 33.009709 0.99890 1 116 12.2105263 0.37310 0 195 20.52632 0.4153 0 113 526.000 21.482890 0.893100 1 31 98.00 31.632653 0.9318 1 17 900 1.8888889 0.29830 0 713 950.000 75.05263 0.6900 0 694 17 2.449568 0.3163 0 0 0.000000 0.2466 0 7 199 3.5175879 0.39980 0 68 199.000 34.170854 0.9826 1 274 950 28.842105 0.9832 1 2.315741 0.4476 2 2.911600 0.70420 2 0.6900 0.6839 0 2.92850 0.6794 2 8.845841 0.6188 6 Yes 0 0 \$0 0 0 \$0 0
02020000300 02 020 000300 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 6308 1834 1707 1137 5839 19.47251 0.4988 0 59 1024 5.761719 0.26830 0 11 11 100.00000 0.99780 1 609 1696 35.90802 0.17490 0 620 1707 36.32103 0.215100 0 85 2458 3.458096 0.12670 0 125 4961 2.519653 0.02643 0 0 0.000000 0.003301 0 2744 43.500317 0.99640 1 54 2007 2.690583 0.007821 0 301 1635 18.40979 0.6168 0 11 5308 0.2072344 0.06620 0 2167 6308 34.35320 0.3715 0 1834 24 1.308615 0.27080 0 0 0.0000000 0.2497 0 10 1707 0.5858231 0.1573 0 10 1707 0.5858231 0.07765 0 469 6308 7.435003 0.9359 1 1.135330 0.1355 0 1.690522 0.13070 1 0.3715 0.3677 0 1.69135 0.1520 1 4.888702 0.1113 2 8256 1834 1731 1603 6583 24.35060 0.6772 0 95 1105 8.597285 0.8029 1 7 16 43.750000 0.91050 1 1127 1715 65.71429 0.88900 1 1134 1731 65.51127 0.985700 1 148 3181 4.652625 0.23830 0 80 5243 1.525844 0.08775 0 119 1.4413760 0.00975 0 3086 37.37888 0.9880 1 193 2171.088 8.889551 0.188800 0 136 1429.97 9.510687 0.3216 0 0 7040 0.0000000 0.02391 0 3808 8256.294 46.12239 0.4209 0 1834 127 6.924755 0.4701 0 0 0.000000 0.2466 0 13 1731 0.7510110 0.12710 0 179 1731.395 10.338487 0.7913 1 1673 8256 20.264050 0.9768 1 2.791850 0.5891 2 1.532060 0.07776 1 0.4209 0.4172 0 2.61190 0.5330 2 7.356710 0.4139 5 Yes 0 0 \$0 0 0 \$0 0
02020000400 02 020 000400 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 5991 1360 1246 628 4602 13.64624 0.3404 0 117 924 12.662338 0.81630 1 0 12 0.00000 0.00240 0 761 1234 61.66937 0.78730 1 761 1246 61.07544 0.929600 1 24 1995 1.203008 0.03078 0 55 4075 1.349693 0.01061 0 0 0.000000 0.003301 0 2117 35.336338 0.93430 1 86 1820 4.725275 0.029420 0 138 1246 11.07544 0.3314 0 14 5099 0.2745636 0.07606 0 1539 5991 25.68853 0.2688 0 1360 0 0.000000 0.09395 0 10 0.7352941 0.5653 0 38 1246 3.0497592 0.4365 0 21 1246 1.6853933 0.19700 0 1389 5991 23.184777 0.9762 1 2.127690 0.4021 2 1.374481 0.05613 1 0.2688 0.2660 0 2.26895 0.3836 1 6.039921 0.2480 4 5090 1440 1377 657 4243 15.48433 0.4416 0 82 1435 5.714286 0.5455 0 0 0 NaN NA NA 912 1377 66.23094 0.89700 1 912 1377 66.23094 0.987300 1 28 1928 1.452282 0.05471 0 82 3349 2.448492 0.16300 0 12 0.2357564 0.00585 0 1446 28.40864 0.8460 1 68 1902.717 3.573837 0.008563 0 56 1032.00 5.426357 0.1342 0 9 4411 0.2040354 0.06983 0 2444 5089.955 48.01614 0.4425 0 1440 38 2.638889 0.3255 0 0 0.000000 0.2466 0 7 1377 0.5083515 0.09514 0 92 1377.000 6.681191 0.6436 0 820 5090 16.110020 0.9730 1 2.192110 0.4140 1 1.064443 0.02264 1 0.4425 0.4386 0 2.28384 0.3878 1 5.982893 0.2198 3 Yes 0 0 \$0 0 0 \$0 0
02020000500 02 020 000500 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 1872 979 956 384 1872 20.51282 0.5238 0 30 957 3.134796 0.06633 0 56 149 37.58389 0.39630 0 321 807 39.77695 0.23920 0 377 956 39.43515 0.311800 0 190 1139 16.681299 0.60890 0 314 2109 14.888573 0.48950 0 221 11.805556 0.574800 0 434 23.183761 0.43040 0 307 1475 20.813559 0.894900 1 91 385 23.63636 0.7603 1 129 1793 7.1946458 0.58420 0 1048 1872 55.98291 0.5787 0 979 578 59.039837 0.95260 1 0 0.0000000 0.2497 0 22 956 2.3012552 0.3729 0 78 956 8.1589958 0.68640 0 0 1872 0.000000 0.3743 0 2.000330 0.3676 0 3.244600 0.82880 2 0.5787 0.5727 0 2.63590 0.5502 1 8.459530 0.5669 3 2039 1074 985 624 2039 30.60324 0.7906 1 119 1125 10.577778 0.8901 1 42 138 30.434783 0.56020 0 361 847 42.62102 0.32940 0 403 985 40.91371 0.614800 0 61 1468 4.155313 0.20970 0 350 1966 17.802645 0.95510 1 200 9.8087298 0.22920 0 322 15.79205 0.1707 0 233 1644.283 14.170309 0.581400 0 143 338.00 42.307692 0.9859 1 48 1920 2.5000000 0.35480 0 1060 2039.045 51.98512 0.4840 0 1074 642 59.776536 0.9485 1 0 0.000000 0.2466 0 39 985 3.9593909 0.43720 0 230 985.000 23.350254 0.9573 1 0 2039 0.000000 0.1370 0 3.460300 0.7607 3 2.322000 0.38140 1 0.4840 0.4797 0 2.72660 0.5866 2 8.992900 0.6375 6 Yes 0 0 \$0 0 0 \$0 0
02020000701 02 020 000701 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 5432 2076 1969 1206 5418 22.25914 0.5643 0 264 2765 9.547920 0.62650 0 354 1051 33.68221 0.26640 0 362 918 39.43355 0.23330 0 716 1969 36.36364 0.216100 0 411 3280 12.530488 0.50270 0 1108 5795 19.119931 0.64920 0 354 6.516937 0.200300 0 1479 27.227540 0.65230 0 567 4056 13.979290 0.607400 0 415 1255 33.06773 0.9178 1 73 4960 1.4717742 0.22780 0 3080 5432 56.70103 0.5848 0 2076 273 13.150289 0.63880 0 335 16.1368015 0.8980 1 166 1969 8.4306755 0.7014 0 202 1969 10.2590147 0.76450 1 0 5432 0.000000 0.3743 0 2.558800 0.5224 0 2.605600 0.53860 1 0.5848 0.5788 0 3.37700 0.8627 2 9.126200 0.6476 3 6784 2585 2265 1300 6719 19.34812 0.5567 0 196 3597 5.448985 0.5123 0 356 1275 27.921569 0.45790 0 443 990 44.74747 0.37870 0 799 2265 35.27594 0.419800 0 363 3964 9.157417 0.46990 0 651 6607 9.853186 0.76060 1 437 6.4416274 0.06927 0 2252 33.19575 0.9548 1 945 4355.000 21.699196 0.897900 1 179 1612.00 11.104218 0.3936 0 481 6172 7.7932599 0.65010 0 4356 6784.000 64.20991 0.5963 0 2585 356 13.771760 0.6278 0 424 16.402321 0.9130 1 195 2265 8.6092715 0.68030 0 250 2265.000 11.037528 0.8145 1 7 6784 0.103184 0.3090 0 2.719300 0.5684 1 2.965670 0.73150 2 0.5963 0.5911 0 3.34460 0.8443 2 9.625870 0.7156 5 Yes 0 0 \$0 0 0 \$0 0 
# Find count of NMTC projects after 2010
# Remove all tracts that do not have SVI flag counts for 2010
# Remove all tracts that do not have SVI flag counts for 2020
# Remove all tracts that had an NMTC project before 2010
svi_national_nmtc <- 
  left_join(svi_national_nmtc_eligible, nmtc_awards_post2010, join_by("GEOID_2010_trt" == "GEOID10")) %>%
  mutate(post10_nmtc_project_cnt = if_else(is.na(post10_nmtc_project_cnt), 0, post10_nmtc_project_cnt)) %>%
  mutate(post10_nmtc_dollars = if_else(is.na(post10_nmtc_dollars), 0, post10_nmtc_dollars))%>%
  mutate(post10_nmtc_dollars_formatted = if_else(is.na(post10_nmtc_dollars_formatted), "$0", post10_nmtc_dollars_formatted)) %>%
  mutate(nmtc_flag = if_else(post10_nmtc_project_cnt > 0, 1, 0)) %>% 
  filter(!is.na(F_TOTAL_10)) %>% 
  filter(!is.na(F_TOTAL_20)) %>% 
  filter(pre10_nmtc_project_cnt < 1)

svi_national_nmtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt FIPS_st FIPS_county FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 nmtc_eligibility pre10_nmtc_project_cnt pre10_nmtc_dollars pre10_nmtc_dollars_formatted post10_nmtc_project_cnt post10_nmtc_dollars post10_nmtc_dollars_formatted nmtc_flag
01001020200 01 001 020200 AL Alabama Autauga County 3 South Region 6 East South Central Division 2020 816 730 495 1992 24.84940 0.5954 0 68 834 8.153477 0.57540 0 49 439 11.16173 0.02067 0 105 291 36.08247 0.30190 0 154 730 21.09589 0.09312 0 339 1265 26.79842 0.8392 1 313 2012 15.55666 0.6000 0 204 10.09901 0.3419 0 597 29.55446 0.8192 1 359 1515 23.69637 0.8791 1 132 456 28.947368 0.8351 1 15 1890 0.7936508 0.40130 0 1243 2020 61.53465 0.7781 1 816 0 0.0000000 0.1224 0 34 4.1666667 0.6664 0 13 730 1.780822 0.5406 0 115 730 15.753425 0.8382 1 0 2020 0.0000 0.3640 0 2.70312 0.5665 1 3.27660 0.8614 3 0.7781 0.7709 1 2.5316 0.5047 1 9.28942 0.6832 6 1757 720 573 384 1511 25.41363 0.6427 0 29 717 4.044630 0.4132 0 33 392 8.418367 0.03542 0 116 181 64.08840 0.9086 1 149 573 26.00349 0.40410 0 139 1313 10.58644 0.5601 0 91 1533 5.936073 0.4343 0 284 16.163916 0.5169 0 325 18.49744 0.2851 0 164 1208.000 13.57616 0.4127 0 42 359.0000 11.699164 0.3998 0 0 1651 0.0000000 0.09479 0 1116 1757.000 63.51736 0.7591 1 720 3 0.4166667 0.2470 0 5 0.6944444 0.5106 0 9 573 1.5706806 0.4688 0 57 573.000 9.947644 0.7317 0 212 1757 12.0660216 0.9549 1 2.45440 0.4888 0 1.70929 0.1025 0 0.7591 0.7527 1 2.9130 0.6862 1 7.83579 0.4802 2 Yes 0 0 \$0 0 0 \$0 0
01001020700 01 001 020700 AL Alabama Autauga County 3 South Region 6 East South Central Division 2664 1254 1139 710 2664 26.65165 0.6328 0 29 1310 2.213741 0.05255 0 134 710 18.87324 0.13890 0 187 429 43.58974 0.47090 0 321 1139 28.18262 0.28130 0 396 1852 21.38229 0.7478 0 345 2878 11.98749 0.4459 0 389 14.60210 0.6417 0 599 22.48499 0.4007 0 510 2168 23.52399 0.8752 1 228 712 32.022472 0.8712 1 0 2480 0.0000000 0.09298 0 694 2664 26.05105 0.5138 0 1254 8 0.6379585 0.2931 0 460 36.6826156 0.9714 1 0 1139 0.000000 0.1238 0 125 1139 10.974539 0.7477 0 0 2664 0.0000 0.3640 0 2.16035 0.4069 0 2.88178 0.6997 2 0.5138 0.5090 0 2.5000 0.4882 1 8.05593 0.5185 3 3562 1313 1248 1370 3528 38.83220 0.8512 1 128 1562 8.194622 0.7935 1 168 844 19.905213 0.44510 0 237 404 58.66337 0.8359 1 405 1248 32.45192 0.60420 0 396 2211 17.91045 0.7857 1 444 3547 12.517620 0.7758 1 355 9.966311 0.1800 0 954 26.78271 0.7923 1 629 2593.000 24.25762 0.8730 1 171 797.0000 21.455458 0.7186 0 0 3211 0.0000000 0.09479 0 1009 3562.000 28.32678 0.4668 0 1313 14 1.0662605 0.3165 0 443 33.7395278 0.9663 1 73 1248 5.8493590 0.8211 1 17 1248.000 1.362180 0.1554 0 112 3562 3.1443010 0.8514 1 3.81040 0.8569 4 2.65869 0.5847 2 0.4668 0.4629 0 3.1107 0.7714 3 10.04659 0.7851 9 Yes 0 0 \$0 0 0 \$0 0
01001021100 01 001 021100 AL Alabama Autauga County 3 South Region 6 East South Central Division 3298 1502 1323 860 3298 26.07641 0.6211 0 297 1605 18.504673 0.94340 1 250 1016 24.60630 0.32070 0 74 307 24.10423 0.11920 0 324 1323 24.48980 0.17380 0 710 2231 31.82429 0.8976 1 654 3565 18.34502 0.7018 0 411 12.46210 0.5001 0 738 22.37720 0.3934 0 936 2861 32.71583 0.9807 1 138 825 16.727273 0.5715 0 9 3155 0.2852615 0.25010 0 1979 3298 60.00606 0.7703 1 1502 14 0.9320905 0.3234 0 659 43.8748336 0.9849 1 44 1323 3.325775 0.7062 0 137 1323 10.355253 0.7313 0 0 3298 0.0000 0.3640 0 3.33770 0.7351 2 2.69580 0.6028 1 0.7703 0.7631 1 3.1098 0.7827 1 9.91360 0.7557 5 3499 1825 1462 1760 3499 50.30009 0.9396 1 42 966 4.347826 0.4539 0 426 1274 33.437991 0.85200 1 52 188 27.65957 0.1824 0 478 1462 32.69494 0.61110 0 422 2488 16.96141 0.7638 1 497 3499 14.204058 0.8246 1 853 24.378394 0.8688 1 808 23.09231 0.5829 0 908 2691.100 33.74084 0.9808 1 179 811.6985 22.052524 0.7323 0 8 3248 0.2463054 0.26220 0 1986 3498.713 56.76373 0.7175 0 1825 29 1.5890411 0.3551 0 576 31.5616438 0.9594 1 88 1462 6.0191518 0.8269 1 148 1461.993 10.123166 0.7364 0 38 3499 1.0860246 0.7013 0 3.59300 0.8073 3 3.42700 0.9156 2 0.7175 0.7114 0 3.5791 0.9216 2 11.31660 0.9150 7 Yes 0 0 \$0 0 0 \$0 0
01003010200 01 003 010200 AL Alabama Baldwin County 3 South Region 6 East South Central Division 2612 1220 1074 338 2605 12.97505 0.2907 0 44 1193 3.688181 0.14720 0 172 928 18.53448 0.13090 0 31 146 21.23288 0.09299 0 203 1074 18.90130 0.05657 0 455 1872 24.30556 0.8016 1 456 2730 16.70330 0.6445 0 401 15.35222 0.6847 0 563 21.55436 0.3406 0 410 2038 20.11776 0.7755 1 64 779 8.215661 0.2181 0 0 2510 0.0000000 0.09298 0 329 2612 12.59571 0.3113 0 1220 38 3.1147541 0.4648 0 385 31.5573770 0.9545 1 20 1074 1.862197 0.5509 0 43 1074 4.003724 0.4088 0 0 2612 0.0000 0.3640 0 1.94057 0.3398 1 2.11188 0.2802 1 0.3113 0.3084 0 2.7430 0.6129 1 7.10675 0.3771 3 2928 1312 1176 884 2928 30.19126 0.7334 0 29 1459 1.987663 0.1356 0 71 830 8.554217 0.03726 0 134 346 38.72832 0.3964 0 205 1176 17.43197 0.12010 0 294 2052 14.32749 0.6940 0 219 2925 7.487179 0.5423 0 556 18.989071 0.6705 0 699 23.87295 0.6339 0 489 2226.455 21.96317 0.8122 1 191 783.8820 24.365914 0.7799 1 0 2710 0.0000000 0.09479 0 398 2927.519 13.59513 0.2511 0 1312 13 0.9908537 0.3111 0 400 30.4878049 0.9557 1 6 1176 0.5102041 0.2590 0 81 1176.202 6.886570 0.6115 0 7 2928 0.2390710 0.4961 0 2.22540 0.4183 0 2.99129 0.7634 2 0.2511 0.2490 0 2.6334 0.5496 1 8.10119 0.5207 3 Yes 0 0 \$0 1 408000 \$408,000 1
01003010500 01 003 010500 AL Alabama Baldwin County 3 South Region 6 East South Central Division 4230 1779 1425 498 3443 14.46413 0.3337 0 166 1625 10.215385 0.71790 0 151 1069 14.12535 0.04638 0 196 356 55.05618 0.73830 0 347 1425 24.35088 0.17010 0 707 2945 24.00679 0.7967 1 528 4001 13.19670 0.5005 0 619 14.63357 0.6436 0 790 18.67612 0.1937 0 536 3096 17.31266 0.6572 0 165 920 17.934783 0.6102 0 20 4021 0.4973887 0.32320 0 754 4230 17.82506 0.4023 0 1779 97 5.4525014 0.5525 0 8 0.4496908 0.4600 0 63 1425 4.421053 0.7762 1 90 1425 6.315790 0.5691 0 787 4230 18.6052 0.9649 1 2.51890 0.5121 1 2.42790 0.4539 0 0.4023 0.3986 0 3.3227 0.8628 2 8.67180 0.6054 3 5877 1975 1836 820 5244 15.63692 0.3902 0 90 2583 3.484321 0.3361 0 159 1345 11.821561 0.10530 0 139 491 28.30957 0.1924 0 298 1836 16.23094 0.09053 0 570 4248 13.41808 0.6669 0 353 5247 6.727654 0.4924 0 1109 18.870172 0.6645 0 1144 19.46571 0.3411 0 717 4102.545 17.47696 0.6332 0 103 1286.1180 8.008596 0.2341 0 0 5639 0.0000000 0.09479 0 868 5877.481 14.76823 0.2709 0 1975 26 1.3164557 0.3359 0 45 2.2784810 0.6271 0 9 1836 0.4901961 0.2540 0 116 1835.798 6.318779 0.5811 0 633 5877 10.7708014 0.9507 1 1.97613 0.3410 0 1.96769 0.1961 0 0.2709 0.2686 0 2.7488 0.6077 1 6.96352 0.3406 1 Yes 0 0 \$0 0 0 \$0 0
01003010600 01 003 010600 AL Alabama Baldwin County 3 South Region 6 East South Central Division 3724 1440 1147 1973 3724 52.98067 0.9342 1 142 1439 9.867964 0.69680 0 235 688 34.15698 0.62950 0 187 459 40.74074 0.40290 0 422 1147 36.79163 0.55150 0 497 1876 26.49254 0.8354 1 511 3661 13.95794 0.5334 0 246 6.60580 0.1481 0 1256 33.72718 0.9305 1 496 2522 19.66693 0.7587 1 274 838 32.696897 0.8779 1 32 3479 0.9198045 0.42810 0 2606 3724 69.97852 0.8184 1 1440 21 1.4583333 0.3683 0 321 22.2916667 0.9036 1 97 1147 8.456844 0.8956 1 167 1147 14.559721 0.8209 1 0 3724 0.0000 0.3640 0 3.55130 0.7859 2 3.14330 0.8145 3 0.8184 0.8108 1 3.3524 0.8725 3 10.86540 0.8550 9 4115 1534 1268 1676 3997 41.93145 0.8814 1 294 1809 16.252073 0.9674 1 341 814 41.891892 0.94320 1 204 454 44.93392 0.5438 0 545 1268 42.98107 0.83620 1 624 2425 25.73196 0.9002 1 994 4115 24.155529 0.9602 1 642 15.601458 0.4841 0 1126 27.36331 0.8175 1 568 2989.000 19.00301 0.7045 0 212 715.0000 29.650350 0.8592 1 56 3825 1.4640523 0.53120 0 2715 4115.000 65.97813 0.7732 1 1534 0 0.0000000 0.1079 0 529 34.4850065 0.9685 1 101 1268 7.9652997 0.8795 1 89 1268.000 7.018927 0.6184 0 17 4115 0.4131227 0.5707 0 4.54540 0.9754 5 3.39650 0.9081 2 0.7732 0.7667 1 3.1450 0.7858 2 11.86010 0.9520 10 Yes 0 0 \$0 1 8000000 \$8,000,000 1 
svi_national_nmtc_county_sum <- summarize_county_nmtc(svi_national_nmtc)

svi_national_nmtc_county_sum %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
State County Division post10_nmtc_project_cnt tract_cnt post10_nmtc_project_dollars post10_nmtc_dollars_formatted
AK Aleutians East Borough Pacific Division 1 1 15762500 \$15,762,500
AK Aleutians West Census Area Pacific Division 0 1 0 \$0
AK Anchorage Municipality Pacific Division 1 13 9800000 \$9,800,000
AK Bethel Census Area Pacific Division 0 1 0 \$0
AK Fairbanks North Star Borough Pacific Division 0 4 0 \$0
AK Hoonah-Angoon Census Area Pacific Division 0 1 0 \$0 
svi_divisional_nmtc_county_sum <- summarize_county_nmtc(svi_divisional_nmtc)
svi_divisional_nmtc_county_sum %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
State County Division post10_nmtc_project_cnt tract_cnt post10_nmtc_project_dollars post10_nmtc_dollars_formatted
AK Aleutians East Borough Pacific Division 1 1 15762500 \$15,762,500
AK Aleutians West Census Area Pacific Division 0 1 0 \$0
AK Anchorage Municipality Pacific Division 1 13 9800000 \$9,800,000
AK Bethel Census Area Pacific Division 0 1 0 \$0
AK Fairbanks North Star Borough Pacific Division 0 4 0 \$0
AK Hoonah-Angoon Census Area Pacific Division 0 1 0 \$0 
# Create data frame of NMTC eligible tracts 2010 nationally
svi_national_nmtc10 <- svi_national_nmtc %>% select(GEOID_2010_trt, FIPS_st, FIPS_county, 
    state, state_name, county, region_number, region, division_number, 
    division, F_TOTAL_10, E_TOTPOP_10) %>% rename("F_TOTAL" = "F_TOTAL_10")

