Historic redlining and biodiveristy observations in Los Angeles County

Environmental Justice in LA: Lasting impacts of redlining

1939 redlining map of LA from PBS SoCal

Background

The United States has a long history of systemic racism that continues to permeate society in both obvious and hidden ways. One of the many consequences of this is the fact that poor or marginalized communities are exposed to and harmed by air pollution, hazardous waste, resource extraction, and more at a disproportionate amount. The environmental justice movement was born from this and has been fighting for community protection, enforcement of environmental regulations and policies, and exposing this reality.

One historical reason for the present day conditions can be traced back to the 1930s. As part of the New Deal, the Home Owner’s Loan Corporation (HOLC) rated neighborhoods based on their perceived safety for real estate investment. Their ranking system, (A (green), B (blue), C (yellow), D (red)) was then used to block access to loans for home ownership. As you might guess, racism played a leading role in the designation of grades and led to the phenomenon colloquially known as “redlining.” This practice has had widely-documented consequences on community wealth, environment, and health. ¹ Redlined neighborhoods have less greenery² and are hotter than other neighborhoods.³ Lee and co-authors, in a meta-analysis, found that “gunshot-related injuries, asthma, heat-related outcomes, and multiple chronic conditions were worse in redlined areas.”⁴

Check out coverage by the New York Times.

A recent study found that redlining has not only affected the environments and health hazards communities are exposed to, it has also shaped our observations of biodiversity.⁵ Community or citizen science, whereby individuals share observations of species on their own time, is generating an enormous volume of data. In recent years, apps such as iNaturalist have enabled more people to identify and report various species of plants and animals. This increase, however, has also highlighted areas of missingness–where individuals do not, or cannot, participate in citizen science for various reasons. Ellis-Soto and co-authors found that redlined neighborhoods remain the most undersampled areas across 195 US cities. This gap is highly concerning, because conservation decisions and policies are made based on these data. If impactful decisions are being made on inaccurate data, this could lead to actions that continue to harm poor and marginalized communities.

Check out coverage by EOS.

Analysis

What lasting impacts does redlining have on the county of Los Angeles? Now that we know that bird biodiversity data tend to be undersampled in redlined neighborhoods, is this true in LA?

Let’s answer these questions by:

• Using EJScreen data to map current environmental and demographic indicators in LA
• Comparing current conditions across census block groups in LA that were historically redlined
• Comparing percent of reported bird observations across areas in LA that were historically redlined

Data Descriptions

Three datasets will be used to answer our questions: US EPA EJScreen data, digitized HOLC maps, and bird biodiveristy observations in LA.

EJScreen

The United States Environmental Protection Agency’s Environmental Justice Screening and Mapping Tool is an effort to provide national data for advancing their goals and supporting others interested in environmental justice.

According to the US EPA website:

This screening tool and data may be of interest to community residents or other stakeholders as they search for environmental or demographic information. It can also support a wide range of research and policy goals. The public has used EJScreen in many different locations and in many different ways.

EPA is sharing EJScreen with the public:
- to be more transparent about how we consider environmental justice in our work,
- to assist our stakeholders in making informed decisions about pursuing environmental justice and,
- to create a common starting point between the agency and the public when looking at issues related to environmental justice.

EJScreen provides environmental and demographic information for the US at the Census tract and block group levels. Block group data has been downloaded, and stored locally, from the EPA site.

Mapping Inequality

A team of researchers, led by the Digital Scholarship Lab at the University of Richmond have digitized maps and information from the HOLC as part of the Mapping Inequality project.

This analysis uses maps of HOLC grade designations for Los Angeles via URL. Information on the data can be found here.⁶

Biodiversity observations

The Global Biodiversity Information Facility is the largest aggregator of biodiversity observations in the world. Observations typically include a location and date that a species was observed. This analysis will use bird observations in 2022.

Legacy of Redlining in present-day LA

Wastewater Discharge

The wastewater discharge indicator in EJScreen assigns a score to each census block group based on the pollution and toxicity of nearby streams and rivers. LA has limited access to local clean water and often deals with extremely polluted water, especially after a storm. Let’s visualize the wastewater discharge indicator for LA census block groups and add points for block groups that are above the 95th percentile of national values for wastewater discharge. Feel free to zoom in on the map to gain a better understanding of where the highest wastewater discharge levels are in the county.

