Journal of Global Resources
doi.org/10.46587/JGR.2020.v06i02.014

Volume 6 (02)

July 2020
Page 100-109
ISSN: 2395-3160 (Print), 2455-2445 (Online)

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ARE SMALL URBAN CENTERS SUSTAINABLE? A STUDY OF THE NORTH TWENTYFOUR PARGANAS OF WEST BENGAL
Piyashee Mallik
School of Oceanographic Studies, Jadavpur University, Kolkata, India
Email: piyasheemallik@gmail.com
Abstract: A substantial proportion of the population of the developing nations either lives insmall urban centers or are inhabitants of rural areas relying on small urban centers foraccess to markets, services, and government facilities. Urban development should be
guided by sustainable planning and management vision to ensure mixed-use development.
Measuring sustainability in urban areas is a challenging task in itself as it encompassessocial, political, economic and environmental components. This study focusses on thesustainability of small urban centers in the North Twenty-Four Parganas district of West
Bengal. Hot Spot Analysis and Principal Component Analysis conducted on several chosenparameters resulted in the identification of such urban centers which can act as growthpoles as well as small urban centers which if supported by local and national governments
have the ability to sustain in the long run.
Key words: Small Urban Centers, Urban development, Sustainability, Hotspot Analysis
Introduction
Sustainable urban development consequent upon the successful management of urban growthis pivotal to the implementation of the United Nations 2030 Agenda for Sustainable
Development (UN Nations). This becomes highly significant in developing nations where agradual and sustained rural to urban migration has been noticed in recent times. The 2018
Revision of World Urbanization Prospects produced by the Population Division of the UN
Department of Economic and Social Affairs (UN DESA) reveals that India, China, and Nigeria
collectively will account for 35 percent of the projected urban population growth of the world
between 2018 and 2050 (United Nations, 2018). In the case of India, the level of urbanization inWest Bengal increased from 27.81 percent in 2001 to 31.16 percent in 2011 (Census of India,
2011). One of the striking features of urbanization that the Census 2011 brought into light is thefact that for the first time the absolute growth in urban population (91 million) was higher than its
rural counterpart thereby influencing the increase in number of Census Towns (CTs) from 1,362
to 3,894 (Bhagat, 2011; Kundu, 2011). Although there is a steady growth of large cities, thepopulation of small urban centers (class II to class V towns based on Government of India’sclassification) in general has shown a marked increase in the last decade. A good number ofthese small urban centers have the qualities (as in, diversified economy and infrastructuralamenities) to attracting a huge number of populations and sustaining them over the years(Denis et al., 2012). Globally, about 1.5 billion people live in these ‘small urban centers’ andAfrica, Asia, and Latin America constitute almost three-quarters of this population. Reports
predict that a large portion of the world’s population will be accommodated in these ‘small urban
centers’ at least until 2025 (Satterthwaite, 2006). Constantinos Dioxides, while stressing thesignificance of small urban centers, argued that the small towns can make themselves reliable
and valuable for their inhabitants by providing employment, certain basic amenities and acommunity life that is often absent in bigger cities (Dioxides, 1965; Abou-Korin, 2014).These
small urban centers, through the mechanism of ‘spread’ and ‘trickling down’, provide basic
necessities in the form of civic amenities, relied upon by the rural areas. Besides, the small
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urban centers promote rural and regional development by contributing directly to production andinstitutional structures (Rondinelli, 1988; Rondinelli, 1991; Evans, 1992; Gaile, 1992 ;) and play
key roles in the successful dissemination of knowledge and technology, widespreadtransmission of government policies and modernization (Hinderink and Titus, 2002;
Satterthwaite and Tacoli, 2003) (Tacoli, 2002; Tacoli, 2003). The global report of UN-Habitat
entitled “Meeting Developmental Goals in Small Urban Centers—Water and Sanitation in theWorld’s Cities 2006” reiterates that despite being important linkages between larger cities andthe rural hinterland by means of flow of capital, goods, and migration of people, the small urban
centers have not been extensively explored till date (Un-Habitat, 2012). Since small urban
centers play a crucial role in strengthening local economic development and reduction ofpoverty (Owusu, 2008) and serve as an interface between agriculture and urban market(Raman et al.,2015), it is imperative to focus on the growth of these small urban centers as wellas their ability to sustain in the future. In case of India in general and West Bengal in particular,
there has been a rapid growth of small urban centers and the fact whether all of these urban
centers will be able to sustain in the coming years or not calls for attention. As a further matter,the provision of basic civic amenities lies behind an urban center’s capability to survive in future,and therefore urban local bodies should ensure that the development of civic amenities remains
at par with the rapid increase of population. In a previous study (Mallik, 2018), we assessed thesustainability of class I towns in the district of North Twenty-Four Parganas. In this study, we
extend that approach by focusing on the small urban centers in the district of North Twenty-FourParganas to explore the recent phenomenon of the gradual upsurge of small urban centers andassess their sustainability in near future.
The district of North Twenty-Four Parganas (22°8” N latitude to 23°16” N latitude and88°18” E Longitude to 89°4” E longitude) (Figure 1) consists of five sub-divisions, namely,
Bongaon, Barasat, Barrackpore, Bidhannagar, and Basirhat. There are 22 Community
Development (C.D.) Blocks, 29 Urban Local Bodies (Statutory Towns) and 78 Census Towns in
the district. As per the 2011 Census, North Twenty-Four Parganas is the second-highest
populated district in the country and the highest populated district in the state with a populationof 1,00,09,781. With a population density of 2,445 persons per sq. km., it secures the third
position among other districts of West Bengal. Besides, with an urban population density of
9,160 persons per sq. km., it ranks second, just after the state capital Kolkata (Census of India,
2011).
The main objectives of this study include the following:i)to examine the pattern of distribution of the urban centers,ii)
to identify the urban centers based on their population size and civic amenities,iii)
To find out the existing gaps in civic amenities.
iv)
to classify the urban centers according to their economic and administrative status,v)
To assess the sustainability pattern of the urban centers.
The pre-field work included going through a wide range of books and published research
articles to have an extensive idea about the growth of urban centers and the parameters related
to their ability to sustain. A pilot survey of the area has been done in the field and visits weremade to the important urban centers and relevant maps and secondary data were collectedfrom Govt. and non-government offices. The secondary data for this work has been collectedfrom different sources namely, the Census of India (2011) and the Bureau of Applied
Economics and Statistics, Govt. of West Bengal. The collected data have been processed using
statistical techniques, namely Hot Spot Analysis (through ArcGIS 10.3) and Principal
Component Analysis (through EXCELSTAT 2014), followed by observation and interpretation of
the analyzed data.
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Figure 01: Location of the Study Area