# Count national-level SVI flags for 2010, create unified fips column
svi_2010_national_county_flags_nmtc <- flag_summarize(svi_national_nmtc10, "E_TOTPOP_10") %>% unite("fips_county_st", FIPS_st:FIPS_county, remove = FALSE, sep="")

# Add suffix to flag columns 2010
colnames(svi_2010_national_county_flags_nmtc)[11:15] <- paste0(colnames(svi_2010_national_county_flags_nmtc)[11:15], 10)

# Create data frame of NMTC eligible tracts 2020 nationally
svi_national_nmtc20 <- svi_national_nmtc %>% select(GEOID_2010_trt, FIPS_st, FIPS_county, 
    state, state_name, county, region_number, region, division_number, 
    division, F_TOTAL_20, E_TOTPOP_20) %>% rename("F_TOTAL" = "F_TOTAL_20")

# Count national-level SVI flags for 2020, create unified fips column
svi_2020_national_county_flags_nmtc <- flag_summarize(svi_national_nmtc20, "E_TOTPOP_20") %>% unite("fips_county_st", FIPS_st:FIPS_county, remove = FALSE, sep="")

# Identify needed columns for 2020, add suffix
colnames(svi_2020_national_county_flags_nmtc)[11:15] <- paste0(colnames(svi_2020_national_county_flags_nmtc)[11:15], "20")

# Filter to needed columns for 2020 to avoid duplicate column column names
svi_2020_national_county_flags_join_nmtc <- svi_2020_national_county_flags_nmtc %>% ungroup() %>% select("fips_county_st", all_of(colnames(svi_2020_national_county_flags_nmtc)[11:15]))
 
# Join 2010 and 2020 data
svi_national_county_flags_nmtc <- left_join(svi_2010_national_county_flags_nmtc, svi_2020_national_county_flags_join_nmtc, join_by("fips_county_st" == "fips_county_st")) 

svi_national_county_flags_nmtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
fips_county_st FIPS_st FIPS_county state state_name county region_number region division_number division flag_count10 pop10 flag_by_pop10 flag_count_quantile10 flag_pop_quantile10 flag_count20 pop20 flag_by_pop20 flag_count_quantile20 flag_pop_quantile20
01001 01 001 AL Alabama Autauga County 3 South Region 6 East South Central Division 14 7982 0.0017539 0.6 0.8 18 8818 0.0020413 0.6 1.0
01003 01 003 AL Alabama Baldwin County 3 South Region 6 East South Central Division 34 38458 0.0008841 0.8 0.4 34 46255 0.0007351 0.8 0.2
01005 01 005 AL Alabama Barbour County 3 South Region 6 East South Central Division 43 21287 0.0020200 0.8 1.0 44 18811 0.0023391 0.8 1.0
01007 01 007 AL Alabama Bibb County 3 South Region 6 East South Central Division 11 17570 0.0006261 0.4 0.2 16 17663 0.0009058 0.6 0.4
01009 01 009 AL Alabama Blount County 3 South Region 6 East South Central Division 12 16995 0.0007061 0.4 0.2 8 16546 0.0004835 0.4 0.2
01011 01 011 AL Alabama Bullock County 3 South Region 6 East South Central Division 21 10923 0.0019225 0.6 1.0 18 10173 0.0017694 0.6 0.8 
svi_national_county_nmtc <- left_join(svi_national_nmtc_county_sum,
                                      svi_national_county_flags_nmtc,
                                    join_by("State" == "state", "County" == "county",
                                            "Division" == "division"))

svi_national_county_nmtc$post10_nmtc_project_cnt[is.na(svi_national_county_nmtc$post10_nmtc_project_cnt)] <- 0

svi_national_county_nmtc$county_name <- paste0(svi_national_county_nmtc$County, ", ", svi_national_county_nmtc$State)

svi_national_county_nmtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
State County Division post10_nmtc_project_cnt tract_cnt post10_nmtc_project_dollars post10_nmtc_dollars_formatted fips_county_st FIPS_st FIPS_county state_name region_number region division_number flag_count10 pop10 flag_by_pop10 flag_count_quantile10 flag_pop_quantile10 flag_count20 pop20 flag_by_pop20 flag_count_quantile20 flag_pop_quantile20 county_name
AK Aleutians East Borough Pacific Division 1 1 15762500 \$15,762,500 02013 02 013 Alaska 4 West Region 9 8 3703 0.0021604 0.4 1.0 5 3389 0.0014754 0.2 0.8 Aleutians East Borough, AK
AK Aleutians West Census Area Pacific Division 0 1 0 \$0 02016 02 016 Alaska 4 West Region 9 6 1774 0.0033822 0.2 1.0 6 950 0.0063158 0.2 1.0 Aleutians West Census Area, AK
AK Anchorage Municipality Pacific Division 1 13 9800000 \$9,800,000 02020 02 020 Alaska 4 West Region 9 72 64432 0.0011175 1.0 0.4 87 69679 0.0012486 1.0 0.6 Anchorage Municipality, AK
AK Bethel Census Area Pacific Division 0 1 0 \$0 02050 02 050 Alaska 4 West Region 9 8 1386 0.0057720 0.4 1.0 10 1404 0.0071225 0.4 1.0 Bethel Census Area, AK
AK Fairbanks North Star Borough Pacific Division 0 4 0 \$0 02090 02 090 Alaska 4 West Region 9 13 17281 0.0007523 0.4 0.2 17 20094 0.0008460 0.6 0.4 Fairbanks North Star Borough, AK
AK Hoonah-Angoon Census Area Pacific Division 0 1 0 \$0 02105 02 105 Alaska 4 West Region 9 4 1888 0.0021186 0.2 1.0 5 2073 0.0024120 0.2 1.0 Hoonah-Angoon Census Area, AK 
# Create data frame of NMTC eligible tracts 2020 nationally
svi_divisional_nmtc10 <- svi_divisional_nmtc %>% select(GEOID_2010_trt, FIPS_st, FIPS_county, 
    state, state_name, county, region_number, region, division_number, 
    division, F_TOTAL_10, E_TOTPOP_10) %>% rename("F_TOTAL" = "F_TOTAL_10")

# Count divisional-level SVI flags for 2010, create unified fips column
svi_2010_divisional_county_flags_nmtc <- flag_summarize(svi_divisional_nmtc10, "E_TOTPOP_10") %>% unite("fips_county_st", FIPS_st:FIPS_county, remove = FALSE, sep="")

# Add suffix to flag columns 2010
colnames(svi_2010_divisional_county_flags_nmtc)[11:15] <- paste0(colnames(svi_2010_divisional_county_flags_nmtc)[11:15], "10")

# Create data frame of NMTC eligible tracts 2020 nationally
svi_divisional_nmtc20 <- svi_divisional_nmtc %>% select(GEOID_2010_trt, FIPS_st, FIPS_county, 
    state, state_name, county, region_number, region, division_number, 
    division, F_TOTAL_20, E_TOTPOP_20) %>% rename("F_TOTAL" = "F_TOTAL_20")

# Count divisional-level SVI flags for 2020, create unified fips column
svi_2020_divisional_county_flags_nmtc <- flag_summarize(svi_divisional_nmtc20, "E_TOTPOP_20") %>% unite("fips_county_st", FIPS_st:FIPS_county, remove = FALSE, sep="")

# Identify needed columns for 2020
colnames(svi_2020_divisional_county_flags_nmtc)[11:15] <- paste0(colnames(svi_2020_divisional_county_flags_nmtc)[11:15], "20")

# Filter to needed columns for 2020 to avoid duplicate column column names
svi_2020_divisional_county_flags_join_nmtc <- svi_2020_divisional_county_flags_nmtc %>% ungroup() %>% select("fips_county_st", all_of(colnames(svi_2020_divisional_county_flags_nmtc)[11:15]))
 
# Join 2010 and 2020 data
svi_divisional_county_flags_nmtc <- left_join(svi_2010_divisional_county_flags_nmtc, svi_2020_divisional_county_flags_join_nmtc, join_by("fips_county_st" == "fips_county_st")) 

svi_divisional_county_flags_nmtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
fips_county_st FIPS_st FIPS_county state state_name county region_number region division_number division flag_count10 pop10 flag_by_pop10 flag_count_quantile10 flag_pop_quantile10 flag_count20 pop20 flag_by_pop20 flag_count_quantile20 flag_pop_quantile20
02013 02 013 AK Alaska Aleutians East Borough 4 West Region 9 Pacific Division 9 3703 0.0024305 0.2 1.0 6 3389 0.0017704 0.2 1.0
02016 02 016 AK Alaska Aleutians West Census Area 4 West Region 9 Pacific Division 4 1774 0.0022548 0.2 1.0 6 950 0.0063158 0.2 1.0
02020 02 020 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 56 64432 0.0008691 0.8 0.2 73 69679 0.0010477 0.8 0.4
02050 02 050 AK Alaska Bethel Census Area 4 West Region 9 Pacific Division 8 1386 0.0057720 0.2 1.0 9 1404 0.0064103 0.2 1.0
02090 02 090 AK Alaska Fairbanks North Star Borough 4 West Region 9 Pacific Division 14 17281 0.0008101 0.4 0.2 15 20094 0.0007465 0.4 0.2
02105 02 105 AK Alaska Hoonah-Angoon Census Area 4 West Region 9 Pacific Division 4 1888 0.0021186 0.2 1.0 7 2073 0.0033767 0.2 1.0 
svi_divisional_county_nmtc <- left_join(svi_divisional_nmtc_county_sum, 
                                        svi_divisional_county_flags_nmtc,
                                    join_by("State" == "state", "County" == "county",
                                            "Division" == "division"))

svi_divisional_county_nmtc$post10_nmtc_project_cnt[is.na(svi_divisional_county_nmtc $post10_nmtc_project_cnt)] <- 0

svi_divisional_county_nmtc$county_name <- paste0(svi_divisional_county_nmtc$County, ", ", svi_divisional_county_nmtc$State)

svi_divisional_county_nmtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
State County Division post10_nmtc_project_cnt tract_cnt post10_nmtc_project_dollars post10_nmtc_dollars_formatted fips_county_st FIPS_st FIPS_county state_name region_number region division_number flag_count10 pop10 flag_by_pop10 flag_count_quantile10 flag_pop_quantile10 flag_count20 pop20 flag_by_pop20 flag_count_quantile20 flag_pop_quantile20 county_name
AK Aleutians East Borough Pacific Division 1 1 15762500 \$15,762,500 02013 02 013 Alaska 4 West Region 9 9 3703 0.0024305 0.2 1.0 6 3389 0.0017704 0.2 1.0 Aleutians East Borough, AK
AK Aleutians West Census Area Pacific Division 0 1 0 \$0 02016 02 016 Alaska 4 West Region 9 4 1774 0.0022548 0.2 1.0 6 950 0.0063158 0.2 1.0 Aleutians West Census Area, AK
AK Anchorage Municipality Pacific Division 1 13 9800000 \$9,800,000 02020 02 020 Alaska 4 West Region 9 56 64432 0.0008691 0.8 0.2 73 69679 0.0010477 0.8 0.4 Anchorage Municipality, AK
AK Bethel Census Area Pacific Division 0 1 0 \$0 02050 02 050 Alaska 4 West Region 9 8 1386 0.0057720 0.2 1.0 9 1404 0.0064103 0.2 1.0 Bethel Census Area, AK
AK Fairbanks North Star Borough Pacific Division 0 4 0 \$0 02090 02 090 Alaska 4 West Region 9 14 17281 0.0008101 0.4 0.2 15 20094 0.0007465 0.4 0.2 Fairbanks North Star Borough, AK
AK Hoonah-Angoon Census Area Pacific Division 0 1 0 \$0 02105 02 105 Alaska 4 West Region 9 4 1888 0.0021186 0.2 1.0 7 2073 0.0033767 0.2 1.0 Hoonah-Angoon Census Area, AK

LIHTC Data Wrangling

lihtc_eligible_flag <- lihtc_eligible %>% 
  select("fips", "state", "county", "stcnty", "tract", "metro", "cbsa", "qct_2010") %>% 
  rename("GEOID10" = "fips") %>% 
  mutate(lihtc_eligibility = if_else(qct_2010 == 1, "Yes", "No")) %>% 
  filter(tolower(lihtc_eligibility) == "yes") %>% 
  select(GEOID10, lihtc_eligibility)

lihtc_eligible_flag %>% head() 

## # A tibble: 6 × 2
##   GEOID10     lihtc_eligibility
##   <chr>       <chr>            
## 1 01003010600 Yes              
## 2 01005950200 Yes              
## 3 01005950300 Yes              
## 4 01005950400 Yes              
## 5 01005950600 Yes              
## 6 01005950700 Yes

lihtc_projects10 <- lihtc_projects %>% 
  filter(yr_alloc < 8000) %>% 
  filter(yr_alloc <= 2010) %>% 
  count(fips2010) %>% 
  rename("pre10_lihtc_project_cnt" = "n")

lihtc_projects10 %>% head() 

##      fips2010 pre10_lihtc_project_cnt
## 1 01001020300                       2
## 2 01001020500                       5
## 3 01001021100                       1
## 4 01003010200                       1
## 5 01003010600                       1
## 6 01003010703                       1

lihtc_dollars10 <- lihtc_projects %>% 
  filter(yr_alloc < 8000) %>% 
  filter(yr_alloc <= 2010) %>%
  select(fips2010, allocamt)

lihtc_dollars10$allocamt[is.na(lihtc_dollars10$allocamt)] <- 0

lihtc_dollars10 <- lihtc_dollars10 %>% 
  group_by(fips2010) %>% 
  summarise(pre10_lihtc_project_dollars = sum(allocamt, na.rm = TRUE))

lihtc_dollars10 %>% head() 

## # A tibble: 6 × 2
##   fips2010    pre10_lihtc_project_dollars
##   <chr>                             <dbl>
## 1 01001020300                      216593
## 2 01001020500                     2250459
## 3 01001021100                       53109
## 4 01003010200                           0
## 5 01003010600                      376889
## 6 01003010703                      717113

lihtc_projects10 <- left_join(lihtc_projects10, lihtc_dollars10, join_by(fips2010 == fips2010))

lihtc_projects10 %>% head()

##      fips2010 pre10_lihtc_project_cnt pre10_lihtc_project_dollars
## 1 01001020300                       2                      216593
## 2 01001020500                       5                     2250459
## 3 01001021100                       1                       53109
## 4 01003010200                       1                           0
## 5 01003010600                       1                      376889
## 6 01003010703                       1                      717113

lihtc_projects20 <- lihtc_projects %>% 
  filter(yr_alloc < 8000) %>% 
  filter(yr_alloc > 2010) %>% 
  filter(yr_alloc < 2021) %>% 
  count(fips2010) %>% 
  rename("post10_lihtc_project_cnt" = "n")

lihtc_projects20 %>% head() 

##      fips2010 post10_lihtc_project_cnt
## 1 01003010500                        1
## 2 01003011403                        1
## 3 01003011601                        1
## 4 01005950900                        1
## 5 01009050102                        1
## 6 01017954600                        2

lihtc_dollars20 <- lihtc_projects %>% 
  filter(yr_alloc < 8000) %>% 
  filter(yr_alloc > 2010) %>% 
  filter(yr_alloc < 2021) %>% 
  select(fips2010, allocamt)

lihtc_dollars20$allocamt[is.na(lihtc_dollars20$allocamt)] <- 0

lihtc_dollars20 <- lihtc_dollars20 %>% 
  group_by(fips2010) %>% 
  summarise(post10_lihtc_project_dollars = sum(allocamt, na.rm = TRUE))

lihtc_dollars20 %>% head() 

## # A tibble: 6 × 2
##   fips2010    post10_lihtc_project_dollars
##   <chr>                              <dbl>
## 1 01003010500                       481325
## 2 01003011403                       828342
## 3 01003011601                       887856
## 4 01005950900                       400758
## 5 01009050102                       463000
## 6 01017954600                       950192

lihtc_projects20 <- left_join(lihtc_projects20, lihtc_dollars20, join_by(fips2010 == fips2010))

lihtc_projects20 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
fips2010 post10_lihtc_project_cnt post10_lihtc_project_dollars
01003010500 1 481325
01003011403 1 828342
01003011601 1 887856
01005950900 1 400758
01009050102 1 463000
01017954600 2 950192 
svi_divisional_lihtc10 <- left_join(svi_divisional, lihtc_projects10, join_by("GEOID_2010_trt" == "fips2010"))

