1. What variable — a count, or a rate?

2. What colours — which palette, in which direction?

3. What breaks — how do continuous values become discrete classes?

Most bad maps are bad because of decision 1 or decision 3.

Homicides come from SESNSP. Population comes from the census. Boundaries come from INEGI. Three sources, three files.

They are joined on INEGI’s geostatistical code. 31050 is Mérida, Yucatán: 31 for the state, 050 for the municipio.

In our file the state layer calls it cve_ent and the municipio layer calls it cvegeo — the same code, one with the municipio digits attached.

Read code columns as text, and the zero survives:

read.csv("estados.csv", colClasses = c(cve_ent = "character"))

Or use readr::read_csv(), which leaves them alone.

Using a qualitative palette for ordered data — or a rainbow for anything — invents structure that is not in the data.

• Perceptually uniform — equal steps in data look like equal steps in colour

• Colourblind-safe — about 8% of men have some form of colour vision deficiency

• Prints in greyscale — the order survives a black-and-white printer

direction = -1 makes dark mean high. For a variable like homicide, dark-is-bad matches what readers expect — and it survives photocopying better.

For choropleths you almost always want binned — the eye cannot read a continuous ramp accurately. Which raises the real question: where do the bins go?

A binned choropleth reduces a continuous variable to a handful of colours.

That reduction is a choice, and it is the single biggest lever on what the reader sees.

Never pick breaks before you have looked at the distribution.

Right-skewed, but not degenerate. Mean 23.7, standard deviation 21.7. Eleven states are below 10; four are above 50.

Now let us see what each one does to the map.

Twenty-one of thirty-two states in the first class. Equal interval divides the range, and the range is stretched by Colima at 91.

It is honest about magnitude — the classes really are equally wide — but it wastes four of your five colours on eight states.

Use it when data are roughly uniform, or when round-number breaks matter more than resolution.

Six or seven states per class, so the whole map varies and you can read the middle of the distribution.

But look at the colour bar: the swatches are wildly unequal in width. The top class runs from 43 to 91; the bottom from 1.7 to 8.

Almost none of the same states.

By count, the State of México is third — 2,286 homicides a year.

By rate, it is 13.5 per 100,000, well below the national average. It has 17 million people.

Colima has 668 homicides — barely a quarter of México’s — and the worst rate in the country at 91.3.

Doctor Coss, Nuevo León has the worst homicide rate in Mexico: 735 per 100,000 — eight times Colima.

It has 1,360 inhabitants and averaged 10 homicides a year. One more incident moves the rate by 74 points.

• Pool years — we already used a three-year mean rather than one year

• Suppress small denominators — grey out units below a population threshold and say so in the caption

• Aggregate — report at state level where municipio counts are thin

• Smooth — empirical Bayes shrinks unreliable rates toward the regional mean

The map on the next slide does the simplest of these: it shows municipios, but greys out those under 10,000 people.

• Title states the finding, not the variable name — “Homicide is concentrated in…”, not “Homicide rate by state”

• Legend has units — per 100,000, not rate

• Sources in the caption, always, and the period in the subtitle

• theme_void() — latitude and longitude gridlines are chart junk on a thematic map

• coord_sf(crs = 6372) — never publish a Mexican map in Web Mercator

delete_dsn = TRUE overwrites; without it, a second run errors instead of replacing the file.

Hover over a state. That is the whole point.

A leaflet map is JavaScript. In a Quarto HTML document it depends on external files — unless you say otherwise: