How each factor is placed: rank, z-score and min-max

You cannot add cafés to people

A score blends factors measured in different units: 22 cafés within a 10-minute walk, 40 transit stops within a 10-minute walk, and the residents of the hex. Before any of that can be combined, each factor has to be turned into a position from 0 to 100 - where does this hex (one hexagon on the map, about 350 m across, roughly 0.1 km²) sit, for this factor, among all the others? Only then can positions be weighted and blended.

There is more than one honest way to do that, and they give different maps. How each factor is placed is the control that chooses. Its three options - “Where it ranks”, “How far from typical” and “Lowest to highest” - are what the field calls percentile rank, z-score and min-max normalization. Analysts argue about these; here they are one dropdown, in plain words, and this post is about what each one does to a London map.

How each factor is placedthe field's term: Normalization (rank · z-score · min-max)

London Score map with each factor placed by how far it sits from typical: a broad solid yellow core across inner London, and a mix of green, teal and purple across the outer boroughs — Cafés and transit stops within a 10-minute walk, plus residents, each placed by 'How far from typical' and then combined. Inner London, well above typical on cafés and transit, glows; ordinary outer hexes sit lower.

The three ways, side by side

Where it ranks

Sort every hex from lowest to highest and place each by its position in the queue. The median sits at 50, the top at 100. One freak hex cannot stretch the scale. The size of a gap is lost.

How far from typical

Measure how many spread-widths (standard deviations) a hex sits from the typical value. A big gap reads as a big gap. Three spread-widths either side become 0 and 100; anything beyond is capped.

Lowest to highest

Place each hex on the straight line from the smallest value to the largest. Gaps keep their size exactly. One extreme hex stretches the whole scale and squashes everyone else.

Five hexes, cafés counted: 1, 2, 3, 4 and 40

Where it rankseven steps, whatever the values: 0 · 25 · 50 · 75 · 100
How far from typicaltypical = 10, spread ≈ 15, mapped from ±3 spreads: 35 · 36 · 36 · 37 · 86
Lowest to highest: 0 · 3 · 5 · 8 · 100

The 40-café hex is the same in all three. What changes is everyone else: ranked, they are spread evenly; by distance from typical, they cluster near the middle with the outlier well clear; lowest-to-highest crushes them into the bottom tenth.

Switching it on

It is the last card in the Score map's Add measures pop-up, under “How the factors combine” - deliberately, because that card decides how factors trade off against each other and this one only changes how each is measured on the way in.

Where to click

Map type
Score
Add measures
How each factor is placed
pick one of three

The How each factor is placed card, subtitled Whether the size of a gap counts, or only the order, with the dropdown set to How far from typical and its description: each hex is placed by how far it sits from the typical amount, so a big gap reads as a big gap - and a single extreme hex is capped instead of flattening everywhere else — The card, set to 'How far from typical'. Each option's description says what it does to the map, not which statistic computes it.

The same card set to Lowest to highest, with its description warning that one extreme hex stretches the whole scale and pushes everywhere else together — 'Lowest to highest' is honest about its weakness in the description itself.

Same city, three maps

Three factors - cafés and transit stops within a 10-minute walk, plus residents - three placements, nothing else changed. At city scale the three look more alike than you might expect, and for a plain reason: the Score's legend colors by Equal share of hexes unless you change it, so each map spends every color on about the same number of hexes. The maps can only differ in which hexes get which color, not in how much of London is bright. Look at the outer boroughs, where middling hexes trade places between maps; the size of the differences lives in each hex's score, which is what the hex cards in the next section show.

London Score map with each factor placed by where it ranks: a broad yellow center and a speckled mix of yellow, green and teal across the outer boroughs — Where it ranks. The outer boroughs are the most speckled of the three: small differences in order become a full step of color.

London Score map with each factor placed by how far from typical, with a solid yellow core and more teal and purple in the outer boroughs — How far from typical. A solid bright core - inner London is well above typical on cafés and transit - while ordinary outer hexes settle lower.

London Score map with each factor placed lowest to highest, close to the How far from typical map, with a solid yellow core and cooler outer boroughs — Lowest to highest. Close to How far from typical in order - though, as the next section shows, a hex's score can be far lower, because the most café-packed hexes stretch the whole scale.

One hex, three scores

The same London hex under each placement, as its hex card - what hovering a hex shows - tells it. The hex has no café and no transit stop of its own, but 22 cafés and 40 transit stops within a 10-minute walk, and residents around the middle of London's hexes. Nothing about the hex changed. Its score moved by 73 points.

