# Multi-scale model assessment

This vignette walks through how to use waywiser to assess model predictions at multiple spatial scales, using the `ny_trees` data in waywiser, adapted from that post.

First things first, we’ll set up our environment, loading a few packages and telling sf to download the coordinate reference system for our data, if needed:

    library(sf)
    library(tidyr)
    library(dplyr)
    library(waywiser)
    invisible(
      sf_proj_search_paths(
        file.path(tools::R_user_dir("waywiser", "data"))
      )
    )
    invisible(sf_proj_network(TRUE))

The data we’re working with is extremely simple, reflecting the number of trees and amount of aboveground biomass (“AGB”, the total amount of aboveground woody bits) at a number of plots across New York State. We can plot it to see that there’s some obvious spatial dependence in this data – certain regions have clusters of much higher AGB values, while other areas (such as the area around New York City to the south) have clusters of much lower AGB.

    library(ggplot2)

    ny_trees %>%
      ggplot() +
      geom_sf(aes(color = agb), alpha = 0.4) +
      scale_color_distiller(palette = "Greens", direction = 1)

Because our focus here is on model assessment, not model fitting, we’re going to use an extremely simple linear regression to try and model AGB across the state. We’ll predict AGB as being a linear function of the number of trees at each plot, and then we’re going to use this model to predict expected AGB:

    agb_lm <- lm(agb ~ n_trees, ny_trees)
    ny_trees$predicted <- predict(agb_lm, ny_trees)

Now we’re ready to perform our multi-scale assessments. The `ww_multi_scale()` function supports two different methods for performing assessments: first, you can pass arguments to `sf::st_make_grid()` (via `...`), specifying the sort of grids that you want to make. For instance, if we wanted to make grids with apothems (the distance from the middle of a grid cell to the middle of its sides) ranging from 10km to 100km long, we can call the function like this:

    cell_sizes <- seq(10, 100, 10) * 1000
    ny_multi_scale <- ww_multi_scale(
      ny_trees,
      agb,
      predicted,
      cellsize = cell_sizes
    )

    ny_multi_scale

We’ve now got a tibble with estimates for our model’s RMSE and MAE at each scale of aggregation! We can use this information to better understand how our model does when predictions are being aggregated across larger units than a single plot; for instance, our model generally does better at larger scales of aggregation:

    ny_multi_scale %>%
      unnest(.grid_args) %>%
      ggplot(aes(x = cellsize, y = .estimate, color = .metric)) +
      geom_line()

Note that we used the `.grid_args` column, which stores the arguments we used to make the grid, to associate our performance estimates with their corresponding `cellsize`.

In addition to our top-level performance estimates, our `ny_multi_scale` object also includes our true and estimated AGB, aggregated to each scale, in the `.grid` column. This lets us easily check what our predictions look like at each level of aggregation:

    ny_multi_scale$.grid[[9]] %>%
      filter(!is.na(.estimate)) %>%
      ggplot(aes(fill = .estimate)) +
      geom_sf() +
      scale_fill_distiller(palette = "Greens", direction = 1)

In addition to specifying systematic grids via `sf::st_make_grid()`, `ww_multi_scale()` also allows you to provide your own aggregation units. For instance, we can use the `tigris` package to download census block group boundaries, as well as county and county subdivision boundaries, for the state of New York. We can then provide those `sf` objects straight to `ww_multi_scale`.

    suppressPackageStartupMessages(library(tigris))

    ny_block_groups <- block_groups("NY")
    ny_county_subdivisions <- county_subdivisions("NY")
    ny_counties <- counties("NY")

    ny_division_assessment <- ww_multi_scale(
      ny_trees,
      agb,
      predicted,
      grids = list(
        ny_block_groups,
        ny_county_subdivisions,
        ny_counties
      )
    )

    ny_division_assessment %>%
      mutate(
        division = rep(c("Block group", "County subdivision", "County"), each = 2)
      ) %>%
      ggplot(aes(x = division, y = .estimate, fill = .metric)) +
      geom_col(position = position_dodge())

By providing grids directly to `ww_multi_scale()`, we can see how well our model performs when we aggregate predictions to more semantically meaningful levels than the systematic grids.