1. 1 W

2. Creating the MOT dataset Applications considered here:-
Using motor vehicle testing data to investigate spatial patterns of vehicle and energy use
What is the MOT project?
Creating the MOT dataset
Applications considered here:-
General spatial patterns
Average age of cars – an application of GWR techniques
Average distance per car – links to transport modelling and pollution.

3. 1. What is the MOT project?

4. MOT dataset (test data) MOT dataset (vehicle data)
Test date
Test type and result
Odometer reading
Location of test (Postcode Area)
MOT dataset (vehicle data)
First use date
Make, model and colour
Engine size
Fuel type
Vehicle class
Stock tables (vehicle data)
Keeper location (LSOA/ Data Zone)
Private or commercial
CO2 value
MOT dataset and DfT data linked by Unique ID

5. Aberdeen Data Safe Haven
MOT data (DVSA)
325 million tests
Varying intervals between tests
One row per test
Vehicle stock data (DVLA)
56 million vehicles
Annual or quarterly recording points
One row per vehicle
Vehicles master table – one row per vehicle; columns contain quarterly attributes
Local area tables – one row per LSOA or Data Zone; columns contain average or total values

6. Simplification processes (1)
Condense to be three vehicle classes; focus on 4/4A

7. Complicating issues No information on when vehicles leave the fleet
No information on unlicensed vehicles, or vehicles that are scrapped or go abroad within the first three years
About 30% vehicles don’t have a CO2 value (separate estimate inferred from engine size and fuel type)
4% of vehicles don’t have a location (leave out as mostly between keepers)
Some vehicles have an Northern Irish location (leave out as relatively few)

8. 2. What is the MOT data-set being used for?

9. 15 Potential uses of the data This slide will move on in 20 seconds!
Differences between places
Trends over time
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Links to socio-demographics
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10. 2. Spatial variations in Car characteristics and Use Some examples

11. 2A. General variations

12. What variables?
The work reported here has concentrated on 5 variables from the MOT data-set: covering all private cars registered at the time of the 2011 Census-
Mean number of private cars per person
Mean Age of private cars
Mean Size of private cars (ccs),
Percent Diesel fuelled (%), and
Average Kilometres per Private Car (km/yr)

13. General spatial variations
Non-hierarchical cluster analysis
10 clusters
Variables standardised
Differences
Urban vs London (clusters 5 and 6)
Rural (age of cars) Cluster 2 vs 3
Role of coastal retirement areas (cluster 8)

14. 2B. Average age of car

15. Examples of standard regressions
Age
Average kms per car
Variable
Cumulative R2
Impact
GWR
SE3
19.0
-ve Y
SDEN
35.7 N
Density
28.0
+ve
P65+
48.5
Dist500
36.5
SRAIL
54.9
SE1
43.2
60.8
SE4
48.1
RAIL
62.7
PAPART
52.5
PTRANS
63.5
55.6
SE8
64.3
SE6
56.9
65.2
PHLD
58.8
PPRIMARY
65.9
POTHER
59.5
Dist5
66.8
-SE1
Total R2
88.7
PPUBLIC
60.1
CarsPerPerson
60.6
PTERR
61.5
SE7
62.0
PFINAN
62.6
90.3

16. Average age by MSOA
Regression residuals from standard regression model

17. Spatial Regression techniques
A wide variety of techniques available. Looked at:-
Spatial Error
Errors spatially correlated
Spatial Lag
Values spatially correlated
Geographically Weighted Regressions (GWR)
Regression coefficients vary spatially

18. Regression residuals from standard regression model
Regression residuals from GWR model

19. Examples of spatial patterns in coefficients Correlation between coefficients?

20. Understanding spatial patterns from GWR models
These patterns may arise because:-
there is real spatial variation in the impact of a variable across the study area - we just cannot explain it.
There may be underlying variables not included in the regression model which are causing the patterns observed (mis-specification), and
The patterns are a statistical artefact of the GWR process itself.
The GWR approach is known to be prone to high levels of collinearity – that is the coefficients of the model are highly correlated, which can lead to a great deal of uncertainty in understanding the responses to a single variable when they are highly correlated with another variable, for instance (Wheeler, 2007).

21. 2C. Average distance per car

22. Average distance per car
Looking at relationship with transport model outputs
No exact comparisons
Different units of measurement (per ‘time-period’ and per ‘year’)
Issues of non-home-based trips, private vs business cars
Main factor in explaining spatial variations in emissions per car
Variations in distance per car are more important explaining spatial variations than variations in emissions per km per car
How do we analyse the spatial variation in emissions?

23. Who emits and where?

24. What next from the MOT project?
Work with DfT to ensure a legacy product, open to others
Continue work on transport & energy justice
Studies of car characteristics and use to assess the role of location. Do relationships with socio-economic factors vary by location -Use of spatial analysis techniques such as Geographic Weighted Regression (GWR). –Look at the distribution of annual distances run by cars
Add the temporal element – currently is feasible

25. Acknowledgements
The work has been undertaken under EPSRC Grant EP/K000438/1.
Grateful thanks to members of DfT, VOSA, DVLA and DECC, SPT, DSC and who have provided advice and support for this work
Website is:
Author PS
We currently have an international survey running to understand the scope to undertake such work in other countries. Can you help by completing this for your country:
The work has been undertaken under EPSRC Grant EP/K000438/1. Grateful thanks to members of DfT, VOSA, DVLA and DECC, who have provided advice and support for this work, and to other members of the project team
Grateful thanks to members of DfT, VOSA, DVLA and DECC, who have provided advice and support for this work, and to other members of the project team: Dr Oliver Turnbull at University of Bristol, Simon Ball and Paul Emmerson at TRL Ltd, and Dr Jo Barnes at the University of the West of England.
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