ANALYSING THE DIVERSITY OF DEPRIVED AREAS IN MUMBAI… · MUMBAI, INDIA MONIKA KUFFER ... For...

ANALYSING THE DIVERSITY OF DEPRIVED AREAS IN MUMBAI, INDIA

MONIKA KUFFER ([email protected])

KARIN PFEFFER, RICHARD SLIUZAS, ISA BAUD, MARTIN VAN MAARSEVEEN

Source: Google Earth

� Global urbanization trends

� Data on slums – deprived areas

� Mapping slum areas

� Mapping the diversity of deprived areas

CONTENT OF PRESENTATIONOVERVIEW

� Mapping the diversity of deprived areas

� Some concluding remarks

15/07/201636th EARSeL Symposium - M. Kuffer, 2016

URBANIZATION RATESAFRICA – ASIA – LATIN AMERICA - CARRIBBEAN

36th EARSeL Symposium - M. Kuffer, 2016 15/07/2016

URBAN POPULATION OF MEGA CITIES

Delhi population recently estimated by UN 25 million

2030 -> 36 million


SLUM POPULATION


Indian census has three types of slums:

� “notified” by the Government

� “recognized” (not formally notified but recognized by the

Government)

� “identified” (at least 300 people or 60-70 households in congested

DEFINITION COMPLEXITY SLUMSEXAMPLE INDIA

� “identified” (at least 300 people or 60-70 households in congested

and unhygienic environments and lack basic services - visited

and registered by a Charge Officer) (Office of the Registrar

General & Census Commissioner, 2013)

� From 2001 to 2011 the census urban slum population decreased

from 26.3% to 17.4% (Office of the Registrar General & Census

Commissioner, 2013),

� Success of policy initiatives such as the Rajiv Awas Yojana

aiming at a ‘slum free India’ (Ministry of Housing & Urban Poverty

Alleviation, 2010)?15/07/201636th EARSeL Symposium - M. Kuffer, 2016

� Several urban renewal projects, e.g. Sabarmati River

Front Development (SRFD), the Kankaria Lakefront

Development and the Bus Rapid Transit System (BRTS)

project

� For implementing the projects around 29,000 centrally

located slum houses were demolished.

URBAN DEVELOPMENT PROJECTSEXAMPLE AHMEDABAD

located slum houses were demolished.

Households (when being “eligible”) were offered to be

resettled to a site in the urban periphery.

(Patel et al., 2015).


Source: Kotadiya, 2016

EXAMPLE OF RESETTLEMENTSAHMEDABAD (ROYCHOWDHURY, 2013)


WHERE ARE THE POOR – DEPRIVED – SLUMS?MUMBAI

� Municipal data often not up-to-

date

� Hotspots of poor extracted from

census data

0 52.5Kilometers


Multiple deprivation Index

Ward Boundaries

census data

� But, wards are heterogeneous

Kuffer, Pfeffer, Baud & Sliuzas (2013)

WHERE ARE THE POOR – DEPRIVED – SLUMS?MUMBAI

WorldView – 2

� VNIR: 1.8 m

(8 bands)

� PAN: 0.5 m


THE MORPHOLOGY OF DEPRIVE AREAS

Features Slum areas Formal built-up areas Size • Small (substandard) building sizes • Generally larger building sizes

Density • (Very) high roof coverage densities

• Lack of public (green) spaces within or in the vicinity of slum areas

• Low – moderate density areas

• Provision of public (green spaces) within or in vicinity of planned areas

Pattern • Organic layout structure (no orderly road arrangement and noncompliance with set-back standards)

• Regular layout pattern (showing planned regular roads and compliance with set-back rules)

Site characteristics

• Often at hazardous locations (e.g. flood prone, close to industrial areas, steep slope)

• Land has basic suitability for being built-up

• (Basic) infrastructure is provided

Kuffer, M., Pfeffer, K., & Sliuzas, R. (2016). Slums from Space—15 Years of Slum Mapping

Using Remote Sensing. Remote Sensing, 8(6), 455.


characteristics industrial areas, steep slope)

• Proximity to infrastructure lines and livelihood opportunities

• (Basic) infrastructure is provided

METHODS Total

Number

(Percentage) Contour

model

Machine

learning

Object-based

approach

Pixel-based

approach

Statistical

model

Texture/

Morphology

Visual image

interpretation

Analysis of types of

informal/slum areas

0 1 1 0 1 1 2 6 (6.9%)

Correlation with socio-

economic indicators

0 0 1 3 0 0 1 5 (5.7%)

REMOTE SENSING PUBLICATIONS ON SLUM MAPPINGMETHODS

FO

CI

economic indicators

Identification of slum

areas

0 8 15 3 2 9 11 48 (55.2%)

Extractions of

roofs/roads (objects)

4 0 7 0 0 1 1 13 (14.9%)

Land use/cover

mapping

0 2 4 5 1 3 0 15 (17.2%)

Total Number

(Percentage)

4

(4.6%)

11

(12.6%)

28

(32.2%)

11

(12.6%)

4

(4.6%)

14

(16.1%)

15

(17.2%)

87 (100%)


Kuffer, M., Pfeffer, K., & Sliuzas, R. (2016). Slums from Space—15 Years of Slum Mapping

Using Remote Sensing. Remote Sensing, 8(6), 455.

