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            Geoplanning: Journal of Geomatics and Planning, Vol. 11, No. 2, 2024, 121-138 

 

Original Research 

Informal Settlement Characterization and 

Socio-Economic Vulnerability Assessment in 

Kolkata Metropolitan City, India  

Shravani Banerjee1, Diksha1, Alisha Prasad1, Amit Kumar1,2,3,4* 

1. Department of Geoinformatics, Central University of Jharkhand, Ranchi -834205. India 

2. Department of Geography, Sikkim University, Gangtok-737102, India 

3. Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47906, 

USA 

4. IUCN Commission of Ecosystem Management (South Asia), Gland, Switzerland 

DOI: 10.14710/geoplanning.11.2.121-138  

Abstract 

The study investigates the physical, social, and economic environment of the Kolkata Metropolitan Area (KMA) to elucidate 

the living conditions of informal settlements and its influence on the local environment using geoinformatics and multi-

criteria decision making-analytical hierarchical process (MCDM-AHP). The informal settlements were delineated using 

high-resolution Google Earth imagery and generic ontology informal settlements. knowledge considering building 

characteristics, building density, locations of the dwelling units, and their characteristics. The study exhibits that most 

informal settlements were concentrated in the wards located in the eastern and central parts of the city. The neighborhood 

land-use functions of the major informal settlements indicated that the informal settlements were highly influenced by green 

space (R2=0.97), followed by water bodies (R2=0.74), unplanned settlement (R2=0.68) and planned settlement (R2=0.67) 

in KMA. In addition, the informal settlements were closely associated with very low relief zones (3m to 13m) followed by 

moderate relief zones (13-23m). The municipal ward-level analysis of the physical-socio-economic health conditions 

exhibited that most of the areas located in the low vulnerable zones (53.71 km2; primarily in southern, and eastern 

periphery), followed by very highly vulnerable zones (43.09 km2; primarily in central and northern parts). The study 

provides an insight into urban areas with special reference to informal settlements and necessitates the implication of 

effective policy for poverty alleviation. This study encourages the availability of real-time data that can improve mitigation 

activities in the event of a health disaster, such as SARS COVID-19 through methods for qualitative investigation of 

disadvantaged locations in Kolkata. 
Copyright © 2024 by Authors,  

Published by Universitas Diponegoro Publishing Group.  

This open access article is distributed under a  

Creative Commons Attribution 4.0 International license 

 

1. Introduction  

The proliferation of informal settlements is a complex derivative of the organic development of growing 

cities (Marques & Saraiva, 2017). As per UN Habitat (2022), 20% of the global population resides in inadequate, 

crowded, and unsafe housing, out of which 1 billion live in slums and informal settlements and is expected to 

grow to 2 billion in the next 30 years, which represents roughly 183,000 people each day (Hughes et al., 2021). 

The factors that contribute to the formation and growth of informal settlements in urban areas include but are 

not limited to unprecedented population growth, an influx of rural migrants, uneven industrial development lack 

of employment opportunities, poverty, and inequalities, lack of building space, and concentration of land in few 

hands (Mahabir et al., 2016; Ministry of Housing & Urban Poverty Alleviation Government of India, 2013; Ooi 

& Phua, 2007; Tripathi, 2015; UN Habitat, 2003). In India, informal settlements in urban areas are amalgams of 

different linguistics, religions, and caste groups due to the inclusion of populations from diverse socio-economic 

e-ISSN: 2355-6544 
 
Received: 15 January 2024;  
Revised:  13 September 2024; 
Accepted: 24 September 2024;  
Available Online: 30 November 2024; 
Published: 04 December 2024. 
 
Keywords:  
Slums Ontology, Informal 
Settlements, Geoinformatics, AHP 
 
*Corresponding author(s)  
email: amit.iirs@gmail.com 
 
 
 
 

 

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backgrounds and regions (Schenk, 2010). These unplanned and unauthorized dwellings fail to provide adequate 

livable conditions due to various reasons such as dilapidation, overcrowding, the faulty arrangement of buildings 

and streets; lack of ventilation, light, sanitation facilities, safety and health, or a combination of these factors 

(Martínez et al., 2008; Ministry of Housing & Urban Poverty Alleviation Government of India, 2013).  

The concept of informal settlements and its definition varies from country to region, its characteristics, 

the socio-economic conditions, dilapidated housing conditions; irrespective of location, be it the core or outskirts 

(Richter et al., 2011). Even within the same country slum definitions can vary among various degrees of 

administration (Patel et al., 2014). UN-Habitat defines informal settlements as areas that lack at least one of the 

amenities viz., a durable housing structure, access to clean water, sanitation with ample living space, and secure 

tenure (UN Habitat, 2003). The temporary roof structure- roof, walls, and floor, non-compliance with building 

codes, the dwelling near the toxic waste, flood plain, unstable slopes, and vulnerable sites are also other 

determining variables (UN Habitat, 2003). Metropolitan areas almost everywhere and especially in the cities of 

the ‘Third World’ are occupied by squatters near the city center (Mahabir et al., 2016). Slums have both positive 

and negative impacts on the physical, social as well as the economic health of urban environments. The dwellers 

are prone to health problems (Awadall, 2013; Riley et al., 2007) in many cities in developing countries, especially 

adolescents and young adults due to overcrowding (Satterthwaite, 1993). 

