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Synthetic population

Explainer

A synthetic population is read household by household

An invented neighbourhood of 100 people in 30 households shows how a synthetic population is read and how it is made. No number on this page is about Portugal.

The invented neighbourhood

The invented neighbourhood30 homes and 100 invented people, each beside their home.
  • 0–14
  • 15–24
  • 25–64
  • 65+

An invented neighbourhood: none of these people exists.

Each home is a household

30 households and 100 people, all invented. Choose a home to see who lives there.

View the homes as a table: The 30 invented households and the ages of who lives there
The 30 invented households and the ages of who lives there
HouseholdPeopleAges
1148
2268, 71
3341, 39, 9
4445, 43, 16, 12
5544, 41, 8, 5, 70
6552, 50, 22, 19, 81
7179
8229, 31
9357, 55, 24
10436, 34, 6, 2
11547, 45, 17, 14, 76
12542, 40, 11, 7, 3
13169
14274, 77
15330, 28, 1
16458, 56, 27, 23
17543, 40, 13, 10, 68
18567, 65, 37, 33, 4
19186
20246, 15
21366, 65, 39
22442, 44, 18, 15
23545, 43, 12, 9, 6
24555, 53, 29, 26, 72
25125
26273, 70
27335, 33, 4
28471, 49, 47, 20
29549, 46, 21, 18, 80
30531, 30, 5, 3, 0
View the ages as a table: The 100 invented people, by age
The 100 invented people, by age
AgePeople
Ages 0–1421
Ages 15–2412
Ages 25–6447
Ages 65+20
View the sizes as a table: The 100 invented people, by household size
The 100 invented people, by household size
People in the householdHouseholdsPeople
155
2510
3515
4520
51050

After the fit, the neighbourhood is 2 people away from the table it had to match

In the published population, households are not written by hand like this neighbourhood’s: the model chooses them until the counts come as close as possible to the tables INE publishes for each parish. In miniature, with an invented table standing in for INE’s:

People by age, invented data
AgeThe table to match30 households at randomThe neighbourhood, fitted
Ages 0–14202821
Ages 15–2412912
Ages 25–64484147
Ages 65+201520
Total10093100
Away from the table—232

“Away from the table” adds up the gaps group by group. Table and households invented for this example; in a real parish, the tables are INE’s (2021 Census).

Thirty households drawn at random were 23 people away from the table; the neighbourhood’s thirty, chosen for it, are 2 away. In the published population the fit is done parish by parish, against several of INE’s tables at once (age, marital status, education, work, among others), and it runs once: every published figure is one model run, with no margin. Each parish’s quality tier (A, B or C) says how close it came to its tables.

The published population is built in five steps

  1. Learn the combinations. A model learns, from the 2021 Census public-use sample, which people and households tend to occur together: ages, education, work, who lives with whom.

  2. Generate candidates and fit them to INE’s tables. For each parish the model generates thousands of possible households and gives more weight to those that make the counts match the tables INE published for it: how many people of each age, each education level, in homes of each size. It is the fit in the table above, with many tables at once.

  3. Draw whole households. Whole households are drawn from the candidates, like the homes in the neighbourhood: nobody is split.

  4. Add the residents of care homes. People in care homes and other collective quarters are added from INE’s counts.

  5. Publish with a quality tier. Every published column has a declared meaning, and each parish comes out with a tier (A, B or C), which combines how close it came to the tables and the number of residents.

The full account, in the methodology

A real parish reads the same way

one model run, no marginGenerated people and households, not real people, families, or addresses.

Every 2021 Census parish has its own generated population, fitted to INE’s tables for that parish, with the same questions: who lives alone, ages, work, families.

See a real parish: Aguada de Cima, in Águeda