ACCESSIBILITY EFFECTS OF RELOCATION AND HOUSING PROJECT FOR THE URBAN POOR IN AHMEDABAD, INDIA

A/Prof. Mark Zuidgeest

South African Cities Network/University of Pretoria, 09 April 2018

“Ability to readily move people from place to place”

Key-words: Networks and modes: how to get there? Speed: how fast? Cost: how expensive?

Indicators of success: Vehicle Kilometres Travelled (VKT)

In other words: maximizing movement

MOBILITY

Easiness to enter, reach, and use (aka access)

The ease of reaching goods, services, activities and destinations (together called opportunities) (aka accessibility)

Key-words: Opportunity: A chance for employment, leisure, etc Impedance: Difficulty of getting there Utility: Satisfaction experienced

Indicator of success The ability to reach destinations

ACCESSIBILITY

"Just as automobiles are machines that provide mobility, urban environments - villages, towns and cities - can be thought of as machines that provide accessibility by minimizing the distance among people and their desired goods, service and activities

(shops, schools, jobs, neighbors, etc.).“ [Litman, 2010]

A PARADIGM SHIFT IS NEEDED

“Travel is a derived demand”, i.e. the demand for travel is derived from the demand for spatially-separated activities:

1. Behaviour and choices of people and companies.

2. Locations and type of spatially bound activities, such as residing, working, recreation.

3. Resistance to overcome a distance (impedance), in terms of time, costs and other factors, such as safety & security, comfort etc.

CONCEPTUALIZING ACCESSIBILITY

Point of Origin

Point of destination

(Utility/Benefit)

Transport network

(Impedance)

CONCEPTUALIZING ACCESSIBILITY

our focus

• Contour measures (cumulative opportunity) – measures the cumulative number of (job) opportunities that can be reached in a given time or at certain threshold distance from a specified origin.

• Potential measures (activity based) – discounts the number of (job) opportunities that can be reached from a specified origin.

MEASURING ACCESSIBILITY

Indicator for the effectiveness of the transport system ability to reach employment areas, service locations, centre

areas etc.

Indicator for the availability of services securing a geographical match between resource allocation

and resource needs

ACCESSIBILITY MEASURES – CONT’D

CASE STUDY AHMEDABAD, INDIA

• Ahmedabad is the largest city of Gujarat state on the banks of Sabarmati river and the seventh largest city in India [total area = 190 km2].

• Current population Ahmedabad city is about 5.5 million. • In 1994 many mills faced liquidation and were officially closed

leaving nearly 67,000 of workers jobless.• The percentage of people living in low-income housing is about

40% (25% in slums, 15% in industry estates (chawls))• Jawaharlal Nehru National Urban Renewal Mission (JNNURM),

2005: • urban poor housing relocation scheme • Introduction of Bus Rapid Transit (BRTS) as well as Metro

system (MRT) • What are accessibility effects of the (proposed) relocation

and public transport upgrading schemes for the urban poor?

AHMEDABAD

Conceptual framework

CASE STUDY AHMEDABAD

Urban poor

Physical condition

of housing

-Income

-Education level

Transport

-Modes-Networks-Operation

Employment

-Job type

-Location of jobs

DATA

Analysis activity Key data sources Data source to test model assumptions

Data source for model validation

Determining locations of the urban poor

Locations of slums and chawls (remote sensing)

Expert interviews

Determining locations of employment

Ahmedabad property tax data

Expert interviews

GIS based network modelling

Various networks, AMTS and BRTS with their characteristicsMode use of urban poor

n.a. n.a.

