Gordon Stokes Transport

Where should homes and jobs be located?

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Last updated - Dec 2018

Pages in this series
Map 1 - Basic driving related characterstics of work journeys

2 - Maps with other characterics including other modes of travel.
Slower to load and more complex to use.

3 - Oxford to Cambridge travel patterns

Notes on methodology

Where should we locate homes and jobs for the best affect travel patterns?

The locations of homes and workplaces are a major factor relating to how much, and what type of travel they produce. This is important in terms of traffic congestion, climate change, pollutants, and the health of people (in terms of their levels of activity and the effects of pollutants). Currently it is of especial importance in the UK given the rush to build homes to 'solve the housing crisis', and the recent weakening of planning controls concerning the locations of new build.

The aim is to demonstrate which areas produce high and low levels of car use in relation to other modes of travel, and to show the types of area where different types of mode can flourish, or not. They may also demonstrate the scope for behaviour adaptation.

What's on this page/ in this analysis

The links on the left are to mapping relating to the commuting travel characteristics for England and Wales, using 2011 census data. I hope that they will be of use to those interested in a particular area and comparing that area to others, but also for seeing the types of area where transport impacts of new housing or other development might cause problems.

The 3rd map (Oxford to Cambridge) is a fixed scale set of maps with commentary, related to the scope for non car based travel in the Oxford to Cambridge corridor which is currently the subject of a proposal for an "expressway" and a massive allocation for housing.

The maps show circles describing travel characteristics for each Middle level Super Output Area (MSOA) zone, each of which have a population of around 8000 people.

The two links differ:

  1. The first shows three options:
    1. Car distance driven per working resident in a residential zone
    2. Car distance driven per job located in a zone
    3. The percentage of cars available to all residents of a zone that are used for a journey to work
  2. The second has a large number of options covering modes other than the car, all of which can be displayed per resident or per job
    1. Distance driven per resident (the first two options in Map 1)
    2. Total distance travelled by all modes
    3. Distance driven per driver (the average commute distance
    4. The percentage of all commuting distance that is made as a car driver
    5. An indication of the predominance of short (under 5km) and long (over 30km) commutes

    6. The percentage of journeys made by public transport (bus, train, metro etc and taxi)
    7. The percentage made by 'active travel' modes (walk and cycle)
    8. The percentage made as a driver (different from the % of distance in #4 above)
    9. The percentage made by other modes, mainly car passenger, also motorbike and 'other' modes

In all cases the circles vary in size, usually according to the resident or working population. They also vary in colour, according to some travel behaviour related measure.

You can zoom in and out using the buttons at the top left, and use the blue movement buttons to move in different directions.
I'm sorry but you can't use the mouse wheel or drag the map. Firstly, I don't know how to, and secondly, even if I did I think it would take some time for the map to change by which time you'd have zoomed in or out way to far, or moved across to Japan!

Help with interpreting the maps

Small section example maps are shown below:

Driver distance per resident (left) and Driver distance per job (right)

Average distances maps (Map Set 2)

When on the map page, clicking on each coloured button displays a different map.

Kms driven per resident or job (also on Map 1)
Distance driven divided by working residents or number of jobs. The average is about 6kms, with 25% of zones under 4kms and 25% over 8kms.

Kms travelled by all
Total distance is divide by residents, or number of jobs. The average is about 9km with 25% below 7kms and 25% over 11kms. Mauve is over 13kms.

Km distance per driver
Total distance driven divided by the number of residents/ workers who drive to work, giving the average distance for those who drive. The average is about 11kms with the quartiles at 9 and 13kms. Mauve is over 15kms.

% of travel distance that is as driver
The total driver commutes is divided by the total distance of commutes. Red and mauve show cases where over 75% and 90% is as driver.

Indications of areas with predominance of short or long journeys (Map Set 2)

When on the map page, clicking on each coloured button displays a different map.
These maps give an indication as to the extent to which shorter journeys to work (less than 5kms) outnumber longer journeys (over 30kms). The red circles are for longer journeys while the green ones represent shorter journeys. In effect, the greener the image, the more short journeys predominate.
One is for the journeys people make from home, while the other is for journeys centred on job locations.


Numbers of trips by different groups of modes (Map Set 2)

When using the map page, clicking on each coloured button displays a different map.

Public transport
The proportion who's main method of travel to work is bus, train, metro or taxi, with each category being broad in its definition. So bus includes works buses, and metro includes all forms of light rail.
The national average is about 8% while the lower and upper quartiles are about 5% and 14%. In 10% of cases over 29% use public transport - virtually all of these are in London with a few in Birmingham, Manchester and other large cities.

Active travel
The percentage who's main method of travel to work is walking or cycling.
The national average is about 15% while the lower and upper quartiles are about 12% and 20%. In 10% of cases over 24% walk or cycle - These are found in the centres of most cities, and throughout Oxford, Cambridge, York Cambridge and a many smaller towns.

The percentage who's main method of travel to work is driving a car or van.
The national average is 65%, while the quartiles are 52% and 74%. In 10% of cases over 80% drive. The lower figures are found in London, other large cities and historic cities, where public transport and active modes are more common. The highest rates are found in rural areas, but also many suburban areas. For workplaces central locations have low figures, but out of centre locations have high rates of driving.

The percentage who's main method of travel to work is none of the above, but who do travel to work. This is mainly car passenger, and also powered two wheelers, and "other" which can include ferries etc.
The national average is 7%, while the quartiles are 5% and 8%. In 10% of cases over 10% drive. Teh patterns are complex, but it seems that the highest rates are found in areas where wages are low and where car ownership is low. It seems a good indicator of deprivation.

% of all residents' cars used for commute
This divides the number of residents who record driving a car to work as their main mode by the number of cars recorded as available to all in the residential zone. It's comparing two different sources, but is illuminating. The averahe is 54% with the quartiles at 46 and 60%. In the highest 10% over 63% of cars are used for commuting. The patterns are, again, complex, as an area with an ageing population may have many cars which will not be used for commuting. In affluent areas car ownership is higher so more cars may be used by non workers. But in areas with higher levels of deprivation but poor public transport there may be an element of 'forced' car ownership.


What do the maps tell us? (work in progress)

The maps contain a huge amount of information and this can be interpreted in different ways. So far I have looked at the maps and compared what they show with what I know of urban, suburban and rural geography. I'm working on using regression and other analysis techniques to make more quantitative sense of them. In the meantime, and to my mind the main conclusions are:

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Gordon Stokes, 2019