Combining Spatial Network Analysis with demographics to ... · Combining Spatial Network Analysis with demographics to study the effect of segregation on cycling mode share AET European

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Combining Spatial Network Analysis with demographics

to study the effect of segregation on cycling mode share

Crispin Cooper, Cardiff University

Ringo Chan, Arup

AET European Transport Conference

Dublin Castle October 2018

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1. Spatial Network Analysis

2. Socio-Demographics

3. Case study

4. Comparing findings

5. Conclusions and future opportunities

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Combining Spatial Network Analysis with demographics to study the effect of segregation on cycling mode share

AET European Transport Conference October 2018

What is sDNA?Detailed network model

Landuse information

Active travel network

Traffic and cyclist counts

Route suitability and quality

Model inputs:

Benefits:- Detailed spatial network

- Modelling at link level, the number of

trips from every link to every other link

based on physical attractiveness to

cyclists

- Modelling with different aversion

factors to examine cyclist’s sensitivity to

road traffic and levels of segregation

Landuse density Road traffic

Network quality Cycling provision

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Spatial network analysis (sDNA)A unified spatial network model that brings together all physical determinants of cycling activities

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Socio-demographic model

A logistic regression model of cycling mode share, using relevant

socio-economic variables and variables representing the transport

system and the physical determinants of cycling.

Socio-demographic variables

- Gender

- Car ownership

- Ethnic background

- Household income

- Socio-economic classification

- Population density

Transport system variables

- Volume of road traffic

- road condition

- Quality of cycling provision

Physical factor variables

- Commuting distance

- Prescience of motor

- Hilliness

- Weather condition

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The study area

Cardiff, Capital city of Wales- 10th largest city in the UK

- Urban population 0.5M

- Increasingly young population

- Growing minority ethnic population (from 7% in 1991 to 15% in 2011)

- Some of the most affluent and most deprived neighbourhoods in Wales

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The study area

Cardiff Cycling Strategy- Currently 9.2% of people cycle to work, compared to 56.1% by car

- Vision is to double cycling mode share to 18.4% by 2026

- 16 km of fully segregated cycle superhighways

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Comparing results

R2 = 0.45R2 = 0.35 R2 = 0.49

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Comparing results

WIM

D

Prop_

Emp_

SEC7

Prop_

EA_

NWhi

te

Prop_

D_20

kmov

er

Prop_

Emp_

SEC1

1

Prop_

Emp_

SEC1

2

Prop_

D_20

km

Prop_

Emp_

SEC5

Prop_

Emp_

SEC2

Prop_

EA_

Male

Prop_

Emp_

50_59

Avg_t

6_Ln

k8000

Prop_

D_10

km

Avg_t

8_Ln

k3000

Avg_t

8_Ln

k1100

0

Prop_

Emp_

HQ

Avg_t

6_Ln

k1100

0

Avg_t

8_Ln

k1500

0

StdCoeff -0.11 -0.05 -0.05 -0.03 -0.01 -0.01 -0 -0 0.002 0.012 0.016 0.023 0.041 0.095 0.106 0.204 0.241 0.252

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

StdCoeffSocio-demographical results reflect known findings

in the UK with valid variables being:

• level of deprivation, ethnic and social

background, and academic qualification.

Accessibility results show new valid variables that

influence cycle journeys:

• 5-10km journeys has the most influence and

longer journeys become serious deterrence

• the higher the influence placed on full and light

segregated routes and on-road route with less

traffic, the more importance in the prediction

Index of multiple deprivation ranks -0.11

NS-SeC 7: Positions with a basic labour contract -0.05

Non-white working adult -0.05

Working adult with high level qualification 0.20

Journeys in the distance band 20km and over -0.03

Journeys in the distance band 5-10km 0.04

Accessibility within 11km perceived distance 0.24

Accessibility within 15km perveived distance 0.25

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Comparing resultsAverage predictions at various parts of the city

Estimated cycling model share

! 1%

! 2%

! 3%

! 4%

! 5%

! 6%

! 7%

! 8%

! 9%

! 10%

Estimated cycling model share

! 1%

! 2%

! 3%

! 4%

! 5%

! 6%

! 7%

! 8%

! 9%

! 10%

Census 4.0%

Predicted 4.8%Census 10.9%

Predicted 7.3%

Census 1.6%

Predicted 1.5%

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Conclusion

Study shows possibility to

1. Combine socio-demographic and spatial network accessibility analysis

2. Examine the impact of road traffic and segregated cycling infrastructure

Potential application:

• Assess the effectiveness of existing infrastructure

• Estimate likely cycling mode share of new development

• Identify areas for improvement, including bike sharing

• Evaluate scheme impact to mode shift or cycling flow

Future opportunities:

• Review in the next Census to examine sensitivity of social-demographic

• New variables, e.g. lifestyle, workstyle and household composition

• ‘Shared bus and cycle lane’

• District/Sub-area groups

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Thank you

Arup

63 St Thomas St

Bristol

BS1 6JZ

UK

Cardiff University

Sustainable Places

33 Plas y Parc

Cardiff

CF10 3BA

UK

Crispin Cooper,[email protected]

Ringo [email protected]

Documents

Combining Spatial Network Analysis with demographics to ... · Combining Spatial Network Analysis with demographics to study the effect of segregation on cycling mode share AET European