The prevailing physical threat of dwellers from various disasters (United Nations Development 

Programme, 2012) and improper housing (Napier, 2007) is mainly due to the low enduring capacity of slum 

dwellers to revive and combat disasters, such as floods and earthquakes, compared with more formal communities  

(Ajibade & McBean, 2014; Braun & Aßheuer, 2011; Ebert et al., 2009). They create an unhealthy milieu (Dana, 

2011; Kjellstrom et al., 2007) due to a lack of basic services, which results in contaminated soil, air, and water 

bodies as well as the overall urban environment at regional and national levels (Richter et al., 2011). This results 

in a perpetuated cycle of deterioration for both slum inhabitants as well as for the environment, with the 

plausibility of impacts extending to communities beyond the informal settlements (Ali & Sulaiman, 2006). 

Consequently, the growth and expansion of informal settlements deteriorate the sustainability of urban 

development at the local, and global level (Patel et al., 2012). The low literacy level of the slum people negatively 

affects the robustness of the urban environment socially as well as economically (Mahabir et al., 2016; Zaman et 

al., 2018). As they are deprived of proper education, there is a lack of awareness among the dwellers which later 

affects the economy of the urban area (Mahabir et al., 2016). Poor sanitation facilities act as the breeding grounds 

for pathogenic bacteria which causes serious health illness (Zaman et al., 2018). The high population density and 

complex social interactions in slum areas, with overcrowding, may result in health cataclysm (Patel & Burke, 

2009) as evident in the case of COVID-19 in Dharavi Slum, Mumbai in April-May 2020. Consequently, this 

nature of housing and living conditions strongly affects all aspects of life in the squatter community. Due to the 

aggregation of census data at administrative units, the characteristics of individual slums become opaque (Kuffer 

et al., 2017). 

Many scholars adopted various approaches and collected information on slums in order to characterize 

informal settlements such as participatory method (Hasan, 2006; Joshi et al., 2002; Lemma et al., 2006), 

integrating census data with GIS as well as analysis of very high-resolution satellite images for slum detection 

(Duque et al., 2018; Sliuzas et al., 2008), simulation models to understand the emergence and expansion of slums 

(Roy et al., 2014). The informal settlements can be differentiated in terms of their surface characteristics and 

texture variations, in the satellite images (Duque et al., 2015). The satellite images area then used to identify, 

classify, and monitor slums in both space and time, providing a deeper understanding of their physical 

manifestations in growing urban surfaces. Very high-resolution images can also be very appropriate to study the 

different characteristics of slum units at different scales more precisely (Hofmann, 2001; Kohli et al., 2012). Many 

methods such as cellular automata and agent-based models have been used to develop dynamic models to 

simulate and project the growth of urban areas (Tripathy & Kumar, 2019) and the evolution of slums (Roy et al., 

2014). The usage of SAR images for urban mapping especially for slum area characterization has proliferated 

recently (Gamba et al., 2011; Kuffer et al., 2016). The nature of housing structure, other neighboring land surface 

features, neighborhood environment characteristics, and topography are vital components of ontology 

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(Hofmann, 2001; Kohli et al., 2012; Shekhar, 2013). The ontology reduces the semantic gap created in image 

interpretation (Durand et al., 2007) and contributes to the identification of features more precisely (Durand et 

al., 2007; Frank, 1997; Tomai et al., 2009). Generic slum ontology has been utilized using very high-resolution 

imagery such as Quick Bird, IKONOS Imagery, etc. to identify slum-dwelling units based on the structural 

compositions of the dwelling units (Kohli et al., 2012; UN Habitat, 2003). 

The generic slum ontology (GSO) consists of characteristics at three spatial levels including object level, 

settlement level, and slum environment. However, remote sensing images have enhanced capacity to identify the 

physical and structural heterogeneity in such environments, which is not captured in census information (Weeks 

et al., 2007). However, satellite images cannot independently capture the socio-economic properties of dwellings 

and slums. Therefore, the amalgamation of the satellite image-based physical properties with census-based socio-

economic properties is crucial in understanding the slum characteristics. Kolkata is one of the largest 

metropolitan cities in India, located in the eastern part of the country, which is an inchoate metropolis, where 

32.9% of families are devoid of basic amenities. There are 0.2% of households in the slum areas of Kolkata, who 

suffer from extreme housing deprivation, i.e., lacking all of the five basic elements of housing including water, 

air, earth, light, and greenery (Patel et al., 2014). In 2011, 44,96,694 people were living in the city of Kolkata. Of 

those, 14,57,273 lived in informal settlements, which were distributed throughout 144 wards of the KMC 

(Kolkata Municipal Corporation), which accounts for 32.4% of the city's urban population (Ray, 2017). The city's 

informal settlements are most concentrated in the east along the Eastern Metropolitan bypass, in the north in 

the Cossipore area, and the west around the dock area. The rise in the informal population with a severe lack of 

basic services has an adverse impact on India's overall target to attain the water and sanitation sector (Ali & 

Islam, 2015).  