A total of 85 towns of North Twenty-Four Parganas have been considered for this study.
These towns are further classified based on their population size, as in towns with 100,000, andthe above population sizes are classified as Class-I towns, towns with 50,000 to 99,999
population are classified as Class -II towns, towns having 20,000 to 49,999 population are Class- III towns, those having population between 10,000 to 19,999 are demarcated as Class - IVtowns and towns having population between 5,000 and 9,999 are designated as Class -V
towns. Towns having a population lower than 5,000 (Class VI towns) have been excluded fromthis study. The definition of small towns, according to Government of India’s official definition, isthe agglomerations with such a range of population that fall under the categories of class II toclass V towns (Pedersen, 2003). The six variables, on which the analyses are based, include
the population of each town, length of road (in km), number of schools, sources of water supply,
number of centers of medical facilities (Hospitals, Health Centers and Nursing Homes) andnumber of cinema halls. In case of absence of numerical data for sources of water supply,
discreet numerical scores have been assigned: two (2) for Bore Well Pumping Station (BWP),
four (4) for Service Reservoir (SR), 6 for Pressure Tank (PT), and eight (8) for Bore Well Tank
(BWT) and ten (10) for Over Head Tank (OHT). In case a service is not available in the town,
the name of the nearest place and its distance from the corresponding town, where the facility isavailable, has been taken into account. For simplification of calculations, a fixed score of 0.5
has been assigned for such cases irrespective of the distance to the nearest facility.
Analysis
ArcGIS 10.3.1 software has been used for conducting Hot Spot Analysis. Hot Spot Analysis
requires calculating the Getis-Ord Gi* statistic (pronounced G-i-star) for each feature in adataset (GIS Analysis (2016). The resultant Z-scores and p-values highlight where features witheither high or low values cluster spatially. This tool looks at each feature within the context of its
nearest features. A feature with a high value is interesting but may not be a statisticallysignificant hot spot. To be a statistically significant hot spot, a feature ought to have a high value
102and be surrounded by other features with high values as well. The local sum for a feature andits neighbors is then compared proportionally to the sum of all features; a statistically significant
Z-score result when the local sum is very different from the expected local sum, and when thatdifference is too large to be the result of random chance. The Gi* statistic returned for eachfeature in the dataset is the Z-score. For statistically significant positive Z-scores the larger theZ-score, the more intense will be the clustering of high values (hot spot). For statisticallysignificant negative Z-scores, a smaller Z-score corresponds to more intense clustering of low
values (cold spot). The Getis-Ord local statistics is given by:∑𝑛𝑗=1 𝑤𝑖,𝑗 𝑥𝑗 − 𝑋̅ ∑𝑛𝑗=1 𝑤𝑖,𝑗
𝐺𝑖∗ =
2
2
− (∑𝑛𝑗=1 𝑤𝑖,𝑗 ) ][𝑛 ∑𝑛𝑗=1 𝑤𝑖,𝑗
√
𝑆
𝑛−1
𝑡ℎ
Where 𝑥𝑗 denotes the attribute value for 𝑗 feature, 𝑤𝑖,𝑗 is the spatial weight between feature 𝑖and 𝑗, 𝑛 is equal to the total number of features and∑𝑛𝑗=1 𝑥𝑗̅𝑋=𝑛and∑𝑛𝑗=1 𝑥𝑗2√
𝑆=
− (𝑋̅)2𝑛This 𝐺𝑖∗ statistic is the Z-score that is computed.
Hot Spot Analysis has been conducted based on GiZScores, taking the following two criteria
into account:i) Combining all the amenities present in each townii) Combining population and all the civic amenities of each town.
Figure 02: Hotspot analysis for Class II (top two figures) and Class III (bottom two figures) towns.