SLUM AREA MAPPING BASED ON IMAGE TEXTUREUTILITY OF GLCM VARIANCE - EXAMPLE AHMEDABAD


RANDOM FOREST CLASSIFICATION EXTRACTION OF SLUM AREAS (HOMOGENEOUS URBAN PATCHES) IN MUMBAI


Kuffer, M., Pfeffer, K., Sliuzas, R., & Baud, I. (2016).

Extraction of slum areas from VHR imagery using GLCM

variance. IEEE J. Sel. Top. Appl. Earth Observ. and Remote

Sens., 9(5), 1830-1840.

Sub-types Indicators - Measure

Informal A

(squatter settlement, semi-

permanent)

� Structure of layout: irregular patterns, absence of

public green space

� Built-up densities: more than 80% of roof coverage

� Building size: small multi-family buildings

Informal B � Structure of layout: irregular patterns,, absence of

BUT HOW MUCH DO WE SIMPLIFY THE REALITY?DELHI DIVERSITY OF DEPRIVED AREAS?

(squatter settlement most

precarious)

public green space


� Building size: no clearly identifiable buildings

� Site suitability: small encroachments

Resettlement colony like

(low standard formal

settlement)

� Structure of layout: regular patterns, clearly

identifiable road network, planned public green

space


� Building size: small multi-family buildings

� Distance: remote settlement

36th EARSeL Symposium - M. Kuffer, 2016

15/07/2016

DETERMENTS OF THE TYPOLOGY OF DEPRIVED AREAS


DIVERSITY OF DEPRIVED AREAS IN MUMBAIA FIRST TYPOLOGY

CLASS 1 CLASS 2 CLASS 3 CLASS 4 CLASS 5 Slum pocket Slum area, small

buildings Slum area, mix

small/larger buildings Basic formal and

chawls Formal areas

Size: Small roofs

Density: High

Patter: Organic

Size: Small roofs

Density: High

Patter: Organic

Size: Small-medium roofs

Density: Mix

Patter: Diverse

Size: Medium roofs

Density: High-medium

Patter: Some structure

Size: Large roofs

Density: Medium - low

Patter: Well structured


Patter: Organic Patter: Organic Patter: Diverse Patter: Some structure Patter: Well structured

INFORMATION EXTRACTION FROM IMAGERYHOMOGENOUS URBAN PATCHES (HUPS)


METHODOLOGY MAPPING THE TYPOLOGY OF DEPRIVATION


LRM: Statistical

significant indicators

INDICATORS


15/07/2016

Indicators B Sig. Odds

ratio

GLCM

variance

41.456 .002 1.514

Constant -6.481 .000 .002

BINARY LOGISTIC REGRESSION MODELPROBABILITY

CLASS INDICATORS B ACCURACY

Class 1 Built-up mean area

GLCM variance

1704.6

-290.4

Nagelkerke R2:

0.88

15/07/2016

GLCM variance

Built-up patch density

Constant

-290.4

-308.5

227.7

0.88

Class 2 GLCM variance

Land cover evenness

Built-up mean area

Constant

-265.4

645.0

415.8

-513.5

Nagelkerke R2:

0.98


Slope mean

GLCM second moment mean

Constant

-974.1

-492.9

-452.1

317.1

Nagelkerke R2:

0.97


Slope mean

GLCM entropy mean

Land cover evenness

Constant

624.87

-755.6

-149.5

427.1

-102.8

Nagelkerke R2:

0.87


MAPPING THE TYPOLOGY OF DEPRIVATIONOVERALL ACCURACY FOR BUILT-UP CLASSES 77%

In particular Class 3 and 4 have the omission and commission problems


BUT HOW MUCH DO WE SIMPLIFY THE REALITY?MUMBAI DIVERSITY OF DEPRIVED AREAS?


CLUSTER OF DEPRIVATION ACROSS WARD BOUNDARIESHEIGHT INDICATES THE VALUE OF DEPRIVATION (BASED ON CENSUS DATA)


� In particular pattern and environment indictors play an important role

to map the typology of deprivation

� The deprived classes have fuzzy boundaries, which are difficult to

capture on ground as well as via morphological indicators.

CONCLUDING REMARKS RAPID DEVELOPMENT TRENDS NEED TIMELY DATA

� Census data at administrative units are commonly “geographically

meaningless” and “smooth local variability”.

� For many planning questions (hazard, transport, planning

interventions) disaggregated spatial data on deprivation is important.


End of the Presentation

Thank you for your attention!


ANALYSING THE DIVERSITY OF DEPRIVED AREAS IN MUMBAI… · MUMBAI, INDIA MONIKA KUFFER ... For...

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