| Title: | Get Geomarker Data Based on ZIP Code |
|---|---|
| Description: | Get geomarker data for ZIP Codes in the contiguous United States. Available data includes racial composition; length and density of primary and secondary roads' land cover classifications including greenness, tree canopy, and imperviousness; measures of community deprivation; NARR cell identifier; and data from the EPA environmental justice index including traffic proximity, ozone concentration, PM2.5 concentration, concentration of PM from diesel traffic, and a respiratory hazard index. |
| Authors: | Erika Rasnick [aut, cre] |
| Maintainer: | Erika Rasnick <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2024-10-29 03:00:38 UTC |
| Source: | https://github.com/geomarker-io/zctaDB |
adds Average Annual Daily Traffic Density. AADT data is from 2017 and corresponds to 2010 ZCTA vintages.
add_aadt_data(data)add_aadt_data(data)
data |
data.frame or tibble with column called 'zcta' at minimum. |
the input data.frame with the following columns appended
moving_roads_density density of roads with moving traffic
(meters of road per square meter of area)
stop_go_roads_density density of roads with stop and go
traffic (meters of road per square meter of area)
moving_traffic_density moving traffic density (vehicle-meters
per square meter of area)
stop_go_traffic_density stop and go traffic density
(vehicle-meters per square meter of area)
moving_truck_density moving truck traffic density
(truck-meters per square meter of area)
stop_go_truck_density stop and go truck traffic
density (truck-meters per square meter of area)
https://degauss.org/aadt
my_data <- data.frame(zcta = c('45229', '45056', '47012')) add_aadt_data(data = my_data)my_data <- data.frame(zcta = c('45229', '45056', '47012')) add_aadt_data(data = my_data)
adds community deprivation index and related variables from the American Community Survey (ACS). Data is available for 2000 and 2010 ZCTAs and for years 2015 and 2018.
add_depindex_data(data)add_depindex_data(data)
data |
data.frame or tibble with column called 'zcta' at minimum. Optionally, another column called 'year', with possible values 2000 to 2019. If no year is given, defaults to 2018 |
the input data.frame with the following columns appended
zcta_year the ZCTA boundary year matched to the input year (2000 or 2010)
dep_year the ACS data year matched to the input year (2015 or 2018)
fraction_assisted_income fraction of households receiving public assistance income
or food stamps or SNAP in the past 12 months
fraction_high_school_edu fraction of population 25 and older with educational attainment
of at least high school graduation (includes GED equivalency)
median_income median household income in the past 12 months in 2015 inflation-adjusted dollars
fraction_no_health_ins fraction of poulation with no health insurance coverage
fraction_poverty fraction of population with income in past 12 months below poverty level
fraction_vacant_housing fraction of houses that are vacant
dep_index a composite measure of the other 6 variables
https://geomarker.io/dep_index/
my_data <- data.frame(zcta = c('45229', '45056', '47012')) add_depindex_data(data = my_data)my_data <- data.frame(zcta = c('45229', '45056', '47012')) add_depindex_data(data = my_data)
adds traffic proximity index, ozone concentration, particulate matter (PM) concentration, PM from diesel traffic, and a respiratory hazard index from the EPA EJ Screen database. Data is available for 2000 and 2010 ZCTAs and for years 2015 to 2018.
add_ejscreen_data(data)add_ejscreen_data(data)
data |
data.frame or tibble with column called 'zcta' at minimum. Optionally, another column called 'year', with possible values 2000 to 2019. If no year is given, defaults to 2018. |
the input data.frame with the following columns appended
zcta_year the ZCTA boundary year matched to the input year (2000 or 2010)
ej_year the EJ Screen data year matched to the input year (2015 - 2018)
traffic_proximity count of vehicles (AADT, avg. annual daily traffic) at
major roads within 500 meters, divided by distance in meters
ozone_conc ozone summer seasonal avg. of daily maximum 8-hour concentration
in air in parts per billion
pm_conc PM2.5 levels in air, µg/m3 annual avg
diesel_pm diesel particulate matter level in air, ug/m3
resp_hazard_ind air toxics respiratory hazard index (ratio of exposure
concentration to health-based reference concentration)
https://www.epa.gov/ejscreen/overview-environmental-indicators-ejscreen
my_data <- data.frame(zcta = c('45229', '45056', '47012')) add_ejscreen_data(data = my_data)my_data <- data.frame(zcta = c('45229', '45056', '47012')) add_ejscreen_data(data = my_data)
adds percentage of land classified as 'green', percentage impervious land, and percentage of land covered by tree canopy from the National Landcover Database. Data is available for 2000 and 2010 ZCTAs and for years 2001, 2006, 2011, and 2016.
