Rural Analyses of Commuting Data

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Rural Analyses of Commuting Data

• Martin Frost
• Centre for Applied Economic Geography
• Birkbeck College, London

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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

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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

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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

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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)

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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

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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 peoples 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

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Long journeys matter in rural areas
Mode of journeys gt 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??)
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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

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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

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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

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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

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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

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Butstrategic 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
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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 its a long time before the 2011
  data will be available!