Hex card reading Strong match · 89 out of 100: Transit stops within a 10-minute walk 33 of 33, in the top 1% of hexes, 0 in this hex · 40 within a 10-minute walk; Café within a 10-minute walk 32 of 34, in the top 7% of hexes, 0 in this hex · 22 within a 10-minute walk; Total population 24 of 33, around the middle; Held back most by Total population — Where it ranks: 89 out of 100 - 33 + 32 + 24 points.

Hex card over the same hex reading Good match · 75 out of 100: Transit stops within a 10-minute walk 33 of 33, Café within a 10-minute walk 23 of 34, Total population 19 of 33; Held back most by Total population — How far from typical: 75 out of 100 - 33 + 23 + 19 points.

Hex card over the same hex reading Weak match · 16 out of 100: Transit stops within a 10-minute walk 11 of 33, Total population 3 of 33, Café within a 10-minute walk 2 of 34; Held back most by Café within a 10-minute walk — Lowest to highest: 16 out of 100 - 11 + 3 + 2 points.

Why the scores move that much

Follow the cafés. 22 within a 10-minute walk puts this hex in the top 7% of London. Ranked, that is almost full points: 32 of 34. By distance from typical it is well above typical, but not off the scale: 23 of 34. Lowest to highest, it is a small fraction of what the most café-packed hexes in central London reach on foot, so it scores near the floor: 2 of 34. Transit stops tell the same story - full points twice, then 11 of 33. None of the three is wrong; each is answering a slightly different question about what “high” means.

You never have to remember which one is on. The Score's Show the math card, at the foot of its Selected settings panel, says it in words.

The Show the math card for a Score: Method 1.5, a map id, one line per factor - Café: Total, within a 10-minute walk, more is better, important; Total population: Total, just this hex, more is better, important; Transit stops: Total, within a 10-minute walk, more is better, important - then Combined: Balanced. Each factor placed by: where it ranks, a Formulas line, a Sources line and a Copy card button — The Score's method card. Its Combined line - 'Each factor placed by: where it ranks.' - names the placing; Copy card copies the whole summary.

Which one should you pick?

Exploring, and want to see the strongest and weakest hexes at a glance? Where it ranks. It always fills the map with contrast and no outlier can break it.
Want genuinely exceptional hexes to stand out, and ordinary ones to look ordinary? How far from typical. It keeps the size of a gap without letting one hex flatten the rest.
Data with no wild outliers, and you care about the true size of every gap? Lowest to highest. It is the most literal, and the most fragile.

The honest advice is the same as for the bivariate map's classification: try all three. If the big picture barely changes, your pattern is robust. If it changes a lot, a few hexes sit on a boundary - which is worth knowing before you act on them.

Open this score of London

The whole idea, in five lines

Every factor is placed on a 0–100 scale before factors combine. Three honest ways: rank, distance from typical, lowest to highest.
Rank ignores the size of gaps and cannot be broken by an outlier. Lowest to highest keeps every gap and is broken by one.
Distance from typical is the middle path: gaps count, but three spread-widths cap the scale so one hex cannot flatten the rest.
The same hex scored 89, 75 and 16 under the three - not because it changed, but because 'high' means something different in each.
Flip between them and watch what moves. Robust patterns stay; boundary cases reveal themselves.

Quick questions

Why do factors need to be 'placed' at all?

Because you cannot add 14 cafés to 1 660 people. Each factor is first converted to a position between 0 and 1, and only then are the positions combined into a score.

What does 'Where it ranks' do?

It places each hex by its rank against every other hex: the median hex sits at 0.5, the top at 1. One extreme hex cannot stretch the scale, but the size of a gap is lost - 40 and 41 cafés look as different as 1 and 40.

What does 'How far from typical' do?

It measures how many spread-widths (standard deviations) a hex sits from the typical value, so a big gap reads as a big gap. Three spread-widths either side of typical become 0 and 1, and anything beyond is capped.

What does 'Lowest to highest' do?

It places each hex on the straight line from the smallest value to the largest. Gaps keep their size, but one extreme hex stretches the whole scale and pushes everywhere else together.

Which one should I use?

Start with 'Where it ranks' for a full spread of color. Switch to 'How far from typical' when you want genuinely exceptional hexes to stand out, and to 'Lowest to highest' only when your data has no wild outliers.

How can I tell which placing a Score uses?

Open Show the math at the foot of the Score's Selected settings panel: its method card has a line such as 'Each factor placed by: where it ranks.'

See it on a real map

The best way to understand the score is to play with it. The full app is on a Free Tier, with no sales call.

Open the live map All guides

About the author

Tinkal Gogoi(LinkedIn profile, opens in a new tab)Founder & Location Data Engineer

Builds MapBees's location-intelligence pipeline end to end — census and Overture Maps ingestion, H3 hex scoring, and the map interface — and writes the methodology behind every number on the site.

How each factor is placed: rank, distance from typical, lowest to highest