Code

# map of LA
# color census block groups by wastewater discharge
# add centroid for block groups above 95th percentile

# filter la dataset for block groups above 95th percentile for wastewater dischare
top_percentile <- la_county %>% 
  filter(P_PWDIS > 95)

# make centroids for block groups above 95th percentile
top_percentile_centroids <- st_centroid(top_percentile)

# map LA county and color census blocks by waste water discharge
m1 <- ggplot() +
  geom_sf(data = la_county, aes(fill = PWDIS),
          lwd = 0.1,
          color = 'gray') +
  scale_fill_viridis_c(direction = -1) +
  # add above 95th percentile centroids
  geom_sf(data = top_percentile_centroids,
          color = 'magenta') +
  labs(x = "Longtitude",
       y = "Latitude",
       fill = "Wastewater discharge") +
  # choose x axis breaks so longitude is readable
  scale_x_continuous(breaks = c(-119, -118.6, -118.2, -117.8)) +
  theme_bw()

# make interactive to better see where the centroids are
ggplotly(m1)

Redlining impacts in numbers

Now let’s use EJScreen data to investigate demographic (% low income) and environmental (PM 2.5 and Superfund proximity) indicators.

Code

high_inc <- la_county %>% 
  # filter to block groups where % of low income is less than 0.05
  filter(LOWINCPCT < 0.05) %>% 
  # take the resulting number of rows and divide by the total number of block groups
  # multiply by 100 to get percent value
  nrow()/nrow(la_county) * 100

print(paste(round(high_inc,2), "% of LA census block groups have less than 5% of the population considered low income"))

[1] "6.11 % of LA census block groups have less than 5% of the population considered low income"

That means that the majority of LA census block groups have over 5% of the population considered as low income.

Code

# percent of census block groups that are above 80th percentile for both PM 2.5 and Superfund proximity
pm_sf <- la_county %>% 
  filter(P_PM25 > 80 & P_PNPL > 80) %>% 
  nrow() / nrow(la_county) * 100

print(paste(round(pm_sf,2), "% of LA census block groups are above the 80th percentile for both PM 2.5 and Superfund proximity"))

[1] "17.36 % of LA census block groups are above the 80th percentile for both PM 2.5 and Superfund proximity"

Over 1/6 of LA census block groups are excessively exposed to unhealthy air quality and hazardous waste.

LA redlining

How do these stats compare in areas that were historically redlined?

Let’s take a look at these areas and their grades first:

Code

# plot redlining areas, color by HOLC Grade
m2 <- ggplot() +
  geom_sf(data = LA_redlining, aes(fill = grade)) +
  scale_fill_discrete(type = c("green", "blue", "yellow", "red", "grey")) +
  labs(x = "Longtitude",
       y = "Latitude",
       fill = "HOLC Grade") +
  theme_bw()

m2

Interestingly, the C and D grades make up the majority of the center of the county, while most of the A and B grades are at the northern part of the county. Let’s investigate the current conditions in these areas. First up, figure out how many current census block groups fall within the designated areas above.

Code

# transfrom LA_redlining to same crs as la_county
LA_redlining3857 <- st_transform(LA_redlining, st_crs(la_county))

# block groups that have intersecting geometries as LA_redlining HOLC grades
# left = FALSE for an inner join
block_groups_holc_join <- st_join(x = la_county, y = LA_redlining3857, left = FALSE)
holc_block_grps <- nrow(block_groups_holc_join)

print(paste(holc_block_grps, "census block groups in LA fall within areas with HOLC grades"))

[1] "6388 census block groups in LA fall within areas with HOLC grades"

LA county has a total of 6591 block groups, so this means that the majority of block groups fall within areas that were historically graded by the HOLC. When we compare current conditions (environmental and demographic) in LA block groups by the grade they were historically assigned, we get this result:

Code

# summary table of means grouped by grade
t1 <- block_groups_holc_join %>%
  st_drop_geometry() %>% 
  group_by(grade) %>% 
  summarise(low_inc_pct_avg = mean(LOWINCPCT, na.rm = TRUE),
            p_pm25_avg = mean(P_PM25, na.rm = TRUE),
            p_low_life_exp_avg = mean(P_LIFEEXPPCT, na.rm = TRUE),
            p_cancer_avg = mean(P_CANCER, na.rm = TRUE)) %>% 
  gt() %>% 
  tab_header(title = "Current EJ Conditions in LA by HOLC grade")

t1

Current EJ Conditions in LA by HOLC grade
grade	low_inc_pct_avg	p_pm25_avg	p_low_life_exp_avg	p_cancer_avg
A	0.1497511	72.16036	23.75982	44.00670
B	0.2420120	76.33898	37.42025	47.68659
C	0.3408981	78.81884	47.88017	54.55468
D	0.3919059	80.23700	53.03621	56.57589
NA	0.3581823	76.72973	50.12409	41.49324

