Exploring the geography of the registered

addresses of car models through a bespoke car

classification

Guy Lansley

Department of Geography, University College London

@GuyLansley

g.lansley@ucl.ac.uk

Outline

• Aims and Objectives

• 2011 UK Car Classification

• Trends and findings

• Exploring car models and society

3

Context

• There is a call to make more government held population

data open source

• There is also a need to acquire more data during

intercensal periods

• Success in Department for Work and Pensions, Land

Registry (England and Wales),…

• However, we still lack good data on affluence or income at

a small area level

• In 2013 there were 29.2 million registered cars in Great

Britain and 75% of UK households had access to at least

one car or van (2011)

4

Cars and society

• The choice of a particular car

model purchase is driven by…

• Budget (& disposable

income)

• Purpose

• For work

• For family

5

Do cars represent everyone?

6

2011 Census Data

% No car/van ownership (2011)

• Car (and van ownership)

• Number of cars/vans per

household

• 0, 1, 2, 3, 4+

• Method of travel to work

• Distance to workplace

• Household characteristics

• Socio-economic characteristics

7

DVLA DATA

• The DVLA hold data on every individual registered car by its

registered address

• Attributes include…

• the model

• engine size

• date of MOT

• age of the vehicle

• Updated quarterly

• Available to purchase

- No detailed data about the owners are available

8

Cars in the UK• Spreadsheet of all car models registered in GB in March 2011

• 1981 different generic models

9

Creating a Car Classification (1)

• The DVLA did not want an individual to be identifiable

from the data, therefore it needed to be aggregated in

some way

• The provision of the data aggregated into a car

classification of 10 segments was proposed

• The car classification lookup table was sent of to The

DVLA (via The DfT) in Summer 2013

• In late 2013 the DVLA found the classification to meet

their data protection requirements at the Lower Super

Output Area (LSOA).

• The data was made available at the 2011 LSOA level for

England and Wales, and 2001 LSOA for Scotland.

10

Creating a Car Classification (2)

• Every car (generic) model registered in 2011 was

assigned a key segment based on the vehicles size,

purpose and marketing.

• Few cars met the criteria for more than one segment,

these were all individually consulted

• Cars were also aggregated into 3 age groups (< 3, 3-10,

>10 years).

• Company cars were also inferred from the registered

titles/names of the owner, these were separated from

the other segments

• The classification only included cars (not motorcycles,

light goods vehicles…)

11

SegmentNumber

of CarsPercent

City Car 1,740,242 6.85%

Supermini 7,212,110 28.40%

Small Family Car 6,730,750 26.50%

Large Family Car 2,954,579 11.63%

MPV 1,343,038 5.29%

Compact Executive 1,923,107 7.57%

Executive 758,042 2.99%

Sports 797,728 3.14%

SUV 1,732,136 6.82%

Luxury 203,003 0.80%

2011 GB Car

Classification

• 10 car segments

• Each segment is disaggregated

into 3 age groups

12

Age of the vehicle

3,934,281 14,865,927 6,793,805

13

Geodemographics (London)

14

Age of the vehicle

< 3

3 - 10

> 10

15

Company cars

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

Age 0-3 Age 3-10 Age >10

City Car Supermini Small Family Car Large Family Car

MPV Compact Executive Executive Sports

SUV Luxury

17

Pearson’s Correlation

Supermini 0.11Small Family Car -0.287 0.264Large Family Car -0.434 0.133 0.308MPV -0.276 -0.13 0.097 0.208Compact Executive

-0.1 -0.618 -0.359 -0.353 -0.049

Executive -0.041 -0.676 -0.475 -0.396 -0.044 0.657SUV 0.05 -0.514 -0.573 -0.374 -0.221 0.175 0.367Sports 0.301 -0.52 -0.551 -0.567 -0.307 0.455 0.561 0.456Luxury 0.032 -0.526 -0.478 -0.358 -0.125 0.494 0.59 0.355 0.549

CC Sm SFC LFC MPV CEx Ex SUV S

London

• The car segments share distinctive spatial associations

between each other

18

The Standardised distribution of cars (London)