Being a coastal city, together with its vast hinterland attracted multiple industries, which enhanced the 

scope of employment (Bhattacharya & Chatterjee, 1973; Ghosh, 2013). The informal settlements grew in central 

parts of Kolkata during the early urbanization of the British Raj with jute and other cotton factories in the 

suburbs (Kundu, 2003). The living condition in urban areas is profoundly influenced by the type of shanty towns 

and living conditions (Das et al., 2012). The growth of informal settlements in the city cannot be prevented as a 

result of excessive urbanization and rural poverty (Roy et al., 2014). Therefore, the physical and socio-economic 

characteristics are needed to analyze the informal settlements more accurately to devise a suitable urban 

development policy pertaining to poverty alleviation and healthier living conditions (Wekesa et al., 2011).  

Thus, this study emphasizes the characterization of slums and analyzes the impact of urban functions on 

slum units in Kolkata. Further, the study focuses on the effect of relief on the slum locations and explains the 

impact of slum areas on the physio-socio-economic health of the urban environment. While much of the previous 

research focuses on the general causes of slum growth and lack of basic services (Das et al., 2012; Patel et al., 

2014), this study offers a more nuanced approach by specifically characterizing slums in Kolkata through a 

combined analysis of their spatial distribution, relief patterns, and socio-economic impacts. Unlike other studies, 

which primarily concentrate on infrastructural deprivation, our research uniquely integrates an understanding 

of urban functions and how they intersect with slum distribution. By examining the role of relief (topography) 

in the formation and persistence of slums, this study fills a critical gap in the literature. Additionally, we explore 

the bidirectional relationship between slum areas and the physio-socio-economic health of the urban 

environment, contributing to the development of targeted policies for urban sustainability and poverty 

alleviation. 

2. Data and Methods 

2.1. Study Area  

Kolkata is the capital of the state of West Bengal and one of the largest metropolitan cities in eastern India 

(Figure 1). It comprises the Kolkata Municipal Corporation (KMC) region and is located between 22⁰ 25’ N to 

23⁰39’ N latitude and 88⁰15’E to 88⁰28’E longitude. It has a jurisdictional area of 187.33 km2 and comprises 141 

electoral wards, as shown in Figure 1. Kolkata has multiple active business centers including the Benoy-Badal-

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Dinesh Bagh area, Burrabazar Area, Shobhabazar, Shyambazar, Chitpur, Esplanade, Park Street, Sudder Street, 

etc. Because the business centers are well spread across the city, residential areas including informal settlements 

are associated and spatially distributed in and around these economic centers. 

 
Figure 1. Location Map of Study Area Representing Kolkata Metropolitan Area in West Bengal, India  

(Dark blue color represents Ganga River) 

2.2. Data 

The study adopted the survey report of the Bustee (habitation) Department, KMA for the year 2001 was 

used. The survey report includes the house structure, sanitary condition, and various socio-economic variables 

including income, number of workers, etc. Remotely sensed data including Google Earth, Sentinel-2A, and 

ASTER Digital Elevation Model (DEM) were used. In addition, the ward-level census variables were also used 

(see Table 1). The data flow diagram and methods of this research are described in Figure 2. 

 
Figure 2. Methodology Flow Chart 

2.2.1. Ontology-based Identification of Informal Settlements 

Sentinel-2 satellite data and 2017 Google Earth imagery were used to identify and delineate informal 

settlement clusters across the KMA. A total of 127 slum pockets were delineated based on their image 

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125 

characteristics on the satellite image and then they were verified using Google Earth. The specification of the 

built form in the satellite imagery was carefully interpreted visually at three different levels, as mentioned in 

Table 2 as adopted from Kohli et al. (2012), 

Table 1. Data Used in the Present Study 

Data Resolution Source Significance 

Survey (2001) Ward level Kolkata Municipal Corporation Demographic, physio-socio-economic aspects 
of informal settlements in each ward 

Sentinel-2A 
(2017) 

10m European Space Agency1 Delineating informal settlements and 
monitoring spatio-temporal changes in land 

cover features 

ASTER DEM 30m United States Geological 
Survey2 

Topography and relief profile of the landscape 

Google Earth 
(2017) 

5m Google Earth desktop 
application 

Cross-checking delineated polygons from 
Sentinel-2A and adding the left-out patches 

Note: 
1) 

https://scihub.copernicus.eu/dhus/ 
2) 

http://earthexplorer.usgs.in/  

 Table 2. Observed Elements in the Satellite Images Related to Informal Settlement   

Level Observation 

Environs Surroundings of the settlement, i.e., its location with respect to neighboring land uses 
prominent land cover features 

Settlement Overall form/shape/density of the settlements 

Object Components of the settlement, such as characteristics of buildings and roads 

Source: Kohli et al., 2012 

2.2.2. Identifying Major Zones of Informal Settlements 

To identify major clusters of informal settlements and analyze different land use functions in the vicinity, 

we conducted proximity analysis using the buffer tool with a 500 m radius. The resulting polygons were 

dissolved to generate larger polygons and a total of eight new polygons were generated. These polygons were 

treated as zones of major informal settlements and were labeled as A, B, C, D, E, F, G, and H for analysis in the 

present study (see Figure 5). 