svi_divisional_lihtc10 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt FIPS_st FIPS_county FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 pre10_lihtc_project_cnt pre10_lihtc_project_dollars
02013000100 02 013 000100 AK Alaska Aleutians East Borough 4 West Region 9 Pacific Division 3703 474 267 1212 3695 32.801082 0.75700 1 111 3163 3.509327 0.08691 0 25 158 15.82278 0.01337 0 17 109 15.59633 0.02605 0 42 267 15.73034 0.004754 0 1082 3017 35.863441 0.8542 1 2060 3112 66.19537 0.9999 1 127 3.429652 0.04240 0 315 8.506616 0.03961 0 182 2849 6.388206 0.07775 0 50 165 30.303030 0.88350 1 1070 3617 29.5825270 0.93700 1 3492 3703 94.30192 0.9141 1 474 8 1.687764 0.29250 0 42 8.8607595 0.8128 1 7 267 2.621723 0.4003 0 77 267 28.8389513 0.96850 1 2969 3703 80.17823 0.9940 1 2.702764 0.5611 3 1.98026 0.23800 2 0.9141 0.9047 1 3.46810 0.8902 3 9.065224 0.6397 9 3389 1199 988 698 3379 20.656999 0.5925 0 86 2414 3.562552 0.2665 0 67 607 11.037891 0.01803 0 74 381 19.42257 0.04067 0 141 988 14.27126 0.006988 0 354 2646 13.378685 0.61070 0 1345 3384 39.745863 0.9997 1 381 11.242254 0.31390 0 443 13.07170 0.0988 0 339 2941.000 11.526692 0.3860 0 135 593.000 22.765599 0.7920 1 334 3276 10.1953602 0.72620 0 2939 3389.000 86.72175 0.81100 1 1199 38 3.169308 0.3474 0 69 5.7547957 0.7806 1 30 988 3.0364372 0.36010 0 220 988.000 22.267207 0.95270 1 1035 3389 30.5399823 0.9843 1 2.476388 0.4947 1 2.31690 0.37850 1 0.81100 0.80380 1 3.42510 0.8683 3 9.029388 0.6419 6 NA NA
02016000100 02 016 000100 AK Alaska Aleutians West Census Area 4 West Region 9 Pacific Division 1774 1056 166 328 1231 26.645004 0.65530 0 15 1370 1.094890 0.01369 0 25 95 26.31579 0.09653 0 16 71 22.53521 0.05099 0 41 166 24.69880 0.029080 0 207 1330 15.563910 0.5839 0 484 973 49.74306 0.9952 1 53 2.987599 0.03180 0 182 10.259301 0.05188 0 147 747 19.678715 0.86420 1 19 96 19.791667 0.66060 0 79 1718 4.5983702 0.46890 0 1154 1774 65.05073 0.6522 0 1056 22 2.083333 0.31610 0 0 0.0000000 0.2497 0 10 166 6.024096 0.6154 0 84 166 50.6024096 0.99320 1 1324 1774 74.63360 0.9935 1 2.277170 0.4443 1 2.07738 0.27780 1 0.6522 0.6454 0 3.16790 0.7874 2 8.174650 0.5311 4 950 694 199 218 719 30.319889 0.7848 1 15 560 2.678571 0.1560 0 11 117 9.401709 0.01305 0 14 82 17.07317 0.03088 0 25 199 12.56281 0.003541 0 48 681 7.048458 0.37250 0 238 721 33.009709 0.9989 1 116 12.210526 0.37310 0 195 20.52632 0.4153 0 113 526.000 21.482890 0.8931 1 31 98.000 31.632653 0.9318 1 17 900 1.8888889 0.29830 0 713 950.000 75.05263 0.69000 0 694 17 2.449568 0.3163 0 0 0.0000000 0.2466 0 7 199 3.5175879 0.39980 0 68 199.000 34.170854 0.98260 1 274 950 28.8421053 0.9832 1 2.315741 0.4476 2 2.91160 0.70420 2 0.69000 0.68390 0 2.92850 0.6794 2 8.845841 0.6188 6 NA NA
02016000200 02 016 000200 AK Alaska Aleutians West Census Area 4 West Region 9 Pacific Division 4485 507 355 1315 4469 29.424927 0.70570 0 87 3859 2.254470 0.03381 0 20 94 21.27660 0.03822 0 35 261 13.40996 0.02120 0 55 355 15.49296 0.004567 0 1292 3728 34.656652 0.8445 1 981 4256 23.04981 0.7621 1 186 4.147157 0.06676 0 384 8.561873 0.03989 0 235 3656 6.427790 0.07933 0 38 204 18.627451 0.62370 0 1458 4397 33.1589720 0.95840 1 3616 4485 80.62430 0.7822 1 507 85 16.765286 0.69850 0 32 6.3116371 0.7700 1 41 355 11.549296 0.7760 1 30 355 8.4507042 0.69980 0 3507 4485 78.19398 0.9938 1 2.350677 0.4664 2 1.76808 0.15490 1 0.7822 0.7742 1 3.93810 0.9690 3 8.839057 0.6119 7 4758 1319 1107 398 4700 8.468085 0.1862 0 144 3404 4.230317 0.3539 0 111 245 45.306122 0.92700 1 93 862 10.78886 0.01250 0 204 1107 18.42818 0.024790 0 297 3527 8.420754 0.43840 0 699 4724 14.796782 0.9132 1 314 6.599412 0.07503 0 822 17.27617 0.2319 0 292 3902.000 7.483342 0.1088 0 99 662.000 14.954683 0.5563 0 433 4586 9.4417793 0.70570 0 3672 4758.000 77.17528 0.71070 0 1319 392 29.719485 0.8272 1 23 1.7437453 0.6618 0 146 1107 13.1887986 0.80180 1 96 1107.000 8.672087 0.73650 0 950 4758 19.9663724 0.9765 1 1.916490 0.3307 1 1.67773 0.11040 0 0.71070 0.70440 0 4.00380 0.9728 3 8.308720 0.5475 4 NA NA
02020000101 02 020 000101 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 5475 1957 1796 296 5463 5.418268 0.08172 0 229 2885 7.937608 0.48540 0 364 1522 23.91590 0.06363 0 39 274 14.23358 0.02232 0 403 1796 22.43875 0.018830 0 121 3476 3.481013 0.1279 0 863 5853 14.74458 0.4836 0 236 4.310502 0.07411 0 1614 29.479452 0.75790 1 524 4028 13.008937 0.54240 0 65 1496 4.344920 0.06955 0 13 5202 0.2499039 0.07262 0 759 5475 13.86301 0.1072 0 1957 0 0.000000 0.09395 0 167 8.5334696 0.8076 1 134 1796 7.461024 0.6695 0 11 1796 0.6124722 0.08007 0 0 5475 0.00000 0.3743 0 1.197450 0.1505 0 1.51658 0.08466 1 0.1072 0.1061 0 2.02542 0.2761 1 4.846650 0.1061 2 5772 2127 1917 416 5772 7.207207 0.1396 0 223 2691 8.286882 0.7821 1 296 1679 17.629541 0.08891 0 30 238 12.60504 0.01632 0 326 1917 17.00574 0.015840 0 74 4011 1.844926 0.07404 0 546 5733 9.523810 0.7456 0 692 11.988912 0.36010 0 1481 25.65835 0.7225 0 771 4252.330 18.131237 0.7949 1 94 1608.796 5.842877 0.1503 0 4 5425 0.0737327 0.05497 0 989 5772.331 17.13346 0.08922 0 2127 28 1.316408 0.2585 0 5 0.2350729 0.5017 0 9 1917 0.4694836 0.09085 0 24 1916.584 1.252228 0.15320 0 114 5772 1.9750520 0.8251 1 1.757180 0.2809 1 2.08277 0.26400 1 0.08922 0.08843 0 1.82935 0.2038 1 5.758520 0.1870 3 NA NA
02020000102 02 020 000102 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 4240 1923 1654 286 4240 6.745283 0.12010 0 198 2385 8.301887 0.51840 0 275 1235 22.26721 0.04612 0 248 419 59.18854 0.73210 0 523 1654 31.62031 0.108600 0 242 2799 8.645945 0.3645 0 838 4982 16.82055 0.5669 0 259 6.108491 0.17290 0 1038 24.481132 0.50340 0 809 3707 21.823577 0.91630 1 97 1071 9.056956 0.24130 0 0 4007 0.0000000 0.02799 0 955 4240 22.52358 0.2295 0 1923 169 8.788351 0.54130 0 147 7.6443058 0.7936 1 33 1654 1.995163 0.3402 0 103 1654 6.2273277 0.58930 0 0 4240 0.00000 0.3743 0 1.678500 0.2760 0 1.86189 0.19010 1 0.2295 0.2271 0 2.63870 0.5519 1 6.408590 0.2952 2 4743 1975 1681 633 4738 13.360067 0.3701 0 75 2465 3.042596 0.1971 0 383 1350 28.370370 0.47830 0 122 331 36.85801 0.20770 0 505 1681 30.04164 0.245400 0 205 3383 6.059710 0.32020 0 330 4638 7.115136 0.6017 0 653 13.767658 0.46830 0 1186 25.00527 0.6899 0 472 3447.726 13.690182 0.5484 0 182 1469.325 12.386640 0.4525 0 0 4485 0.0000000 0.02391 0 756 4743.330 15.93817 0.07455 0 1975 153 7.746835 0.4947 0 156 7.8987342 0.8173 1 17 1681 1.0113028 0.15640 0 0 1681.103 0.000000 0.02249 0 15 4743 0.3162555 0.4726 0 1.734500 0.2739 0 2.18301 0.31050 0 0.07455 0.07389 0 1.96349 0.2552 1 5.955550 0.2162 1 NA NA
02020000201 02 020 000201 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 4085 1543 1532 296 4085 7.246022 0.13670 0 46 2089 2.202011 0.03307 0 302 969 31.16615 0.19620 0 187 563 33.21492 0.13830 0 489 1532 31.91906 0.113500 0 164 2395 6.847599 0.2875 0 811 3466 23.39873 0.7707 1 133 3.255814 0.03812 0 1199 29.351285 0.75230 1 320 2468 12.965964 0.53850 0 171 1083 15.789474 0.52510 0 7 3810 0.1837270 0.06378 0 743 4085 18.18849 0.1723 0 1543 54 3.499676 0.37950 0 7 0.4536617 0.5183 0 32 1532 2.088773 0.3498 0 49 1532 3.1984334 0.36020 0 0 4085 0.00000 0.3743 0 1.341470 0.1875 1 1.91780 0.21070 1 0.1723 0.1705 0 1.98210 0.2583 0 5.413670 0.1694 2 4707 1946 1835 706 4707 14.998938 0.4258 0 88 2269 3.878360 0.3073 0 219 793 27.616646 0.44320 0 527 1042 50.57582 0.53120 0 746 1835 40.65395 0.607400 0 194 2805 6.916221 0.36480 0 464 4274 10.856341 0.8018 1 257 5.459953 0.04652 0 1279 27.17230 0.7970 1 390 2999.274 13.003148 0.4994 0 72 1222.675 5.888728 0.1525 0 26 4201 0.6189003 0.13850 0 1282 4706.670 27.23794 0.21200 0 1946 76 3.905447 0.3758 0 2 0.1027749 0.4966 0 96 1835 5.2316076 0.52940 0 39 1834.897 2.125459 0.25860 0 0 4707 0.0000000 0.1370 0 2.507100 0.5058 1 1.63392 0.09856 1 0.21200 0.21010 0 1.79740 0.1921 0 6.150420 0.2432 2 1 270385 
svi_national_lihtc10 <- left_join(svi_national, lihtc_projects10, join_by("GEOID_2010_trt" == "fips2010"))

svi_national_lihtc10 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt FIPS_st FIPS_county FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 pre10_lihtc_project_cnt pre10_lihtc_project_dollars
01001020100 01 001 020100 AL Alabama Autauga County 3 South Region 6 East South Central Division 1809 771 696 297 1809 16.41791 0.3871 0 36 889 4.049494 0.1790 0 127 598 21.23746 0.20770 0 47 98 47.95918 0.5767 0 174 696 25.00000 0.18790 0 196 1242 15.780998 0.6093 0 186 1759 10.574190 0.3790 0 222 12.271973 0.4876 0 445 24.59923 0.5473 0 298 1335 22.32210 0.8454 1 27 545 4.954128 0.09275 0 36 1705 2.1114370 0.59040 0 385 1809 21.282477 0.4524 0 771 0 0.0000000 0.1224 0 92 11.9325551 0.8005 1 0 696 0.0000000 0.1238 0 50 696 7.183908 0.6134 0 0 1809 0 0.364 0 1.74230 0.28200 0 2.56345 0.5296 1 0.4524 0.4482 0 2.0241 0.2519 1 6.78225 0.3278 2 1941 710 693 352 1941 18.13498 0.4630 0 18 852 2.112676 0.15070 0 81 507 15.976331 0.26320 0 63 186 33.87097 0.2913 0 144 693 20.77922 0.2230 0 187 1309 14.285714 0.6928 0 187 1941 9.634209 0.6617 0 295 15.19835 0.4601 0 415 21.38073 0.4681 0 391 1526 25.62254 0.9011 1 58 555 10.45045 0.3451 0 0 1843 0.0000000 0.09479 0 437 1941 22.51417 0.3902 0 710 0 0.0000000 0.1079 0 88 12.3943662 0.8263 1 0 693 0.0000000 0.09796 0 10 693 1.443001 0.1643 0 0 1941 0.000000 0.1831 0 2.19120 0.4084 0 2.26919 0.3503 1 0.3902 0.3869 0 1.37956 0.07216 1 6.23015 0.2314 2 NA NA
01001020200 01 001 020200 AL Alabama Autauga County 3 South Region 6 East South Central Division 2020 816 730 495 1992 24.84940 0.5954 0 68 834 8.153477 0.5754 0 49 439 11.16173 0.02067 0 105 291 36.08247 0.3019 0 154 730 21.09589 0.09312 0 339 1265 26.798419 0.8392 1 313 2012 15.556660 0.6000 0 204 10.099010 0.3419 0 597 29.55446 0.8192 1 359 1515 23.69637 0.8791 1 132 456 28.947368 0.83510 1 15 1890 0.7936508 0.40130 0 1243 2020 61.534653 0.7781 1 816 0 0.0000000 0.1224 0 34 4.1666667 0.6664 0 13 730 1.7808219 0.5406 0 115 730 15.753425 0.8382 1 0 2020 0 0.364 0 2.70312 0.56650 1 3.27660 0.8614 3 0.7781 0.7709 1 2.5316 0.5047 1 9.28942 0.6832 6 1757 720 573 384 1511 25.41363 0.6427 0 29 717 4.044630 0.41320 0 33 392 8.418367 0.03542 0 116 181 64.08840 0.9086 1 149 573 26.00349 0.4041 0 139 1313 10.586443 0.5601 0 91 1533 5.936073 0.4343 0 284 16.16392 0.5169 0 325 18.49744 0.2851 0 164 1208 13.57616 0.4127 0 42 359 11.69916 0.3998 0 0 1651 0.0000000 0.09479 0 1116 1757 63.51736 0.7591 1 720 3 0.4166667 0.2470 0 5 0.6944444 0.5106 0 9 573 1.5706806 0.46880 0 57 573 9.947644 0.7317 0 212 1757 12.066022 0.9549 1 2.45440 0.4888 0 1.70929 0.1025 0 0.7591 0.7527 1 2.91300 0.68620 1 7.83579 0.4802 2 NA NA
01001020300 01 001 020300 AL Alabama Autauga County 3 South Region 6 East South Central Division 3543 1403 1287 656 3533 18.56779 0.4443 0 93 1552 5.992268 0.3724 0 273 957 28.52665 0.45780 0 178 330 53.93939 0.7152 0 451 1287 35.04274 0.49930 0 346 2260 15.309734 0.5950 0 252 3102 8.123791 0.2596 0 487 13.745413 0.5868 0 998 28.16822 0.7606 1 371 2224 16.68165 0.6266 0 126 913 13.800657 0.46350 0 0 3365 0.0000000 0.09298 0 637 3543 17.979114 0.4049 0 1403 10 0.7127584 0.3015 0 2 0.1425517 0.4407 0 0 1287 0.0000000 0.1238 0 101 1287 7.847708 0.6443 0 0 3543 0 0.364 0 2.17060 0.41010 0 2.53048 0.5116 1 0.4049 0.4011 0 1.8743 0.1942 0 6.98028 0.3576 1 3694 1464 1351 842 3694 22.79372 0.5833 0 53 1994 2.657974 0.22050 0 117 967 12.099276 0.11370 0 147 384 38.28125 0.3856 0 264 1351 19.54108 0.1827 0 317 2477 12.797739 0.6460 0 127 3673 3.457664 0.2308 0 464 12.56091 0.3088 0 929 25.14889 0.7080 0 473 2744 17.23761 0.6211 0 263 975 26.97436 0.8234 1 128 3586 3.5694367 0.70770 0 1331 3694 36.03140 0.5515 0 1464 26 1.7759563 0.3675 0 14 0.9562842 0.5389 0 35 1351 2.5906736 0.60550 0 42 1351 3.108808 0.3415 0 0 3694 0.000000 0.1831 0 1.86330 0.3063 0 3.16900 0.8380 1 0.5515 0.5468 0 2.03650 0.26830 0 7.62030 0.4460 1 2 216593
01001020400 01 001 020400 AL Alabama Autauga County 3 South Region 6 East South Central Division 4840 1957 1839 501 4840 10.35124 0.2177 0 101 2129 4.744011 0.2447 0 310 1549 20.01291 0.17080 0 89 290 30.68966 0.2044 0 399 1839 21.69657 0.10540 0 274 3280 8.353658 0.3205 0 399 4293 9.294200 0.3171 0 955 19.731405 0.8643 1 1195 24.69008 0.5530 0 625 3328 18.78005 0.7233 0 152 1374 11.062591 0.34710 0 10 4537 0.2204100 0.22560 0 297 4840 6.136364 0.1647 0 1957 33 1.6862545 0.3843 0 25 1.2774655 0.5516 0 14 1839 0.7612833 0.3564 0 19 1839 1.033170 0.1127 0 0 4840 0 0.364 0 1.20540 0.13470 0 2.71330 0.6129 1 0.1647 0.1632 0 1.7690 0.1591 0 5.85240 0.1954 1 3539 1741 1636 503 3539 14.21305 0.3472 0 39 1658 2.352232 0.17990 0 219 1290 16.976744 0.30880 0 74 346 21.38728 0.1037 0 293 1636 17.90954 0.1333 0 173 2775 6.234234 0.3351 0 169 3529 4.788892 0.3448 0 969 27.38062 0.9225 1 510 14.41085 0.1208 0 670 3019 22.19278 0.8194 1 148 1137 13.01671 0.4541 0 89 3409 2.6107363 0.64690 0 454 3539 12.82848 0.2364 0 1741 143 8.2136703 0.6028 0 0 0.0000000 0.2186 0 10 1636 0.6112469 0.28340 0 72 1636 4.400978 0.4538 0 0 3539 0.000000 0.1831 0 1.34030 0.1575 0 2.96370 0.7496 2 0.2364 0.2344 0 1.74170 0.16270 0 6.28210 0.2389 2 NA NA
01001020500 01 001 020500 AL Alabama Autauga County 3 South Region 6 East South Central Division 9938 3969 3741 1096 9938 11.02838 0.2364 0 188 4937 3.807981 0.1577 0 426 2406 17.70574 0.11050 0 528 1335 39.55056 0.3753 0 954 3741 25.50120 0.20140 0 293 5983 4.897209 0.1655 0 740 10110 7.319486 0.2211 0 837 8.422218 0.2408 0 3012 30.30791 0.8455 1 759 7155 10.60797 0.2668 0 476 2529 18.821669 0.63540 0 78 9297 0.8389803 0.41110 0 1970 9938 19.822902 0.4330 0 3969 306 7.7097506 0.6153 0 0 0.0000000 0.2198 0 7 3741 0.1871157 0.2535 0 223 3741 5.960973 0.5483 0 0 9938 0 0.364 0 0.98210 0.08468 0 2.39960 0.4381 1 0.4330 0.4290 0 2.0009 0.2430 0 5.81560 0.1905 1 10674 4504 4424 1626 10509 15.47245 0.3851 0 81 5048 1.604596 0.09431 0 321 2299 13.962592 0.17970 0 711 2125 33.45882 0.2836 0 1032 4424 23.32731 0.3109 0 531 6816 7.790493 0.4251 0 301 10046 2.996217 0.1894 0 1613 15.11149 0.4553 0 2765 25.90407 0.7494 0 1124 7281 15.43744 0.5253 0 342 2912 11.74451 0.4019 0 52 9920 0.5241935 0.35230 0 2603 10674 24.38636 0.4160 0 4504 703 15.6083481 0.7378 0 29 0.6438721 0.5037 0 37 4424 0.8363472 0.33420 0 207 4424 4.679023 0.4754 0 176 10674 1.648866 0.7598 1 1.40481 0.1743 0 2.48420 0.4802 0 0.4160 0.4125 0 2.81090 0.63730 1 7.11591 0.3654 1 5 2250459
01001020600 01 001 020600 AL Alabama Autauga County 3 South Region 6 East South Central Division 3402 1456 1308 735 3402 21.60494 0.5199 0 134 1720 7.790698 0.5436 0 242 1032 23.44961 0.28010 0 62 276 22.46377 0.1035 0 304 1308 23.24159 0.14070 0 301 2151 13.993491 0.5510 0 355 3445 10.304790 0.3656 0 386 11.346267 0.4232 0 931 27.36626 0.7200 0 440 2439 18.04018 0.6912 0 143 924 15.476190 0.52900 0 4 3254 0.1229256 0.19840 0 723 3402 21.252205 0.4519 0 1456 18 1.2362637 0.3507 0 433 29.7390110 0.9468 1 16 1308 1.2232416 0.4493 0 28 1308 2.140673 0.2298 0 0 3402 0 0.364 0 2.12080 0.39510 0 2.56180 0.5288 0 0.4519 0.4477 0 2.3406 0.4048 1 7.47510 0.4314 1 3536 1464 1330 1279 3523 36.30429 0.8215 1 34 1223 2.780049 0.23780 0 321 1111 28.892889 0.75870 1 67 219 30.59361 0.2305 0 388 1330 29.17293 0.5075 0 306 2380 12.857143 0.6480 0 415 3496 11.870709 0.7535 1 547 15.46946 0.4760 0 982 27.77149 0.8327 1 729 2514 28.99761 0.9488 1 95 880 10.79545 0.3601 0 0 3394 0.0000000 0.09479 0 985 3536 27.85633 0.4608 0 1464 0 0.0000000 0.1079 0 364 24.8633880 0.9300 1 0 1330 0.0000000 0.09796 0 17 1330 1.278196 0.1463 0 0 3536 0.000000 0.1831 0 2.96830 0.6434 2 2.71239 0.6156 2 0.4608 0.4569 0 1.46526 0.08976 1 7.60675 0.4440 5 NA NA 
svi_divisional_lihtc20 <- left_join(svi_divisional_lihtc10, lihtc_projects20, join_by("GEOID_2010_trt" == "fips2010"))

