README.md

In dKvale/mnrisks2011: MNRISKS

mnrisks

Helper functions and data from Minnesota's 2011 NEI air modeling results.

1. Install
2. Load model inputs
3. Block group spatial averages
4. Add data

1. Install

install.packages("remotes")

remotes::install_github("dkvale/mnrisks")

2. Load model inputs

library(mnrisks)
library(knitr)

Load all modeling receptors

all_receptors <- get_receptors()

kable(head(all_receptors))

| receptor|geoid | lat| long| utm_x| utm_y|county | fips| total_bg_receptors| metro| |--------:|:------------|-------:|--------:|--------:|-------:|:------|-----:|------------------:|-----:| | 46318|270017701001 | 46.7195| -93.1178| 490997.4| 5174001|AITKIN | 27001| 86| 0| | 46383|270017701001 | 46.7195| -93.0785| 494000.8| 5173997|AITKIN | 27001| 86| 0| | 46514|270017701001 | 46.7465| -93.1178| 491001.9| 5177001|AITKIN | 27001| 86| 0| | 46526|270017701001 | 46.7461| -93.3142| 475999.8| 5176998|AITKIN | 27001| 86| 0| | 46579|270017701001 | 46.7457| -93.4320| 467001.5| 5176996|AITKIN | 27001| 86| 0| | 46591|270017701001 | 46.7465| -93.0786| 493996.2| 5176997|AITKIN | 27001| 86| 0|

Load a single block group's receptors

points <- get_receptors(bg_id = c(271090017012))

plot(points$lat, points$long, pch = 16)

kable(head(points))

| receptor|geoid | lat| long| utm_x| utm_y|county | fips| total_bg_receptors| metro| |--------:|:------------|-------:|--------:|--------:|-------:|:-------|-----:|------------------:|-----:| | 4597|271090017012 | 44.0699| -92.4932| 540584.5| 4879761|OLMSTED | 27109| 3| 0| | 4602|271090017012 | 44.0709| -92.4917| 540704.0| 4879873|OLMSTED | 27109| 3| 0| | 4606|271090017012 | 44.0714| -92.4965| 540319.2| 4879926|OLMSTED | 27109| 3| 0|

Block group Census 2010 data

bg_census <- get_blockgroups()

kable(head(bg_census))

| fid| geoid| metro| county_fips| population| poverty150_count| area| percent_in_poverty| population_density|county | |---:|------------:|-----:|-----------:|----------:|----------------:|--------:|------------------:|------------------:|:------| | 1| 270030501154| 1| 27003| 1495| 264| 23395500| 17.658863| 197.9400|ANOKA | | 2| 270030501153| 1| 27003| 1146| 145| 11626200| 12.652705| 305.3315|ANOKA | | 3| 270030502181| 1| 27003| 1810| 13| 11347200| 0.718232| 494.0997|ANOKA | | 4| 270030502182| 1| 27003| 1341| 0| 6212700| 0.000000| 668.6105|ANOKA | | 5| 270030502191| 1| 27003| 1318| 21| 7044300| 1.593323| 579.5652|ANOKA | | 6| 270030502192| 1| 27003| 1862| 0| 2190600| 0.000000| 2632.9424|ANOKA |

3. Calculate Block group spatial averages using the area coverage of each receptor

View the block group areas for each receptor

receptor_areas <- receptor_bg_areas()

kable(head(receptor_areas))

|geoid | receptor| area_wts| |:------------|--------:|--------:| |270531260001 | 22213| 0.0005| |270531260001 | 22550| 0.2202| |270531260001 | 22393| 0.3994| |270531260001 | 22530| 0.3786| |270531260001 | 22354| 0.0013|

The spatial_bg_avg function will calculate block group averages by joining the area fractions above to MNRISKS receptor results.

library(readr)
library(dplyr)

setwd("X:/Agency_Files/Outcomes/Risk_Eval_Air_Mod/_Air_Risk_Evaluation/Staff Folders/Dorian/Mnrisks/R/mnrisks2011")

df <- read_csv("data/onRoad - Inhalation - DieselPM risk ratio.csv")

names(df)[7] <- "Inhalation Cancer Risk"


# For a single pollutant
chrysene <- filter(df, `COPC Name` == "Chrysene")

bg_avg <- chrysene %>% 
          group_by(`Block Group`) %>%
          summarize(avg_inh_cancer_risk = spatial_bg_avg(values       = chrysene$`Inhalation Cancer Risk`, 
                                                         receptors    = chrysene$Receptor,
                                                         bg_geoids    = `Block Group`,
                                                         results_only = TRUE))

kable(head(arrange(bg_avg, -sum_of_area_wts)))


# For multiple pollutants
bg_avg <- df %>% 
          group_by(`Block Group`, `COPC Name`) %>%
          summarize(Pollutant = `COPC Name`[1],
                    avg_inh_cancer_risk = spatial_bg_avg(values       = filter(df, `COPC Name` == Pollutant)$`Inhalation Cancer Risk`, 
                                                         receptors    = filter(df, `COPC Name` == Pollutant)$Receptor,
                                                         bg_geoids    = `Block Group`,
                                                         results_only = TRUE))

kable(head(bg_avg))

|geoid | sum_of_area_wts| bg_avg_Inhalation Cancer Risk| |:------------|---------------:|-----------------------------:| |270531054001 | 0.8071| 1e-07| |270531054002 | 0.0842| 1e-07| |270531044002 | 0.0220| 1e-07| |270530059012 | 0.0122| 1e-07| |270531261001 | 0.0094| 1e-07| |270531261003 | 0.0001| 1e-07|

4. Add data

usethis::use_data(new_data)

devtools::document()

dKvale/mnrisks2011 documentation built on Feb. 18, 2022, 5:43 a.m.