For this assignment I chose the article The Lasting Legacy Of Redlining published on February 9th, 2022 by Ryan Best and Eliana Mejia to see how past practices based racial demographics are still affecting the neighborhoods even 50 years after the the practice was banned. In this article, the arthors compared data from Home Owners’ Loan Corporation from 1935-1940 of redlined cities to the demographics information collected on the 2020 census in the same cities to understand if the racial demographic have changed since banning of redline practice. Unfortunately, most redline neighborhoods are still deeply segregated.
To understand how the HOLC was determining the grade I looked at the data for Pittsburgh, PA and tried to compare percentage of each race to the surrounding area. I rearranged the column surr_area_pct_white and surr_area_pct_black to be in front of the column pct_white so that I can see the percentages side by side from the source file metro-grades.csv and displayed the result with below R code.
pittsburgh_grade <- metro_grade |>
filter(metro_area == "Pittsburgh, PA") |>
relocate(surr_area_pct_white:surr_area_pct_black, .before = pct_white) |>
select(metro_area:holc_grade, surr_area_pct_white:pct_black)
pittsburgh_grade## # A tibble: 4 × 6
## metro_area holc_grade surr_area_pct_white surr_area_pct_black pct_white
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Pittsburgh, PA A 74.9 12.9 79.3
## 2 Pittsburgh, PA B 74.9 12.9 71.5
## 3 Pittsburgh, PA C 74.9 12.9 62.8
## 4 Pittsburgh, PA D 74.9 12.9 53.1
## # ℹ 1 more variable: pct_black <dbl>Through this example, I can see that the location quotient was calculated by dividing percentage of race population by the surrounding race population. This shows that compare to the surrounding area, each demographic was more or less represented in the HOLC graded areas. Looking at the data with only HoLC grade A and D with the location quotient of each race we can see that most places with a grade A has lq_white closer to 1 or more.
grade_lq <- metro_grade |>
filter(holc_grade %in% c("A","D") ) |>
select(metro_area, holc_grade,lq_white:lq_other)
grade_lq## # A tibble: 276 × 7
## metro_area holc_grade lq_white lq_black lq_hisp lq_asian lq_other
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Akron, OH A 0.94 1.41 1 0.46 0.97
## 2 Akron, OH D 0.57 2.76 1.45 0.74 1.21
## 3 Albany-Schenectady-Tr… A 1.09 0.66 0.77 1.21 0.72
## 4 Albany-Schenectady-Tr… D 0.51 3.35 1.83 0.62 1.26
## 5 Allentown-Bethlehem-E… A 1.1 0.69 0.87 0.43 1.22
## 6 Allentown-Bethlehem-E… D 0.93 0.92 1.21 0.54 1.19
## 7 Altoona, PA A 1.03 0.08 2.09 1.18 0.78
## 8 Altoona, PA D 0.97 1.61 0.9 0.73 1.18
## 9 Amarillo, TX A 1.62 0.38 0.46 0.62 1.57
## 10 Amarillo, TX D 0.52 1.07 1.53 0.74 0.77
## # ℹ 266 more rows