add_landcover_data(data)add_landcover_data(data)
data |
data.frame or tibble with column called 'zcta' at minimum. Optionally, another column called 'year', with possible values 2000 to 2019. If no year is given, defaults to 2016. |
the input data.frame with the following columns appended
zcta_year the ZCTA boundary year matched to the input year (2000 or 2010)
nlcd_year the National Landcover data year matched to the input year (2001, 2006, 2011, or 2016)
treecanopy_year if tree canopy data was not available in the nlcd_year, the
year of the tree canopy data, otherwise NA
pct_green percentage of pixels within ZCTA classified as green (defined as all NLCD land cover
codes except water, ice/snow, developed medium intensity, developed high intensity, and rock/sand/clay)
pct_impervious average percent impervious for pixels within ZCTA
pct_treecanopy average percent tree canopy for pixels within ZCTA
https://www.mrlc.gov/
my_data <- data.frame(zcta = c('45229', '45056', '47012')) add_landcover_data(data = my_data)my_data <- data.frame(zcta = c('45229', '45056', '47012')) add_landcover_data(data = my_data)
adds NARR cell identifier based on ZCTA centroid for subsequent use with the addNarrData package
add_narr_cell_zcta(data)add_narr_cell_zcta(data)
data |
data.frame or tibble with column called 'zcta' at minimum. |
the input data.frame with the following columns appended
narr_cell NARR cell indentifier corresponding to the
NARR cell that intersects the ZCTA centroid
https://geomarker.io/addNarrData/
my_data <- data.frame(zcta = c('45229', '45056', '47012')) add_narr_cell_zcta(data = my_data)my_data <- data.frame(zcta = c('45229', '45056', '47012')) add_narr_cell_zcta(data = my_data)
adds numbers and percentages of white non-Hispanic and black non-Hispanic residents from the 2018 ACS for given ZIP codes, as well as a racial index of concentration at the extremes (ICE). Data is available for 2010 ZCTAs in the contiguous United States.
add_race_data(data)add_race_data(data)
data |
data.frame or tibble with column called 'zcta' at minimum. |
the input data.frame with the following columns appended
total total population of ZCTA
wnh number of white non-Hispanic residents
bnh number of black non-Hispanic residents
racial_ice racial index of concentration at the extremes (wnh-bnh)/total)
pct_wnh percent white wnh/totalx100
pct_bnh percent black bnh/totalx100
my_data <- data.frame(zcta = c('45229', '45056', '47012')) add_race_data(data = my_data)my_data <- data.frame(zcta = c('45229', '45056', '47012')) add_race_data(data = my_data)
adds length (m) and density (m/km2) of primary and secondary roads as defined by 2018 TIGER Line files. Data is available for 2000 and 2010 ZCTAs.
add_road_data(data)add_road_data(data)
data |
data.frame or tibble with column called 'zcta' at minimum. Optionally, another column called 'year', with possible values 2000 to 2019. Years 2000 to 2009 will be matched to 2000 ZCTAs and years 2010 to 2019 will be matched to 2010 ZCTAs. If no year is given, defaults to 2018. |
the input data.frame with the following columns appended
primary_road_length length (m) of primary roads in ZCTA
primary_road_density length(m) of primary roads in ZCTA divided by area of ZCTA (km2)
secondary_road_length length (m) of secondary roads in ZCTA
secondary_road_density length(m) of secondary roads in ZCTA divided by area of ZCTA (km2)
my_data <- data.frame(zcta = c('45229', '45056', '47012')) add_road_data(data = my_data)my_data <- data.frame(zcta = c('45229', '45056', '47012')) add_road_data(data = my_data)