Contour based accessibility modelling

BSUP housing locations Focus Group discussions

Gravity based accessibility modelling

Ahmedabad household surveyDistance decay curves

Focus Group discussions

Current and proposed public transport modes 3D – ArcGIS mutli-modal network model, allowing for transfers

INTEGRATED MULTI-MODAL TRANSPORT NETWORK

NETWORK SETTINGS

LOCATIONS OF THE URBAN POOR AND THEIR JOBS

Remote sensing data to capture housing types Classify level of poverty (least poor, middle poor, very poor) Slums and chawls combined, Worker Density per 0.25ha

DENSITY OF POTENTIAL WORKERS

±

Legend

slum location

riverdensity potential workers

[per .25 hect.]7. - 1010. - 2525. - 9090. - 400400. - 1,970 0 2.5 5

Km

Formal employment - all job sectors combined, 400 x 400m. Grid:

LOCATIONS OF EMPLOYMENT

Job sectors, partly as proxies for informal employment:• Industrial• Retail• Government• Education• Transport and logistics• Office and commercial jobs• All jobs combined (shown here)

Categorized as:• Casual labour jobs• Salaried jobs• Self employment jobs

± 0 2.5 5Km

Legend

Employment [ jobs]

1 - 100

100 - 250

250 - 500

500 - 1000

1000 - 3000

roads

river /lake

Connecting workers and potential jobs (expert knowledge – Ray (2010) and focus groups)

LINKING POOR WORKERS AND JOBS

Urban poor

Least poorMiddle-poorVery poor

Employment

SalariedSelf employmentCasual labour

Transport network

DISTRIBUTION OF URBAN POOR CLASSES AND EMPLOYMENT CATEGORIES

JnNURM, Basic Services to Urban Poor (BSUP) program

Socially & Economically Weaker Section Housing (SEWSH) scheme

21 SEWSH locations 976 new buildings relocating 78,080 poor

Used as a scenario in this study

SOCIALLY & ECONOMICALLY WEAKER SECTION HOUSING (SEWSH)

S1: IMPACT OF SEWSH RELOCATION PROGRAMME

9

8

7

65

4

3

1

2120

19

18

17 16 15

141312

11

10

± 0 2.5 5Km

Legend

sewsh location

AMTS lines

roads

SEWSH

Contour [min]

0 - 10

10 - 20

20 - 30

30 - 45

45 - 60

river/lakeModes: Walking

AMTS

9

8

7

65

4

3

1

2120

19

18

17 16 15

141312

11

10

± 0 2.5 5Km

Legend

sewsh location

AMTS lines

BRTS lines

MRTS metro

roads

Contour [min]

0 - 10

10 - 20

20 - 30

30 - 45

45 - 60

river/lake

Modes: WalkingAMTS

BRTS phase 1&2Metro

pointid Name1 Shahwadi2 Vatava3 Vatva Site A & Site B4 Calico Mill5 Ahmedabad Cotton Mill, Sarangpur6 Kesar Hind Mill Ni Chali7 Rustam Mill8 Vivekanand Mil9 Raipur Mill

10 Saraspur Mill11 Vijay Mill12 Odhav-1, 18713 Odhav-3, 2314 Odhav-3, 3715 Odhav-3, 3816 Odhav -3, 5117 Odhav - 3, 8618 Vadaj BSUP19 Ajit Mill, Rakhiyal20 Ishanpur21 Bag-e-Firdosh

Without BRTS/MRTS With BRTS/MRTS

Bars: walking (left), walking + AMTS (middle) and all modes (right)

IMPACT OF SEWSH RELOCATION PROGRAMME

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

45-60

30-45

20-30

10-20

0-10

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b o

pp

ort

un

itie

s

9

8

7

65

4

3

1

2120

19

18

17 16 15

141312

11

10

± 0 2.5 5Km

Legend

sewsh location

AMTS lines

BRTS lines

MRTS metro

roads

Contour [min]

0 - 10

10 - 20

20 - 30

30 - 45

45 - 60

river/lake

Mode: WalkingAMTS

BRTS phase 1&2Metro

pointid Name1 Shahwadi2 Vatava3 Vatva Site A & Site B4 Calico Mill5 Ahmedabad Cotton Mill, Sarangpur6 Kesar Hind Mill Ni Chali7 Rustam Mill8 Vivekanand Mil9 Raipur Mill

