ANALYSING THE SPATIAL HETEROGENEITY OF POVERTY USING REMOTE SENSING: TYPOLOGY OF POVERTY AREAS USING...

Carsten Jürgens (Ed.): Remote Sensing - New Challenges of High Resolution, Bochum 2008

ISBN 978-3-925143-79-3 158

ANALYSING THE SPATIAL HETEROGENEITY OF POVERTY USING REMOTE SENSING: TYPOLOGY OF POVERTY AREAS USING SELECTED

RS BASED INDICATORS

Richard Sliuzas1 and Monika Kuffer1

1. International Institute for Geo-Information Science and Earth Observation (ITC), Department of Urban and Regional Planning and Geo-information Management [email protected] [email protected]

ABSTRACT

In this paper we discuss methods and tools to spatially locate areas of urban poverty. Much work on poverty analysis uses city wide statistics that hides the spatial heterogeneity of urban poverty. For spatially targeting poverty alleviation interventions, disaggregated spatial information is needed on various dimensions of the phenomenon. An important aspect in this regard is how poverty is conceptualized, measured and analyzed within a specific local context.

The starting point for our work is an analysis of urban poverty in Delhi using a livelihood approach and based on 2001 census data at ward level. We use high resolution imagery (Ikonos 2001/2002) to examine the physical development of a selection of these wards to develop a methodological approach for combining additional indicators of physical development to the poverty index. Spatial indicators of poverty areas extracted from classified imagery on location characteristics of poverty areas (such as roof coverage densities, lack of proper road network characterised by irregular lay-out of settlements) are used. Spatial indicators are used to understand the heterogeneity within several deprived wards. We also differentiate between types of poverty areas (e.g. high density central slum areas versus developing poverty areas in the fringe of the municipal corporation) aim-ing eventually at the development of a typology of poverty areas.

The results indicate that poverty is not spatially uniform within the selected deprived wards, and hence demonstrate how data aggregation can hide spatial variation of poverty. By comparing be-tween poverty areas an initial typology is extracted, highlighting the multidimensional of the phe-nomenon. Information on the spatial heterogeneity as well as the typology are important informa-tion for local authorities to improve the targeting of poverty alleviation interventions.

INTRODUCTION

Global concern for the proliferation and consequences of urban poverty is a component of the Mil-lennium Campaign and the related Millennium Development Goals (MDGs). Current estimates are that around one billion people live in slum conditions in the developing world and it is expected that the number will grow up to 1.4 billion in 2020. Although the terms urban poverty and slums are not synonymous they are likely to be very highly correlated. Poor living conditions found in slums will generally give rise to relatively low housing costs, whether for purchase or for rental, and therefore one may expect that a very high proportion of the urban poor will live in slums.

It is therefore not surprising that the MDG campaign calls for international action to improve the lives of at least 100 million slum dwellers by the year 2020. A series of 5 indicators have been de-fined for the identification of slum dwellers: secure tenure, adequate access to safe water, access to acceptable forms of sanitation, overcrowding, and the durability of housing considering both the quality of the structures as well as the site conditions in terms of hazards (i). UN-HABITAT has made a considerable effort into the collection and analysis of statistics on slum dwellers from exist-ing data sources, particularly the Multiple Indicators Cluster Surveys (MICs) and the Demographic and Health Surveys (DHS) and in some cases Census data. UN-HABITAT now regularly publishes


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the number of people living in precarious housing conditions at country level and it is now expand-ing its work into creation of city level databases. Such statistics are used to inform the debate at various political and administrative levels on the state of slum development and should provide information to policy makers on the effectiveness of interventions that are aimed at alleviating ur-ban poverty through slum improvement or other such strategies.

Given the expected increase in the number of slum dwellers there is also a growing need for the development of sound methods to identify and spatially define slum areas in various contexts. This is important both for the international debate and monitoring of MDG performance but perhaps more importantly for targeting poverty alleviation work at local level. The character of such slum areas may vary considerably however. There are cities in which relatively small “pockets of pov-erty’ may be found (ii) or in which large parts of the urban fabric are comprised by a very heteroge-neous mix of slums, better quality housing and other land uses (iii) or others in which slums are typically large contiguous parts of the urban fabric (iv). Moreover, there is considerable variation in terms of building materials, architectural styles, and topographic conditions to be dealt with.

