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Replies

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

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

Hot spot analysis extends the interpretation of local measures by classifying the cluster centers. The class depends on the statistic value and which quadrant of the Moran plot the observation lands. Interpretation exs: High - cluster of high values Low-High - low value center, high value neighbors

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

tmap_options_reset()
tmap_theme_notebook()

notebook_colors <- unname(swatches::read_ase(here::here("palettes/Forest Floor.ase")))

tm_shape(clust_mcg_iturn_pres15) +
tm_polygons(col = "#4A3728") +
tm_fill(
fill = "clust_type",
fill.scale = tm_scale(
values = c(notebook_colors[[5]], notebook_colors[[6]], notebook_colors[[7]]),
value.na = notebook_colors[[2]],
label.na = "Not Determined"
),
fill.legend = tm_legend(
"Hotspot Type",
frame = FALSE,
position = tm_pos_on_top(
pos.v = 0.4,
pos.h = 0.66
)
)
) +
tm_facets_wrap("method_type", ncol = 2) +
tm_title(
"High and Low Voter Turnout Clusters in Poland (2015)",
size = 2,
fontface = "bold",
# get title flush with left side to line up with subtitle
# every number except 0.05 is default
padding = c(0.25, 0.15, 0.25, 0.25)
) +
tm_title(
"Comparing Local Moran's I, Local Getis-Ord G, and Local Geary's C methods",
size = 1.25
) +
tm_layout(
panel.labels = c(
"MoranI-Permutation",
"Getis-OrdG-Permutation",
"GearyC-Permutation"
)
)

110

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

Spatio-temporal kriging is an interpolation method that models autocorrelation using a variogram. It uses semi-variance which can be interpreted inversely of spatial autocorrelation (i.e. ↑semi-variance,↓spatial autocorrelation). The value is a function of distance and in this case, also time.

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