iiinova — where your digital self keeps evolving

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

First up is neighbor algorithms and spatial weights which represent spatial autocorrelation using relationships between nodes on a graph. Econometric regression and autocorrelation tests make use of these. This is a map of NY census tracts illustrating k-NN where all nodes have at least k neighbors.

$source - https://r-spatial.github.io/spdep/articles/nb_sf.html#k-nearest-neighbours full code - https://ercbk.github.io/Data-Science-Notebook/qmd/geospatial-spatial-weights.html#sec-geo-swgt-nbalg-distb nb2tbl <- function(nb_list, coords, color = "black") { nb_len <- length(nb_list) if (length(color) == 1) { nb_col <- rep(color, nb_len) } else { nb_col <- color } # flattens neighbor vectors into 1 vector cardnb <- card(nb_list) # source node indexes i <- rep(1:nb_len, cardnb) # neighbor node indexes j <- unlist(nb_list) nb_tib <- tibble::tibble( # segment coords x_i = st_coordinates(coords)[, 1, drop = TRUE][i], x_j = st_coordinates(coords)[, 1, drop = TRUE][j], y_i = st_coordinates(coords)[, 2, drop = TRUE][i], y_j = st_coordinates(coords)[, 2, drop = TRUE][j], # segment colors seg_col = nb_col[i] ) return(nb_tib) } ny_nb_ll <- knn2nb(knearneigh(ny_ct_ll, k = 4, longlat = TRUE)) # Convert neighbors list to tibble tbl_nb <- nb2tbl(ny_nb_ll, ny_ct_ll) ggplot() + geom_sf(data = ny_sf_ll, fill = "#FFFDF9FF") + geom_sf(data = ny_ct_ll, color = "#000E54") + geom_segment(aes(x = x_i, y = y_i, xend = x_j, yend = y_j), data = tbl_nb, color = "#000E54") + ggtitle( "Central New York", subtitle = "k-NN with k = 4" )$

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

Spatial lags of neighbors are the number of steps away from a node. The neighbors at a particular spatial lag are that lag’s order of neighbors (e.g. 4th lag has 4th order neighbors). I was having trouble visualizing exactly what what that meant, so I thought it was finally time to try gganimate.

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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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