©NERIP 2005 Commuting and Workplace Research Phase 2 23 rd November 2005 Michael Jackson North East Regional Information Partnership

©NERIP 2005 Introduction  TRL Ltd completed three areas of work: - Have you changed your commuting pattern too? Who’s working in the Key Employment Centres of the North East? Does where you live affect your propensity to commute?  Sources of data Census th April 2001

©NERIP 2005 Have you changed your commuting patterns too?  Why has there been an increase in commuting within the North East?  Is it because: - Increases in working residents? Increases in employment places? Changes in the behaviour of workers? Some of everything?

©NERIP 2005 Have you changed your commuting patterns too?  Observed changes between 1991 and 2001  Expected changes between 1991 and 2001 Assuming no change in behavioural patterns  Difference between Observed and Expected Changes in behaviour? Causes

Observed changes Blue are increases Red are decreases Circles represent intra- district flows Arrows give direction of flow Only flow changes greater than 200 workers are shown

©NERIP 2005 Observed changes  4.8% increase in workplaces, 5.8% increase in working residents (within NE).  Not uniform growth Employed residents – Stockton +11.6%, Alnwick +11.0%, Redcar -0.9% Workplaces – Durham +14.5%,Sunderland +15.3%, Sedgefield -8.6%, Redcar -6.6%  Decline in most intra-district flows Easington -11%, Sedgefield -15% Exceptions are Sunderland (+6%) and Stockton on Tees (+4.6%).  Increase in most inter-district flows Largest percentage increases are small flows but A number of exceptions – around Middlesbrough, Hartlepool, Gateshead and Castle Morpeth

©NERIP 2005 What are the causes of the changes in the level of commuting?  Assuming 2 main factor: - Increases in working residents and or employment places Changes in the propensity to commute further  Why? RES will result in higher economic activity Does the current transport infrastructure have the capacity to support this growth? Are the commuting behaviours optimum for the capacity of the infrastructure or what are the challenges for policy intervention?  How? Investigate the effects of economic growth only Deduce the effects of the changes in commuting behaviour Mathematical technique known as ‘Furnessing’ - also known as Bi-proportionating.

Expected changes in commuting flows assuming no change in underlying commuting behaviour Blue are increases Red are decreases Circles represent intra-district flows Arrows give direction of flow Only flow changes greater than 200 workers are shown

Observed changes Blue are increases Red are decreases Circles represent intra- district flows Arrows give direction of flow Only flow changes greater than 200 workers are shown

Expected changes in commuting flows assuming no change in underlying commuting behaviour Blue are increases Red are decreases Circles represent intra-district flows Arrows give direction of flow Only flow changes greater than 200 workers are shown

©NERIP 2005 Expected Changes in Commuting patterns  Intra District Flows Most districts would see increases in intra-district trips Durham City, Sunderland, Stockton >12% Declines in Redcar & Cleveland and Sedgefield 6%  Inter District Flows But most districts see a modest increase towards flows to major towns and cities Some decline especially from Gateshead & Easington and to Sedgefield Clearly major epicentres of Tyne & Wear, Durham City and Darlington/Stockton

©NERIP 2005 Estimating changes in commuting behaviour  Changes in travel behaviour = Observed changes ( ) - Expected changes ( )  Assume all the unexplained change is due to changing commuting behaviour – in reality will include some measurement errors

Changes in underlying commuting behaviour Blue are increases Red are decreases Circles represent intra-district flows Arrows give direction of flow Only flow changes greater than 200 workers are shown

Changes in underlying commuting behaviour Tyneside + North Durham Blue are increases Red are decreases Circles represent intra-district flows Arrows give direction of flow Only flow changes greater than 200 workers are shown

Changes in underlying commuting behaviour Tees Valley Blue are increases Red are decreases Circles represent intra-district flows Arrows give direction of flow Only flow changes greater than 200 workers are shown

©NERIP 2005 Changes in Commuting behaviour  Intra District Flows All intra district flows reducing Largest % reductions - Durham City, Chester le Street and Teesdale >10% Largest absolute reductions – Districts of TW, Durham City, Darlington, Stockton  Inter District Flows Most districts see a modest increase towards flows to major towns and cities Northumberland 7 to 34% (Berwick) Tyne Wear 11 to 25% (Newcastle) County Durham 6 to 29% (Durham City) Tees Valley 10 to 29% (Darlington) But some two flows also increase Tynedale & Newcastle, TW districts, Durham City & CLS, 4 of the TV districts

