Rural Analyses of Commuting Data
Martin FrostCentre for Applied Economic Geography
Birkbeck College, London
The importance of commuting analyses for rural policy
A key source of evidence on the inter-dependencies between towns, villages and dispersed populations in rural areas as the role of a place centred land-based sector declines in relative importance
A source of evidence for inter-dependencies that cross the traditional ‘urban-rural’ divide
Significant for insights into sustainability that the environmental footprint of these journeys have
Significant for analysis of the drivers of productivity growth in rural areas
Four facets of commuting evidence based on Census records
The challenge of coding workplace and mode of travel information
The issue of small cell adjustment of Census counts The limitations and implications of table specifications
at different areal scales The problems of approximating ‘settlements’ from
aggregations of Output Areas and Wards
These issues hold for all commuting analyses – but often have a greater impact on rural analyses because of relatively sparse flows and small settlements
Workplace coding in the Census (2001)
All hinges of the Census Form question - ‘What is the address of the place where you work in
your main job?’ Census Quality Report suggests that ‘Respondent
difficulties’ included ‘respondents who have put down a part-time job,
people who have more than one occupation and those who were unsure as to which was their main job’
‘Item non-response’ was 7.8% - a few estimated from ‘Method of Travel’ question but 6.4% imputed
Coding relies on using an identifiable postcode in the address response
Workplace coding in the Census (2001)
A little more worrying was that ONS checks on the accuracy of automatic scanning of Census forms (contracted out to Lockheed Martin) showed them to be 86.1% accurate compared with an agreed target of 94.5%
Although ONS claim that many were affected by ‘impossible’ postcodes in only the final two characters of the code
In addition is the problem of households with more than one address
Plus the growing problem of irregular patterns of travel to multiple workplaces (about which we know very little)
Mode of travel coding in the Census (2001)
‘Respondent difficulties’ included ‘the most common was the use of different methods of
travel on different days. Other respondents used two methods of travel and ticked more than one. A number of respondents mentioned the method of transport they used in the course of their work.’
Item non-response was 6.3% with 5.0% ultimately imputed
Accurate data capture accuracy was high at 99.3% reflecting the ‘tick box’ nature of the Census Form response
The products of coding difficulties
The possible sources of error may occur independently but can also interact to produce ‘improbable’ journeys
Intuitively, it seems to many experienced users of Census work travel data that these problems have a stronger influence in 2001 than before
Some of this may be that people’s lives and journeys are becoming more complicated and more dispersed
Some may be the result of coding difficulties
The ‘improbable’ journeys can have a significant influence of average and median journey distances – particularly for individual modal groups – and on estimates of ‘environmental impacts’ of travel
Long journeys matter in rural areas
Mode % of journeys > 15kms
% of person kms
Person kms
Car 11.8 49.7 29,959,081
Bus 5.5 37.3 738,441
Cycle 5.0 44.9 631,315
But about 7 million person kms of car commuting contributed by people who state they drive more than 150kms (each way per day??)
Possible ‘cut-offs’ for ‘improbable’ journeys
One approach is to use National Travel Survey data to estimate speeds of commuting travel by mode – and then apply ‘common sense’ upper limits
In some work we have applied a three hour cut-off.
But…. this would eliminate all the journeys of more than 150kms included on the previous slide
Numbers commuting from London by Underground
People (per Output Area)
3
4
5
6
7 - 13
The issue of small cell adjustment
Travel to work tables (particularly for small areal units such as Output Areas or Wards) are notoriously sparse
To maintain anonymity small cell adjustment sets any values of 1 or 2 travellers between any pair of areas to either 0 or 3
The effect is constrained to be neutral over the total extent of any table – but it may not be neutral for individual origins or destinations
The positive side is that all previous Censuses measure work travel on a 10% sample of returns
Small cell adjustment – a simple test
Travel between North Hertfordshire and London estimated by adding up all constituent Output Areas, Wards and treating Local Authority as a whole
Output Areas 5,735 9.6% of employed residents
Wards 5,840 9.8%
Local Authority 5,692 9.7%
Table specifications
One big issue for the work travel analysis of relatively small places – there is no male/female breakdown of travellers at the Output Area scale
We know that there are still significant differences between the average journey lengths of men and women (male journeys tend to be longer across almost all labour market sub-groups)
Analyses including a gender component are forced to approximate settlements (rather badly) by ward level definitions – emphasises issue of approximating settlement boundaries
Lowestoft
Thetford
Ipswich Urban Area
Harwich
Felixstowe
Sudbury
Haverhill
Bury St Edmunds/Fornham All Saints
ColchesterBraintree
Great Yarmouth Urban Area
Key
Urban Areas
OA Approximation
Divergence between settlement boundaries and output area approximations:Suffolk
Bury St Edmunds/Fornham All Saints
Key
Urban Areas
OA Approximation
OA Boundaries
Divergence between settlement boundaries and output area approximations:Bury St Edmunds
Bury St Edmunds/Fornham All Saints
Key
Urban Areas
OA Approximation
Ward Approximation
Ward Boundaries
Divergence between settlement boundaries, output area and ward approximations:Bury St Edmunds
The effects on rural analyses of work travel
Often limited to using ward-level approximations of settlements
A particularly severe problem for the current definitions of what is ‘rural’
Difficult to use travel distances to estimate environmental impact of travel as mode groups often have inflated average and median distances
Difficult to map ‘catchment areas’ around settlements
Partly because travel directions and links are very complex Partly because small cell adjustment can have significant
influence of relatively small settlements
Difficult to focus on the characteristics of individual settlements
But……strategic views are still viable – the changing pattern of commuting, 1981-2001
(% change in commuters) From LS Town LS Village S Town S Village
To
Metro Urban 12.0 20.1 60.8 85.3
Large Urban 13.1 20.2 107.4 21.5
Other Urban 17.6 15.4 67.5 71.1
Market Towns
26.6 11.0 62.4 43.4
Less Sparse Town
-25.1 15.8 32.9 12.0
Less Sparse Village
30.0 -22.9 53.7 18.2
Sparse Town 76.8 63.0 -19.8 9.6
Sparse Village
65.5 40.7 0.0 -26.1
Concluding comments
Many of the data quality issues are difficult to quantify – and lead to considerable uncertainty particularly at local scales
It is highly uncertain whether environmental impacts of commuting and urban form/expansion can be adequately tackled – which is a pity
Analyses work best when meaningful aggregation is possible - but the ONS classification of rural areas (which has an upper settlement size limit of 10k residents) will usually need to be extended to include a classification of ‘urban’ as well as ‘rural’ settlements
At a ‘strategic’ level these ageing results are still relevant – it’s a long time before the 2011 data will be available!