Based on the averages calculated, the HOLC grade for a census block group in LA appears to be indicative of the percent of the population that is low income, as well as the percentiles they fall within for environmental justice indicators. Groups with a grade of “A” had the lowest average percent of low income (15%), and had an average of the lowest percentiles, comparatively, for PM 2.5, low life expectancy, and air toxics cancer risk. In comparison, areas with a grade of “D” had the highest average percent of low income (39%) and had the highest average percentiles for PM 2.5, low life expectancy, and air toxics cancer risk.

Investigate the legacy of redlining in biodiversity observations

Let’s add bird observations in LA from the year 2022 to our analysis. We want to know if the redlined neighborhoods in LA are undersampled in terms of bird observations. Calculating the percent of total observations in each HOLC grade should get us to an answer.

Code

# plot % of bird observations by HOLC
ggplot(data = holc_birds_pct) +
  geom_col(aes(x = grade, y = bird_pct_obs, fill = grade),
           show.legend = FALSE) +
  scale_fill_discrete(type = c("green", "blue", "yellow", "red", "grey")) +
  labs(x = "HOLC Grade",
       y = "% Bird Observations",
       title = "Bird Observations in Redlining Categories") +
  theme_bw()

Based on the findings of Ellis-Soto and co-authors, we would expect to see high percentages in A and B areas and low percentages in C and D areas. Surprisingly, the largest share of bird observations are in areas that were historically graded a C, the second largest share in areas historically graded a D, or historically redlined areas of Los Angeles. This could potentially be a result of LA’s population skyrocketing since 1933 and consequently leading to gentrification of redlined neighborhoods.

Conclusion

Redlining has lasting impacts on environmental and demographic indicators in LA county census block groups. When comparing across grades (A to D), we saw percent of low income increase as well as the percentiles for low life expectancy and exposure to PM 2.5 and air toxics with cancer risk. Therefore, in line with the research, health and quality of life decrease in historically redlined areas. However, we surprisingly found that these areas accounted for a high proportion of bird biodiversity observations, in contrast with the findings of Ellis-Soto and co-authors. This may be for a multitude of reasons that are not yet apparent. The gentrification of redlined neighborhoods is definitely a possibility.

Footnotes

1. Gee, G. C. (2008). A multilevel analysis of the relationship between institutional and individual racial discrimination and health status. American journal of public health, 98(Supplement_1), S48-S56.

2. Nardone, A., Rudolph, K. E., Morello-Frosch, R., & Casey, J. A. (2021). Redlines and greenspace: the relationship between historical redlining and 2010 greenspace across the United States. Environmental health perspectives, 129(1), 017006.

3. Hoffman, J. S., Shandas, V., & Pendleton, N. (2020). The effects of historical housing policies on resident exposure to intra-urban heat: a study of 108 US urban areas. Climate, 8(1), 12.

4. Lee E. K., Donley G., Ciesielski T. H., Gill I., Yamoah O., Roche A., Martinez R., Freedman D. A. (2022). Health outcomes in redlined versus non-redlined neighborhoods: A systematic review and meta-analysis. Soc Sci Med, 294, 114696. doi: 10.1016/j.socscimed.2021.114696.

5. Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. Robert K. Nelson and Edward L. Ayers, accessed October 17, 2023, https://dsl.richmond.edu/panorama/redlining/

6. Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. Robert K. Nelson and Edward L. Ayers, accessed October 17, 2023, https://dsl.richmond.edu/panorama/redlining/

Citation

BibTeX citation:

@online{herbst2023,
  author = {Herbst, Amanda},
  title = {Historic Redlining and Biodiveristy Observations in {Los}
    {Angeles} {County}},
  date = {2023-12-15},
  url = {https://amandaherbst.github.io/blog/2023-12-15-eds223-la-redlining-biodiversity-obs/},
  langid = {en}
}

For attribution, please cite this work as:

Herbst, Amanda. 2023. “Historic Redlining and Biodiveristy Observations in Los Angeles County.” December 15, 2023. https://amandaherbst.github.io/blog/2023-12-15-eds223-la-redlining-biodiversity-obs/.