2.2.3. Analytical Hierarchical Process (AHP) for Urban Physio-Social-Economic Vulnerability 

Analysis 

All the eight ward-level factors were assigned relative weights based on the respective number of classes 

such that a higher weight complements unfavorable health conditions with reference to physical, social, and 

economic characteristics of the informal settlements and vice-versa (Table 3). The weights were assigned based 

on an informed assumption, local field experience, as well as expertise based on the relative contribution of each 

class to the suitability of informal settlements within each ward. The individual weights were normalized using 

Satty’s AHP technique (Saaty, 1980). The normalization process reduces the subjectivity associated with the 

assigned weights of the thematic maps and their features as recommended by Saaty (1980) to maintain 

consistency. The Consistency Ratio (CR) for each theme and unit, initially, principal Eigenvalue (λ) was 

computed by the Eigenvector technique followed by the Consistency Index (CI) (Equation 1) was calculated from 

the following Multi-Criteria equation (MCE) (Saaty, 1980): 

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CI = (λ_max - n) / (n – 1) ……………. (Equation. 1) 

where n is the number of criteria. Using the above equation, the CR (Equation 2) was computed using (Saaty, 

1980): 

CR = CI / RCI ………………………. (Equation. 2) 

where RCI is an acronym for Random Consistency Index. CR value of less than 0.1 indicates good consistency 

(Saaty, 1980). If CR=0.1, the comparison is inconsistent and requires reconsideration of weights. 

2.2.4. Ward level factors 

The following information was compiled at the ward level and examined in order to describe the wards 

according to the state of the informal settlements and to comprehend how the latter interact with the 

environment (see Table 3). 

Table 3. Description for the Ward Level Factors 

Variables Description 

Number of informal 

settlements 

The number of delineated informal settlements in each ward 

Number of dwellers in 
the informal settlements 

Population of informal residents was based on the survey report, KMA 

Population density of the 

informal settlements 

This was computed by dividing the total population of the informal settlements’ clusters with 
the area covered by the informal settlement’s clusters in that ward 

Percentage of the kutcha 

and pucca houses 

There are various types of housing structures in the informal settlements in Kolkata 
metropolitan area, which were primarily broadly classified into Kutcha houses and Pucca 
houses based on the dominance of the housing material used for house construction. The 
kutcha structure comprises roof materials and wall materials that vary from tile, tin to khapra 
(mud tiles) and thatched roof and 'mud' etc., respectively. Whereas, the Pucca structure 
consisted of hutments with a brick wall, cemented wall and RCC roof. The percentage of 
kutcha/ pucca houses is based on the total number of kutcha/ pucca houses with respect to 
total houses in KMA 

Per capita sanitation 

facility 

This is the sanitation facility per person in the informal settlements with respect to the total 

population of informal settlements in KMA 

Percentage of literates This is the number of literate persons expressed in percentage with respect to the total 
number of people in the informal household 

Workers aged below 18  The number of workers below the age of 18 years 

Land use function This comprises various land use functions in the KMA obtained from Sentinel 2A. Google 

Earth and field-based information 

Relief The average elevation (in meters) of informal settlement patches within each ward. The 

elevation is from the mean sea level as obtained from the ASTER data 

Note: All the parameters were normalized between zero to one such that the most significant zones get the highest value and vice-versa 

 

 

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2.2.5. Contributing Factors to Physio-Socio-Economic Health Vulnerability  

Number of informal settlement patches: The number of informal settlements within a municipal ward 

represents the discreteness of the informal settlements and the influence of either geographic or socio-economic 

or both. The wards with a higher number of informal settlements will have a relatively higher population density 

and are less suitable with reference to the health of urban environments and vice-versa. Therefore, the wards 

with informal settlements clustered in the range >49 and 37-48 were assigned higher weightage with reference 

to the proliferation of poor and unhealthy environments (Table 3, Figure 8a). Percentage of Kutcha Houses in 

clusters of informal settlements: The presence of different types of kutcha houses in the urban areas poses obstruction 

with reference to the development of the urban areas, thus hampering the overall setting of the urban 

environment. Therefore, the wards with more percentage of kutcha houses i.e., 72-96 and more than 96 in the 

slum clusters result in poor urban health (Agarwal, 2011; Awadall, 2013; Gambo et al., 2012), thus are more 

vulnerable and have been given a higher weightage (Table 3, Figure 8g). 

No. of persons per household in informal settlements: Overcrowding contributes to the growing psycho-social 

health problems of many urban dwellers in developing countries, especially adolescents and young adults 

(Satterthwaite, 1993). As the number of persons in a dwelling increase, the availability of necessary resources is 

compromised which leads to an unhealthy environment. Therefore, an informal settlement with a higher number 

of persons per household was given higher weightage and vice-versa (Table 3, Figure 8c).Percentage of literate in 

informal settlements population: The literacy of the people living in the informal settlements clusters is important 

for determining the social health condition of the urban environment in many ways such as better 

accomplishment of health and nutritional status, economic growth, and empowerment of the community (Lahon, 

2017; Mahabir et al., 2016; Pawar & Mane, 2013). Thus, the wards with more than 50% of the literate in informal 

settlements’ population will tend to provide a healthier environment, thus are more suitable and given lower 

weightage (Table 3, Figure 8d). 

Per capita sanitation facility in informal settlements: Lack of access to sanitation leads to the presence of 

pathogenic microorganisms, which in turn affects health but also affects social and economic development 

(Awadall, 2013; Hanchett et al., 2003). Thus, the wards with poor sanitation facilities (i.e., per capita sanitation 

0.04-0.08 and less than 0.04) were considered more vulnerable and unsuitable in terms of urban health (Agarwal, 

2011) and have been given higher weightage (Table 3, Figure 8e). Engagement of children of informal settlements 

for earning: The poverty, lack of good schools, and growth of the informal economy were considered as the vital 

causes for engaging the children for earning. Thus, the wards with a higher number of children labor (workers 

below the age of 18 i.e., 70-100 and greater than 100) were more vulnerable and were given a higher weightage 

(Table 3, Figure 8f). 

Relief of informal settlements: The informal settlements lying in low-lying areas are highly susceptible to 

waterlogging/ flood inundation (Braun & Aßheuer, 2011). Thus, the low-elevation zones are more vulnerable 

and have been given higher weightage and vice-versa (Table 3, Figure 8h). LU Functions in the proximity of 

informal settlements: Based on previous knowledge and examples from existing literature (Kundu, 2003; Shekhar 

& others, 2013; Uddin, 2018), places close to land-use functions including green space, waterbody, open-land, 

and institutions; are prone to the formation of informal settlements, were assigned a higher weightage. On the 

contrary, land use functions, which negatively influence the proliferation of informal settlements, were assigned 

lower weightage (Table 3, Figure 8i). 

These ward-level factors were converted to raster layers of 30m cell size for high spatial accuracy. Each 

sub-class of each feature was assigned the respective Eigenvectors in their attribute table. Finally, the AHP-

based normalized weighted maps were spatially accumulated in a GIS environment. The resulting map was 

reclassified into five classes, namely, ‘very high’, ‘high’, ‘moderate’, ‘low’, and ‘very low’ vulnerable zones with 

reference to the health of urban environments 

 

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Table 4. Normalized Weight based on AHP with Reference to the Health of Urban Environment Assigned to 

Various Classes of Input Parameters 

Theme Rank 
Normalized 

Weight 
Sub 
Class 

Rank 
Normalized 

Weight 
Theme Rank 

Normalized 
Weight 

Sub Class Rank 
Normalized 

Weight 

Number of 
Slum units 

1 0.17 

<12 1 0.16 

Number of 
workers 

(under 18 
years age) 

6 1.04 

<10 1 0.16 

13-24 3 0.29 10-40 3 0.3 

25-36 5 0.64 40 - 70 5 0.55 

37-48 7 1.23 70 - 100 7 1.29 

>49 9 2.68 >100 9 2.7 

Percentage 
of Kutcha 
Houses 

7 1.5 

<24 1 0.18 

Relief 8 2.13 

>43 1 0.17 

24-48 3 0.3 33-43 3 0.3 

48-72 5 0.54 23-33 5 0.6 

72-96 7 1.26 13-23 7 1.31 

>96 9 2.72 <13 9 2.62 

Population 
Density 

3 0.34 

<0.06
6 

1 0.18 

Land use 
Functions 

9 2.98 

Others 1 0.16 

0.06-
0.13 

3 0.32 Green Space 1 0.18 

0.13-
0.19 

5 0.56 Water Bodies 2 0.19 

0.19-
0.24 

7 1.2 Institutional 3 0.26 

>0.24 9 2.75 Cantonment 4 0.36 

Percentage 
of literate 
in slum 

population 

4 0.49 

>80 1 0.16 Industrial 5 0.42 

60-80 3 0.29 Administrative 5 0.61 

40-60 5 0.53 Historical 5 0.66 

20-40 8 1.36 Service Sector 6 0.97 

<20 9 2.65 Mixed 7 1.21 

Per Capita 
Sanitation 

5 0.71 

>0.16 1 0.16 
Planned 

Residential 
8 1.48 

0.12-
0.16 

3 0.3 Commercial 8 1.86 

0.08-
0.12 

5 0.56 
Unplanned 
Residential 

9 2.4 

0.04-
0.08 

7 1.26 Slum Areas 9 2.84 

<0.04 9 2.72    

3. Results 

The study describes the ward-level characterization of informal settlements, the distribution and 

interaction of different land-use functions with informal settlements, and the analysis of vulnerability zones. 

3.1. Characterization of Wards based on Informal Settlements 

3.1.1. Number of Informal Settlements Clusters 

The study indicated that the informal settlements were distributed unevenly in the different wards of 

KMA (Figure 3a and b). A very high number of informal settlement clusters (i.e., number of informal settlements 

>49) was observed in the ward located at the eastern part of KMA (ward no. (WN) 66). Followed by wards 

located in the western parts (WN 58 and 73), comprising a high number of informal settlement clusters (37-48). 

The moderate occurrence of informal settlement clusters was explicitly located in the central (WN 65) and 

southwestern parts (WN 82). The remaining wards of the city had low (13-24) and very low (<12) occurrence 

of informal settlement clusters.  

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3.1.2. Population Distribution 

The spatial distribution of population in informal settlements (Figure 3b) exhibited a high population 

density (population density > 0.24) throughout the city and there are only three wards without any clusters of 

informal settlements (WN 42, 45, 87). The high (> 0.24) to very high population density (0.19-0.24) of informal 

dwellers was observed in the wards located in north-west directions. Whereas the moderate population density 

(0.13-0.19) of informal dwellers was found in the wards located in the northern and central parts (WN 40, 28, 

69, 90) of KMA. The remaining wards consisted of the lowest population density (<0.06) of informal dwellers. 

