A Dorling cartogram is a type of map that replaces geographic shapes with proportional circles, preserving spatial relationships while emphasizing data values. In this tutorial, we’ll walk through how to build a Dorling cartogram using census tract data for Minneapolis–Saint Paul, focusing on median home value (MHV) and neighborhood clustering.
# Define ACS variables
vars <- c(
income = "B19013_001",
education = "B15003_022",
total_edu = "B15003_001",
white = "B02001_002",
black = "B02001_003",
hispanic = "B03003_003",
total_pop = "B03003_001"
)
# Download ACS data for Hennepin and Ramsey counties
mn_data <- get_acs(
geography = "tract",
variables = vars,
state = "MN",
county = c("Hennepin", "Ramsey"),
geometry = TRUE,
year = 2021
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select(GEOID, NAME, variable, estimate) %>%
pivot_wider(names_from = variable, values_from = estimate) %>%
mutate(
pct_white = white / total_pop,
pct_black = black / total_pop,
pct_hispanic = hispanic / total_pop,
pct_bachelors = education / total_edu
) %>%
drop_na(pct_white, pct_black, pct_hispanic, pct_bachelors, income)mn_sf_proj <- st_transform(mn_wide, crs = 3857)
mn_sf_proj$income_w <- mn_sf_proj$income / 10000
mn_dorling <- cartogram_dorling(mn_sf_proj, "income_w", k = 0.8)cluster_df <- st_drop_geometry(mn_sf_proj) %>%
select(income, pct_white, pct_black, pct_hispanic, pct_bachelors)
cluster_scaled <- scale(cluster_df)
pam_result <- pam(cluster_scaled, k = 5)
mn_dorling$cluster <- factor(pam_result$clustering)# Load harmonized LTDB data
URL1 <- "https://github.com/DS4PS/cpp-529-fall-2020/raw/main/LABS/data/rodeo/LTDB-2000.rds"
URL2 <- "https://github.com/DS4PS/cpp-529-fall-2020/raw/main/LABS/data/rodeo/LTDB-2010.rds"
URLmd <- "https://github.com/DS4PS/cpp-529-fall-2020/raw/main/LABS/data/rodeo/LTDB-META-DATA.rds"
d1 <- readRDS(gzcon(url(URL1))) %>% select(-year)
d2 <- readRDS(gzcon(url(URL2))) %>% select(-year)
md <- readRDS(gzcon(url(URLmd)))
d <- merge(d1, d2, by = "tractid") %>%
merge(md, by = "tractid")
# Standardize GEOID
d$tractid2 <- as.numeric(gsub("-", "", gsub("fips", "", d$tractid)))
# Merge with spatial object
mn_dorling$GEOID_num <- as.numeric(mn_dorling$GEOID)
mn_dorling <- merge(mn_dorling, d, by.x = "GEOID_num", by.y = "tractid2", all.x = TRUE)mn_dorling <- mn_dorling %>%
mutate(
p.col = 100 * col00 / ag25up00,
p.vacant = 100 * vac00 / hu00,
p.prof = 100 * prof00 / empclf00,
mhv.00 = mhmval00 * 1.28855,
mhv.10 = mhmval12,
mhv.change = mhv.10 - mhv.00,
mhv.growth = 100 * (mhv.change / mhv.00),
log.p.col = log(p.col + 1),
log.p.vacant = log(p.vacant + 1),
log.p.prof = log(p.prof + 1)
)# Convert to sp object
mn_sp <- as_Spatial(mn_dorling)
row.ids <- sapply(slot(mn_sp, "polygons"), function(x) slot(x, "ID"))
row.names(mn_sp) <- row.ids
# Reproject to lat-long
mn_sp <- spTransform(mn_sp, CRS("+proj=longlat +datum=WGS84"))
# Save to file
geojson_write(mn_sp, file = "mn_final.geojson", geometry = "polygon")## <geojson-file>
## Path: mn_final.geojson
## From class: SpatialPolygonsDataFrameThis tutorial demonstrated how to build a Dorling cartogram using R. We scaled census predictors, applied PAM clustering, and visualized the results with proportional circles. Cartograms are powerful tools for communicating spatial patterns in urban data, especially when paired with interactive dashboards or policy analysis.