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Estimate Spatial Markov Transition Dynamics

Source: R/spatial-markov.R
spatial_markov.Rd

Estimates transition matrices conditioned on the class of each unit's spatial lag at the start of the transition period.

Usage

spatial_markov(
  data,
  id,
  time,
  value,
  geometry = NULL,
  listw = NULL,
  nb = NULL,
  k = 5,
  lag_k = k,
  class_method = c("pooled_quantile", "time_quantile", "fixed"),
  zero.policy = TRUE
)

Arguments

data

A long data frame or sf object.

id, time, value

Columns identifying spatial unit, time, and value.

geometry

An sf tibble with one row per spatial unit (a single time slice's geography), carrying nb and wt list-columns produced by sfdep::st_contiguity() and sfdep::st_weights(). The preferred input.

listw

A row-standardized spdep listw object. Accepted for compatibility with prior workflows; use geometry for new code.

nb

A spdep neighbor list, used only when both geometry and listw are NULL. Converted with spdep::nb2listw(style = "W").

k

Number of value classes.

lag_k

Number of spatial-lag classes.

class_method

Value classification method passed to classify_dynamics().

zero.policy

Passed to spdep lag/weight helpers.

Value

A grd_spatial_markov object.

Examples

panel <- data.frame(
  id = rep(1:4, each = 3),
  year = rep(2020:2022, times = 4),
  value = c(1, 2, 3, 2, 3, 4, 4, 3, 5, 5, 6, 7)
)

grid <- sf::st_sf(
  id = 1:4,
  geometry = sf::st_make_grid(
    sf::st_bbox(c(xmin = 0, ymin = 0, xmax = 2, ymax = 2)),
    n = c(2, 2)
  )
) |>
  dplyr::mutate(
    nb = sfdep::st_contiguity(geometry),
    wt = sfdep::st_weights(nb)
  )

spatial <- spatial_markov(panel, id, year, value, geometry = grid, k = 2)

spatial
#> <grd_spatial_markov>
#> 2 states, 2 lag states, 8 observed transitions
#> # A tibble: 8 × 8
#>   lag_class from_state to_state transition     n probability lag_lower lag_upper
#>   <ord>     <ord>      <ord>    <chr>      <int>       <dbl>     <dbl>     <dbl>
#> 1 Q1        Q1         Q1       Q1 -> Q1       2       0.5        2.33      3.67
#> 2 Q1        Q1         Q2       Q1 -> Q2       2       0.5        2.33      3.67
#> 3 Q1        Q2         Q1       Q2 -> Q1       1       0.333      2.33      3.67
#> 4 Q1        Q2         Q2       Q2 -> Q2       2       0.667      2.33      3.67
#> 5 Q2        Q1         Q1       Q1 -> Q1       1       1          3.67      5.33
#> 6 Q2        Q1         Q2       Q1 -> Q2       0       0          3.67      5.33
#> 7 Q2        Q2         Q1       Q2 -> Q1       0      NA          3.67      5.33
#> 8 Q2        Q2         Q2       Q2 -> Q2       0      NA          3.67      5.33
lag_intervals(spatial)
#> # A tibble: 2 × 4
#>   class lower upper type       
#>   <ord> <dbl> <dbl> <chr>      
#> 1 Q1     2.33  3.67 spatial_lag
#> 2 Q2     3.67  5.33 spatial_lag
transition_matrix(spatial, "count", lag_class = "Q1")
#>    Q1 Q2
#> Q1  2  2
#> Q2  1  2