3.1.3. Physical Conditions of the Informal Dwellers 

The wards were categorized based on the percentage of kutcha houses into five major classes, viz., very 

high (>96), high (72-96), moderate (48-72), low (24-48), and very low (<24) (Figure 3c). Most wards were found 

to have a higher percentage of kutcha houses (72-96 and >96). The low percentage of kutcha houses (<24) were 

found in four wards (42,45,67,87), located at the western, eastern, and central parts of the KMA. The moderate 

concentration of Kutcha houses (48-72) was observed in a few wards located in the southern and southeastern 

periphery. It was observed that the wards comprising active business centers have a high concentration (72-96) 

of kutcha houses mainly in the Central parts of KMA. In a few remaining wards, the concentration of kutcha 

houses was observed very high. On the contrary, the availability of sanitation facilities for informal dwellers in 

all the wards is poor (less than 1.2) (Figure 3d). There is only one ward, in which the condition is good (greater 

than 1.6 in ward number 101). The poor defecation facility leads to the poorer environment of the informal 

settlements’ areas and the nearby areas, which give rise to various health problems. 

3.1.4. Economic Condition of Dwellers of the Informal Settlements 

The study indicates the dominance of the wards in medium (42.89 to 64.33 USD i.e., Rs. 3200 to Rs.4800) 

and low-income (Rs.1600-3200) groups. Very low- (Rs. <1600) and low-income groups were mainly located in 

the southern, eastern, and northern parts, respectively (Figure 3e). While the medium groups are mostly 

centered in the central, and western peripheral parts of Kolkata. So, the informal settlement dwellers of the 

extreme peripheral wards of the city belong to a low-income group and are below the poverty line, whereas 

informal settlement dwellers located in the wards of the central part of the city were in a better condition (Rs. 

>4800). 

3.1.5. Literacy Rate in Informal Settlements 

In informal settlements, the literacy rate is much affected by poverty. (Figure 3f). The study revealed that 

a very low and low (20%-40%) literacy rate was found in only 9.9% of the wards (14 out of 141 wards). In the 

remaining wards, a medium to very high literacy rate (>80%) was observed. It is evident that moderate (40%-

60%) to high (60%-80%) literacy rate was found in 112 wards out of 141.  

3.1.6. Social Condition of Informal Settlements (based on the Engagement of Children in 

Earning)  

There were only 7.09% (10 wards out of 141) wards in KMA that have moderate to very high numbers of 

children (age <18 years) engaged in earning in the slum clusters.  The very high (100) to high numbers (70-100) 

of child workers (aged below 18 years) were found in the wards located in upper central, central, and southern 

parts followed by moderate numbers of child workers in the central and western parts of KMA. (Figure 3g). It 

is revealed from the map of the percentage of total earning members in informal settlements (Figure 3h) that the 

highest concentration of earning or employed informal settlement dwellers (>44%) are situated in the upper 

central part of the city. The concentration of earning members was observed primarily in low to very low in 

central, western, and northern parts. 

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Figure 3. Ward-wise distribution map representing (a) number of slum clusters, (b) population density, 

(c) percentage of kutcha houses, (d) per capita sanitation facility, (e) average monthly income, (f) total literates, 

(g) number of below 18 age workers, (h) percentage of total earning members, (i) percentage of pucca houses, 

in the slums of Kolkata municipal corporation 

3.2. Land-Use Functions 

To study the characteristics of the informal settlement discreetly, the major informal settlement areas and 

urban land-use (LU) function were delineated. The study exhibits that a major part of the city is occupied with 

unplanned residential areas (52.5% of KMA), followed by green spaces (27.3%) comprising vegetation and 

agricultural lands, with the intrusion of unplanned settlements in between (Figure 4). The central part of the 

city consists of the commercial areas and the administrative units covering 4.06% of KMA (6.91 km2) (Table 4). 

The industrial areas (4.53%) are mainly located in the western part, along the periphery of the Hugli River and 

the eastern part of the city. The service sector (0.70%), which comprises the dock areas and other transportation 

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services, is near the Hugli River as well as yards of railways and trams (Table 4). The water bodies and wetlands 

are primarily located in the eastern part of the city along with green spaces. Kolkata is well distributed with 

small water bodies within the city except for the CBD patches. Ponds and lakes occupy ~4.85% of KMA (8.77 

km2) (Table 4). The mixed land used functions (2.25%) comprising both commercials along with residential areas, 

banks, institutes, hospitals, etc. cover mostly the central and western part of the city. The rest of the region is 

classified as others (1.89%) including the major open spaces, vacant and arable lands of the city which mainly 

occupy the areas along the canal and the wetlands of the eastern fringe. 

 
Figure 4. Major Land Use Functions in Kolkata 

Table 5. Area Statistics of the Land Use Functions in KMC 

LULC Functions Area (km2) 

Administrative 0.42 

Commercial 6.92 

Planned Residential 2.05 

Unplanned Residential 94.98 

Institutional 0.24 

Mixed 4.06 

Cantonment 0.41 

Historical 0.66 

Green space 49.41 

Industrial 8.19 

Service sector 1.26 

Water bodies 8.77 

Others 3.42 

Total 180.78 

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3.3. Geospatial Analysis of Land Use Functions within the Proximity of Informal Settlements  

The buffer zones of major clusters of informal settlements (A, B, C, D, E, F, G, and H) were examined with 

their associated land-use functions, both, spatially and statistically to deduce the level of control of various urban 

functions on formation and propagation of informal settlements (Figure 5). Pearson's correlation coefficient (r) 

was computed for different land-use functions and informal settlement dwellers based on the data tabulated in 

Table 6.  

