griddy

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
• summarize sojourn times, first-passage times, and scalar Markov mobility
• test whether transition dynamics differ across regimes
• return tidy tables and ggplot2 plots

Version 0.2.0 extends the core workflow with chain summaries and inferential tests for transition-regime homogeneity.

Installation

# CRAN release
install.packages("griddy")

# Development version
install.packages("pak")
# pak::pak("dshkol/griddy")
install.packages("griddy", repos = c('https://dshkol.r-universe.dev', 'https://cloud.r-project.org'))

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.

Documentation

The pkgdown site is organized around:

• core workflow and concepts
• tidycensus and cancensus examples showing applications with US and Canadian census data
• prior-art comparison against estdaR and spdyn
• performance benchmarking notes