©NERIP 2005 Examples of Changes in Commuting Behaviour

©NERIP 2005 Why has there been and increase in level of commuting?  Increases in working residents and or employment places? Yes, +5.8% & +4.8%  Changes in the propensity to commute further Yes Major impact of the change appears to be behavioural Concentrated around urban areas of Tyne Wear and southern Tees Valley  More individuals are commuting, longer distances, to more places !

©NERIP 2005 Why has commuting behaviours changed?  Full investigation is beyond scope of study  However some of the reasons could be:- Occupational structures Car ownership Highway infrastructure Domestic arrangements Travel costs Public Transport infrastructure

©NERIP 2005 Policy Angle -RSS  RSS aim is: - “to reduce the need to travel, particularly by private modes of transport”  Evidence suggests: - More travelling, longer journeys More inter district journeys  Policy Challenges Commuting is likely to continue to increase as economic growth continues – how can the transport infrastructure be more efficiently used? More focus on “City Region” transport solutions Improving accessibility to “non-town centre” sites by public services

©NERIP 2005 Have you changed your commuting patterns too?  Main Report  Appendix A Spreadsheet containing data relating to the components of commuting flow changes 1991 to 2001  Any Questions?

©NERIP 2005 Commuting and Workplace Research Phase 2 Who’s working in the Key Employment Centres of the North East? 23 rd November 2005 Michael Jackson North East Regional Information Partnership

©NERIP 2005 Who’s working in the Key Employment Centres of the North East?  Definitions of Key Employment Centres  Analysis of Largest 20 Centres By age of worker Socio-economic status Social Class Commuting distances Commuting mode  Key Employment Centre Profiles

©NERIP 2005 Defining Key Employment Centres  Where do workers travel to for employment?  Even the use of ward data is not fine enough Possible to have two employment centres in the same ward (Newcastle Business Park and City-centre both in Castle ward) Many employment centres straddle more than one ward  Use of Output Area Workplace data from 2001 Census, plus  2001 Origin-destination Output Area data  No equivalent 1991 data  Data limitations Individual records swapped between Output Areas Cell with small numbers subject to perturbations Some rounding of very small cell-sizes (mod 3) Where possible use highest level of data possible Where a choice use table with fewest cells aggregated Origin-destination tables worst affected

©NERIP 2005 Defining Key Employment Centres  Types of data limited at Output Area (and ward level) for workplace data ONS increase in concern for confidentiality has led to only few tables for workplace data at Output Area level  Age  Socio-economic classification  Social Grade  Commuting distances  Commuting mode  Origin and Destination flows by mode and sex  Available data is subject to censoring –ONS claim still better than using a 10% sample as in 1991 census.

©NERIP 2005 Defining Key Employment Centres  Previous work identified (3,500 workplaces or 12 workplaces per ha) based on ward data Need to define cores for employment centres then additional adjacent areas  Criteria for this study:- Employment Core - Output area with >2,000 workplaces or Employment Core – Output area with workplace density >50 per ha forming the core for an agglomeration of OAs totalling over 2,000 workplaces Adjacent areas - contiguous output areas with workplace density >50 per ha to form full employment centre  Need for additional judgement - business parks & industrial sites per ha (OAs >1,600 persons)

©NERIP 2005 Example of Employment centre - Cramlington Built up area and output areas

©NERIP 2005 Example of employment centre definition Cramlington Stippling = OAs with 2,000 workplaces Orange = OAs with >50 workplaces per ha.