 
Figure 5. Major Land Use Functions in the Proximity of the Slum Areas of (a) Zone A, (b) Zone B, (c) 

Zone C, (d) Zone D, (e) Zone E, (f) Zone F, (g) Zone G, and (h) Zone H 

Table 6. Quantitative Analysis of the Control of Different Land Use Functions on Geographical 

Distribution of Informal Settlement (Slum) 

LULC Functions R Value Control Level w.r.t Slum Formation 

Planned Settlement 0.67 Moderate 

Unplanned Settlement 0.68 Moderate 

Water Bodies 0.74 Major 

Green Space 0.97 Major 

Industries 0.003 Very Less 

Mixed 0.002 Very Less 

Service 0.00 Very Less 

Educational 0.00 Very Less 

Historical 0.0004 Very Less 

Cantonment 0.00 Very Less 

Others 0.22 Less 

The very high correlation of informal settlements was observed with green spaces (R=0.97), followed by 

water bodies (R = 0.74) exhibiting the major control level of these land-use functions on slum formation and 

proliferation. Also, a high correlation of informal settlements was observed with unplanned settlements (R= 

0.68) and planned settlements (R=0.67) (Figure 6). The commercial zones and informal settlement dwellers are 

also related to a good correlation value of R =0.60. In contrast, the association of informal settlements with the 

industrial areas was very poor (R=0.003). 

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Figure 6. Graph showing Correlation between slum population and (a) planned settlement, (b) unplanned 

residential, (c) water bodies, (d) green space, (e) commercial area, (f) other features, (g) mixed land use, (h) 

industrial Area 

3.4. Topographical Influence Over Informal Settlements 

The relief of buffer zones of major informal settlement clusters was analyzed to study the influence of relief 

over the proliferation of informal settlements (Figure 7). Although the overall relief of the Kolkata metropolitan 

area was low (3-51 meters above MSL) due to its proximity to the Bay of Bengal, the informal settlement clusters 

are confined to the relief zones ranging between 3m to 23m. Most of the informal settlement units are confined 

to the lowest relief (<13m). 

 
Figure 7. Relief variation in the proximity of the slum areas of (a) Zone A, (b) Zone B, (c) Zone C, (d) 

Zone D, (e) Zone E, (f) Zone F, (g) Zone G, and (h) Zone H. 

a) b) c) 

d) e) f) 

g) 

h) 

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3.5. Urban Physio-Socio-Economic Vulnerability Modelling 

Various parameters related to informal settlement characteristics, LU function, and topographical 

variability were employed to deduce the physio-socio-economic vulnerable zones in KMA using AHP and GIS 

(Figure 8). The resultant zones exhibit that the major parts have moderate (37.80 km2) to low vulnerability 

(53.71 km2), primarily comprising western, southern to eastern parts of KMA (Figure 9). Contrary the high and 

very highly vulnerable (70.26 km2) zones were located in the central, northern, and northeastern parts of the 

city. While the southern and eastern peripheral areas were the least (20.35 km2) vulnerable in terms of the 

physical, social, and economic deprivation and health of the urban environment. 

 

 
 

Figure 8. Contribution of (a) No. of slum clusters, (b) Percentage of kutcha houses, (c) Population 

Density, (d) Percentage of Literates, (e) Per capita sanitation, (f) Workers below 18 years of age, (g) Relief, (h) 

Land Use Functions to Urban Health Condition within the Municipal Wards of Kolkata Municipal Boundary  

 
Figure 9. Urban Physical-Socio-Economic Vulnerability in the Kolkata Metropolitan Area 

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4. Discussion 

The high concentration of informal settlements and high density of slum dwellers in eastern and central 

parts may be attributed to the availability of required vacant space in proximity to the workplace of dwellers. 

The nature of the distribution of the informal settlement population implies that the density of the informal 

settlements’ population varies inversely with the distance from the city’s geographical center, barring very few 

exceptions (Mahabir et al., 2016). This can be attributed to a large number of CBDs or economic centers (multiple 

nuclei) in and around the Kolkata Metropolitan Area. The very high proportion of kutcha houses in most of the 

informal settlements (primarily in the central and the northern parts) of KMA represents lower socio-economic 

conditions of the informal settlements. Despite a higher literacy rate in all the informal settlement clusters, the 

lack of proper sanitation facilities is evident, which complements poor health and morbidity due to infections in 

urban areas in many ways (Kundu, 2003). The central part of the KMA, with intense commercial activities, 

affords higher informal occupations to slum dwellers as compared to the periphery. Although the poor economic 

conditions compel the majority of family members to work, the number of children (age <18 years) engaged in 

earning was very less, and the latter were mostly associated with industrial activities. 

In Kolkata, the very high association of informal settlements with green spaces and water bodies 

corroborated the higher suitability of such dwelling units under tree shades and water bodies, which provide a 

natural roof to solar insulation and rain, and base for constructing houses (Gopal & Nagendra, 2014; Kohli, 2015). 

