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

Just finished studying the topics covered in Spatial Data Science with applications in R (r-spatial.org/book/). Thought I'd share some of the charts I made along the way. Mostly recreations of some of the plots from the sources along with some context. 1/14 😬 #rstats #rspatial #geospatial #gis 🧵

r-spatial.org

Spatial Data Science

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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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