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+83401,0, +99586,0, +99683,0, +50563,0, +50467,0, +57551,0, +59483,0, +71935,0, +69030,0, +49871,0, +69034,0, +58210,0, +67739,0, +88034,0, +67864,0, +69131,0, +99564,0, +67846,0, +69101,0, +67446,0, +59477,0, +49952,0, +69336,0, +57572,0, +39459,0, +57661,0, +59464,0, +65583,0, +58440,0, +58443,0, +57465,0, +56735,0, +4779,0, +68961,0, +56258,0, +69217,0, +82638,0, +56264,0, +68722,0, +983,0, +38925,0, diff --git a/code/EconomicVar_Tract_2010_ACS.R b/code/EconomicVar_Tract_2010_ACS.R new file mode 100644 index 00000000..7ac1757d --- /dev/null +++ b/code/EconomicVar_Tract_2010_ACS.R @@ -0,0 +1,60 @@ +# Author: Ashlynn Wimer +# Date: 07/28/23 +# About: This script uses tidycensus to grab tract level economic Decennial Census +# and ACS data. + + +# Libraries +library(tidycensus) +library(tidyr) +library(dplyr) +library(purrr) + +# Get a list of all relevant states' FIPS codes. +states_fips <- tigris::fips_codes |> + filter(!state_code %in% c('60', '72', '66', '69', '78', '74')) |> + select(state_code) |> unique() + +states_fips <- states_fips$state_code + +# Acquire data +econ2010 <- map_dfr(.x = states_fips, ~ get_acs(geography = 'tract', + variables = c( + NMMLFU = 'B12006_006', NMMLF = 'B12006_004', + NMFLFU = 'B12006_011', NMFLF = 'B12006_009', + MMLFU = 'B12006_017', MMLF = 'B12006_015', + MFLFU = 'B12006_022', MFLF = 'B12006_020', + SMLFU = 'B12006_028', SMLF = 'B12006_026', + SFLFU = 'B12006_033', SFLF = 'B12006_031', + WMLFU = 'B12006_039', WMLF = 'B12006_037', + WFLFU = 'B12006_044', WFLF = 'B12006_042', + DMLFU = 'B12006_050', DMLF = 'B12006_048', + DFLFU = 'B12006_055', DFLF = 'B12006_053', + pci = 'B19301_001', cntPov = 'B06012_002', + cntPovUni = 'B06012_001'), + year = 2010, state=.x)) + +# Clean the data +econ2010 <- econ2010 |> + select(GEOID, variable, estimate) |> + spread("variable", "estimate") |> + mutate(unempP = round(100 * (NMMLFU + NMFLFU + MMLFU + + MFLFU + SMLFU + SFLFU + + WMLFU + WFLFU + DMLFU + + DFLFU) / + (NMMLFU + NMMLF + NMFLFU + NMFLF + + SMLFU + SMLF + SFLFU + SFLF + + MMLFU + MMLF + MFLFU + MFLF + + WMLFU + WMLF + WFLFU + WFLF + + DMLFU + DFLF + DFLFU + DFLF), + 2), + povP = round(100 * cntPov / cntPovUni, 2) + )|> + select(GEOID, unempP, povP, pciE = pci) + +# Final cleaning +econ2010 <- econ2010 |> + map_df(~ ifelse(is.nan(.x), NA, .x)) + +# Save +write.csv(econ2010, "../data_final/EC03_2010_T_ACS.csv", row.names = FALSE) diff --git a/code/access_OTP.R b/code/access_OTP.R new file mode 100644 index 00000000..3219f38f --- /dev/null +++ b/code/access_OTP.R @@ -0,0 +1,170 @@ +# Author: Ashlynn Wimer +# Date: July 17th, 2023 +# About: This script prepares the nearest distance metrics to Opioid Treatment +# Programs at the tract and zip level, merges those metrics into the drive time +# metrics calculated in a separate script, cleans the files, and then saves them. + +# It is essentially just a modification of access_mentalhealth.R, a script +# written by Susan Paykin in January 2021. + +# Libraries # +library(dplyr) +library(stringr) +library(sf) +library(units) + +# Read in point dataset +opioid_treatment_progs <- read.csv("Access Metrics - Health Resources/Tract/Driving/OpioidTreatmentProgram_Geocoded.csv") + +# Convert to spatial data +otp.sf <- st_as_sf(opioid_treatment_progs, + coords = c('Longitude', 'Latitude'), + crs = 4326) + +### Nearest Resource Analysis + +# Read in location Data +zips <- read_sf("../data_final/geometryFiles/tl_2018_zcta/zctas2018.shp") +tracts <- read_sf("../data_final/geometryFiles/tl_2018_tract/tracts2018.shp") + +zips <- zips |> st_transform(3857) +tracts <- tracts |> st_transform(3857) +otp.sf <- otp.sf |> st_transform(3857) + +### Nearest OTP - ZCTAs ---- + +# Create centroids for ZCTAs +zipCentroids <- st_centroid(zips) + +# Identify the OTP that is nearest to the zip centroid +nearestOTP_index <- st_nearest_feature(zipCentroids, otp.sf) +nearestOTP <- otp.sf[nearestOTP_index,] + +# Calculate distance +minDistZipsOTP <- st_distance(zipCentroids, nearestOTP, by_element = TRUE) +head(minDistZipsOTP) + +# Convert to miles +minDistZipsOTP_mi <- set_units(minDistZipsOTP, "mi") + +# Merge data - everything is ordered so cbind works. +minDistZipsOTP_sf <- cbind(zips, minDistZipsOTP_mi) +head(minDistZipsOTP_sf) + +# Clean up +minDistZipsOTP_sf <- minDistZipsOTP_sf |> + select(ZCTA = ZCTA5CE10, + minDisOTP = minDistZipsOTP_mi) |> + mutate(ZCTA = str_pad(ZCTA, width=5, side='left', pad='0')) |> + st_drop_geometry() + +### Nearest OTP - Tracts ---- + +# Create centroids for tracts +tractCentroids <- st_centroid(tracts) + +# Identify the OTP that is nearest to the tract centroid +nearestOTP_index <- st_nearest_feature(tractCentroids, otp.sf) +nearestOTP <- otp.sf[nearestOTP_index,] + +# Calculate distance +minDistTractsOTP <- st_distance(tractCentroids, nearestOTP, by_element = TRUE) +head(minDistTractsOTP) + +# Convert units to miles. +minDistTractsOTP_mi <- set_units(minDistTractsOTP, "mi") + +# Merge data - everything is ordered so cbind works. +minDistTractsOTP_sf <- cbind(tracts, minDistTractsOTP_mi) +head(minDistTractsOTP_sf) + +# Clean up data +minDistTractsOTP_sf <- minDistTractsOTP_sf |> + select(GEOID, STATEFP, COUNTYFP, + TRACTCE, minDisOTP = minDistTractsOTP_mi) |> + mutate(GEOID = str_pad(GEOID, width=11, side='left', pad='0')) |> + st_drop_geometry() + +### Merge travel access metrics -- Tracts ##### + +# Read in driving times, rename variables and clean GEOID +tract_drive <- read.csv('Access Metrics - Health Resources/Tract/Driving/opioidtreatmentprogram_drive_tract.csv') |> + rename( + GEOID = origin, + countDrive = count.within.30, + timeDrive = minutes + ) |> + mutate(GEOID = str_pad(GEOID, width=11, side='left', pad='0')) +head(tract_drive) + +# Merge +otp_accessT <- left_join(tract_drive, minDistTractsOTP_sf, by='GEOID') +head(otp_accessT) + +otp_accessT <- otp_accessT |> + select(GEOID, minDisOTP, timeDrive, countDrive) +head(otp_accessT) + +# Save file +write.csv(otp_accessT, "../data_final/Access07_T.csv", row.names=FALSE) + +##### Merge travel access metrics - Zip Code ##### + +# Read in driving metrics, rename variables, fix ZCTAs +otp_drive <- read.csv('Access Metrics - Health Resources/ZIP Code/Driving/opioidtreatmentprogram_drive_zip.csv') |> + rename(ZCTA = origin, + countDrive = count.within.30, + timeDrive = minutes) |> + mutate(ZCTA = str_pad(ZCTA, width=5, side='left', pad='0')) + +# Merge +otp_accessZ <- left_join(otp_drive, minDistZipsOTP_sf, by='ZCTA') +head(otp_accessZ) + +otp_accessZ <- otp_accessZ |> + select(ZCTA, minDisOTP, countDrive, timeDrive) + +head(otp_accessZ) + +# Save file +write.csv(otp_accessZ, "../data_final/Access07_Z.csv", row.names=FALSE) + +##### County Access ##### + +tract_OTP <- read.csv("../data_final/Access07_T.csv") + +tract_OTP$GEOID <- str_pad(tract_OTP$GEOID, width=11, side='left', pad = '0') + +tract_OTP$COUNTYFP <- substr(tract_OTP$GEOID, 1, 5) + +county_OTP <- tract_OTP |> + group_by(COUNTYFP) |> + summarise(cntT = n(), + cntTimeDrive = sum(timeDrive <= 30), + avTimeDrive = round(mean(timeDrive), 2) + ) |> + mutate(pctTimeDrive = round(cntTimeDrive / cntT, 2)) + +head(county_OTP) + +##### State Access ##### + +tract_OTP$STATEFP <- substr(tract_OTP$GEOID, 1, 2) + +state_OTP <- tract_OTP |> + group_by(STATEFP) |> + summarise(cntT = n(), + cntTimeDrive = sum(timeDrive <= 30, na.rm = TRUE), + avTimeDrive = round(mean(timeDrive, na.rm=TRUE), 2) + ) |> + mutate(pctTimeDrive = round(cntTimeDrive / cntT, 2)) + +head(state_OTP) + +### Save state and county files + +# County +write.csv(county_OTP, "../data_final/Access07_C.csv", row.names=FALSE) + +# State +write.csv(state_OTP, '../data_final/Access07_S.csv', row.names=FALSE) diff --git a/data_final/Access07_C.csv b/data_final/Access07_C.csv new file mode 100644 index 00000000..6fb868f9 --- /dev/null +++ b/data_final/Access07_C.csv @@ -0,0 +1,3235 @@ +"COUNTYFP","cntT","cntTimeDrive","avTimeDrive","pctTimeDrive" +"01001",12,1,39.64,0.08 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