svi_divisional_lihtc20 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt FIPS_st FIPS_county FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 pre10_lihtc_project_cnt pre10_lihtc_project_dollars post10_lihtc_project_cnt post10_lihtc_project_dollars
02013000100 02 013 000100 AK Alaska Aleutians East Borough 4 West Region 9 Pacific Division 3703 474 267 1212 3695 32.801082 0.75700 1 111 3163 3.509327 0.08691 0 25 158 15.82278 0.01337 0 17 109 15.59633 0.02605 0 42 267 15.73034 0.004754 0 1082 3017 35.863441 0.8542 1 2060 3112 66.19537 0.9999 1 127 3.429652 0.04240 0 315 8.506616 0.03961 0 182 2849 6.388206 0.07775 0 50 165 30.303030 0.88350 1 1070 3617 29.5825270 0.93700 1 3492 3703 94.30192 0.9141 1 474 8 1.687764 0.29250 0 42 8.8607595 0.8128 1 7 267 2.621723 0.4003 0 77 267 28.8389513 0.96850 1 2969 3703 80.17823 0.9940 1 2.702764 0.5611 3 1.98026 0.23800 2 0.9141 0.9047 1 3.46810 0.8902 3 9.065224 0.6397 9 3389 1199 988 698 3379 20.656999 0.5925 0 86 2414 3.562552 0.2665 0 67 607 11.037891 0.01803 0 74 381 19.42257 0.04067 0 141 988 14.27126 0.006988 0 354 2646 13.378685 0.61070 0 1345 3384 39.745863 0.9997 1 381 11.242254 0.31390 0 443 13.07170 0.0988 0 339 2941.000 11.526692 0.3860 0 135 593.000 22.765599 0.7920 1 334 3276 10.1953602 0.72620 0 2939 3389.000 86.72175 0.81100 1 1199 38 3.169308 0.3474 0 69 5.7547957 0.7806 1 30 988 3.0364372 0.36010 0 220 988.000 22.267207 0.95270 1 1035 3389 30.5399823 0.9843 1 2.476388 0.4947 1 2.31690 0.37850 1 0.81100 0.80380 1 3.42510 0.8683 3 9.029388 0.6419 6 NA NA NA NA
02016000100 02 016 000100 AK Alaska Aleutians West Census Area 4 West Region 9 Pacific Division 1774 1056 166 328 1231 26.645004 0.65530 0 15 1370 1.094890 0.01369 0 25 95 26.31579 0.09653 0 16 71 22.53521 0.05099 0 41 166 24.69880 0.029080 0 207 1330 15.563910 0.5839 0 484 973 49.74306 0.9952 1 53 2.987599 0.03180 0 182 10.259301 0.05188 0 147 747 19.678715 0.86420 1 19 96 19.791667 0.66060 0 79 1718 4.5983702 0.46890 0 1154 1774 65.05073 0.6522 0 1056 22 2.083333 0.31610 0 0 0.0000000 0.2497 0 10 166 6.024096 0.6154 0 84 166 50.6024096 0.99320 1 1324 1774 74.63360 0.9935 1 2.277170 0.4443 1 2.07738 0.27780 1 0.6522 0.6454 0 3.16790 0.7874 2 8.174650 0.5311 4 950 694 199 218 719 30.319889 0.7848 1 15 560 2.678571 0.1560 0 11 117 9.401709 0.01305 0 14 82 17.07317 0.03088 0 25 199 12.56281 0.003541 0 48 681 7.048458 0.37250 0 238 721 33.009709 0.9989 1 116 12.210526 0.37310 0 195 20.52632 0.4153 0 113 526.000 21.482890 0.8931 1 31 98.000 31.632653 0.9318 1 17 900 1.8888889 0.29830 0 713 950.000 75.05263 0.69000 0 694 17 2.449568 0.3163 0 0 0.0000000 0.2466 0 7 199 3.5175879 0.39980 0 68 199.000 34.170854 0.98260 1 274 950 28.8421053 0.9832 1 2.315741 0.4476 2 2.91160 0.70420 2 0.69000 0.68390 0 2.92850 0.6794 2 8.845841 0.6188 6 NA NA NA NA
02016000200 02 016 000200 AK Alaska Aleutians West Census Area 4 West Region 9 Pacific Division 4485 507 355 1315 4469 29.424927 0.70570 0 87 3859 2.254470 0.03381 0 20 94 21.27660 0.03822 0 35 261 13.40996 0.02120 0 55 355 15.49296 0.004567 0 1292 3728 34.656652 0.8445 1 981 4256 23.04981 0.7621 1 186 4.147157 0.06676 0 384 8.561873 0.03989 0 235 3656 6.427790 0.07933 0 38 204 18.627451 0.62370 0 1458 4397 33.1589720 0.95840 1 3616 4485 80.62430 0.7822 1 507 85 16.765286 0.69850 0 32 6.3116371 0.7700 1 41 355 11.549296 0.7760 1 30 355 8.4507042 0.69980 0 3507 4485 78.19398 0.9938 1 2.350677 0.4664 2 1.76808 0.15490 1 0.7822 0.7742 1 3.93810 0.9690 3 8.839057 0.6119 7 4758 1319 1107 398 4700 8.468085 0.1862 0 144 3404 4.230317 0.3539 0 111 245 45.306122 0.92700 1 93 862 10.78886 0.01250 0 204 1107 18.42818 0.024790 0 297 3527 8.420754 0.43840 0 699 4724 14.796782 0.9132 1 314 6.599412 0.07503 0 822 17.27617 0.2319 0 292 3902.000 7.483342 0.1088 0 99 662.000 14.954683 0.5563 0 433 4586 9.4417793 0.70570 0 3672 4758.000 77.17528 0.71070 0 1319 392 29.719485 0.8272 1 23 1.7437453 0.6618 0 146 1107 13.1887986 0.80180 1 96 1107.000 8.672087 0.73650 0 950 4758 19.9663724 0.9765 1 1.916490 0.3307 1 1.67773 0.11040 0 0.71070 0.70440 0 4.00380 0.9728 3 8.308720 0.5475 4 NA NA NA NA
02020000101 02 020 000101 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 5475 1957 1796 296 5463 5.418268 0.08172 0 229 2885 7.937608 0.48540 0 364 1522 23.91590 0.06363 0 39 274 14.23358 0.02232 0 403 1796 22.43875 0.018830 0 121 3476 3.481013 0.1279 0 863 5853 14.74458 0.4836 0 236 4.310502 0.07411 0 1614 29.479452 0.75790 1 524 4028 13.008937 0.54240 0 65 1496 4.344920 0.06955 0 13 5202 0.2499039 0.07262 0 759 5475 13.86301 0.1072 0 1957 0 0.000000 0.09395 0 167 8.5334696 0.8076 1 134 1796 7.461024 0.6695 0 11 1796 0.6124722 0.08007 0 0 5475 0.00000 0.3743 0 1.197450 0.1505 0 1.51658 0.08466 1 0.1072 0.1061 0 2.02542 0.2761 1 4.846650 0.1061 2 5772 2127 1917 416 5772 7.207207 0.1396 0 223 2691 8.286882 0.7821 1 296 1679 17.629541 0.08891 0 30 238 12.60504 0.01632 0 326 1917 17.00574 0.015840 0 74 4011 1.844926 0.07404 0 546 5733 9.523810 0.7456 0 692 11.988912 0.36010 0 1481 25.65835 0.7225 0 771 4252.330 18.131237 0.7949 1 94 1608.796 5.842877 0.1503 0 4 5425 0.0737327 0.05497 0 989 5772.331 17.13346 0.08922 0 2127 28 1.316408 0.2585 0 5 0.2350729 0.5017 0 9 1917 0.4694836 0.09085 0 24 1916.584 1.252228 0.15320 0 114 5772 1.9750520 0.8251 1 1.757180 0.2809 1 2.08277 0.26400 1 0.08922 0.08843 0 1.82935 0.2038 1 5.758520 0.1870 3 NA NA NA NA
02020000102 02 020 000102 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 4240 1923 1654 286 4240 6.745283 0.12010 0 198 2385 8.301887 0.51840 0 275 1235 22.26721 0.04612 0 248 419 59.18854 0.73210 0 523 1654 31.62031 0.108600 0 242 2799 8.645945 0.3645 0 838 4982 16.82055 0.5669 0 259 6.108491 0.17290 0 1038 24.481132 0.50340 0 809 3707 21.823577 0.91630 1 97 1071 9.056956 0.24130 0 0 4007 0.0000000 0.02799 0 955 4240 22.52358 0.2295 0 1923 169 8.788351 0.54130 0 147 7.6443058 0.7936 1 33 1654 1.995163 0.3402 0 103 1654 6.2273277 0.58930 0 0 4240 0.00000 0.3743 0 1.678500 0.2760 0 1.86189 0.19010 1 0.2295 0.2271 0 2.63870 0.5519 1 6.408590 0.2952 2 4743 1975 1681 633 4738 13.360067 0.3701 0 75 2465 3.042596 0.1971 0 383 1350 28.370370 0.47830 0 122 331 36.85801 0.20770 0 505 1681 30.04164 0.245400 0 205 3383 6.059710 0.32020 0 330 4638 7.115136 0.6017 0 653 13.767658 0.46830 0 1186 25.00527 0.6899 0 472 3447.726 13.690182 0.5484 0 182 1469.325 12.386640 0.4525 0 0 4485 0.0000000 0.02391 0 756 4743.330 15.93817 0.07455 0 1975 153 7.746835 0.4947 0 156 7.8987342 0.8173 1 17 1681 1.0113028 0.15640 0 0 1681.103 0.000000 0.02249 0 15 4743 0.3162555 0.4726 0 1.734500 0.2739 0 2.18301 0.31050 0 0.07455 0.07389 0 1.96349 0.2552 1 5.955550 0.2162 1 NA NA NA NA
02020000201 02 020 000201 AK Alaska Anchorage Municipality 4 West Region 9 Pacific Division 4085 1543 1532 296 4085 7.246022 0.13670 0 46 2089 2.202011 0.03307 0 302 969 31.16615 0.19620 0 187 563 33.21492 0.13830 0 489 1532 31.91906 0.113500 0 164 2395 6.847599 0.2875 0 811 3466 23.39873 0.7707 1 133 3.255814 0.03812 0 1199 29.351285 0.75230 1 320 2468 12.965964 0.53850 0 171 1083 15.789474 0.52510 0 7 3810 0.1837270 0.06378 0 743 4085 18.18849 0.1723 0 1543 54 3.499676 0.37950 0 7 0.4536617 0.5183 0 32 1532 2.088773 0.3498 0 49 1532 3.1984334 0.36020 0 0 4085 0.00000 0.3743 0 1.341470 0.1875 1 1.91780 0.21070 1 0.1723 0.1705 0 1.98210 0.2583 0 5.413670 0.1694 2 4707 1946 1835 706 4707 14.998938 0.4258 0 88 2269 3.878360 0.3073 0 219 793 27.616646 0.44320 0 527 1042 50.57582 0.53120 0 746 1835 40.65395 0.607400 0 194 2805 6.916221 0.36480 0 464 4274 10.856341 0.8018 1 257 5.459953 0.04652 0 1279 27.17230 0.7970 1 390 2999.274 13.003148 0.4994 0 72 1222.675 5.888728 0.1525 0 26 4201 0.6189003 0.13850 0 1282 4706.670 27.23794 0.21200 0 1946 76 3.905447 0.3758 0 2 0.1027749 0.4966 0 96 1835 5.2316076 0.52940 0 39 1834.897 2.125459 0.25860 0 0 4707 0.0000000 0.1370 0 2.507100 0.5058 1 1.63392 0.09856 1 0.21200 0.21010 0 1.79740 0.1921 0 6.150420 0.2432 2 1 270385 NA NA 
svi_national_lihtc20 <- left_join(svi_national_lihtc10, lihtc_projects20, join_by("GEOID_2010_trt" == "fips2010"))

svi_national_lihtc20 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt FIPS_st FIPS_county FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 pre10_lihtc_project_cnt pre10_lihtc_project_dollars post10_lihtc_project_cnt post10_lihtc_project_dollars
01001020100 01 001 020100 AL Alabama Autauga County 3 South Region 6 East South Central Division 1809 771 696 297 1809 16.41791 0.3871 0 36 889 4.049494 0.1790 0 127 598 21.23746 0.20770 0 47 98 47.95918 0.5767 0 174 696 25.00000 0.18790 0 196 1242 15.780998 0.6093 0 186 1759 10.574190 0.3790 0 222 12.271973 0.4876 0 445 24.59923 0.5473 0 298 1335 22.32210 0.8454 1 27 545 4.954128 0.09275 0 36 1705 2.1114370 0.59040 0 385 1809 21.282477 0.4524 0 771 0 0.0000000 0.1224 0 92 11.9325551 0.8005 1 0 696 0.0000000 0.1238 0 50 696 7.183908 0.6134 0 0 1809 0 0.364 0 1.74230 0.28200 0 2.56345 0.5296 1 0.4524 0.4482 0 2.0241 0.2519 1 6.78225 0.3278 2 1941 710 693 352 1941 18.13498 0.4630 0 18 852 2.112676 0.15070 0 81 507 15.976331 0.26320 0 63 186 33.87097 0.2913 0 144 693 20.77922 0.2230 0 187 1309 14.285714 0.6928 0 187 1941 9.634209 0.6617 0 295 15.19835 0.4601 0 415 21.38073 0.4681 0 391 1526 25.62254 0.9011 1 58 555 10.45045 0.3451 0 0 1843 0.0000000 0.09479 0 437 1941 22.51417 0.3902 0 710 0 0.0000000 0.1079 0 88 12.3943662 0.8263 1 0 693 0.0000000 0.09796 0 10 693 1.443001 0.1643 0 0 1941 0.000000 0.1831 0 2.19120 0.4084 0 2.26919 0.3503 1 0.3902 0.3869 0 1.37956 0.07216 1 6.23015 0.2314 2 NA NA NA NA
01001020200 01 001 020200 AL Alabama Autauga County 3 South Region 6 East South Central Division 2020 816 730 495 1992 24.84940 0.5954 0 68 834 8.153477 0.5754 0 49 439 11.16173 0.02067 0 105 291 36.08247 0.3019 0 154 730 21.09589 0.09312 0 339 1265 26.798419 0.8392 1 313 2012 15.556660 0.6000 0 204 10.099010 0.3419 0 597 29.55446 0.8192 1 359 1515 23.69637 0.8791 1 132 456 28.947368 0.83510 1 15 1890 0.7936508 0.40130 0 1243 2020 61.534653 0.7781 1 816 0 0.0000000 0.1224 0 34 4.1666667 0.6664 0 13 730 1.7808219 0.5406 0 115 730 15.753425 0.8382 1 0 2020 0 0.364 0 2.70312 0.56650 1 3.27660 0.8614 3 0.7781 0.7709 1 2.5316 0.5047 1 9.28942 0.6832 6 1757 720 573 384 1511 25.41363 0.6427 0 29 717 4.044630 0.41320 0 33 392 8.418367 0.03542 0 116 181 64.08840 0.9086 1 149 573 26.00349 0.4041 0 139 1313 10.586443 0.5601 0 91 1533 5.936073 0.4343 0 284 16.16392 0.5169 0 325 18.49744 0.2851 0 164 1208 13.57616 0.4127 0 42 359 11.69916 0.3998 0 0 1651 0.0000000 0.09479 0 1116 1757 63.51736 0.7591 1 720 3 0.4166667 0.2470 0 5 0.6944444 0.5106 0 9 573 1.5706806 0.46880 0 57 573 9.947644 0.7317 0 212 1757 12.066022 0.9549 1 2.45440 0.4888 0 1.70929 0.1025 0 0.7591 0.7527 1 2.91300 0.68620 1 7.83579 0.4802 2 NA NA NA NA
01001020300 01 001 020300 AL Alabama Autauga County 3 South Region 6 East South Central Division 3543 1403 1287 656 3533 18.56779 0.4443 0 93 1552 5.992268 0.3724 0 273 957 28.52665 0.45780 0 178 330 53.93939 0.7152 0 451 1287 35.04274 0.49930 0 346 2260 15.309734 0.5950 0 252 3102 8.123791 0.2596 0 487 13.745413 0.5868 0 998 28.16822 0.7606 1 371 2224 16.68165 0.6266 0 126 913 13.800657 0.46350 0 0 3365 0.0000000 0.09298 0 637 3543 17.979114 0.4049 0 1403 10 0.7127584 0.3015 0 2 0.1425517 0.4407 0 0 1287 0.0000000 0.1238 0 101 1287 7.847708 0.6443 0 0 3543 0 0.364 0 2.17060 0.41010 0 2.53048 0.5116 1 0.4049 0.4011 0 1.8743 0.1942 0 6.98028 0.3576 1 3694 1464 1351 842 3694 22.79372 0.5833 0 53 1994 2.657974 0.22050 0 117 967 12.099276 0.11370 0 147 384 38.28125 0.3856 0 264 1351 19.54108 0.1827 0 317 2477 12.797739 0.6460 0 127 3673 3.457664 0.2308 0 464 12.56091 0.3088 0 929 25.14889 0.7080 0 473 2744 17.23761 0.6211 0 263 975 26.97436 0.8234 1 128 3586 3.5694367 0.70770 0 1331 3694 36.03140 0.5515 0 1464 26 1.7759563 0.3675 0 14 0.9562842 0.5389 0 35 1351 2.5906736 0.60550 0 42 1351 3.108808 0.3415 0 0 3694 0.000000 0.1831 0 1.86330 0.3063 0 3.16900 0.8380 1 0.5515 0.5468 0 2.03650 0.26830 0 7.62030 0.4460 1 2 216593 NA NA
01001020400 01 001 020400 AL Alabama Autauga County 3 South Region 6 East South Central Division 4840 1957 1839 501 4840 10.35124 0.2177 0 101 2129 4.744011 0.2447 0 310 1549 20.01291 0.17080 0 89 290 30.68966 0.2044 0 399 1839 21.69657 0.10540 0 274 3280 8.353658 0.3205 0 399 4293 9.294200 0.3171 0 955 19.731405 0.8643 1 1195 24.69008 0.5530 0 625 3328 18.78005 0.7233 0 152 1374 11.062591 0.34710 0 10 4537 0.2204100 0.22560 0 297 4840 6.136364 0.1647 0 1957 33 1.6862545 0.3843 0 25 1.2774655 0.5516 0 14 1839 0.7612833 0.3564 0 19 1839 1.033170 0.1127 0 0 4840 0 0.364 0 1.20540 0.13470 0 2.71330 0.6129 1 0.1647 0.1632 0 1.7690 0.1591 0 5.85240 0.1954 1 3539 1741 1636 503 3539 14.21305 0.3472 0 39 1658 2.352232 0.17990 0 219 1290 16.976744 0.30880 0 74 346 21.38728 0.1037 0 293 1636 17.90954 0.1333 0 173 2775 6.234234 0.3351 0 169 3529 4.788892 0.3448 0 969 27.38062 0.9225 1 510 14.41085 0.1208 0 670 3019 22.19278 0.8194 1 148 1137 13.01671 0.4541 0 89 3409 2.6107363 0.64690 0 454 3539 12.82848 0.2364 0 1741 143 8.2136703 0.6028 0 0 0.0000000 0.2186 0 10 1636 0.6112469 0.28340 0 72 1636 4.400978 0.4538 0 0 3539 0.000000 0.1831 0 1.34030 0.1575 0 2.96370 0.7496 2 0.2364 0.2344 0 1.74170 0.16270 0 6.28210 0.2389 2 NA NA NA NA
01001020500 01 001 020500 AL Alabama Autauga County 3 South Region 6 East South Central Division 9938 3969 3741 1096 9938 11.02838 0.2364 0 188 4937 3.807981 0.1577 0 426 2406 17.70574 0.11050 0 528 1335 39.55056 0.3753 0 954 3741 25.50120 0.20140 0 293 5983 4.897209 0.1655 0 740 10110 7.319486 0.2211 0 837 8.422218 0.2408 0 3012 30.30791 0.8455 1 759 7155 10.60797 0.2668 0 476 2529 18.821669 0.63540 0 78 9297 0.8389803 0.41110 0 1970 9938 19.822902 0.4330 0 3969 306 7.7097506 0.6153 0 0 0.0000000 0.2198 0 7 3741 0.1871157 0.2535 0 223 3741 5.960973 0.5483 0 0 9938 0 0.364 0 0.98210 0.08468 0 2.39960 0.4381 1 0.4330 0.4290 0 2.0009 0.2430 0 5.81560 0.1905 1 10674 4504 4424 1626 10509 15.47245 0.3851 0 81 5048 1.604596 0.09431 0 321 2299 13.962592 0.17970 0 711 2125 33.45882 0.2836 0 1032 4424 23.32731 0.3109 0 531 6816 7.790493 0.4251 0 301 10046 2.996217 0.1894 0 1613 15.11149 0.4553 0 2765 25.90407 0.7494 0 1124 7281 15.43744 0.5253 0 342 2912 11.74451 0.4019 0 52 9920 0.5241935 0.35230 0 2603 10674 24.38636 0.4160 0 4504 703 15.6083481 0.7378 0 29 0.6438721 0.5037 0 37 4424 0.8363472 0.33420 0 207 4424 4.679023 0.4754 0 176 10674 1.648866 0.7598 1 1.40481 0.1743 0 2.48420 0.4802 0 0.4160 0.4125 0 2.81090 0.63730 1 7.11591 0.3654 1 5 2250459 NA NA
01001020600 01 001 020600 AL Alabama Autauga County 3 South Region 6 East South Central Division 3402 1456 1308 735 3402 21.60494 0.5199 0 134 1720 7.790698 0.5436 0 242 1032 23.44961 0.28010 0 62 276 22.46377 0.1035 0 304 1308 23.24159 0.14070 0 301 2151 13.993491 0.5510 0 355 3445 10.304790 0.3656 0 386 11.346267 0.4232 0 931 27.36626 0.7200 0 440 2439 18.04018 0.6912 0 143 924 15.476190 0.52900 0 4 3254 0.1229256 0.19840 0 723 3402 21.252205 0.4519 0 1456 18 1.2362637 0.3507 0 433 29.7390110 0.9468 1 16 1308 1.2232416 0.4493 0 28 1308 2.140673 0.2298 0 0 3402 0 0.364 0 2.12080 0.39510 0 2.56180 0.5288 0 0.4519 0.4477 0 2.3406 0.4048 1 7.47510 0.4314 1 3536 1464 1330 1279 3523 36.30429 0.8215 1 34 1223 2.780049 0.23780 0 321 1111 28.892889 0.75870 1 67 219 30.59361 0.2305 0 388 1330 29.17293 0.5075 0 306 2380 12.857143 0.6480 0 415 3496 11.870709 0.7535 1 547 15.46946 0.4760 0 982 27.77149 0.8327 1 729 2514 28.99761 0.9488 1 95 880 10.79545 0.3601 0 0 3394 0.0000000 0.09479 0 985 3536 27.85633 0.4608 0 1464 0 0.0000000 0.1079 0 364 24.8633880 0.9300 1 0 1330 0.0000000 0.09796 0 17 1330 1.278196 0.1463 0 0 3536 0.000000 0.1831 0 2.96830 0.6434 2 2.71239 0.6156 2 0.4608 0.4569 0 1.46526 0.08976 1 7.60675 0.4440 5 NA NA NA NA 
svi_divisional_lihtc20 <- svi_divisional_lihtc20 %>% 
  filter(is.na(pre10_lihtc_project_cnt)) %>% 
  filter(post10_lihtc_project_cnt >= 1) %>% 
  select(GEOID_2010_trt, pre10_lihtc_project_cnt, pre10_lihtc_project_dollars, post10_lihtc_project_cnt, post10_lihtc_project_dollars)

