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"
)
)](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:dcv32lv6xgstilwv3klodild/bafkreifkw7cczse4wnoi5csxfmqtamzrwpu7fy2ihctluxeop6f4gfo2da)