19

1 2 3 4 5 6 7 8

City Car 0.234 0.342 0.286 0.05 0.025 -0.117 -0.193 -0.498

Supermini -0.538 -0.392 0.195 -0.362 0.532 0.554 0.489 0.036

Small Family Car -0.417 -0.394 -0.036 -0.431 0.29 0.395 0.391 0.331

Large Family Car -0.597 -0.616 -0.21 -0.264 0.389 0.538 0.573 0.501

MPV -0.246 -0.244 -0.076 -0.087 0.04 0.161 0.143 0.354

Compact Executive 0.548 0.4 -0.084 0.139 -0.597 -0.615 -0.553 .009

Executive 0.641 0.485 -0.107 0.282 -0.617 -0.644 -0.584 -0.132

Sports 0.724 0.629 0.037 0.28 -0.467 -0.613 -0.585 -0.455

SUV 0.437 0.444 -0.014 0.68 -0.264 -0.404 -0.397 -0.403

Luxury 0.449 0.32 -0.086 0.243 -0.43 -0.457 -0.403 -0.105

1. Higher managerial, administrative and professional occupations 5. Lower supervisory and technical occupations

2. Lower managerial, administrative and professional occupations 6. Semi-routine occupations

3. Intermediate occupations 7. Routine occupations

4. Small employers and own account workers 8. Never worked and long-term unemployed

Socio-Economic Classification (2011 Census)

• The car has been compared to 2011 Census data for England and Wales

• Distinctive relationships between car model segments and socio-

economics

House Price Data

Unstandardised Standardised

• House prices were considered as

an additional proxy for asset wealth

• All house sales are recorded by the

Land Registry, the data includes the

post code of the property, the date

of sale, the sale price and the

accommodation (I.e. flat, terraced,

semi-detached, detached).

• House sale data downloaded from

the Land Registry (England &

Wales) for 2011.

• The sale data were restandardised

by the structure of the household.

• This data were then aggregated

into LSOAs, creating an average for

each area.

21

1. Higher managerial, administrative and professional occupations

1.1 Large employers and higher managerial and administrative occupations

1.2 Higher professional occupations

City Car 0.234 0.26 0.211Supermini -0.538 -0.502 -0.515Small Family Car -0.417 -0.487 -0.369Large Family Car -0.597 -0.55 -0.573MPV -0.246 -0.268 -0.223Compact Executive 0.548 0.407 0.555Executive 0.641 0.555 0.626Sports 0.724 0.712 0.681SUV 0.437 0.565 0.371Luxury 0.449 0.452 0.42

Car SegmentAverage

House Sale Value

Standardised House

Price IndexCity Car 0.425 0.416Supermini -0.64 -0.677Small Family Car -0.58 -0.567Large Family Car -0.597 -0.589MPV -0.361 -0.361Compact Executive 0.395 0.412Executive 0.619 0.605Sports 0.686 0.717SUV 0.74 0.717Luxury 0.603 0.66

Age SegmentUnder 3 years 0.607 0.561Between 3 & 10 years

0.021 -0.033

Above 10 years -0.502 -0.452

Cars and inferred social standing

22

K-means Clustering

• A clustering algorithm was used to group LSOAs into 7 distinctive groups

based on the DVLA car data and relevant 2011 Census variables

• K-means clustering was used – Top-down clustering method with pre-

defined number of clusters, all cases are assigned to their ‘nearest’ cluster

Variables:

• Car Segments

• Car Age Group

• Number of Cars per Household

• Method of Travel to Work

• Distance Travelled to Work

• Population Density

• NS-SEC (National Statistics Socio-Economic Classification)

• Age Structure

Number of Groups: 7

Cases: LSOA (England and Wales only), 34753 in total

23

1 2 3 4 5 6 7City CarSuperminiSmall Family CarLarge Family CarMPVCompact ExecutiveExecutiveSportsSUVLuxury

Age of the Vehicle< 33 - 10> 10Number of cars per

household01234

Travel to work by carAverage distance

Pop’ Density

NS-SEC

1 and 2

356 and 7

8

Age StructureAge 0-7

Age 8-15Age 20-29

Age 30-44Age 45-64Age 65+

1 3.09

2 12.79

3 5.40

4 8.45

5 24.79

6 27.90

7 17.58

1 2 3 4 5 6 7

K-means clustering

Low

High

% Size of each group

24

K-means clustering

Bristol

• The distribution of the k-means

classification groups clearly reflect the

geodemographic composition across

England and Wales

25

K-means clustering

House Prices

26

AcknowledgementsGratitude goes to Jeremy Grove from the Department of Transport and his colleague Mike Dark for instigating

the data request to the DVLA, and the Data Sharing team at DVLA for assessing and approving the request.

Conclusions

• Car models and age can provide fruitful information

about society at the small area level

• The composition of car models is a more useful

indicator of socio-economics than car ownership /

access to cars variables.

• The car classification also indicates life stage of

households at an aggregate level

• The classification would require further consultation –

exceptions still exist

• Future research?