# View data
svi_divisional_lihtc20 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt pre10_lihtc_project_cnt pre10_lihtc_project_dollars post10_lihtc_project_cnt post10_lihtc_project_dollars
02020000202 NA NA 2 1651298
02020001300 NA NA 1 0
02122000900 NA NA 1 0
02122001000 NA NA 1 0
02180000200 NA NA 1 0
02261000200 NA NA 1 0 
svi_national_lihtc20 <- svi_national_lihtc20 %>% 
  filter(is.na(pre10_lihtc_project_cnt)) %>% 
  filter(post10_lihtc_project_cnt >= 1) %>% 
  select(GEOID_2010_trt, pre10_lihtc_project_cnt, pre10_lihtc_project_dollars, post10_lihtc_project_cnt, post10_lihtc_project_dollars)

# View data
svi_national_lihtc20 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt pre10_lihtc_project_cnt pre10_lihtc_project_dollars post10_lihtc_project_cnt post10_lihtc_project_dollars
01003010500 NA NA 1 481325
01003011601 NA NA 1 887856
01017954600 NA NA 2 950192
01021060101 NA NA 1 812048
01039962600 NA NA 1 434742
01043964900 NA NA 1 1046201 
# Filter SVI divisional data to remove all tracts that had a project in 2010 or before:
svi_divisional_lihtc <-  svi_divisional %>% 
  filter(! GEOID_2010_trt %in% lihtc_projects10$fips2010)

# Merge SVI divisional data with post 2010 project data, create flag for projects (1 for tracts that have LIHTC project, 0 for those that do not):
svi_divisional_lihtc <- left_join(svi_divisional_lihtc, 
                                  svi_divisional_lihtc20, 
                                  join_by("GEOID_2010_trt" == "GEOID_2010_trt")) %>% 
                        mutate(pre10_lihtc_project_cnt = replace_na(pre10_lihtc_project_cnt, 0),
                               post10_lihtc_project_cnt = replace_na(post10_lihtc_project_cnt, 0),
                               pre10_lihtc_project_dollars = replace_na(pre10_lihtc_project_dollars, 0),
                               post10_lihtc_project_dollars = replace_na(post10_lihtc_project_dollars, 0),
                               lihtc_flag = if_else(post10_lihtc_project_cnt >= 1, 1, 0))

# Finally, we want to filter our dataset to only have tracts that are eligible for the LIHTC program and that have SVI data:
svi_divisional_lihtc <- left_join(svi_divisional_lihtc, lihtc_eligible_flag, 
                                  join_by("GEOID_2010_trt" == "GEOID10")) %>%
                        filter(tolower(lihtc_eligibility) == "yes") %>%
                        filter(!is.na(F_TOTAL_10)) %>% 
                        filter(!is.na(F_TOTAL_20)) 


# View data
svi_divisional_lihtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt FIPS_st FIPS_county FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 pre10_lihtc_project_cnt pre10_lihtc_project_dollars post10_lihtc_project_cnt post10_lihtc_project_dollars lihtc_flag lihtc_eligibility
02050000100 02 050 000100 AK Alaska Bethel Census Area 4 West Region 9 Pacific Division 9481 2776 2127 4499 9422 47.74995 0.9162 1 923 3537 26.09556 0.9936 1 224 1570 14.26752 0.010260 0 35 557 6.283663 0.012980 0 259 2127 12.17677 0.003169 0 1431 4685 30.54429 0.8055 1 2901 9557 30.35471 0.8979 1 688 7.256619 0.24940 0 3678 38.79338 0.9771 1 1085 5745 18.88599 0.8446 1 418 1677 24.92546 0.7894 1 771 8382 9.1982820 0.64930 0 9146 9481 96.46662 0.9412 1 2776 3 0.1080692 0.18850 0 14 0.5043228 0.5274 0 992 2127 46.638458 0.9944 1 1814 2127 85.28444 0.9993 1 0 9481 0.000000 0.3743 0 3.616369 0.7794 4 3.50980 0.9107 3 0.9412 0.9315 1 3.08390 0.7535 2 11.15127 0.8587 10 10311 2692 2104 5779 10267 56.28713 0.9839 1 870 3667 23.72512 0.9967 1 232 1494 15.52878 0.05333 0 95 610 15.57377 0.02547 0 327 2104 15.54183 0.009597 0 1228 5181 23.701988 0.78920 1 1639 10294 15.92190 0.9319 1 812 7.875085 0.12960 0 4008 38.87111 0.9926 1 1259 6286.0000 20.02864 0.8560 1 483 1769.0000 27.30356 0.8759 1 188 9020 2.0842572 0.31690 0 10181 10311.000 98.73921 0.9760 1 2692 1 0.0371471 0.16590 0 31 1.1515602 0.6200 0 1024 2104 48.669201 0.9978 1 1793 2104.0000 85.21863 0.9993 1 477 10311 4.626127 0.9233 1 3.711297 0.8199 4 3.17100 0.8206 3 0.9760 0.9674 1 3.70630 0.9326 3 11.56460 0.9233 11 0 0 0 0 0 Yes
02050000300 02 050 000300 AK Alaska Bethel Census Area 4 West Region 9 Pacific Division 1386 725 439 460 1383 33.26103 0.7628 1 118 596 19.79866 0.9694 1 38 308 12.33766 0.008283 0 10 131 7.633588 0.014190 0 48 439 10.93394 0.002703 0 168 777 21.62162 0.7013 0 477 1475 32.33898 0.9213 1 160 11.544011 0.55680 0 464 33.47763 0.8938 1 122 955 12.77487 0.5244 0 99 318 31.13208 0.8947 1 4 1284 0.3115265 0.08126 0 1161 1386 83.76623 0.8084 1 725 0 0.0000000 0.09395 0 8 1.1034483 0.6032 0 90 439 20.501139 0.8956 1 261 439 59.45330 0.9957 1 0 1386 0.000000 0.3743 0 3.357503 0.7224 3 2.95096 0.7007 2 0.8084 0.8000 1 2.96275 0.7006 2 10.07961 0.7498 8 1404 742 369 597 1379 43.29224 0.9267 1 152 646 23.52941 0.9965 1 50 267 18.72659 0.11360 0 27 102 26.47059 0.08218 0 77 369 20.86721 0.046030 0 149 794 18.765743 0.71930 0 345 1404 24.57265 0.9915 1 115 8.190883 0.14420 0 484 34.47293 0.9690 1 139 920.0005 15.10869 0.6447 0 89 276.0002 32.24635 0.9371 1 6 1243 0.4827031 0.11630 0 1240 1404.000 88.31906 0.8327 1 742 0 0.0000000 0.08271 0 9 1.2129380 0.6256 0 112 369 30.352304 0.9725 1 223 369.0005 60.43353 0.9961 1 94 1404 6.695157 0.9478 1 3.680030 0.8126 3 2.81130 0.6519 2 0.8327 0.8253 1 3.62471 0.9189 3 10.94874 0.8637 9 0 0 0 0 0 Yes
02070000100 02 070 000100 AK Alaska Dillingham Census Area 4 West Region 9 Pacific Division 2569 1354 584 1037 2565 40.42885 0.8513 1 236 853 27.66706 0.9954 1 68 398 17.08543 0.016280 0 34 186 18.279570 0.034550 0 102 584 17.46575 0.006339 0 384 1303 29.47045 0.7955 1 1140 2710 42.06642 0.9814 1 217 8.446867 0.33900 0 940 36.59011 0.9531 1 311 1728 17.99769 0.8126 1 94 442 21.26697 0.7005 0 203 2363 8.5907744 0.63010 0 2410 2569 93.81082 0.9081 1 1354 0 0.0000000 0.09395 0 14 1.0339734 0.5974 0 186 584 31.849315 0.9650 1 367 584 62.84247 0.9966 1 0 2569 0.000000 0.3743 0 3.629939 0.7830 4 3.43530 0.8919 2 0.9081 0.8988 1 3.02725 0.7274 2 11.00059 0.8430 9 2801 1444 718 1191 2792 42.65759 0.9224 1 183 1059 17.28045 0.9849 1 94 487 19.30185 0.12840 0 51 231 22.07792 0.05382 0 145 718 20.19499 0.039410 0 265 1619 16.368129 0.67640 0 552 2801 19.70725 0.9721 1 353 12.602642 0.39670 0 862 30.77472 0.9114 1 295 1939.1327 15.21299 0.6517 0 200 579.0000 34.54231 0.9555 1 49 2513 1.9498607 0.30380 0 2536 2801.124 90.53509 0.8619 1 1444 1 0.0692521 0.16740 0 10 0.6925208 0.5747 0 255 718 35.515320 0.9868 1 481 718.0000 66.99164 0.9972 1 230 2801 8.211353 0.9566 1 3.595210 0.7924 3 3.21910 0.8382 2 0.8619 0.8543 1 3.68270 0.9288 3 11.35891 0.9048 9 0 0 0 0 0 Yes
02122000100 02 122 000100 AK Alaska Kenai Peninsula Borough 4 West Region 9 Pacific Division 251 428 138 90 251 35.85657 0.7982 1 29 145 20.00000 0.9707 1 54 90 60.00000 0.930300 1 0 48 0.000000 0.005509 0 54 138 39.13043 0.301700 0 21 186 11.29032 0.4631 0 198 460 43.04348 0.9847 1 6 2.390438 0.02129 0 61 24.30279 0.4943 0 56 395 14.17722 0.6201 0 18 57 31.57895 0.8999 1 0 233 0.0000000 0.02799 0 205 251 81.67331 0.7907 1 428 0 0.0000000 0.09395 0 20 4.6728972 0.7396 0 7 138 5.072464 0.5709 0 17 138 12.31884 0.8207 1 0 251 0.000000 0.3743 0 3.518400 0.7575 3 2.06358 0.2722 1 0.7907 0.7826 1 2.59945 0.5334 1 8.97213 0.6292 6 531 307 131 193 523 36.90249 0.8743 1 74 324 22.83951 0.9958 1 23 92 25.00000 0.32780 0 4 39 10.25641 0.01129 0 27 131 20.61069 0.043330 0 6 389 1.542417 0.05899 0 220 523 42.06501 0.9998 1 12 2.259887 0.01198 0 111 20.90395 0.4394 0 50 412.0000 12.13592 0.4328 0 23 72.0000 31.94445 0.9342 1 0 512 0.0000000 0.02391 0 437 531.000 82.29756 0.7611 1 307 0 0.0000000 0.08271 0 16 5.2117264 0.7700 1 11 131 8.396947 0.6735 0 42 131.0000 32.06107 0.9796 1 111 531 20.903955 0.9772 1 2.972220 0.6420 3 1.84229 0.1603 1 0.7611 0.7544 1 3.48301 0.8841 3 9.05862 0.6447 8 0 0 0 0 0 Yes
02180000100 02 180 000100 AK Alaska Nome Census Area 4 West Region 9 Pacific Division 5766 2016 1373 3052 5552 54.97118 0.9552 1 519 2134 24.32052 0.9899 1 224 852 26.29108 0.095960 0 94 521 18.042227 0.033620 0 318 1373 23.16096 0.021070 0 580 2709 21.41011 0.6970 0 1988 5811 34.21098 0.9380 1 299 5.185571 0.11630 0 2214 38.39750 0.9740 1 580 3550 16.33803 0.7460 0 439 1083 40.53555 0.9715 1 95 5090 1.8664047 0.26950 0 5430 5766 94.17274 0.9125 1 2016 15 0.7440476 0.22730 0 27 1.3392857 0.6231 0 495 1373 36.052440 0.9787 1 1167 1373 84.99636 0.9991 1 187 5766 3.243149 0.8747 1 3.601170 0.7768 3 3.07730 0.7608 2 0.9125 0.9031 1 3.70290 0.9385 3 11.29387 0.8751 9 5901 2111 1441 2939 5789 50.76870 0.9667 1 554 2224 24.91007 0.9980 1 237 1047 22.63610 0.23610 0 56 394 14.21320 0.02099 0 293 1441 20.33310 0.040350 0 586 2969 19.737285 0.73470 0 1202 5852 20.53999 0.9780 1 469 7.947806 0.13320 0 2245 38.04440 0.9906 1 590 3606.9999 16.35708 0.7143 0 532 1175.0000 45.27660 0.9916 1 161 5296 3.0400302 0.39890 0 5578 5901.000 94.52635 0.9154 1 2111 6 0.2842255 0.17600 0 23 1.0895310 0.6155 0 602 1441 41.776544 0.9943 1 1240 1441.0000 86.05135 0.9993 1 351 5901 5.948144 0.9413 1 3.717750 0.8217 3 3.22860 0.8410 2 0.9154 0.9073 1 3.72640 0.9367 3 11.58815 0.9255 9 0 0 0 0 0 Yes
02290000100 02 290 000100 AK Alaska Yukon-Koyukuk Census Area 4 West Region 9 Pacific Division 1127 969 515 482 1127 42.76841 0.8749 1 165 551 29.94555 0.9970 1 104 386 26.94301 0.106600 0 16 129 12.403101 0.019980 0 120 515 23.30097 0.022180 0 216 727 29.71114 0.7981 1 492 1121 43.88938 0.9865 1 65 5.767524 0.14900 0 329 29.19255 0.7462 0 193 825 23.39394 0.9394 1 85 247 34.41296 0.9316 1 13 1049 1.2392755 0.20170 0 960 1127 85.18190 0.8206 1 969 0 0.0000000 0.09395 0 30 3.0959752 0.7027 0 83 515 16.116505 0.8480 1 333 515 64.66019 0.9969 1 0 1127 0.000000 0.3743 0 3.678680 0.7918 4 2.96790 0.7088 2 0.8206 0.8122 1 3.01585 0.7215 2 10.48303 0.7894 9 1118 1030 445 516 1097 47.03737 0.9495 1 94 463 20.30238 0.9929 1 68 346 19.65318 0.13910 0 22 99 22.22222 0.05448 0 90 445 20.22472 0.039600 0 125 703 17.780939 0.70070 0 161 1099 14.64968 0.9100 1 159 14.221825 0.49590 0 338 30.23256 0.8989 1 158 761.0000 20.76216 0.8764 1 88 218.0000 40.36697 0.9809 1 0 1038 0.0000000 0.02391 0 1001 1118.000 89.53488 0.8480 1 1030 0 0.0000000 0.08271 0 17 1.6504854 0.6556 0 76 445 17.078652 0.8684 1 274 445.0000 61.57303 0.9965 1 58 1118 5.187835 0.9330 1 3.592700 0.7918 3 3.27601 0.8582 3 0.8480 0.8405 1 3.53621 0.8979 3 11.25292 0.8955 10 0 0 0 0 0 Yes 
# Filter SVI national data to remove all tracts that had a project in 2010 or before:
svi_national_lihtc <-  svi_national %>% 
  filter(! GEOID_2010_trt %in% lihtc_projects10$fips2010)

# Merge SVI national data with post 2010 project data, create flag for projects (1 for tracts that have LIHTC project, 0 for those that do not):
svi_national_lihtc <- left_join(svi_national_lihtc, 
                                  svi_national_lihtc20, 
                                  join_by("GEOID_2010_trt" == "GEOID_2010_trt")) %>% 
                        mutate(pre10_lihtc_project_cnt = replace_na(pre10_lihtc_project_cnt, 0),
                               post10_lihtc_project_cnt = replace_na(post10_lihtc_project_cnt, 0),
                                pre10_lihtc_project_dollars = replace_na(pre10_lihtc_project_dollars, 0),
                               post10_lihtc_project_dollars = replace_na(post10_lihtc_project_dollars, 0),
                               lihtc_flag = if_else(post10_lihtc_project_cnt >= 1, 1, 0))

# Finally, we want to filter our dataset to only have tracts that are eligible for the LIHTC program and that have SVI data:
svi_national_lihtc <- left_join(svi_national_lihtc, lihtc_eligible_flag, 
                                  join_by("GEOID_2010_trt" == "GEOID10")) %>%
                        filter(tolower(lihtc_eligibility) == "yes") %>%
                        filter(!is.na(F_TOTAL_10)) %>% 
                        filter(!is.na(F_TOTAL_20)) 


