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process_indicators.R
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process_indicators.R
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# code base to run individual environmental indicator functions
# set up environment -----------------
source("setup.R")
# read in processed prison polygons
prisons <- read_sf("data/processed/prisons/study_prisons.shp")
# run individual indicator functions -------------------
# flood risk (takes 1-2 days to run)
flood_risk <- calc_flood_risk(sf_obj = prisons)
# wildfire risk
wildfire_risk <- calc_wildfire_risk(
sf_obj = prisons,
file = "data/raw/wildfire_risk/whp2020_GeoTIF/"
)
# ozone
ozone <- calc_ozone(
sf_obj = prisons,
folder = "data/raw/air_quality/o3_daily/",
dist = 1000, years = c(2014, 2016)
)
# pm2.5 (may change dataset, check for more recent years)
pm25 <-
calc_pm25(
sf_obj = prisons,
folder = "data/raw/air_quality/pm2.5_sedac/",
dist = 1000,
years = c(2017, 2019),
save = FALSE
)
# pesticides
pesticides <- calc_pesticides(prisons,
folder = "data/raw/pesticides/ferman-v1-pest-chemgrids-v1-01-geotiff",
dist = 1000,
save = TRUE)
# traffic proximity (takes 1.5 days to run on Desktop comp)
traffic_prox <- calc_traffic_proximity(
sf_obj = prisons,
file = "data/processed/traffic_proximity/aadt_2018.RData"
)
# calculate Risk Management Plan (RMP) facility proximity
rmp_prox <- calc_rmp_proximity(
sf_obj = prisons,
file = "data/raw/EPA_RMP/EPA_Emergency_Response_(ER)_Risk_Management_Plan_(RMP)_Facilities.csv"
)
# calculate NPL facility proximity
npl_prox <- calc_npl_proximity(
sf_obj = prisons,
file = "data/processed/npl_addresses_geocoded_arc_sf.csv"
)
# calculate Haz waste facility proximity
haz_prox <- calc_haz_waste_proximity(
sf_obj = prisons,
file = "data/processed/hazardous_waste/TSD_LQGs.csv"
)
# component score calculation (pulling in files saved above) ---------------
## climate scores -------------
flood_risk <- read_csv("outputs/flood_risk_2023-03-10.csv") %>%
select(FACILITYID, flood_risk = flood_risk_percent)
wildfire_risk <- read_csv("outputs/wildfire_risk_2023-08-29.csv") %>%
select(-geometry)
heat_exp <- read_csv("data/processed/heat_exposure/lst_average.csv")
canopy_cover <- read_csv("data/processed/canopy_cover/prison_canopy_AK.csv") %>%
bind_rows(read_csv("data/processed/canopy_cover/prison_canopy_HI.csv")) %>%
bind_rows(read_csv("data/processed/canopy_cover/prison_canopy_CONUS.csv"))
climate_scores <- list(flood_risk, wildfire_risk, heat_exp, canopy_cover) %>%
# convert FACILITYID to character for all to make sure they join
purrr::map(~ .x %>% mutate(FACILITYID = as.character(FACILITYID))) %>%
purrr::reduce(left_join, by = "FACILITYID") %>%
# calculate percentile columns for each raw indicator
mutate(across(
where(is.numeric),
.fns = list(pcntl = ~ cume_dist(.) * 100),
.names = "{col}_{fn}"
)) %>%
# need to inverse canopy cover since high value is good
mutate(percent_tree_cover_pcntl = cume_dist(desc(percent_tree_cover)) * 100) %>%
rowwise() %>%
# calculate climate component score (average all indicator percentile values per prison
mutate(climate_score = gm_mean(c_across(contains("pcntl"))))
## env exposures scores ----------------
ozone <- read_csv("outputs/ozone_2023-08-15.csv")
pm25 <- read_csv("outputs/pm25_2023-08-10.csv")
pesticides <- read_csv("outputs/pesticides_2023-08-14.csv")
traffic <- read_csv("outputs/traffic_prox_2023-05-13.csv")
exposure_scores <- list(ozone, pm25, pesticides, traffic) %>%
# convert FACILITYID to character for all to make sure they join
purrr::map(~ .x %>% mutate(FACILITYID = as.character(FACILITYID))) %>%
purrr::reduce(left_join, by = "FACILITYID") %>%
# calculate percentile columns for each raw indicator
dplyr::mutate(across(
where(is.numeric),
.fns = list(pcntl = ~ cume_dist(.) * 100),
.names = "{col}_{fn}"
)) %>%
rowwise() %>%
# calculate climate component score (geometric mean of indicator percentiles)
mutate(exposure_score = gm_mean(c_across(contains("pcntl"))))
## env effects scores -------------------
npl <- read_csv("data/processed/npl_prox_2023-05-16.csv")
rmp <- read_csv("data/processed/rmp_prox_2023-05-16.csv")
haz <- read_csv("data/processed/haz_prox_2023-05-16.csv")
effects_scores <- list(npl, rmp, haz) %>%
purrr::reduce(left_join, by = "FACILITYID") %>%
# make facility ID character
mutate(FACILITYID = as.character(FACILITYID)) %>%
# calculate percentile columns for each raw indicator
dplyr::mutate(across(
where(is.numeric),
.fns = list(pcntl = ~ cume_dist(.) * 100),
.names = "{col}_{fn}"
)) %>%
rowwise() %>%
# calculate climate component score (average all indicator percentile values per prison
mutate(effects_score = gm_mean(c_across(contains("pcntl"))))
# final data frame ----------------------------
final_df <- list(climate_scores, exposure_scores, effects_scores) %>%
purrr::reduce(left_join, by = "FACILITYID") %>%
# remove rowwise
ungroup() %>%
mutate(
final_risk_score = rowMeans(select(., contains("score"))),
final_risk_score_pcntl = cume_dist(final_risk_score) * 100
)%>%
# join with original prison facility metadata
mutate(FACILITYID = as.character(FACILITYID)) %>%
left_join(prisons, by = "FACILITYID")
# save as shapefile and csv
final_df %>% st_as_sf() %>% st_write(paste0("outputs/final_df_", Sys.Date(), ".gpkg", driver="GPKG"))
final_df %>%
select(-geometry) %>%
write_csv(paste0("outputs/final_df_", Sys.Date(), ".csv"))