One of the clear messages derived from the work of UN-Habitat’s analysis of global slum dweller statistics is the significant effect that multiple-deprivation has on the well-being of urban popula-tions. Families having deficiencies in 2 or more of the 5 slum indicators tend to be significantly worse off in terms of other indicators such as child mortality for example. The issue of concern to us in this current work, however, is to see whether areas with concentrations of urban poverty from a statistical viewpoint also have clearly recognizable physical/spatial development problems. In this process we make use of an ongoing study of urban poverty in Delhi by researchers from the Uni-versity of Amsterdam and the School of Planning and Architecture, Delhi (v).

BACKGROUND TO THE DELHI STUDY AREA

The city of Delhi provides a relevant example to develop and test methods for slum mapping that might be used in conjunction with poverty analysis and mapping approaches based upon statistics. The Municipal Corporation of Delhi (MCD) is one of India’s fastest growing megacities, with an estimated population in 2001 of 13.8 million people over an area of 1450 km2, making it also one of the world’s most densely populated cities. Population density in the MCD area averages 9500 per-sons/km2 but can reach as high as 150,000 persons/km2 locally. Such high densities can be an indication of poverty concentrations and the ability to identify and spatially define variations in the physical density of the urban fabric can therefore be important.

Recently an analysis of poverty in Delhi has been undertaken based on a livelihoods approach using census statistics from 2001(v). Their approach quantifies and localizes poverty at census ward level through the use of a set of indices that relate to the levels of 4 different types of capital in each ward: social, human, financial and physical. The physical capital indices incorporates 3 variables (% of households using hand pumps, % households without latrine, % households over-crowded) which are quite similar to the slum indicators of UN-Habitat. In essence this is a multiple deprivation analysis that provides 4 measures of capital and 1 combined index of deprivation for each ward. One of the key findings of this work is that several wards, distributed over the city, can be considered as poverty hotspots. Other important conclusions are the need to look beyond slums when targeting poverty and that the census wards are typically very heterogeneous in terms of their physical and socio-economic development and levels of capital. It is therefore important to develop methods that could enable a more detailed examination and characterization of the cen-sus wards, one that recognizes this heterogeneity and provides a more detailed view of the distri-bution and level of urban poverty in the city.


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METHODOLOGY TO EXTRACT SPATIAL HETEROGENEITY OF POVERTY:

The main focus of this study is to explore the possibility of extracting information about physically deprived areas employing indicators derived from remotely sensed imagery. The two main re-search questions are:

Q1: How much is a spatial delineation of physically deprived areas corresponding with the multiple deprivation index?

Q2: How homogeneous is the development of the selected census wards in Delhi?

For the classification of types of physically deprived areas it was important to establish groups of residential areas in Delhi based on literature (v and vi) and local knowledge from Dr. Sada Karup-pannan, University of South Australia (vii). As the focus is on deprived areas details on other resi-dential (better-off) areas were not considered. In a first stage the following groups have been es-tablished: Informal (unauthorized) settlements; Regularized unauthorized settlements; Slum clus-ters; Resettlement colonies and Other residential.

From a first explorative overview of possibly deprived areas (see figure 1) it can be concluded that the most problematic deprived areas are the informal areas, namely unauthorized settlements and slum clusters but also the formal very high density resettlement colonies (v). Both (formal and in-formal areas) have in common that they suffer from poor housing conditions and overcrowding.

In most informal areas basic services like water supply, sanitary facilities and proper road network are problematic as well as they suffer from lack of public green spaces. The resettlement colonies area better-off in terms of provision of basic services, but suffer from their remote location being far away from employment opportunities. The regularized unauthorized settlements are less problem-atic as they have been mostly provided with basic services and show in general better housing conditions and less overcrowding. The decision was made to not include them as physical expres-sion of deprivation. Therefore only the unauthor-ized settlement, slum clusters and the resettlement colonies are selected. In the further discussion they area called informal A (unauthorized/informal settlements) and informal B (slum clusters) and formal high density (mostly resettlement colonies).

Based on this initial typology of physically deprived areas a set of indictors and measure was devel-oped (see table 1) that has the potential to deline-ate deprived from better-off areas in high resolution satellite images. The overview gives a summary of indicators and measures to delineate physically deprived areas. It groups the indicators into two main domains; morphology and location. Most of the areas are well described by the domain mor-phology in which measures of regularity and width of roads, roof-coverage density and mix of green spaces are most relevant. Locational aspects are important for finding specific areas e.g. the resettlement colonies.

In a next step this set of indicators is used to describe the differences between the three selected types of physically deprived areas (see table 2). These indicators are building the conceptual frame to extract different deprived areas by visual image interpretation as well it is intended to use them as starting point for further information extraction by remote sensing methods.

Typology of physically deprived areas

� Informal A: irregular patterns, road are not clearly identifiable, small build-ing sizes, lack of public green space, insufficient basic services

� Informal B: irregular patterns, road are not identifiable, very small building sizes, small encroachments at mostly hazardous locations (proximity to rail-ways/high-ways/river etc), lack of pub-lic green space, no basic services

� Formal high density: regular patterns, clearly identifiable road network, small buildings sizes, remote areas


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Type of area Sample Areas (scale 1:2,500)

Informal (unauthorized) settlements:

These are residential areas that came

up without planning approvals mostly

on privately owned land. Not all areas

can be characterized as sub-standard.

Historically they have been developing

on the periphery of the urban area.

Regularized unauthorized

settlements:

Several informal (unauthorized)

settlements have been regularized by

the city government, which also

includes the provision of basic

services.

Slum clusters:

Slum clusters are in general illegal

occupations of land, mostly

encroaching government land. Besides

its location they are also characterized

by very high housing density and lack

of basic services (e.g. water,

sanitation).

Resettlement colonies:

As part of the relocation policy of slum

dwellers these alternative sites are

usually located at the periphery of the

city. These areas are characterized by

poor housing conditions, overcrowding

and are mostly unattractive due to the

distance to possible employment.

Figure 1: Description of possibly deprived areas


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Table 1: Set of spatial variables to delineate physically deprived areas

Table 2: Spatial variables to differentiate between different residential types

Built-up Sub-types Indicators - Measure

Informal A (unauthorized settlements)

Structure of layout: irregular patterns, no access roads of more than 3 meters, absence of public green space

Built-up densities: more than 80% of roof coverage within the built-up areas

Building size: small multi-family buildings

Informal Built-up

Informal B (slum clusters)

Structure of layout: irregular patterns, no access roads of more than 3 meters, absence of public green space


Building size: small buildings

Site suitability: small encroachments at mostly hazardous loca-tions (proximity to railways/high-ways/river etc)

Formal Built-up

Formal high density (low standard neighbor-hood mainly resettle-ment colonies)

Structure of layout: regular patterns, clearly identifiable road network, planned public green space


Building size: small multi-family buildings

Distance from city centre: remote settlement, > 10 km from centre

For answering the two main research questions both visual as well as automatic information ex-traction from Ikonos images are used. Besides the Ikonos images (acquisition dates: 03-06-2001 and 30-04-2002) (and an Aster image of 2003), the census ward boundaries and incomplete road vector data no other relevant spatial information have been accessible. Access to spatial data can be a rather complex issue in Indian cities, local authorities have mostly no or very poor spatial data while commercial companies are selling better dataset for high prices. One of the major issues currently being worked on is for example the correct geo-referencing of the census ward bounda-ries.

Indicator Measure

Structure of layout

Absence of planned access ways (width of less than 3 me-ters and no regularity)

Absence of public green (no mix of built-up and green spaces)

Built-up density Crowding of buildings – more than 80% of roof coverage

Urban morphology

Building size Small dwelling size

Site suitability Encroachments on hazardous locations (proximity to rail-ways/high-ways/river etc)

Location

Distance from city centre

Remote (more than 10 km from centre) versus central loca-tion


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For answering Question 1 (relation between spatial variable and the multiple deprivation index) two main methods are employed:

1. Visual interpretation based on the set of spatial variables (see table 2) of the Ikonos image (minimum mapping unit of 0.25 ha) extracted the following residential classes: Informal A, Informal B, Formal high density and Other residential.

2. Supervised classification of the Ikonos image, using local knowledge and Google Earth as refer-ence. Collection of ground-truth information is ongoing but not yet available.

For answering Question 2 (the homogeneity of the wards) the results of the visual image interpre-tation is analyzed and discussed.

For testing the methods several wards have been selected. The poor geographic accuracy of the ward boundaries (they are only sketch maps from the census bureau) limited the selection. Only those wards were included where correction of the geometry was possible as well as they had to be covered by the 3 Ikonos scenes. The second criterion for selecting wards was that they should represent the range of multiple deprivation index (v) well. The index range for entire Delhi is be-tween 0.16 (lowest) and 0.55 (highest). The range of the finally six selected wards is between 0.27 (Laxmi Nagar) and 0.55 (Mangolpuri North) see figure 2 below.

Figure 2: Selected census wards (displayed on Terra Aster 22-09-2003)


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DISCUSSION OF RESULTS

The results of the visual image interpretation for two wards are shown below (see figure 3), on the left ward Laxmi Nagar has an index of 0.27 and Mangolpuri North with an index of 0.55. Presently ground validation of the result is ongoing, thus no final conclusion on the accuracy of the interpre-tation can be stated. The result of the visual interpretation was used to calculate the % of physi-cally derivate areas of the total residential area of the ward.

Figure 3: Left: Ward Laxmi Nagar (index 0.27) Right: Mangolpuri North (index 0.55)

Besides a visual image interpretation the area covered by vegetation has been extracted from the Ikonos images using a supervised maximum likelihood classification of 5 major land cover classes (water, bare soil, vegetation, built-up and roads) with an overall classification accuracy of 77%1, while the accuracy of the class vegetation was with 92% quite high. Using the extracted vegetation cover the % of vegetation cover per ward was calculated. One major limitation of this method is that without contextual information conclusions about accessibility of these green areas cannot be drawn. Thus this indicator can only give an overall trend – showing the relation between depriva-tion and lack of green spaces (not public green). Here too we are awaiting the collection of sup-plementary data from the field.

Both extracted indicators are compared with the multiple deprivation index (see table 3). At present we are limited by the small number for wards for which reliable data on physical development is available, implying that we must be cautious in drawing hard conclusions. However, there are indi-cations of positive relation between the multiple deprivation index and the % of physically deprived areas. The main outlier is the ward Minto Road, most of which is commercial and institutional land use as well as some residential multi-storey buildings (which we are as yet unable to discriminate from the non residential multi-storey buildings). Consequently the share of physically deprived area is probably overestimated. The relation between the multiple deprivation index and % of vegetation cover is less strong and again Minto Road is the main outlier, as most of the institu-tional/commercial areas have a good mix with green areas while the deprived clusters are very compact and contain little or no green spaces.

1 The most problematic classes were roads and built-up, in particular small roads and built-up areas have been misclas-

sified.


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0

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60

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0.2 0.3 0.4 0.5 0.6

Multiple Deprivation Index

% o

f p

hysic

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dep

rivate

are

as

Minto Road

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Minto Road

The relation is also visualized in two scatter plots (see figure 4) the trend shows that areas that score high on the multiple deprivation index also score high on the % of physically deprived areas and low on % of vegetation cover, except for the ward Minto Road.

Table 3: Comparing the multiple deprivation index and percentage of physically deprived area and vegetation cover for 6 sample wards in Delhi

Name Multiple Deprivation In-dex

% physically deprived areas

% vegetation cover

Total ward area in ha

Laxmi Nagar 0.27 16.7 31.6 377

Shastri Nagar 0.29 8.7 19.1 203

Minto Road 0.43 73.0 36.1 374

Jaffrabad 0.50 42.2 8.8 107

Mangolpuri South 0.53 63.6 11.8 157

Mangolpuri North 0.55 64.4 7.3 315

Correlation with Index

0.83 -0.65

Figure 4: Scatter plots of the multiple deprivation index and the percentage of physically deprived areas (red circle showing Minto Road)

After finding an initial answer to the first research question relating the spatial delineation of physi-cally deprived areas with the multiple deprivation index, the second question is explored by analyz-ing the intra-ward heterogeneity using the results of the visual image interpretation. The charts below (see figure 5) show the percentage of the different urban land use (agricultural areas are excluded) of the total urban area per ward. The two wards with the lowest multiple deprivation in-dex (Laxmi Nagar and Shastri Nagar) show that the majority of the area is other residential and other urban. But in both wards pockets of deprived areas can be found. Minto Road shows again stark dissimilarity, the majority of the ward is not residential but within the residential area the physically deprived areas are dominant. Comparing the 3 wards with the highest multiple depriva-tion index (Jaffrabad, Mangolpuri South and North) a higher share of physically deprived areas is indicated. Main difference between Jaffrabad and Mangolpuri South/North is that the later two


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Jaffrabad

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other residential

informal A

informal Bformal high density

other urban

Mangolpuri North

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informal A


other urban

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70other residential

informal A


other urban

Laxmi Nagar

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informal A


other urban

Mangolpuri South

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informal A


other urban

wards are dominated by formal high density areas (resettlement areas) and Jaffrabad by informal areas.

