iiinova — where your digital self keeps evolving

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ercbk.bsky.social @ercbk.bsky.social · 17/06/2026

This heatmap (tidyplots) and table (tinytable) summarize the previous map but for all nodes. For the first lag (first row) in the heatmap, the brightest yellow cell says around 16 census tracts have 6 First Order neighbors. Connectedness peaks around the 3rd lag. More explicitly shown in the table.

$source - https://r-spatial.github.io/spdep/articles/nb.html#higher-order-neighbours full code - https://ercbk.github.io/Data-Science-Notebook/qmd/geospatial-spatial-weights.html#sec-geo-swgt-diag plot_col <- function(d, color, ...) { ggplot(d, aes(x = tot_neigh, y = tot_nodes)) + geom_col(color = color, fill = color) + # rm axes w/o using theme elts scale_x_continuous(expand = c(0, 0)) + scale_y_continuous(expand = c(0, 0)) + theme_void() } tt(dat_nb_wide) |> style_tt(background = "#FFFDF9FF") |> # headers style_tt(i = 0, bold = TRUE, color = "#000E54") |> # row names style_tt(j = 1, bold = TRUE, color = "#000E54") |> # only color nonzero values style_tt(i = dat_nb_wide > 0, background = "#0095A8FF", color = "white") |> # add column chart plot_tt(j = 26, fun = plot_col, data = lst_tbl_nb, color = "#0095A8FF", height = 2)$

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ercbk.bsky.social @ercbk.bsky.social · 17/06/2026

As mentioned before, spatial autocorrelation tests use spatial weights created from neighbor graphs. Most measures have global and local versions. One, Moran's I, has an extension called a Moran Plot. Among other things, it labels locations with strong influence on the measure.

source - https://r-spatial.org/book/15-Measures.html#local-measures
full code - https://ercbk.github.io/Data-Science-Notebook/qmd/association-spatial.html#sec-assoc-spat-global-ex

data(pol_pres15, package = "spDataLarge")
pol_pres15 <- pol_pres15 |>
janitor::clean_names()

iturnout <- pol_pres15 |>
st_drop_geometry() |>
pull(i_turnout)

lw_w_pol <- nb_poly_pol |> nb2listw(style = "W")

infl_turnout_pol <- moran.plot(
iturnout,
listw = lw_w_pol,
labels = pol_pres15$TERYT,
cex = 1,
pch = ".",
xlab = "I round turnout",
ylab = "lagged turnout"
)

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ercbk.bsky.social @ercbk.bsky.social · 17/06/2026

For the local Moran's I test, units with I statistics significantly below or above the expected value indicate negative or positive autocorrelation respectively. p-values should be adjusted. Using the 0.005 threshold, p-values in darker blue shades indicate potential cluster centers of voter turnout

source - https://r-spatial.org/book/15-Measures.html#local-morans-i_i
full code - https://ercbk.github.io/Data-Science-Notebook/qmd/association-spatial.html#sec-assoc-spat-local-ex

pv_brks <- c(0, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1)

tmap_options_reset()
tmap_theme_charcoal()

tm_shape(pv_iturn_pres15) +
tm_polygons(col = "gray40") +
tm_fill(
fill = "p_value",
fill.scale = tm_scale(breaks = pv_brks, values = "scico.davos"),
fill.legend = tm_legend(
"Adjusted P-Values (BH)",
frame = FALSE,
position = tm_pos_on_top(
pos.v = 0.4,
pos.h = 0.66
)
)
) +
tm_facets_wrap("pval_type", ncol = 2) +
tm_title(
"Local Moran's I P-Values",
size = 2,
fontface = "bold"
) +
tm_title("With regards to Poland's voter turnout (2015)", size = 1.25) +
tm_layout(
panel.labels = c(
"Analytical-Conditional",
"Permutation-Std.Deviate",
"Permutation-Rank"
)
)

110