# View data
svi_national_lihtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt FIPS_st FIPS_county FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 pre10_lihtc_project_cnt pre10_lihtc_project_dollars post10_lihtc_project_cnt post10_lihtc_project_dollars lihtc_flag lihtc_eligibility
01005950700 01 005 950700 AL Alabama Barbour County 3 South Region 6 East South Central Division 1753 687 563 615 1628 37.77641 0.8088 1 17 667 2.548726 0.06941 0 41 376 10.90426 0.01945 0 62 187 33.15508 0.2464 0 103 563 18.29485 0.04875 0 264 1208 21.85430 0.7570 1 201 1527 13.163065 0.4991 0 368 20.992584 0.89510 1 462 26.354820 0.66130 0 211 1085 19.44700 0.7505 1 107 399 26.81704 0.8048 1 0 1628 0.000000 0.09298 0 861 1753 49.11580 0.7101 0 687 17 2.474527 0.4324 0 38 5.5312955 0.6970 0 3 563 0.5328597 0.3037 0 19 563 3.374778 0.3529 0 233 1753 13.29150 0.9517 1 2.18306 0.4137 2 3.20468 0.8377 3 0.7101 0.7035 0 2.7377 0.6100 1 8.83554 0.6264 6 1527 691 595 565 1365 41.39194 0.8765 1 37 572 6.468532 0.6776 0 70 376 18.617021 0.38590 0 92 219 42.00913 0.4736 0 162 595 27.22689 0.4454 0 280 1114 25.13465 0.8942 1 105 1378 7.619739 0.5505 0 383 25.081860 0.88450 1 337 22.069417 0.51380 0 237 1041.0000 22.76657 0.8360 1 144 413.0000 34.86683 0.9114 1 11 1466 0.7503411 0.40700 0 711 1527.0000 46.56189 0.6441 0 691 13 1.881331 0.3740 0 37 5.3545586 0.7152 0 0 595 0.0000000 0.09796 0 115 595.0000 19.327731 0.8859 1 149 1527 9.757695 0.9470 1 3.4442 0.7707 2 3.55270 0.9403 3 0.6441 0.6387 0 3.02006 0.7337 2 10.66106 0.8537 7 0 0 0 0 0 Yes
01011952100 01 011 952100 AL Alabama Bullock County 3 South Region 6 East South Central Division 1652 796 554 564 1652 34.14044 0.7613 1 46 816 5.637255 0.33630 0 96 458 20.96070 0.19930 0 62 96 64.58333 0.8917 1 158 554 28.51986 0.29220 0 271 1076 25.18587 0.8163 1 155 1663 9.320505 0.3183 0 199 12.046005 0.47180 0 420 25.423729 0.60240 0 327 1279 25.56685 0.9151 1 137 375 36.53333 0.9108 1 0 1590 0.000000 0.09298 0 1428 1652 86.44068 0.8939 1 796 0 0.000000 0.1224 0 384 48.2412060 0.9897 1 19 554 3.4296029 0.7145 0 45 554 8.122744 0.6556 0 0 1652 0.00000 0.3640 0 2.52440 0.5138 2 2.99308 0.7515 2 0.8939 0.8856 1 2.8462 0.6637 1 9.25758 0.6790 6 1382 748 549 742 1382 53.69030 0.9560 1 40 511 7.827789 0.7730 1 110 402 27.363184 0.71780 0 45 147 30.61224 0.2307 0 155 549 28.23315 0.4773 0 181 905 20.00000 0.8253 1 232 1382 16.787265 0.8813 1 164 11.866860 0.27170 0 250 18.089725 0.26290 0 258 1132.0000 22.79152 0.8368 1 99 279.0000 35.48387 0.9162 1 33 1275 2.5882353 0.64520 0 1347 1382.0000 97.46744 0.9681 1 748 0 0.000000 0.1079 0 375 50.1336898 0.9922 1 0 549 0.0000000 0.09796 0 37 549.0000 6.739526 0.6039 0 0 1382 0.000000 0.1831 0 3.9129 0.8785 4 2.93280 0.7342 2 0.9681 0.9599 1 1.98506 0.2471 1 9.79886 0.7570 8 0 0 0 0 0 Yes
01015000300 01 015 000300 AL Alabama Calhoun County 3 South Region 6 East South Central Division 3074 1635 1330 1904 3067 62.08021 0.9710 1 293 1362 21.512482 0.96630 1 180 513 35.08772 0.65450 0 383 817 46.87882 0.5504 0 563 1330 42.33083 0.70280 0 720 2127 33.85049 0.9148 1 628 2835 22.151675 0.8076 1 380 12.361744 0.49340 0 713 23.194535 0.45030 0 647 2111 30.64898 0.9708 1 298 773 38.55110 0.9247 1 0 2878 0.000000 0.09298 0 2623 3074 85.32856 0.8883 1 1635 148 9.051988 0.6465 0 6 0.3669725 0.4502 0 68 1330 5.1127820 0.8082 1 303 1330 22.781955 0.9029 1 0 3074 0.00000 0.3640 0 4.36250 0.9430 4 2.93218 0.7233 2 0.8883 0.8800 1 3.1718 0.8070 2 11.35478 0.9009 9 2390 1702 1282 1287 2390 53.84937 0.9566 1 102 1066 9.568480 0.8541 1 158 609 25.944171 0.67520 0 286 673 42.49629 0.4856 0 444 1282 34.63339 0.6634 0 467 1685 27.71513 0.9180 1 369 2379 15.510719 0.8562 1 342 14.309623 0.40850 0 548 22.928870 0.57100 0 647 1831.0000 35.33588 0.9862 1 202 576.0000 35.06944 0.9130 1 16 2134 0.7497657 0.40690 0 1896 2390.0000 79.33054 0.8451 1 1702 96 5.640423 0.5329 0 0 0.0000000 0.2186 0 0 1282 0.0000000 0.09796 0 186 1282.0000 14.508580 0.8308 1 43 2390 1.799163 0.7727 1 4.2483 0.9395 4 3.28560 0.8773 2 0.8451 0.8379 1 2.45296 0.4602 2 10.83196 0.8718 9 0 0 0 0 0 Yes
01015000500 01 015 000500 AL Alabama Calhoun County 3 South Region 6 East South Central Division 1731 1175 743 1042 1619 64.36072 0.9767 1 124 472 26.271186 0.98460 1 136 461 29.50108 0.48970 0 163 282 57.80142 0.7919 1 299 743 40.24226 0.64910 0 340 1270 26.77165 0.8389 1 460 1794 25.641026 0.8722 1 271 15.655690 0.70190 0 368 21.259388 0.32190 0 507 1449 34.98965 0.9885 1 150 386 38.86010 0.9269 1 0 1677 0.000000 0.09298 0 1559 1731 90.06355 0.9123 1 1175 50 4.255319 0.5128 0 4 0.3404255 0.4480 0 0 743 0.0000000 0.1238 0 122 743 16.419919 0.8473 1 0 1731 0.00000 0.3640 0 4.32150 0.9362 4 3.03218 0.7679 2 0.9123 0.9038 1 2.2959 0.3818 1 10.56188 0.8244 8 940 907 488 586 940 62.34043 0.9815 1 59 297 19.865320 0.9833 1 100 330 30.303030 0.79220 1 58 158 36.70886 0.3497 0 158 488 32.37705 0.6020 0 199 795 25.03145 0.8930 1 118 940 12.553192 0.7770 1 246 26.170213 0.90530 1 118 12.553192 0.08233 0 383 822.5089 46.56484 0.9984 1 30 197.8892 15.16000 0.5363 0 0 889 0.0000000 0.09479 0 898 940.3866 95.49264 0.9489 1 907 0 0.000000 0.1079 0 2 0.2205072 0.4456 0 2 488 0.4098361 0.23670 0 146 487.6463 29.939736 0.9404 1 0 940 0.000000 0.1831 0 4.2368 0.9379 4 2.61712 0.5593 2 0.9489 0.9409 1 1.91370 0.2196 1 9.71652 0.7468 8 0 0 0 0 0 Yes
01015000600 01 015 000600 AL Alabama Calhoun County 3 South Region 6 East South Central Division 2571 992 796 1394 2133 65.35396 0.9789 1 263 905 29.060773 0.98990 1 121 306 39.54248 0.75940 1 209 490 42.65306 0.4481 0 330 796 41.45729 0.68030 0 641 1556 41.19537 0.9554 1 416 1760 23.636364 0.8383 1 220 8.556982 0.24910 0 584 22.714897 0.41610 0 539 1353 39.83740 0.9955 1 243 466 52.14592 0.9783 1 30 2366 1.267963 0.48990 0 1944 2571 75.61260 0.8440 1 992 164 16.532258 0.7673 1 8 0.8064516 0.5110 0 46 796 5.7788945 0.8329 1 184 796 23.115578 0.9049 1 614 2571 23.88176 0.9734 1 4.44280 0.9548 4 3.12890 0.8088 2 0.8440 0.8362 1 3.9895 0.9792 4 12.40520 0.9696 11 1950 964 719 837 1621 51.63479 0.9467 1 157 652 24.079755 0.9922 1 22 364 6.043956 0.01547 0 129 355 36.33803 0.3420 0 151 719 21.00139 0.2303 0 363 1387 26.17159 0.9048 1 351 1613 21.760694 0.9435 1 249 12.769231 0.32090 0 356 18.256410 0.27140 0 332 1259.7041 26.35540 0.9135 1 136 435.6156 31.22018 0.8775 1 0 1891 0.0000000 0.09479 0 1463 1949.9821 75.02633 0.8219 1 964 14 1.452282 0.3459 0 8 0.8298755 0.5269 0 19 719 2.6425591 0.61120 0 197 719.0542 27.397100 0.9316 1 329 1950 16.871795 0.9655 1 4.0175 0.9001 4 2.47809 0.4764 2 0.8219 0.8149 1 3.38110 0.8712 2 10.69859 0.8583 9 0 0 0 0 0 Yes
01015002101 01 015 002101 AL Alabama Calhoun County 3 South Region 6 East South Central Division 3872 1454 1207 1729 2356 73.38710 0.9916 1 489 2020 24.207921 0.97860 1 20 168 11.90476 0.02541 0 718 1039 69.10491 0.9332 1 738 1207 61.14333 0.96900 1 113 725 15.58621 0.6035 0 664 3943 16.839970 0.6495 0 167 4.313016 0.05978 0 238 6.146694 0.02255 0 264 2359 11.19118 0.3027 0 94 263 35.74144 0.9050 1 46 3769 1.220483 0.48250 0 1601 3872 41.34814 0.6572 0 1454 761 52.338377 0.9504 1 65 4.4704264 0.6738 0 5 1207 0.4142502 0.2791 0 113 1207 9.362055 0.7004 0 1516 3872 39.15289 0.9860 1 4.19220 0.9133 3 1.77253 0.1304 1 0.6572 0.6511 0 3.5897 0.9337 2 10.21163 0.7885 6 3238 1459 1014 1082 1836 58.93246 0.9735 1 251 1403 17.890235 0.9767 1 31 155 20.000000 0.44920 0 515 859 59.95343 0.8554 1 546 1014 53.84615 0.9535 1 134 916 14.62882 0.7033 0 251 3238 7.751699 0.5588 0 167 5.157505 0.03597 0 169 5.219271 0.02111 0 323 1667.0000 19.37612 0.7205 0 94 277.0000 33.93502 0.9040 1 0 3164 0.0000000 0.09479 0 1045 3238.0000 32.27301 0.5125 0 1459 607 41.603838 0.9185 1 65 4.4551062 0.6949 0 24 1014 2.3668639 0.57900 0 85 1014.0000 8.382643 0.6775 0 1402 3238 43.298332 0.9876 1 4.1658 0.9263 3 1.77637 0.1225 1 0.5125 0.5082 0 3.85750 0.9661 2 10.31217 0.8160 6 0 0 0 0 0 Yes 
svi_national_lihtc_county_sum <- summarize_county_lihtc(svi_national_lihtc)

svi_national_lihtc_county_sum %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
State County Division post10_lihtc_project_cnt tract_cnt post10_lihtc_project_dollars post10_lihtc_dollars_formatted
AK Bethel Census Area Pacific Division 0 2 0 \$0
AK Dillingham Census Area Pacific Division 0 1 0 \$0
AK Kenai Peninsula Borough Pacific Division 0 1 0 \$0
AK Nome Census Area Pacific Division 0 1 0 \$0
AK Yukon-Koyukuk Census Area Pacific Division 0 2 0 \$0
AL Barbour County East South Central Division 0 1 0 \$0 
svi_divisional_lihtc_county_sum <- summarize_county_lihtc(svi_divisional_lihtc)
svi_divisional_lihtc_county_sum %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
State County Division post10_lihtc_project_cnt tract_cnt post10_lihtc_project_dollars post10_lihtc_dollars_formatted
AK Bethel Census Area Pacific Division 0 2 0 \$0
AK Dillingham Census Area Pacific Division 0 1 0 \$0
AK Kenai Peninsula Borough Pacific Division 0 1 0 \$0
AK Nome Census Area Pacific Division 0 1 0 \$0
AK Yukon-Koyukuk Census Area Pacific Division 0 2 0 \$0
CA Alameda County Pacific Division 1 23 1590984 \$1,590,984 
# Create data frame of LIHTC eligible tracts 2010 nationally
svi_national_lihtc10 <- svi_national_lihtc %>% select(GEOID_2010_trt, FIPS_st, FIPS_county, 
    state, state_name, county, region_number, region, division_number, 
    division, F_TOTAL_10, E_TOTPOP_10) %>% rename("F_TOTAL" = "F_TOTAL_10")

# Count national-level SVI flags for 2010, create unified fips column
svi_2010_national_county_flags_lihtc <- flag_summarize(svi_national_lihtc10, "E_TOTPOP_10") %>% unite("fips_county_st", FIPS_st:FIPS_county, remove = FALSE, sep="")

# Add suffix to flag columns 2010
colnames(svi_2010_national_county_flags_lihtc)[11:15] <- paste0(colnames(svi_2010_national_county_flags_lihtc)[11:15], 10)

# Create data frame of LIHTC eligible tracts 2020 nationally
svi_national_lihtc20 <- svi_national_lihtc %>% select(GEOID_2010_trt, FIPS_st, FIPS_county, 
    state, state_name, county, region_number, region, division_number, 
    division, F_TOTAL_20, E_TOTPOP_20) %>% rename("F_TOTAL" = "F_TOTAL_20")

# Count national-level SVI flags for 2020, create unified fips column
svi_2020_national_county_flags_lihtc <- flag_summarize(svi_national_lihtc20, "E_TOTPOP_20") %>% unite("fips_county_st", FIPS_st:FIPS_county, remove = FALSE, sep="")

# Identify needed columns for 2020, add suffix
colnames(svi_2020_national_county_flags_lihtc)[11:15] <- paste0(colnames(svi_2020_national_county_flags_lihtc)[11:15], "20")

# Filter to needed columns for 2020 to avoid duplicate column column names
svi_2020_national_county_flags_join_lihtc <- svi_2020_national_county_flags_lihtc %>% ungroup() %>% select("fips_county_st", all_of(colnames(svi_2020_national_county_flags_lihtc)[11:15]))
 
# Join 2010 and 2020 data
svi_national_county_flags_lihtc <- left_join(svi_2010_national_county_flags_lihtc, svi_2020_national_county_flags_join_lihtc, join_by("fips_county_st" == "fips_county_st")) 

svi_national_county_flags_lihtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
fips_county_st FIPS_st FIPS_county state state_name county region_number region division_number division flag_count10 pop10 flag_by_pop10 flag_count_quantile10 flag_pop_quantile10 flag_count20 pop20 flag_by_pop20 flag_count_quantile20 flag_pop_quantile20
01005 01 005 AL Alabama Barbour County 3 South Region 6 East South Central Division 6 1753 0.0034227 0.2 0.8 7 1527 0.0045842 0.2 1.0
01011 01 011 AL Alabama Bullock County 3 South Region 6 East South Central Division 6 1652 0.0036320 0.2 0.8 8 1382 0.0057887 0.4 1.0
01015 01 015 AL Alabama Calhoun County 3 South Region 6 East South Central Division 40 15130 0.0026438 0.8 0.6 37 11783 0.0031401 0.8 0.8
01023 01 023 AL Alabama Choctaw County 3 South Region 6 East South Central Division 12 5578 0.0021513 0.6 0.4 15 5412 0.0027716 0.6 0.8
01031 01 031 AL Alabama Coffee County 3 South Region 6 East South Central Division 12 8139 0.0014744 0.6 0.2 13 8517 0.0015264 0.6 0.2
01033 01 033 AL Alabama Colbert County 3 South Region 6 East South Central Division 10 1983 0.0050429 0.4 1.0 8 1931 0.0041429 0.4 1.0 
svi_national_county_lihtc <- left_join(svi_national_lihtc_county_sum,
                                      svi_national_county_flags_lihtc,
                                    join_by("State" == "state", "County" == "county",
                                            "Division" == "division"))

svi_national_county_lihtc$post10_lihtc_project_cnt[is.na(svi_national_county_lihtc$post10_lihtc_project_cnt)] <- 0

svi_national_county_lihtc$county_name <- paste0(svi_national_county_lihtc$County, ", ", svi_national_county_lihtc$State)

svi_national_county_lihtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
State County Division post10_lihtc_project_cnt tract_cnt post10_lihtc_project_dollars post10_lihtc_dollars_formatted fips_county_st FIPS_st FIPS_county state_name region_number region division_number flag_count10 pop10 flag_by_pop10 flag_count_quantile10 flag_pop_quantile10 flag_count20 pop20 flag_by_pop20 flag_count_quantile20 flag_pop_quantile20 county_name
AK Bethel Census Area Pacific Division 0 2 0 \$0 02050 02 050 Alaska 4 West Region 9 18 10867 0.0016564 0.6 0.4 20 11715 0.0017072 0.8 0.4 Bethel Census Area, AK
AK Dillingham Census Area Pacific Division 0 1 0 \$0 02070 02 070 Alaska 4 West Region 9 9 2569 0.0035033 0.4 0.8 10 2801 0.0035702 0.4 0.8 Dillingham Census Area, AK
AK Kenai Peninsula Borough Pacific Division 0 1 0 \$0 02122 02 122 Alaska 4 West Region 9 7 251 0.0278884 0.2 1.0 8 531 0.0150659 0.4 1.0 Kenai Peninsula Borough, AK
AK Nome Census Area Pacific Division 0 1 0 \$0 02180 02 180 Alaska 4 West Region 9 9 5766 0.0015609 0.4 0.2 10 5901 0.0016946 0.4 0.4 Nome Census Area, AK
AK Yukon-Koyukuk Census Area Pacific Division 0 2 0 \$0 02290 02 290 Alaska 4 West Region 9 18 2300 0.0078261 0.6 1.0 21 2153 0.0097538 0.8 1.0 Yukon-Koyukuk Census Area, AK
AL Barbour County East South Central Division 0 1 0 \$0 01005 01 005 Alabama 3 South Region 6 6 1753 0.0034227 0.2 0.8 7 1527 0.0045842 0.2 1.0 Barbour County, AL 
# Create data frame of LIHTC eligible tracts 2020 nationally
svi_divisional_lihtc10 <- svi_divisional_lihtc %>% select(GEOID_2010_trt, FIPS_st, FIPS_county, 
    state, state_name, county, region_number, region, division_number, 
    division, F_TOTAL_10, E_TOTPOP_10) %>% rename("F_TOTAL" = "F_TOTAL_10")

# Count divisional-level SVI flags for 2010, create unified fips column
svi_2010_divisional_county_flags_lihtc <- flag_summarize(svi_divisional_lihtc10, "E_TOTPOP_10") %>% unite("fips_county_st", FIPS_st:FIPS_county, remove = FALSE, sep="")

# Add suffix to flag columns 2010
colnames(svi_2010_divisional_county_flags_lihtc)[11:15] <- paste0(colnames(svi_2010_divisional_county_flags_lihtc)[11:15], "10")

# Create data frame of LIHTC eligible tracts 2020 nationally
svi_divisional_lihtc20 <- svi_divisional_lihtc %>% select(GEOID_2010_trt, FIPS_st, FIPS_county, 
    state, state_name, county, region_number, region, division_number, 
    division, F_TOTAL_20, E_TOTPOP_20) %>% rename("F_TOTAL" = "F_TOTAL_20")

# Count divisional-level SVI flags for 2020, create unified fips column
svi_2020_divisional_county_flags_lihtc <- flag_summarize(svi_divisional_lihtc20, "E_TOTPOP_20") %>% unite("fips_county_st", FIPS_st:FIPS_county, remove = FALSE, sep="")

# Identify needed columns for 2020
colnames(svi_2020_divisional_county_flags_lihtc)[11:15] <- paste0(colnames(svi_2020_divisional_county_flags_lihtc)[11:15], "20")

# Filter to needed columns for 2020 to avoid duplicate column column names
svi_2020_divisional_county_flags_join_lihtc <- svi_2020_divisional_county_flags_lihtc %>% ungroup() %>% select("fips_county_st", all_of(colnames(svi_2020_divisional_county_flags_lihtc)[11:15]))
 
# Join 2010 and 2020 data
svi_divisional_county_flags_lihtc <- left_join(svi_2010_divisional_county_flags_lihtc, svi_2020_divisional_county_flags_join_lihtc, join_by("fips_county_st" == "fips_county_st")) 

svi_divisional_county_flags_lihtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
fips_county_st FIPS_st FIPS_county state state_name county region_number region division_number division flag_count10 pop10 flag_by_pop10 flag_count_quantile10 flag_pop_quantile10 flag_count20 pop20 flag_by_pop20 flag_count_quantile20 flag_pop_quantile20
02050 02 050 AK Alaska Bethel Census Area 4 West Region 9 Pacific Division 18 10867 0.0016564 0.6 0.4 20 11715 0.0017072 0.6 0.6
02070 02 070 AK Alaska Dillingham Census Area 4 West Region 9 Pacific Division 9 2569 0.0035033 0.2 1.0 9 2801 0.0032131 0.4 1.0
02122 02 122 AK Alaska Kenai Peninsula Borough 4 West Region 9 Pacific Division 6 251 0.0239044 0.2 1.0 8 531 0.0150659 0.2 1.0
02180 02 180 AK Alaska Nome Census Area 4 West Region 9 Pacific Division 9 5766 0.0015609 0.2 0.4 9 5901 0.0015252 0.4 0.4
02290 02 290 AK Alaska Yukon-Koyukuk Census Area 4 West Region 9 Pacific Division 17 2300 0.0073913 0.6 1.0 19 2153 0.0088249 0.6 1.0
06001 06 001 CA California Alameda County 4 West Region 9 Pacific Division 186 92323 0.0020147 1.0 0.6 170 101788 0.0016701 1.0 0.6 
svi_divisional_county_lihtc <- left_join(svi_divisional_lihtc_county_sum, 
                                        svi_divisional_county_flags_lihtc,
                                    join_by("State" == "state", "County" == "county",
                                            "Division" == "division"))

svi_divisional_county_lihtc$post10_lihtc_project_cnt[is.na(svi_divisional_county_lihtc $post10_lihtc_project_cnt)] <- 0

svi_divisional_county_lihtc$county_name <- paste0(svi_divisional_county_lihtc$County, ", ", svi_divisional_county_lihtc$State)

svi_divisional_county_lihtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
State County Division post10_lihtc_project_cnt tract_cnt post10_lihtc_project_dollars post10_lihtc_dollars_formatted fips_county_st FIPS_st FIPS_county state_name region_number region division_number flag_count10 pop10 flag_by_pop10 flag_count_quantile10 flag_pop_quantile10 flag_count20 pop20 flag_by_pop20 flag_count_quantile20 flag_pop_quantile20 county_name
AK Bethel Census Area Pacific Division 0 2 0 \$0 02050 02 050 Alaska 4 West Region 9 18 10867 0.0016564 0.6 0.4 20 11715 0.0017072 0.6 0.6 Bethel Census Area, AK
AK Dillingham Census Area Pacific Division 0 1 0 \$0 02070 02 070 Alaska 4 West Region 9 9 2569 0.0035033 0.2 1.0 9 2801 0.0032131 0.4 1.0 Dillingham Census Area, AK
AK Kenai Peninsula Borough Pacific Division 0 1 0 \$0 02122 02 122 Alaska 4 West Region 9 6 251 0.0239044 0.2 1.0 8 531 0.0150659 0.2 1.0 Kenai Peninsula Borough, AK
AK Nome Census Area Pacific Division 0 1 0 \$0 02180 02 180 Alaska 4 West Region 9 9 5766 0.0015609 0.2 0.4 9 5901 0.0015252 0.4 0.4 Nome Census Area, AK
AK Yukon-Koyukuk Census Area Pacific Division 0 2 0 \$0 02290 02 290 Alaska 4 West Region 9 17 2300 0.0073913 0.6 1.0 19 2153 0.0088249 0.6 1.0 Yukon-Koyukuk Census Area, AK
CA Alameda County Pacific Division 1 23 1590984 \$1,590,984 06001 06 001 California 4 West Region 9 186 92323 0.0020147 1.0 0.6 170 101788 0.0016701 1.0 0.6 Alameda County, CA

Exploratory Data Analysis

NMTC in Pacific Division

svi_divisional_county_nmtc_projects <- svi_divisional_county_nmtc %>% filter(post10_nmtc_project_cnt > 0)

Data Summary

summary(svi_divisional_county_nmtc_projects$flag_count10)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1.00   26.25   56.00  301.68  199.75 9210.00

summary(svi_divisional_county_nmtc_projects$post10_nmtc_project_dollars)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##    669550   9561257  18612500  41859846  35824250 987407086

There’s a wide range of flags among counties in the Pacific Division. Some counties have as few as 1 flag or as many as 9,210 flags (Los Angeles County). The next highest number of flags after the maximum is 1,451. This explains the large difference between the median (56 flags) and mean (302 flags); the median is a more realistic average for the division because the mean is dragged significantly higher by the maximum.

Similarly, NMTC project award totals in Pacific Division counties ranged from $667,550 to $987,407,086. Los Angeles County accounts for the maximum cost here as well. The next highest county total comes in at $221,738,411. Similar to the flags, Los Angeles County’s total is an outlier that skews the data to the right, creating a significant difference between the median and mean.

Let’s visualize this effect in a scatterplot.

# Scatterplot
# y is our independent variable (NMTC Project Dollars),  
# x is our dependent variable (SVI flag count)
ggplot2::ggplot(svi_divisional_county_nmtc_projects,
                aes(x=flag_count10,
                    y=post10_nmtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format())

Based on the summary statistics and scatterplot, the NMTC data for the Pacific Division may have a leptokurtic distribution. This is important to note because extremes can distort correlation, regression, and k-means clustering. However, this analysis does not exclude Los Angeles County as an outlier because it’s a natural part of the population/geography under study. In fact, Los Angeles is the most populous county in the United States, and a 2023 U.S. Census Bureau report states that 374 tracts within the county, representing 1.6 million people, have experienced persistent poverty. We can predict that, at least by count, the population need and number of qualified projects for tax credits like the NMTC may be higher in LAC than in most other counties in the division.