103

The subfigures on the left side represent hotspot analysis based on the sum of amenities (displayed
through orange circles). In the sub-figures on the right side, hotspot analysis based on the sum of
population and amenities (displayed through green circles) is superimposed on the hotspot analysis
based on the sum of amenities only.

The two-way Hot Spot Analysis indicates that Taki, Noapara, Patulia, Ruiya, Chak
Kanthalia, Chandpur, Jafarpur, Kaugachhi, Teghari, Itinda and Mohanpur have sufficient civic
amenities as well as a higher concentration of population. Therefore, these towns can be said tohave reached an optimum level of urbanization and can act as growth poles i.e. urban centerswith certain attributes that facilitate the flow of goods and services among individual
households, firms, and governmental entities. These facilities, if augmented by a measure ofinvestment support, will trigger regional and economic development in their hinterlands. They
can act as service centers and hubs for surrounding not-so -developed urban centers, exploiting
their potential as attractive places to live and work and thus might achieve sustainability.
Baduria, Nebadhai Duttapukur, Talbandha, Muragachhi, Deara, Digha, Bilkanda, Purba
Narayanpur, and Bandipur are seen to have achieved somewhat sufficiency in civic amenities
but their population strength is not so high i.e. the amenities present in these towns arecomparatively more than what their current population requires. This means these towns are
somewhat going through the process of urbanization and have the ability to accommodate more
people based on their more than a sufficient number of civic amenities (Figures 2 - 4). Thissituation would warrant a focus on the towns’ economic development so as to ensure thegeneration of income and employment. The Hotspot Analysis has also helped to identify
another category of towns. The civic amenities present in towns like Garulia, Sadpur, Betpuli,
Gangapur, Chikanpara, Dogachhia, Bara Bamonia, Anarberia, Dhanyakuria, etc. are notproportional to the total population of these towns. This indicates that, just like the Third World
cities, these towns have grown physically and demographically without proper development of
Figure 03: Hotspot Analysis for Class IV Towns

The subfigure on the left side represents hotspot analysis based on the sum of amenities (displayed
through orange circles). In the subfigure on the right side, hotspot analysis based on the sum of
population and amenities (displayed through green circles) is superimposed on the hotspot analysis
based on the sum of amenities only.