The blue-green zones are vacant land, primarily owned by the government, and are the least interference sites 

for informal dwellers. These zones are often vulnerable and overlooked sites; as evident in KMA, most of them 

are the low-lying relief zones (<13m) that are prone to flooding caused by frequent cyclones and heavy rainfall 

(Bose & Ghosh, 2015; Braun & Aßheuer, 2011; Jha & Bairagya, 2013). On the other hand, the proximity of the 

blue-green zones to the water bodies, increases the risk of hygiene, triggering water-borne diseases like malaria, 

cholera etc. The high correlation of informal settlements with unplanned and planned settlements indicates the 

high plausibility of informal job and service opportunities, whereas the dwellers in proximity to commercial 

zones probably could not afford to bear daily transport charges to reach residential areas for job opportunities. 

The high physio-socio-economic vulnerability in central and northern parts of KMA indicated the poor 

socio-economic condition of slum dwellers thereby affecting the local urban environment. It is crucial to 

understand the vulnerability of informal settlements, with complex informal social intersections, to any typical 

disaster or pandemic due to poor infrastructure and economic conditions. An example is evident during the 

outbreak of COVID-19, which has disrupted the economy human health, and livelihood at local to global scales 

(Lal et al., 2020). The cataclysm affected the dwellers of Dharavi slum of Mumbai during April-May 2020 with 

the meteoric upheaval of the number of positive cases in a very short period (PTI, 2020). Similarly, a few densely 

populated slums in north Kolkata observed a rapid turn into COVID-19 hotspots during April-May 2020, 

complemented by the lack of space (Basu, 2020). The findings of the present study about the socio-economic 

health vulnerability of informal dwellers are crucial to understanding and mitigating COVID-19 hotspots.  

The integration of high-resolution Google Earth imagery with the generic ontology of informal 

settlements in the present study based on geoinformatics and multi-criteria decision-making-analytical 

hierarchical process (MCDM-AHP), allows for a better understanding of the environment of informal 

settlements within the Kolkata Metropolitan Area (KMA). Moreover, the study establishes a strong correlation 

between informal settlements and their proximity to environmental features, which highlights how land use and 

the natural environment impact the distribution and conditions of informal settlements. The study also 

highlights the influence of geography on settlement patterns and suggested a differentiated risk profile across 

the municipal wards. These findings offer valuable insights for urban planning and policymaking, especially in 

addressing the challenges of informal settlements and improving living conditions in vulnerable areas. 

5. Conclusion 

The present study aimed at analyzing the physio-socio-economic settings of the Kolkata Metropolitan 

Area (KMA) through a geoinformatics approach and applying a multi-criteria decision-making-analytical 

hierarchical process (MCDM-AHP) to further explain the living conditions of informal settlements (slum 

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dwellers) and its influence on the local environment. In the present study, informal settlement clusters were 

delineated using ontological properties and were used to characterize the urban area (municipal wards) based on 

its physio-socio-economic condition using geoinformation. While the informal settlement concentration was 

observed mostly in the peripheral areas, the population density of informal settlement dwellers was concentrated 

in the central part of the city. The city core witnesses the lowest percentage of pucca houses in informal 

settlements and low per capita sanitation facilities, primarily in the wards lying along the Hugli River in southern 

parts of the city. Although the per capita income of informal settlement inhabitants was low, the level of literacy 

is moderately higher, and the percentage of children engaged in work is very low in many of the informal 

settlement clusters located in northern, eastern, and southern parts of KMA.  

The present study asserts a high correlation of informal settlement clusters with the water bodies and 

green space. The finding was corroborated by the geographical locations of most of the informal settlements in 

the low-lying areas (<4 m). The study exhibited that the highly vulnerable zones in KMA are in the central parts 

and northern parts, whereas the southern, eastern, and western peripheral areas were mostly low vulnerable 

zones. However, the study had some limitations, the use of a generic ontology for informal settlements, 

considering factors such as building density and characteristics, may overlook unique, location-specific factors 

that vary spatially. These generalized assumptions might not fully reflect the diversity of informal settlements 

across the Kolkata Metropolitan Area (KMA). The study also has not considered other significant parameters 

such as social networks, local governance, or environmental hazards like flooding, which can also impact living 

conditions.  

Future studies may incorporate such factors and can also investigate the vulnerability of informal 

settlements to climate change, particularly considering flood risk, heatwaves, and sea-level rise, given that many 

of these settlements are in low-lying areas of Kolkata. Following the COVID-19 pandemic, future research could 

assess how informal settlements have adapted to the challenges posed by health crises and what strategies have 

emerged to improve resilience against future pandemics or similar disruptions. This may also include studying 

the role of government and non-governmental organizations in crisis mitigation. Future research needs to 

explore the integration of informal settlements in sustainable urban planning for more comprehensive policies, 

although improvements have been made. The study necessitates site-specific informal settlement redevelopment 

strategies to improve the conditions of informal settlement dwellers and the urban environment. It is 

recommended that measures to improve the status of slums should include raising awareness and increasing 

community participation wherever possible. In addition, the study encourages the timely availability of data, 

which can ameliorate mitigation activities in the event of cataclysms, such as COVID-19.  

6. Acknowledgments 

The authors are thankful to Kolkata Municipal Corporation for providing ward-based data and their 

support during the field visits and the Copernicus Hub for providing Sentinel 2A/B satellite data. 

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