# Pearson's r calculation
cor(svi_divisional_county_nmtc_projects$flag_count10, svi_divisional_county_nmtc_projects$post10_nmtc_project_dollars, method = "pearson")

## [1] 0.9603179

The correlation between flag count and project award totals is very strong and positive in the Pacific Division. If we exclude LAC, the correlation drops meaningfully.

svi_d_c_temp <- svi_divisional_county_nmtc_projects %>% 
  filter(County != "Los Angeles County")
cor(svi_d_c_temp$flag_count10, svi_d_c_temp$post10_nmtc_project_dollars, method = "pearson")

## [1] 0.5307725

The test excluding LAC returns a moderate, positive correlation, but the association does not disappear or reverse. Still, it’s a pretty large drop. Let’s do a couple more tests.

svi_d_c_temp_CAnotLAC <- svi_divisional_county_nmtc_projects %>% 
  filter(State == "CA") %>% 
  filter(County != "Los Angeles County")
cor(svi_d_c_temp_CAnotLAC$flag_count10, svi_d_c_temp_CAnotLAC$post10_nmtc_project_dollars, method = "pearson")

## [1] 0.5461931

svi_d_c_temp_notCA <- svi_divisional_county_nmtc_projects %>% 
  filter(State != "CA")
cor(svi_d_c_temp_notCA$flag_count10, svi_d_c_temp_notCA$post10_nmtc_project_dollars, method = "pearson")

## [1] 0.5248953

These tests show that correlation is similar among California counties (when excluding LAC) and the Pacific Division (when excluding all CA counties). In both cases, there is a moderate and positive association. What is so unique about LAC? Can it be explained by just its high population and the number of high value projects? In a 2022 neighborhood level analysis of California’s LIHTC program, Basolo et. al found that:

The exclusion of LAC from the state analysis produces one important change in the results. Without LAC in the analysis, there is no statistically significant relationship between neighborhood economic hardship and the location of LIHTC developments. In other words, LIHTC housing in California, except for LAC, is not more likely to be in disadvantaged neighborhoods.

While the Basolo et. al study uses a measure of economic hardship for the LIHTC, their result implies that programmatic and/or market conditions in LAC could also influence this analysis due to overlapping measures between the economic hardship index and SVI. Something is different about LAC, but Basolo et. al also struggled to identify what. Our NMTC data tentatively supports the idea that more than just “population” explains LAC as an outlier. If this holds true, we can expect to see this occur in the LIHTC section below as well.

As it stands, in the Pacific Division, counties with more social vulnerability flags in 2010 received more NMTC dollars in 2011-2020. The boxplot below further visualizes the skewed distribution and identifies other outliers. However, the data does not adjust the number of flags by population, so the list of outliers below also represents the most populous counties in the Pacific Division.

boxplot(svi_divisional_county_nmtc_projects$flag_count10)


boxplot.stats(svi_divisional_county_nmtc_projects$flag_count10)$out %>% sort(decreasing = TRUE)

## [1] 9210 1451 1305  978  931  831  815  669  596

svi_divisional_county_nmtc_projects %>% 
  select(county_name, flag_count10, post10_nmtc_dollars_formatted) %>% 
  arrange(desc(flag_count10)) %>% 
  head(9) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
county_name flag_count10 post10_nmtc_dollars_formatted
Los Angeles County, CA 9210 \$987,407,086
Riverside County, CA 1451 \$40,871,824
San Diego County, CA 1305 \$221,738,411
Orange County, CA 978 \$12,463,033
Fresno County, CA 931 \$115,258,480
Sacramento County, CA 831 \$3,630,000
Alameda County, CA 815 \$119,049,224
Kern County, CA 669 \$31,290,000
Santa Clara County, CA 596 \$41,829,813

K-Means Clustering

svi_divisional_nmtc_cluster <- svi_divisional_county_nmtc_projects %>% 
                            select(county_name, post10_nmtc_project_dollars, 
                                   flag_count10) %>% 
                            remove_rownames %>% 
                            column_to_rownames(var="county_name")

# Remove nulls, if in dataset
svi_divisional_nmtc_cluster <- na.omit(svi_divisional_nmtc_cluster)


# Scale numeric variables
svi_divisional_nmtc_cluster <- scale(svi_divisional_nmtc_cluster)


svi_divisional_nmtc_cluster %>% head(5)

##                               post10_nmtc_project_dollars flag_count10
## Aleutians East Borough, AK                     -0.2292355   -0.2754180
## Anchorage Municipality, AK                     -0.2816092   -0.2311900
## Wade Hampton Census Area, AK                   -0.1795408   -0.2744770
## Yukon-Koyukuk Census Area, AK                  -0.3005472   -0.2594206
## Alameda County, CA                              0.6780208    0.4830462

set.seed(123)
k2_nmtc_div <- kmeans(svi_divisional_nmtc_cluster, centers = 2, nstart = 25)
set.seed(123)
k3_nmtc_div <- kmeans(svi_divisional_nmtc_cluster, centers = 3, nstart = 25)
set.seed(123)
k4_nmtc_div <- kmeans(svi_divisional_nmtc_cluster, centers = 4, nstart = 25)
set.seed(123)
k5_nmtc_div <- kmeans(svi_divisional_nmtc_cluster, centers = 5, nstart = 25)

# plots to compare
p_k2_nmtc_div <- factoextra::fviz_cluster(k2_nmtc_div, geom = "point", data = svi_divisional_nmtc_cluster) + ggtitle("k = 2 w/ LAC")

p_k3_nmtc_div <- factoextra::fviz_cluster(k3_nmtc_div, geom = "point", data = svi_divisional_nmtc_cluster) + ggtitle("k = 3 w/ LAC")

p_k4_nmtc_div <- factoextra::fviz_cluster(k4_nmtc_div, geom = "point",  data = svi_divisional_nmtc_cluster) + ggtitle("k = 4 w/ LAC")

p_k5_nmtc_div <- factoextra::fviz_cluster(k5_nmtc_div, geom = "point",  data = svi_divisional_nmtc_cluster) + ggtitle("k = 5 w/ LAC")

grid.arrange(p_k2_nmtc_div, p_k3_nmtc_div, p_k4_nmtc_div, p_k5_nmtc_div, nrow = 2)


elbow_plot(svi_divisional_nmtc_cluster)

##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
## [1] 11
## [1] 12
## [1] 13
## [1] 14
## [1] 15

LAC’s inclusion degrades the visibility of the k-means clusters in the bottom left of the chart. It also impacts the usability of the elbow plot, making it meaningless for us. This is because the dataset violates the k-means clustering assumption that data has no outliers (IBM, n.d.). If we accepted the validity of this elbow plot, it would mean there are two groups: LAC by itself and the remaining 77 counties in the Pacific Division in a group. This is pretty unlikely.

In every iteration of k = n, LAC comprises its own cluster. This is good in the sense that the k-means isn’t clustering a widely spaced group with LAC. However, we struggle to see the centroids in the bottom left corner, and our elbow plot isn’t functional. We can force it by removing LAC, then add 1 to that elbow plot’s number to add LAC back in. This is not valid according to the original elbow plot, but technically, the model is already overfitted because k-means is not the ideal clustering method for the Pacific Division when including LAC for the aforementioned reasons.

svi_divisional_nmtc_cluster2 <- svi_divisional_county_nmtc_projects %>% 
                            select(county_name, post10_nmtc_project_dollars, 
                                   flag_count10) %>% 
                            filter(county_name != "Los Angeles County, CA") %>% 
                            remove_rownames %>% 
                            column_to_rownames(var="county_name")

# Remove nulls, if in dataset
svi_divisional_nmtc_cluster2 <- na.omit(svi_divisional_nmtc_cluster2)


# Scale numeric variables
svi_divisional_nmtc_cluster2 <- scale(svi_divisional_nmtc_cluster2)


svi_divisional_nmtc_cluster2 %>% head(5)

##                               post10_nmtc_project_dollars flag_count10
## Aleutians East Borough, AK                     -0.3964835   -0.6021680
## Anchorage Municipality, AK                     -0.5675732   -0.4422586
## Wade Hampton Census Area, AK                   -0.2341456   -0.5987657
## Yukon-Koyukuk Census Area, AK                  -0.6294380   -0.5443284
## Alameda County, CA                              2.5672545    2.1401091

set.seed(123)
k2_nmtc_div2 <- kmeans(svi_divisional_nmtc_cluster2, centers = 2, nstart = 25)
set.seed(123)
k3_nmtc_div2 <- kmeans(svi_divisional_nmtc_cluster2, centers = 3, nstart = 25)
set.seed(123)
k4_nmtc_div2 <- kmeans(svi_divisional_nmtc_cluster2, centers = 4, nstart = 25)
set.seed(123)
k5_nmtc_div2 <- kmeans(svi_divisional_nmtc_cluster2, centers = 5, nstart = 25)

# plots to compare
p_k2_nmtc_div2 <- factoextra::fviz_cluster(k2_nmtc_div2, geom = "point", data = svi_divisional_nmtc_cluster2) + ggtitle("k = 2 w/o LAC")

p_k3_nmtc_div2 <- factoextra::fviz_cluster(k3_nmtc_div2, geom = "point", data = svi_divisional_nmtc_cluster2) + ggtitle("k = 3 w/o LAC")

p_k4_nmtc_div2 <- factoextra::fviz_cluster(k4_nmtc_div2, geom = "point",  data = svi_divisional_nmtc_cluster2) + ggtitle("k = 4 w/o LAC")

p_k5_nmtc_div2 <- factoextra::fviz_cluster(k5_nmtc_div2, geom = "point",  data = svi_divisional_nmtc_cluster2) + ggtitle("k = 5 w/o LAC")

grid.arrange(p_k2_nmtc_div2, p_k3_nmtc_div2, p_k4_nmtc_div2, p_k5_nmtc_div2, nrow = 2)


elbow_plot(svi_divisional_nmtc_cluster2)

##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
## [1] 11
## [1] 12
## [1] 13
## [1] 14
## [1] 15

Spacing decreases after 4 clusters, so we’ll add 1 cluster to account for the outlier in its own group for a total of 5 clusters. We can see that k = 4 in svi_divisional_nmtc_cluster2 overlaps with k = 5 in svi_divisional_nmtc_cluster after we account for the outlier as the fifth cluster. So k5_nmtc_div will serve as the best cluster matrix.

grid.arrange(p_k4_nmtc_div2, p_k5_nmtc_div, nrow = 1)


p_k5_nmtc_div


svi_divisional_nmtc_cluster_label <- as.data.frame(svi_divisional_nmtc_cluster) %>%
                                  rownames_to_column(var = "county_name") %>%
                                  as_tibble() %>%
                                  mutate(cluster = k5_nmtc_div$cluster) %>%
                                  select(county_name, cluster)

svi_divisional_county_nmtc_projects2 <- left_join(svi_divisional_county_nmtc_projects, svi_divisional_nmtc_cluster_label, join_by(county_name == county_name))

# View county counts in each cluster
table(svi_divisional_county_nmtc_projects2$cluster)

## 
##  1  2  3  4  5 
##  5 57 12  1  3

Cluster 1

# Cluster 1 Scatterplot
# y is our independent variable (NMTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 1) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_nmtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format())  


svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 1) %>%
  select(flag_count10,post10_nmtc_project_dollars) %>%
  cor(method = "pearson")

##                             flag_count10 post10_nmtc_project_dollars
## flag_count10                   1.0000000                   0.1134538
## post10_nmtc_project_dollars    0.1134538                   1.0000000

Cluster 1 shows no association.

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 1) %>%
  select(county_name, flag_count10, post10_nmtc_dollars_formatted) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
county_name flag_count10 post10_nmtc_dollars_formatted
Kern County, CA 669 \$31,290,000
Orange County, CA 978 \$12,463,033
Riverside County, CA 1451 \$40,871,824
Sacramento County, CA 831 \$3,630,000
Santa Clara County, CA 596 \$41,829,813

Cluster 2

# Cluster 2 Scatterplot
# y is our independent variable (NMTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 2) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_nmtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 


svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 2) %>%
  select(flag_count10,post10_nmtc_project_dollars) %>%
  cor(method = "pearson")

##                             flag_count10 post10_nmtc_project_dollars
## flag_count10                   1.0000000                   0.1464225
## post10_nmtc_project_dollars    0.1464225                   1.0000000

Cluster 2 shows no association between data points. This cluster represents more than 73% of the counties in the Pacific Region.

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 2) %>%
  select(county_name, flag_count10, post10_nmtc_dollars_formatted) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
county_name flag_count10 post10_nmtc_dollars_formatted
Aleutians East Borough, AK 9 \$15,762,500
Anchorage Municipality, AK 56 \$9,800,000
Wade Hampton Census Area, AK 10 \$21,420,000
Yukon-Koyukuk Census Area, AK 26 \$7,644,000
Butte County, CA 171 \$22,355,000
Contra Costa County, CA 346 \$24,726,000
Del Norte County, CA 25 \$39,615,000
Humboldt County, CA 75 \$22,330,000
Madera County, CA 117 \$10,864,000
Merced County, CA 283 \$18,982,900
Napa County, CA 36 \$29,651,000
Santa Barbara County, CA 231 \$9,568,778
Santa Cruz County, CA 106 \$8,342,000
Siskiyou County, CA 56 \$18,625,000
Solano County, CA 163 \$8,500,000
Stanislaus County, CA 394 \$8,614,600
Tehama County, CA 40 \$13,000,000
Ventura County, CA 396 \$20,200,000
Yolo County, CA 83 \$6,790,000
Honolulu County, HI 343 \$35,830,900
Maui County, HI 38 \$22,038,000
Baker County, OR 16 \$8,148,000
Clackamas County, OR 54 \$980,000
Clatsop County, OR 9 \$11,640,000
Coos County, OR 38 \$35,804,300
Crook County, OR 12 \$5,820,000
Curry County, OR 9 \$12,610,000
Hood River County, OR 1 \$16,100,000
Jackson County, OR 102 \$9,558,750
Josephine County, OR 54 \$20,480,000
Klamath County, OR 55 \$6,547,500
Lake County, OR 9 \$7,275,000
Lane County, OR 175 \$28,810,000
Lincoln County, OR 31 \$2,988,434
Linn County, OR 41 \$17,640,000
Malheur County, OR 24 \$19,730,000
Marion County, OR 110 \$4,800,000
Polk County, OR 17 \$12,480,000
Umatilla County, OR 26 \$29,975,000
Wallowa County, OR 4 \$3,750,000
Wasco County, OR 18 \$3,884,000
Washington County, OR 104 \$9,081,000
Adams County, WA 19 \$30,510,000
Benton County, WA 58 \$15,480,000
Clallam County, WA 31 \$7,620,320
Columbia County, WA 4 \$19,500,000
Cowlitz County, WA 60 \$669,550
Ferry County, WA 14 \$16,005,000
Grant County, WA 57 \$15,520,000
Mason County, WA 27 \$12,330,000
Okanogan County, WA 37 \$33,838,866
Pend Oreille County, WA 18 \$19,310,000
Skagit County, WA 45 \$9,994,000
Spokane County, WA 199 \$15,328,238
Whatcom County, WA 38 \$5,940,000
Whitman County, WA 20 \$15,757,500
Yakima County, WA 200 \$18,600,000

Cluster 3

# Cluster 3 Scatterplot
# y is our independent variable (NMTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 3) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_nmtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 


svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 3) %>%
  select(flag_count10,post10_nmtc_project_dollars) %>%
  cor(method = "pearson")

##                             flag_count10 post10_nmtc_project_dollars
## flag_count10                   1.0000000                   0.2320538
## post10_nmtc_project_dollars    0.2320538                   1.0000000

Cluster 3 shows a weak, positive association.

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 3) %>%
  select(county_name, flag_count10, post10_nmtc_dollars_formatted) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
county_name flag_count10 post10_nmtc_dollars_formatted
San Francisco County, CA 316 \$68,150,600
San Luis Obispo County, CA 56 \$47,941,400
San Mateo County, CA 161 \$47,864,844
Shasta County, CA 135 \$48,005,000
Hawaii County, HI 56 \$99,555,000
Douglas County, OR 45 \$66,520,000
Multnomah County, OR 262 \$52,382,352
Clark County, WA 133 \$64,280,000
Grays Harbor County, WA 47 \$83,180,621
King County, WA 358 \$111,041,500
Pierce County, WA 290 \$76,005,801
Snohomish County, WA 146 \$47,437,840

Cluster 4

# Cluster 4 Scatterplot
# y is our independent variable (NMTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 4) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_nmtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 


svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 4) %>%
  select(flag_count10,post10_nmtc_project_dollars) %>%
  cor(method = "pearson")

##                             flag_count10 post10_nmtc_project_dollars
## flag_count10                          NA                          NA
## post10_nmtc_project_dollars           NA                          NA

Cluster 4 contains only our outlier.

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 4) %>%
  select(county_name, flag_count10, post10_nmtc_dollars_formatted) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
county_name flag_count10 post10_nmtc_dollars_formatted
Los Angeles County, CA 9210 \$987,407,086

Cluster 5

# Cluster 5 Scatterplot
# y is our independent variable (NMTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 5) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_nmtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 


svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 5) %>%
  select(flag_count10,post10_nmtc_project_dollars) %>%
  cor(method = "pearson")

##                             flag_count10 post10_nmtc_project_dollars
## flag_count10                   1.0000000                   0.9664253
## post10_nmtc_project_dollars    0.9664253                   1.0000000

Cluster 5 has a very strong, positive association. These counties are technically outliers as well, but not to the same degree as LAC.

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 5) %>%
  select(county_name, flag_count10, post10_nmtc_dollars_formatted) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
county_name flag_count10 post10_nmtc_dollars_formatted
Alameda County, CA 815 \$119,049,224
Fresno County, CA 931 \$115,258,480
San Diego County, CA 1305 \$221,738,411

Bivariate Map

divisional_county_sf <- svi_county_map2010 %>% select(COUNTYFP, STATEFP, geometry)

divisional_county_sf %>% head(5)

## Simple feature collection with 5 features and 2 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -2327771 ymin: -195140.7 xmax: -1953556 ymax: 782440.1
## Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic
##   COUNTYFP STATEFP                       geometry
## 1      035      06 MULTIPOLYGON (((-1987497 61...
## 2      049      06 MULTIPOLYGON (((-1992512 76...
## 3      075      06 MULTIPOLYGON (((-2327608 35...
## 4      083      06 MULTIPOLYGON (((-2157081 -1...
## 5      091      06 MULTIPOLYGON (((-2025220 47...

