griddy is an R package for geospatial distribution dynamics with sf and tidy data. It is inspired by PySAL giddy and by established spatial distribution dynamics work, especially Rey (2001). Prior R implementations exist, including estdaR and spdyn, but neither is on CRAN or designed along tidy principles. This package is designed to work best with long data and sf native spatial workflows.
Current scope:
- classify longitudinal spatial values into comparable classes
- estimate classic Markov transition matrices
- estimate spatial Markov transition matrices conditioned on spatial lag class
- compute simple rank mobility
- return tidy tables and
ggplot2plots
The initial CRAN release focuses on the core classification, Markov, spatial Markov, rank mobility, and plotting workflows.
Installation
# install.packages("pak")
pak::pak("dshkol/griddy")The minimal example below also uses spData for state geometry:
install.packages("spData")What it does
griddy keeps the workflow keyed by explicit id, time, and value columns instead of matrix row position. The analytical outputs preserve transition labels, class intervals, and spatial-lag intervals so they can be inspected, joined, and plotted without reverse-engineering array dimensions.
Minimal example
The bundled usjoin panel is the canonical PySAL giddy reference dataset: 48 contiguous US states, per-capita personal income, 1929 to 2009.
library(griddy)
library(dplyr)
library(sf)
library(sfdep)
library(spData)
data(usjoin)
geom <- us_states |>
filter(NAME %in% usjoin$name) |>
arrange(NAME) |>
mutate(
nb = st_contiguity(geometry),
wt = st_weights(nb)
)
panel <- usjoin |>
filter(name %in% geom$NAME) |>
arrange(name, year)
classes <- classify_dynamics(panel, name, year, income, k = 5)
classic <- markov_dynamics(classes, name, year, class)
spatial <- spatial_markov(panel, name, year, income, geometry = geom, k = 5)
classic$transitions |> select(id, from_time, to_time, transition) |> head()
lag_intervals(spatial)spatial_markov() takes a geometry argument: an sf tibble with one row per spatial unit and nb / wt list-columns produced by sfdep. This keeps the spatial frame, neighbor structure, and row-standardization choice in one tidy object. listw and nb arguments remain accepted for compatibility with existing workflows.