10 Saraspur Mill11 Vijay Mill12 Odhav-1, 18713 Odhav-3, 2314 Odhav-3, 3715 Odhav-3, 3816 Odhav -3, 5117 Odhav - 3, 8618 Vadaj BSUP19 Ajit Mill, Rakhiyal20 Ishanpur21 Bag-e-Firdosh

IMPACT OF SEWSH RELOCATION PROGRAMME

Specifically looking at locations 1, 13, 14, 15, 16

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= +/- 1 km usingthe roads

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20

10

0 1 2Km

0 1 2Km

Legend

AMTS stops

#* BRTS stops

&- SEWSH location

AMTS lines

BRTS lines

roads

employment

Clearly the housing relocation project (versus BRTS/MRTS) has more losers than winners

IMPACT OF RELOCATION ON JOB ACCESSIBILITY

0-30 min walking walking + AMTS all modesBefore relocation 78,000 jobs 148,000 jobs 215,000 jobs

After relocation 40,000 jobs 61,000 jobs 80,000 jobsWinners 4 locations

(5, 6, 10, 11)3 locations(5 ,6 , 10)

3 locations(5, 6, 10)

Losers 17 locations(1-4, 7-9, 12-21)

18 locations(1-4, 7-9, 11-21)

18 locations(1-4, 7-9, 11-21)

30-60 min Walking walking + AMTS all modesBefore relocation 353,000 jobs 798,000 jobs 910,000 jobs

After relocation 155,000 jobs 545,000 jobs 637,000 jobsWinners 6 locations

(5-10)7 locations(5-11)

9 locations(5 – 11, 19, 20)

Losers 15 locations(1-4, 11-21)

14 locations(1-4, 12-21)

12 locations(1-4, 12-18)

The potential of opportunities for interaction

with Wj the number of jobs in location j, cij the generalized cost of travelling between i and j, and f(cij) the distance decay function

Distance decay functions (two modes):

CALCULATING POTENTIAL JOB OPPORTUNITIES

)exp()( j

ijjj

ijji cWcfWA

CALCULATING POTENTIAL JOB OPPORTUNITIES City-wide Potential accessibility analysis (least poor to salaried jobs)

CITY-WIDE POTENTIAL ACCESSIBILITY ANALYSIS

Ratio of job-based potential accessibility for all potential workers comparing all public transport options with walking and AMTS only.

Impact mostly linear, and in close vicinity of the proposed systems.

Relative to walking/AMTS alone

EFFECT OF BICYCLE FEEDERS (ALL POOR)

Overall the level of potential accessibility for the locations improves by 135% on average for the 21 SEWSH locations

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±Legend

SEWSH potential jobs

200,000

cycling contribution

walk + PT

#* BRTS stops

_̂ MRTS stations

BRTS buslines

MRTS lines

roads

river / lake

0 2.5 5Km

OVERALL PUBLIC TRANSPORT IMPACT

Walk

+ AM

TS

+ BRTS 1

+ BRTS 2

+ MRT

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

SEWSH

Least poor

Middle poor

Very poor

SEWSH

Least poor

Middle poor

Very poor

0%5%

10%1

5%

8%

4%

13%

8%

14%

9%

+ BRTS 1 + BRTS 2 + MRT

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The very poor workers benefit least

CONCLUSIONS

There is variation between accessibility to jobs for different urban poor groups.

Compared to walking, the existing and proposed public transport improvements do improve job accessibility considerably.

BRTS/MRTS impact is mainly in the vicinity of the systems. Cycling provides a good first/last-mile access to and from the

proposed BRTS/MRTS, combining the strengths of both NMT/PT.

The housing relocation scheme clearly hasn’t

considered transport and promotes exclusion These effects are likely to be exacerbated when including

affordability.

PROJECT TEAM: CASE STUDY AHMEDABAD

The World Bank group Nupur Gupta, Andrew Salzberg and Samuel Zimmerman

University of Twente – Faculty ITC Mark Zuidgeest (PI) (formerly), Mark Brussel and Martin van

Maarseveen Frans van den Bosch and Nguyen Ngoc Quang Talat Munshi (CEPT University, Ahmedabad)