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genuine resources to build essential infrastructure and indispensable services for their
populations. This suggests that in the long run these towns may either get merged to other large
towns or cease to exist until and unless sufficient civic amenities (namely, availability of public
facilities, efficacious urban governance, all-inclusive growth management policies forsustainable urban growth, etc.), which can sustain the burgeoning inner and peri-urban
populations, are incorporated in these towns. An important thing to note here is that the ability ofthese small urban centers to sustain in the long run does not merely depend on the provision ofbasic civic amenities and services. This is because there lies a phenomenally rich and diverse
set of circumstances that are unique to each urban center. An urban center will only sustain in
the future if it gets the much-needed support from local and national governments for local
development and resource mobilization which is further dependent upon the urban centers’potential and constraints.
Figure 04: Hotspot analysis for Class V Towns

The subfigure on the left side represents hotspot analysis based on the sum of amenities (displayed
through orange circles). In the subfigure on the right side, hotspot analysis based on the sum of
population and amenities (displayed through green circles) is superimposed on the hotspot analysis
based on the sum of amenities only.

.
Principal Component Analysis has been performed to identify the towns that haveundergone optimum urbanization and towns, which are going through urbanization. PCA has
been performed for each class of towns separately and each such analysis reveals different
dominant variables for each class of towns. Factor loadings for Class II Towns reveal road
length, sources of water supply, the number of schools and number of cinema halls has positive
correlations and they are highly correlated. Here, the first factor explains 70.25 percent
variability. Four variables namely road length, sources of water supply, number of schools andnumber of cinema halls have major contributions. However, the number of medical facilities
becomes dominant in secondary factor loading. Among class II towns, only Baduria shows up
as a town going through urbanization (Figure 5).

105

Figure 05: Observation chart (observations in the PCA space) for Class II Towns
2.5
New Barrackpore

2
1.5
1
0.5
0
-3

-2

-1

-0.5

0

1

2

Garulia

3

Baduria-1-1.5

Each town is plotted according to their factor scores, which represent their observation coordinates on the
PCA dimensions.

Based on the first factor loadings of Class III Towns, it can be said that road length andnumber of medical facilities are dominant variables. In secondary loading, however, watershows dominance but road length and number of medical facilities also contribute positively. If
we move on to the third factor loadings, number of cinema halls emerges as the most dominantvariable. Gobardanga shows high positive loading in both primary and secondary stages, whichindicates its optimality of urbanization (Figure 6).
Figure 06: Observation chart (observations in the PCA space) for Class III Towns
2.5
Ghuni

2
1.5
Gobardanga

1
0.5
0
-3

-2

-1

-0.5
Taki

-1

0

Nebadhai
Duttapukur
Shimulpur

1

2

3

-1.5
Each town is plotted according to their factor scores, which represent their observation coordinates on the
PCA dimensions.

Principal Component Analysis of Class IV towns shows that sources of water supply andthe number of medical facilities show high dominance in primary loading; in secondary stage thenumber of cinema halls is the most dominant variable. Contrastingly, the number of schools
shows a strikingly high loading in the third factor loading. In the fourth factor loading, all thevariables except sources of water supply show positive influence. So, it can be said that thepeople residing at Maslandapur, Chandpur, Guma, Dhakuria, and Sulanggari have access toadequate amenities as evident from the biplot. Noapara, Barrackpore Cantonment, Deara andKaugachhi have sufficient sources of water supply and medical facilities but they lack in cinema
halls (Figure 7).

106Figure 07: Observation chart (observations in the PCA space) for Class IV Towns
4
Maslandapur

3
2
Bira
Chatta Baria-3

1

ChandpurJaipulMuragachha
Talbandha
Jafarpur
0
Patulia
Guma
Chak Kanthalia-2-1 Teleni Para 0

-1

Koira

Dhakuria
Sulanggari

1

Noapara2

Kaugachhi

3

Deara

4

Barrackpore
Cantonment

-2
Ruiya

-3

Each town is plotted according to their factor scores, which represent their observation coordinates on the
PCA dimensions.