# Join our NMTC projects data with our shapefile geocoordinates
svi_divisional_county_nmtc_sf <- left_join(svi_divisional_county_nmtc_projects, divisional_county_sf, join_by("FIPS_st" == "STATEFP", "FIPS_county" == "COUNTYFP"))

svi_divisional_county_nmtc_sf %>% head(5) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
State County Division post10_nmtc_project_cnt tract_cnt post10_nmtc_project_dollars post10_nmtc_dollars_formatted fips_county_st FIPS_st FIPS_county state_name region_number region division_number flag_count10 pop10 flag_by_pop10 flag_count_quantile10 flag_pop_quantile10 flag_count20 pop20 flag_by_pop20 flag_count_quantile20 flag_pop_quantile20 county_name geometry
AK Aleutians East Borough Pacific Division 1 1 15762500 \$15,762,500 02013 02 013 Alaska 4 West Region 9 9 3703 0.0024305 0.2 1.0 6 3389 0.0017704 0.2 1.0 Aleutians East Borough, AK MULTIPOLYGON (((-2385249 -1…
AK Anchorage Municipality Pacific Division 1 13 9800000 \$9,800,000 02020 02 020 Alaska 4 West Region 9 56 64432 0.0008691 0.8 0.2 73 69679 0.0010477 0.8 0.4 Anchorage Municipality, AK MULTIPOLYGON (((-1927463 -1…
AK Wade Hampton Census Area Pacific Division 1 1 21420000 \$21,420,000 02270 02 270 Alaska 4 West Region 9 10 7398 0.0013517 0.4 0.8 11 8298 0.0013256 0.4 0.6 Wade Hampton Census Area, AK MULTIPOLYGON (((-2310112 -1…
AK Yukon-Koyukuk Census Area Pacific Division 1 3 7644000 \$7,644,000 02290 02 290 Alaska 4 West Region 9 26 4027 0.0064564 0.6 1.0 28 3979 0.0070369 0.6 1.0 Yukon-Koyukuk Census Area, AK MULTIPOLYGON (((-1736112 -9…
CA Alameda County Pacific Division 10 134 119049224 \$119,049,224 06001 06 001 California 4 West Region 9 815 563385 0.0014466 1.0 0.8 690 615018 0.0011219 1.0 0.6 Alameda County, CA MULTIPOLYGON (((-2266160 34… 
# Create classes for bivariate mapping 
svi_divisional_county_nmtc_sf <- bi_class(svi_divisional_county_nmtc_sf, x = flag_count10, y = post10_nmtc_project_dollars, style = "quantile", dim = 3)

# View data
svi_divisional_county_nmtc_sf %>% head(5) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
State County Division post10_nmtc_project_cnt tract_cnt post10_nmtc_project_dollars post10_nmtc_dollars_formatted fips_county_st FIPS_st FIPS_county state_name region_number region division_number flag_count10 pop10 flag_by_pop10 flag_count_quantile10 flag_pop_quantile10 flag_count20 pop20 flag_by_pop20 flag_count_quantile20 flag_pop_quantile20 county_name geometry bi_class
AK Aleutians East Borough Pacific Division 1 1 15762500 \$15,762,500 02013 02 013 Alaska 4 West Region 9 9 3703 0.0024305 0.2 1.0 6 3389 0.0017704 0.2 1.0 Aleutians East Borough, AK MULTIPOLYGON (((-2385249 -1… 1-2
AK Anchorage Municipality Pacific Division 1 13 9800000 \$9,800,000 02020 02 020 Alaska 4 West Region 9 56 64432 0.0008691 0.8 0.2 73 69679 0.0010477 0.8 0.4 Anchorage Municipality, AK MULTIPOLYGON (((-1927463 -1… 2-1
AK Wade Hampton Census Area Pacific Division 1 1 21420000 \$21,420,000 02270 02 270 Alaska 4 West Region 9 10 7398 0.0013517 0.4 0.8 11 8298 0.0013256 0.4 0.6 Wade Hampton Census Area, AK MULTIPOLYGON (((-2310112 -1… 1-2
AK Yukon-Koyukuk Census Area Pacific Division 1 3 7644000 \$7,644,000 02290 02 290 Alaska 4 West Region 9 26 4027 0.0064564 0.6 1.0 28 3979 0.0070369 0.6 1.0 Yukon-Koyukuk Census Area, AK MULTIPOLYGON (((-1736112 -9… 1-1
CA Alameda County Pacific Division 10 134 119049224 \$119,049,224 06001 06 001 California 4 West Region 9 815 563385 0.0014466 1.0 0.8 690 615018 0.0011219 1.0 0.6 Alameda County, CA MULTIPOLYGON (((-2266160 34… 3-3 
# Create map with ggplot
svi_divisional_county_nmtc_map <- ggplot() +
  # Map county shapefile, fill with bi_class categories
  geom_sf(data = svi_divisional_county_nmtc_sf, mapping = aes(geometry=geometry, fill = bi_class), color = "white", size = 0.1, show.legend = FALSE) +
  # Set to biscale palette
  bi_scale_fill(pal = "GrPink", dim = 3) +
  # Add state shapefiles for outline
  geom_sf(data=divisional_st_sf, color="black", fill=NA, linewidth=.5, aes(geometry=geometry)) +
  labs(
    title = paste0("Correlation of 2010 ", census_division, " SVI Flag Count \n and 2011 - 2020 NMTC Tax Dollars"),
  ) +
  # Set them to biscale
  bi_theme(base_size = 10)

# Create biscale legend
svi_divisional_county_nmtc_legend <- bi_legend(pal = "GrPink",
                    dim = 3,
                    xlab = "SVI Flag Count",
                    ylab = "NMTC Dollars",
                    size = 8)

# Combine map with legend using cowplot
svi_divisional_county_nmtc_bivarmap <- ggdraw() +
  draw_plot(svi_divisional_county_nmtc_map) +
  # Set legend location
  draw_plot(svi_divisional_county_nmtc_legend, x= -.02,  y = -.05,
 width=.20)


# View map
svi_divisional_county_nmtc_bivarmap

Areas with high NMTC dollars and high SVI flags tend to be located in more densely populated parts of the Pacific Division, including California and the Seattle-metro area. The inland northwest appears to be more likely to have fewer flags, so we see more counties in shades of gray to blue.

LIHTC in Pacific Division

svi_divisional_county_lihtc_projects <- svi_divisional_county_lihtc %>% filter(post10_lihtc_project_cnt > 0)

svi_divisional_county_lihtc_projects <- svi_divisional_county_lihtc_projects %>% filter(post10_lihtc_project_dollars > 0)

Data Summary

summary(svi_divisional_county_lihtc_projects$flag_count10)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    2.00   14.75   25.50  171.14  149.75 2394.00

summary(svi_divisional_county_lihtc_projects$post10_lihtc_project_dollars)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##   250101   745546  2168330  5287061  4488865 50547731

There’s a wide range of flags among the Pacific Division counties, but not as wide as it is for the NMTC data. Some counties have as few as 2 flags or as many as 2,394 flags (Los Angeles County). The next highest number of flags after the maximum is 376. This explains the large difference between the median (26 flags) and mean (171 flags); like for the NMTC data, the median is a more realistic average for the division because the mean is dragged significantly higher by the maximum.

Similarly, LIHTC project award totals in Pacific Division counties ranged from $250,101 to $50,547,731. Los Angeles County accounts for the maximum cost here as well. The next highest county total comes in at
$20,961,962, which is a much smaller gap than for the NMTC project award totals.

Los Angeles County is an outlier that skews the data to the right, creating a significant difference between the median and mean for both variables. Let’s visualize this effect in a scatterplot.

# Scatterplot
# y is our independent variable (LIHTC Project Dollars),  
# x is our dependent variable (SVI flag count)
ggplot2::ggplot(svi_divisional_county_lihtc_projects,
                aes(x=flag_count10,
                    y=post10_lihtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format())

The scatterplot for LIHTC is similar to but less severely skewed than the NMTC data.

# Pearson's r calculation
cor(svi_divisional_county_lihtc_projects$flag_count10, svi_divisional_county_lihtc_projects$post10_lihtc_project_dollars, method = "pearson")

## [1] 0.9418682

There is a very strong and positive correlation between flag count and LIHTC project award totals in the Pacific Division.

svi_d_c_temp2 <- svi_divisional_county_lihtc_projects %>% 
  filter(County != "Los Angeles County")
cor(svi_d_c_temp2$flag_count10, svi_d_c_temp2$post10_lihtc_project_dollars, method = "pearson")

## [1] 0.7026956

If we exclude LAC, the correlation drops. The test excluding LAC returns a strong, positive correlation. This means LAC has a less severe effect on the LIHTC than it did on the NMTC data.

In the Pacific Division, counties with more social vulnerability flags in 2010 received more LIHTC dollars in 2011-2020. The boxplot below further visualizes the skewed distribution and identifies other outliers. However, the data does not adjust the number of flags by population, so the list of outliers below also represents the most populous counties in the Pacific Division.

The study by Basolo et. al also brings up an issue that is important to keep in mind for the LIHTC data. We’re looking at the correlation between award totals and SVI flags because we want to understand how investment changed social vulnerability over time; however, LIHTC is most effectively used when built in areas of low social vulnerability. To see that there’s a strong correlation between dollars and flags is not ideal. This is because such placements segregate low-income households from opportunity and access to key destinations. While it achieves the goal of improving the affordable housing stock, it could harm our regression model through economic segregation. LAC is the perfect example of this.

boxplot(svi_divisional_county_lihtc_projects$flag_count10)


boxplot.stats(svi_divisional_county_lihtc_projects$flag_count10)$out %>% sort(decreasing = TRUE)

## [1] 2394  376

svi_divisional_county_lihtc_projects %>% filter(flag_count10 == 2394) %>% select(county_name, flag_count10, post10_lihtc_dollars_formatted) %>% head() 

##              county_name flag_count10 post10_lihtc_dollars_formatted
## 1 Los Angeles County, CA         2394                    $50,547,731

K-Means Clustering

svi_divisional_lihtc_cluster <- svi_divisional_county_lihtc_projects %>% 
                            select(county_name, post10_lihtc_project_dollars, 
                                   flag_count10) %>% 
                            remove_rownames %>% 
                            column_to_rownames(var="county_name")

# Remove nulls, if in dataset
svi_divisional_lihtc_cluster <- na.omit(svi_divisional_lihtc_cluster)


# Scale numeric variables
svi_divisional_lihtc_cluster <- scale(svi_divisional_lihtc_cluster)


svi_divisional_lihtc_cluster %>% head(5)

##                     post10_lihtc_project_dollars flag_count10
## Alameda County, CA                    -0.3720126   0.03317421
## Butte County, CA                      -0.4766870  -0.34418241
## Fresno County, CA                     -0.2922395  -0.17225070
## Humboldt County, CA                   -0.4092212  -0.35534681
## Kern County, CA                       -0.2010567  -0.11866159

set.seed(123)
k2_lihtc_div <- kmeans(svi_divisional_lihtc_cluster, centers = 2, nstart = 25)
set.seed(123)
k3_lihtc_div <- kmeans(svi_divisional_lihtc_cluster, centers = 3, nstart = 25)
set.seed(123)
k4_lihtc_div <- kmeans(svi_divisional_lihtc_cluster, centers = 4, nstart = 25)
set.seed(123)
k5_lihtc_div <- kmeans(svi_divisional_lihtc_cluster, centers = 5, nstart = 25)

# plots to compare
p_k2_lihtc_div <- factoextra::fviz_cluster(k2_lihtc_div, geom = "point", data = svi_divisional_lihtc_cluster) + ggtitle("k = 2")

p_k3_lihtc_div <- factoextra::fviz_cluster(k3_lihtc_div, geom = "point", data = svi_divisional_lihtc_cluster) + ggtitle("k = 3")

p_k4_lihtc_div <- factoextra::fviz_cluster(k4_lihtc_div, geom = "point",  data = svi_divisional_lihtc_cluster) + ggtitle("k = 4")

p_k5_lihtc_div <- factoextra::fviz_cluster(k5_lihtc_div, geom = "point",  data = svi_divisional_lihtc_cluster) + ggtitle("k = 5")

grid.arrange(p_k2_lihtc_div, p_k3_lihtc_div, p_k4_lihtc_div, p_k5_lihtc_div, nrow = 2)


elbow_plot(svi_divisional_lihtc_cluster)

##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
## [1] 11
## [1] 12
## [1] 13
## [1] 14
## [1] 15

Like the NMTC clustering, LAC degrades the visibility of the centroids in the bottom left and impacts the usability of the elbow plot. If we accept this elbow plot, it would mean LAC is by itself and the rest of the 27 counties in the Pacific Division are in a group.

In every iteration of k = n, LAC comprises its own cluster, a good sign like with the NMTC data. What’s unique about the LIHTC data is that, at k = 4 and k = 5, the model clusters the other outlier independently as well. Let’s remove LAC, then add 1 to that elbow plot’s number to add LAC back in. As stated before, this is not valid according to the original elbow plot, but technically, the model is already overfitted because k-means is not the ideal clustering method for the Pacific Division when including LAC. We’ll leave the other outlier in the data because it belongs to a group in k = 2 and k = 3.

svi_divisional_lihtc_cluster2 <- svi_divisional_county_lihtc_projects %>% 
                            filter(county_name != "Los Angeles County, CA") %>% 
                            select(county_name, post10_lihtc_project_dollars, 
                                   flag_count10) %>% 
                            remove_rownames %>% 
                            column_to_rownames(var="county_name")

# Remove nulls, if in dataset
svi_divisional_lihtc_cluster2 <- na.omit(svi_divisional_lihtc_cluster2)


# Scale numeric variables
svi_divisional_lihtc_cluster2 <- scale(svi_divisional_lihtc_cluster2)


svi_divisional_lihtc_cluster2 %>% head(5)

##                     post10_lihtc_project_dollars flag_count10
## Alameda County, CA                   -0.44285872   0.91809920
## Butte County, CA                     -0.67088766  -0.67842772
## Fresno County, CA                    -0.26907622   0.04898395
## Humboldt County, CA                  -0.52391625  -0.72566225
## Kern County, CA                      -0.07043801   0.27570967

set.seed(123)
k2_lihtc_div2 <- kmeans(svi_divisional_lihtc_cluster2, centers = 2, nstart = 25)
set.seed(123)
k3_lihtc_div2 <- kmeans(svi_divisional_lihtc_cluster2, centers = 3, nstart = 25)
set.seed(123)
k4_lihtc_div2 <- kmeans(svi_divisional_lihtc_cluster2, centers = 4, nstart = 25)
set.seed(123)
k5_lihtc_div2 <- kmeans(svi_divisional_lihtc_cluster2, centers = 5, nstart = 25)

# plots to compare
p_k2_lihtc_div2 <- factoextra::fviz_cluster(k2_lihtc_div2, geom = "point", data = svi_divisional_lihtc_cluster2) + ggtitle("k = 2")

p_k3_lihtc_div2 <- factoextra::fviz_cluster(k3_lihtc_div2, geom = "point", data = svi_divisional_lihtc_cluster2) + ggtitle("k = 3")

p_k4_lihtc_div2 <- factoextra::fviz_cluster(k4_lihtc_div2, geom = "point",  data = svi_divisional_lihtc_cluster2) + ggtitle("k = 4")

p_k5_lihtc_div2 <- factoextra::fviz_cluster(k5_lihtc_div2, geom = "point",  data = svi_divisional_lihtc_cluster2) + ggtitle("k = 5")

grid.arrange(p_k2_lihtc_div2, p_k3_lihtc_div2, p_k4_lihtc_div2, p_k5_lihtc_div2, nrow = 2)


elbow_plot(svi_divisional_lihtc_cluster2)

##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
## [1] 11
## [1] 12
## [1] 13
## [1] 14
## [1] 15

Spacing decreases after 2 clusters, so we’ll add 1 cluster to account for LAC in its own group. We can see that k = 2 in svi_divisional_lihtc_cluster2 is very similar to k = 3 in svi_divisional_lihtc_cluster. So k3_lihtc_div will serve as the best cluster matrix.

grid.arrange(p_k2_lihtc_div2, p_k3_lihtc_div, nrow = 1)


p_k3_lihtc_div


svi_divisional_lihtc_cluster_label <- as.data.frame(svi_divisional_lihtc_cluster) %>%
                                  rownames_to_column(var = "county_name") %>%
                                  as_tibble() %>%
                                  mutate(cluster = k3_lihtc_div$cluster) %>%
                                  select(county_name, cluster)

svi_divisional_county_lihtc_projects2 <- left_join(svi_divisional_county_lihtc_projects, svi_divisional_lihtc_cluster_label, join_by(county_name == county_name))

# View county counts in each cluster
table(svi_divisional_county_lihtc_projects2$cluster)

## 
##  1  2  3 
##  1 21  6

Cluster 1

# Cluster 1 Scatterplot
# y is our independent variable (LIHTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_divisional_county_lihtc_projects2 %>% 
  filter(cluster == 1) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_lihtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 


svi_divisional_county_lihtc_projects2 %>% 
  filter(cluster == 1) %>%
  select(flag_count10,post10_lihtc_project_dollars) %>%
  cor(method = "pearson")

##                              flag_count10 post10_lihtc_project_dollars
## flag_count10                           NA                           NA
## post10_lihtc_project_dollars           NA                           NA

Cluster 1 is made up only of LAC, so there’s no correlation to report.

Cluster 2

# Cluster 2 Scatterplot
# y is our independent variable (LIHTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_divisional_county_lihtc_projects2 %>% 
  filter(cluster == 2) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_lihtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 


svi_divisional_county_lihtc_projects2 %>% 
  filter(cluster == 2) %>%
  select(flag_count10,post10_lihtc_project_dollars) %>%
  cor(method = "pearson")

##                              flag_count10 post10_lihtc_project_dollars
## flag_count10                    1.0000000                    0.3664188
## post10_lihtc_project_dollars    0.3664188                    1.0000000

Cluster 2 has a weak, positive association.

Cluster 3

# Cluster 3 Scatterplot
# y is our independent variable (LIHTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_divisional_county_lihtc_projects2 %>% 
  filter(cluster == 3) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_lihtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 


svi_divisional_county_lihtc_projects2 %>% 
  filter(cluster == 3) %>%
  select(flag_count10,post10_lihtc_project_dollars) %>%
  cor(method = "pearson")

##                              flag_count10 post10_lihtc_project_dollars
## flag_count10                    1.0000000                    0.4555345
## post10_lihtc_project_dollars    0.4555345                    1.0000000

Cluster 3 has a moderate, positive association.

Bivariate Map

# Join our LIHTC projects data with our shapefile geocoordinates
svi_divisional_county_lihtc_sf <- left_join(svi_divisional_county_lihtc_projects, divisional_county_sf, join_by("FIPS_st" == "STATEFP", "FIPS_county" == "COUNTYFP"))

svi_divisional_county_lihtc_sf %>% head(5) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
State County Division post10_lihtc_project_cnt tract_cnt post10_lihtc_project_dollars post10_lihtc_dollars_formatted fips_county_st FIPS_st FIPS_county state_name region_number region division_number flag_count10 pop10 flag_by_pop10 flag_count_quantile10 flag_pop_quantile10 flag_count20 pop20 flag_by_pop20 flag_count_quantile20 flag_pop_quantile20 county_name geometry
CA Alameda County Pacific Division 1 23 1590984 \$1,590,984 06001 06 001 California 4 West Region 9 186 92323 0.0020147 1.0 0.6 170 101788 0.0016701 1.0 0.6 Alameda County, CA MULTIPOLYGON (((-2266160 34…
CA Butte County Pacific Division 1 3 551007 \$551,007 06007 06 007 California 4 West Region 9 17 12025 0.0014137 0.6 0.2 19 13034 0.0014577 0.6 0.4 Butte County, CA MULTIPOLYGON (((-2174433 56…
CA Fresno County Pacific Division 4 8 2383558 \$2,383,558 06019 06 019 California 4 West Region 9 94 36454 0.0025786 1.0 0.8 92 34872 0.0026382 1.0 1.0 Fresno County, CA MULTIPOLYGON (((-2051818 14…
CA Humboldt County Pacific Division 1 2 1221303 \$1,221,303 06023 06 023 California 4 West Region 9 12 8644 0.0013882 0.4 0.2 15 8698 0.0017245 0.6 0.6 Humboldt County, CA MULTIPOLYGON (((-2312278 74…
CA Kern County Pacific Division 8 11 3289492 \$3,289,492 06029 06 029 California 4 West Region 9 118 54631 0.0021599 1.0 0.6 116 54102 0.0021441 1.0 0.8 Kern County, CA MULTIPOLYGON (((-2050419 59… 
# Create classes for bivariate mapping 
svi_divisional_county_lihtc_sf <- bi_class(svi_divisional_county_lihtc_sf, x = flag_count10, y = post10_lihtc_project_dollars, style = "quantile", dim = 3)

# View data
svi_divisional_county_lihtc_sf %>% head(5) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
State County Division post10_lihtc_project_cnt tract_cnt post10_lihtc_project_dollars post10_lihtc_dollars_formatted fips_county_st FIPS_st FIPS_county state_name region_number region division_number flag_count10 pop10 flag_by_pop10 flag_count_quantile10 flag_pop_quantile10 flag_count20 pop20 flag_by_pop20 flag_count_quantile20 flag_pop_quantile20 county_name geometry bi_class
CA Alameda County Pacific Division 1 23 1590984 \$1,590,984 06001 06 001 California 4 West Region 9 186 92323 0.0020147 1.0 0.6 170 101788 0.0016701 1.0 0.6 Alameda County, CA MULTIPOLYGON (((-2266160 34… 3-2
CA Butte County Pacific Division 1 3 551007 \$551,007 06007 06 007 California 4 West Region 9 17 12025 0.0014137 0.6 0.2 19 13034 0.0014577 0.6 0.4 Butte County, CA MULTIPOLYGON (((-2174433 56… 2-1
CA Fresno County Pacific Division 4 8 2383558 \$2,383,558 06019 06 019 California 4 West Region 9 94 36454 0.0025786 1.0 0.8 92 34872 0.0026382 1.0 1.0 Fresno County, CA MULTIPOLYGON (((-2051818 14… 2-2
CA Humboldt County Pacific Division 1 2 1221303 \$1,221,303 06023 06 023 California 4 West Region 9 12 8644 0.0013882 0.4 0.2 15 8698 0.0017245 0.6 0.6 Humboldt County, CA MULTIPOLYGON (((-2312278 74… 1-1
CA Kern County Pacific Division 8 11 3289492 \$3,289,492 06029 06 029 California 4 West Region 9 118 54631 0.0021599 1.0 0.6 116 54102 0.0021441 1.0 0.8 Kern County, CA MULTIPOLYGON (((-2050419 59… 2-2 
# Create map with ggplot
svi_divisional_county_lihtc_map <- ggplot() +
  # Map county shapefile, fill with bi_class categories
  geom_sf(data = svi_divisional_county_lihtc_sf, mapping = aes(geometry=geometry, fill = bi_class), color = "white", size = 0.1, show.legend = FALSE) +
  # Set to biscale palette
  bi_scale_fill(pal = "GrPink", dim = 3) +
  # Add state shapefiles for outline
  geom_sf(data=divisional_st_sf, color="black", fill=NA, linewidth=.5, aes(geometry=geometry)) +
  labs(
    title = paste0("Correlation of 2010 ", census_division, " SVI Flag Count \n and 2011 - 2020 LIHTC Tax Dollars")
  ) +
  # Set them to biscale
  bi_theme(base_size = 10)

# Create biscale legend
svi_divisional_county_lihtc_legend <- bi_legend(pal = "GrPink",
                    dim = 3,
                    xlab = "SVI Flag Count",
                    ylab = "LIHTC Dollars",
                    size = 8)

# Combine map with legend using cowplot
svi_divisional_county_lihtc_bivarmap <- ggdraw() +
  draw_plot(svi_divisional_county_lihtc_map) +
  # Set legend location
  draw_plot(svi_divisional_county_lihtc_legend, x= -.02,  y = -.05,
 width=.20)


# View map
svi_divisional_county_lihtc_bivarmap

Oregon, Hawaii, and Alaska have few counties included in this dataset. California has the most counties represented in the LIHTC data for the Pacific Division. Interestingly, most counties with high LIHTC project award totals and high SVI flags are grouped in southern California.

saveRDS(svi_divisional_lihtc, file = here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_divisional_lihtc.rds")))

saveRDS(svi_national_lihtc, file = here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_national_lihtc.rds")))

saveRDS(svi_divisional_nmtc, file = here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_divisional_nmtc.rds")))

saveRDS(svi_national_nmtc, file = here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_national_nmtc.rds")))

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