treeco

The goal of treeco is to provide R users a tool for calculating the eco benefits of trees. All data used to calculate benefits is ripped from OpenStreetMaps otm-ecoservice repository which was (probably) ripped from i-Tree’s Eco or Streets software. A single tree is represented by 15 rows and 8 columns as there are 15 benefits calculated for every tree. Since tree inventories can be rather large, treeco utilizes the data.table package for speed. All calculations are done on unique species/dbh pairs to avoid redundant computation.

Installation

treeco isn’t available on CRAN but you can install it directly from github using devtools:

# install.packages("devtools")
devtools::install_github("tylurp/treeco")

A reproducible example

We can use the trees dataset to demonstrate how eco_guess and eco_run_all works:

library(dplyr)
library(treeco)

df_trees <- trees %>% 
  mutate(common_name = "Black cherry") %>% 
  select(common_name, Girth) %>% 
  mutate(botanical_name = eco_guess(common_name, "botanical"))

eco_run_all(
  data = df_trees,                  # dataset or path to CSV
  common_col = "common_name",       # common name field
  botanical_col = "botanical_name", # botanical name field
  dbh_col = "Girth",                # dbh field
  region = "PiedmtCLT",             # region code
  n = 0.99,                         # optional, threshold for species guessing
  ) %>% as_tibble()
#> # A tibble: 465 x 8
#>    botanical    common     dbh benefit_value benefit   unit  dollars rn   
#>    <chr>        <chr>    <dbl>         <dbl> <chr>     <chr>   <dbl> <chr>
#>  1 prunus sero… black c…   8.3        0.0776 aq nox a… lb       0.51 1    
#>  2 prunus sero… black c…   8.3        0.026  aq nox d… lb       0.17 1    
#>  3 prunus sero… black c…   8.3        0.0556 aq ozone… lb       0.36 1    
#>  4 prunus sero… black c…   8.3        0.015  aq pm10 … lb       0.04 1    
#>  5 prunus sero… black c…   8.3        0.0633 aq pm10 … lb       0.16 1    
#>  6 prunus sero… black c…   8.3        0.165  aq sox a… lb       0.32 1    
#>  7 prunus sero… black c…   8.3        0.0119 aq sox d… lb       0.02 1    
#>  8 prunus sero… black c…   8.3        0.0146 aq voc a… lb       0.09 1    
#>  9 prunus sero… black c…   8.3       -0.338  bvoc      lb       2.12 1    
#> 10 prunus sero… black c…   8.3       30.3    co2 avoi… lb       0.23 1    
#> # ... with 455 more rows

More examples

Use eco_run to calculate benefits for a single tree:

treeco::eco_run("Common fig", 20, "InlEmpCLM")
#>     common_name dbh benefit_value            benefit unit dollars
#>  1:  Common fig  20        0.2429     aq nox avoided   lb    0.93
#>  2:  Common fig  20        0.2623         aq nox dep   lb    1.01
#>  3:  Common fig  20        0.7716       aq ozone dep   lb    2.96
#>  4:  Common fig  20        0.0602    aq pm10 avoided   lb    0.28
#>  5:  Common fig  20        0.4079        aq pm10 dep   lb    1.89
#>  6:  Common fig  20        0.4813     aq sox avoided   lb    1.17
#>  7:  Common fig  20        0.0353         aq sox dep   lb    0.09
#>  8:  Common fig  20        0.0602     aq voc avoided   lb    0.12
#>  9:  Common fig  20        0.0000               bvoc   lb    0.00
#> 10:  Common fig  20      122.7973        co2 avoided   lb    0.41
#> 11:  Common fig  20        9.0389    co2 sequestered   lb    0.03
#> 12:  Common fig  20     1255.7516        co2 storage   lb    4.19
#> 13:  Common fig  20      189.2000        electricity  kwh   38.12
#> 14:  Common fig  20      834.7837 hydro interception  gal    4.59
#> 15:  Common fig  20     -179.4561        natural gas   lb    1.20

One issue with eco benefits is that they all rely on i-Tree’s master_species_list which is a list of 3,000+ species, therefore a users data needs to fit this list in order to extract benefits. For example, “Commn fig” doesn’t match i-Tree’s “Common fig” because of the typo. So far, there really isn’t a great solution to this. For now, treeco guesses the species code on the fly by quantifying the “similarity”, anything below 90% similar is immediately discarded.

For example, if we misspell “Common fig” as “Commn fig”:

treeco::eco_run("Commn fig", 20, "InlEmpCLM")
#> Species given: [commn fig]
#> Closest match: [common fig]
#> ...
#> Using closest match
#>     common_name dbh benefit_value            benefit unit dollars
#>  1:  Common fig  20        0.2429     aq nox avoided   lb    0.93
#>  2:  Common fig  20        0.2623         aq nox dep   lb    1.01
#>  3:  Common fig  20        0.7716       aq ozone dep   lb    2.96
#>  4:  Common fig  20        0.0602    aq pm10 avoided   lb    0.28
#>  5:  Common fig  20        0.4079        aq pm10 dep   lb    1.89
#>  6:  Common fig  20        0.4813     aq sox avoided   lb    1.17
#>  7:  Common fig  20        0.0353         aq sox dep   lb    0.09
#>  8:  Common fig  20        0.0602     aq voc avoided   lb    0.12
#>  9:  Common fig  20        0.0000               bvoc   lb    0.00
#> 10:  Common fig  20      122.7973        co2 avoided   lb    0.41
#> 11:  Common fig  20        9.0389    co2 sequestered   lb    0.03
#> 12:  Common fig  20     1255.7516        co2 storage   lb    4.19
#> 13:  Common fig  20      189.2000        electricity  kwh   38.12
#> 14:  Common fig  20      834.7837 hydro interception  gal    4.59
#> 15:  Common fig  20     -179.4561        natural gas   lb    1.20

If you are missing a field, you can use eco_guess to try and find it:

x <- c("common fig", "red maple", "fir")
treeco::eco_guess(x, "botanical")
#> [1] "ficus carica" "acer rubrum"  "abies spp"