In case of Class V towns, sources of water supply and the number of schools show highdominance in primary loading; at the same time, the number of medical facilities and thenumber of cinema halls also show positive loading. However, in the secondary stage water andschool show negative loading, but cinema and medical show high positive relationship. Numberof medical facilities is also dominant in the third factor loading. In the fourth factor loading road
length and sources of water supply are dominant variables. Therefore, in this case, the numberof medical facilities and cinema halls emerge as influential variables. Based on this, towns like
Dhania, Bilkanda, Chandrapur, Sonatikiri, Bhatenda, Dhanyakuria, Basina, Uttar Bagundi,
Dandirhat, Jetia, Dakshin Chatra, and Bara emerge as having enough amenities to sustain theircurrent population quite sufficiently (Figure 8).
Figure 08: Observation chart (observations in the PCA space) for Class V Towns
4
Bara

3
2

MohanpurDhaniaBilkanda
SadpurDogachhia
Shibalaya
Sonatikiri Chandrapur
1 Uttar Bagundi
Khorda Bamonia
BasinaDakshin Chatra
PalladahaAnarbaria
Bhatenda
TeghariPurba Narayanpur
Dandirhat
Chak Barbaria Kulberia
Bandipur Digha
Chandpara
Jetia
Balibhara
Bamangachhi
Betpul0 Nokpul Jatragachhi
Dhanyakuria
DeuliaKokapur Mathurapur

Paltapara
Garshyamnagar
Babanpur-4-3-2 Nagdaha-10Chikanpara

Palashi-1

Gangapur

ItindaSadigachhi-21Bara Bamonia
Raigachhi

2

Raghunathpur

3

BankraChandrapur
Champagachhi

Hingalganj
Ichhapur Defense Estate-3-4Each town is plotted according to their factor scores, which represent their observation coordinates on the
PCA dimensions.

107

Conclusion
If the results of Hotspot Analysis and Principal Component Analysis are focused on together, 7
towns, namely, Gobardanga, Chandpur, Noapara, Barrackpore Cantonment, Kaugachhi, Uttar
Bagundi, and Mathurapur come up as towns that have reached the optimum level ofurbanization. These towns act as growth poles that would serve nearby urban centers byconcentrating populations for productive ventures, achieving economies of scale in areas ofenergy, housing, transportation, and supporting land use connectivity (Gantsho 2008).Even
after reaching the optimum level of urbanization, they will continue to attract people from small
urban centers and henceforth are capable of sustaining in the long run. Baduria, Muragachha,
Deara, Bilkanda, Chandrapur, Dandirhat, Basina, Bara, Raigachhi and Kokapur, in terms of
their locational advantages of being resource-based, tend to accommodate more population in
the future. Good local governance, modern planning policies, fostering entrepreneurship, proper
management of these urban centers through information sharing and adoption of principles thatcater to strengthening linkages between these centers and surrounding areas would help thesetowns grow into large urban centers in the coming years. Rest of the towns has not been taken
into consideration because of either of the following reasons (which, if not supported by quality
infrastructure, would potentially fail to sustain in the future):i)Meager civic amenities (as revealed by hotspot analysis) incapable of fulfilling theneeds of the people. Towns that fall into this category most likely will either getmerged into other large towns or cease to exist.
ii)
Negative loadings on either the first or second factors or on both the factors.
It can be said that these towns have qualified the urban status because of their population
size without necessary infrastructures. Kingsley Davies defined urbanization as a process of theswitchover of human settlements from rural areas to urban centers (Kingsley, 1962). In the longprocess of urbanization, there develops an almost completely urbanized society, with the
majority of the people living in just a few large places. In the present instance, a rapid processof urbanization has been taking place as the small urban centers boast of better social andeconomic conditions consequent upon the significant increase in service sectors. This analysis
also recognized some urban centers, which, if not supported by quality infrastructure, would
potentially fail to sustain in the future. Nonetheless, there remain certain unexplored areas thatprove to be of ample scope for future academic research in this field.
Acknowledgement
The author thanks Dr. Uttam Mukhopadhyay for his ideas and valuable suggestions in enriching
the article and Dr. Dipanwita Chattopadhyay for her relevant and helpful inputs related to
ArcGIS analyses.
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