©NERIP 2005 Example of employment centre definition - Cramlington – Final definition of centre

Key Employment Centres of the North East 2001

©NERIP 2005 Key Employment Centres – Top 20

©NERIP 2005 Key Employment Centres – Next 30 +1

Comparing age structure of top 20 Centres

Comparing socio-economic structure of Top 20 Centres

Comparing social grade structure of top 20 Centres

Comparing workers commuting distances to top 20 Centres

Comparing workers mode of travel to top 20 Centres

©NERIP 2005 Policy Angle -RSS  RSS aim is: - “to reduce the need to travel, particularly by private modes of transport”  Evidence suggests: - Private car is the preferred mode of transport Even in Newcastle City Centre 40% use car and 60% in Middlesbrough  Policy Challenges Aligning public transport with the needs of commuters – in particular in City Centres

©NERIP 2005 Who’s working in the Key Employment Centres of the North East?  Main Report  Appendix C Spreadsheet containing the list of 50 Key Employment Centres in the region  Employment Centre Profiles 27 Detailed Profiles Durham_City_Centre_Employment_Centre_EC31_August_2005  Any Questions?

©NERIP 2005 Commuting and Workplace Research Phase 2 Does where you live affect your propensity to commute? 23 rd November 2005 Michael Jackson North East Regional Information Partnership

©NERIP 2005 Does where you live affect your propensity to commute?  Worklessness context Worklessness = economically active but unemployed + economically inactive and in receipt of certain benefits.  Is the role of geography important in employment deprivation? Areas of deprivation and worklessness often have poor access to jobs and limited travel horizons.  Local variations in accessibility may restrict the jobs available for workers (and potential workers) in deprived areas

©NERIP 2005 Defining sample and comparator areas  Worklessness sample areas - aggregations of Output Areas in the 10 th (worst) decile for the country as a whole.  Can only be highlighted by comparing with adjacent but with less economically deprived populations (Comparator areas) Defined as adjacent to study area but containing Output Areas in the 3rd-8 th deciles – ideally.  Sample areas had:- Deprived areas and non-deprived areas in close proximity > employed persons (sampling considerations) Both sample and comparator areas were either both rural or both urban. 17 sample areas chosen some rural (mostly ex-coalfield areas), some urban (mostly suburban estates)

©NERIP 2005 Study areas – example Area 7 Horsley Hills (South Shields) Worklessness Deciles Dark Green = 1 st and 2nd decile (best) Light Green = 3 rd - 8 th decile Orange = 9 th decile Red = 10 th decile (worst) Shading represents study areas. Hatching = Worklessness study area Stippling = Comparator area

©NERIP 2005 Relationship of study area to built-up area - Sample area 7 - Horsley Hills (South Shields) PINK = WORKLESSNESS SAMPLE AREA - BLUE = COMPARATOR AREA

Location of sample areas

©NERIP 2005 Characteristics of sample areas  Ideally:- Sample and comparator areas would have the same population and worker characteristics but:  In ALL cases the characteristics were different Obviously unemployment Also car ownership (across all households in area) Generally relatively fewer female workers in sample area but there are examples of the converse (Wallsend, Haswell)  Compare travel to work characteristics Graphs of commuting distance and mode-choice Numbers on Y axis – last digit (1= sample area, 2= comparator)

©NERIP 2005 Distance to work characteristics

©NERIP 2005 Mode Choice characteristics

©NERIP 2005 Commuting characteristics  Workers in study areas travel less than comparator areas in nearly every case (equal in Wallsend and similar in Doxford Park)  Use of the car is much less in study areas than in comparator  Results expected given difference in car ownership but:-  Even considering car users only study area car drivers travel to less distant districts especially Newcastle. Example of Wallsend. Also true for 15 out of /17 study areas.

©NERIP 2005 Study area 16 - Wallsend PINK = Sample area BLUE = Comparator

©NERIP 2005 Workplaces for the Wallsend study area (Area 16) % of commuters in an area travelling to different districts

©NERIP 2005 Worklessness and Commuting - Conclusions  Any comparison of worklessness areas with adjacent areas with lower unemployment is complicated by the difference in population characteristics  Access to car is important – Comparators use car more to work Travel further to work BUT even car drivers travel further in areas of lower unemployment compared to worklessness areas.  Occupational differences?  Data limitations to further work.

©NERIP 2005 Does where you live affect your propensity to commute?  Main Report  Appendix E Spreadsheet containing the characteristics of the workers from areas of high “worklessness” and those in less deprived areas used in this study  Any Questions?

©NERIP 2005 Commuting and Workplace Research Phase 2 Main Report rd November 2005 Michael Jackson North East Regional Information Partnership