Figure 5: Intra-ward heterogeneity of urban land use classes (showing the % of urban land use classes of the total urban area per ward)

In general it can be concluded that informal A and B constitute in neither of the wards large areas in terms of size. But considering roof-coverage density and typical population densities in such areas quite a large number of people are probably living in them. Moreover, also in the relatively less deprived areas, pockets of informal A and B are found. E.g. in the case of Laxmi Nagar small pockets with very high roof-coverage density indicate that such deprived areas are hidden within the good average score of the ward.

CONCLUSION

The work presented above should be seen as the first steps in a more elaborate spatial analysis of multiple-deprivations in Delhi. Problems in geo-referencing vector data for wards with the available image clearly limited the scope of the analysis to date and therefore the conclusions are at present more indicative than based on sound empirical evidence.

Although based on the limited number of wards, we observe indications that the % of physically deprived areas is strongly correlated to the index of deprivation. Further work needs to be done to increase the number of wards covered and to improve the definition and delineation of the different classes of residential areas. For the variable of green space we observe a weaker correlation with


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the index of deprivation. An apparent weakness in the current approach to assessing green space availability is the question of access. Further, it may be worthwhile to also consider the availability of accessible public green spaces within neighboring wards as well as those within each ward, in order to adjust the data for apparent boundary effects associated with the so-called modifiable areal unit problem.

On the issue of the homogeneity of census tracts the image interpretation data shows that consid-erable heterogeneity in terms of functions and deprivation levels should be expected within the wards. This very much concurs with the findings of the deprivation analysis which provides a basis for our work.

In order to maximize the potential of remote sensing in urban poverty mapping for Indian cities there is an urgent need to improve the quality and availability of GIS based datasets that can sup-port and integrated approach to analysis. Access to geometrically correct vector datasets at ap-propriate scales that provide details of topographic base data, urban development, land uses, and the location of public land and facilities would greatly enhance the extraction of value form remote sensing products. It is to be regretted that the analysis of important issues are compelled to make use of remote sensing images as stand alone products.

Further research after ground-truth data collection is intending to test object oriented approaches for extracting different types of deprived areas using the set of spatial variable. While spatial statis-tics will be tested to measure the heterogeneity versus homogeneity within the wards.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the supply of Ikonos images of 6 June 2001 and 30 April 2002 donated by Geoeye Foundation for the purposes of this work. The authors further acknowledge the cooperation and inputs from Dr. Sada Karuppannan, University of South Australia, Prof. Isa Baud and Dr. Karin Pfeffer, University of Amsterdam and Dr. Sridharan, SPA, Delhi.

REFERENCES

i Martinez-Martin, J.M., Mboup, G., Sliuzas, R.V. and Steinj, A., 2008. Trends in urban and slum indicators across developing world cities, 1990- 2003. Habitat international, 32: 86-108.

ii G. B. Hall, N. W. Malcolm and J. M. Piwowar, “Integration of remote sensing and GIS to detect pockets of urban poverty: the case of Rosario, Argentina”, Transactions in GIS, 5, 3, 235-254, 2001.

iii Lemma, T., Sliuzas, R.V., Kuffer, M. 2006. A participatory approach to monitoring slum condi-tions: an example from Ethiopia. Participatory Learning and Action 54: 54-58.

iv Sliuzas, R.V. 2004. Managing informal settlements: a study using geo-information in Dar es Salaam, Tanzania. ITC Publication Series No. 112, ITC, Enschede & University of Utrecht.

v Baud, I.S.A., Sridharan, N. and Pfeffer, K. 2006. Mapping urban poverty for local governance in an Indian Mega-city: the case of Delhi. Working paper 1 for IDPAD project New Forms of Urban Governance.

vi Kundu, A., 2004.: Provision of tenurial security for the urban poor in Delhi: recent trends and future perspectives. Habitat International, 28: 259-274.

vii Karuppannan, S. (2000): - Modelling urban land use in developing countries: a GIS approach to planning. Unpublished PhD thesis University of Melbourne.

ANALYSING THE SPATIAL HETEROGENEITY OF POVERTY USING REMOTE SENSING: TYPOLOGY OF POVERTY AREAS USING...

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