Introduction

Urbanisation means concentration of population in a point (town or city) or multiplication of points (Tisdale 1942) and urbanisation can be defined from demographic, economic, social, morphological and functional perspectives (Khullar 2006). The definition of urban vary from country to country and the Indian Census considers two types of urban i.e. Statutory Town [ST] which is also known as civic town and it is defined as the places with a municipality, corporation, cantonment board or notified town area committee, etc. and the second category i.e. Census Town [CT] is defined by demographic and economic parameters which includes three essential criteria viz. minimum population size 5000, minimum population density 400 persons per sq.km. and minimum 75 per cent of male non-agricultural worker of a place.

The process of being urban is closely associated with the economic prospects of urban centre (Dao 2002), industrialisation (Gollin, Jedwab and Vollrath 2016), initial population and status of the city (Abhishek et al. 2017) as well as commercialisation, social benefits, services, job opportunities and better way of living, etc. Urbanisation is positive factor for overall development of a region because about 65 per cent gross domestic production comes from urban sector (Planning commission of India 2008). Urban centre is concerned with the changes of production pattern, social and economic transformation and geographical shift of population (Wirth 1938; Sharma 2003; Kumari 2015) and the urban centres reveals a system in terms of interdependence, interlink in a spatial organisation which is known as urban system (Short 1984). The study of urban system or city size distribution is very useful in analysing the economic forces (centripetal or centrifugal) operate in the region (Sharma 2003; Gabaix and Ioannides 2004), urban inequality (Manaeva and Rastvortseva 2016), disparity in regional development (Soo 2005). It is the fact that during globalisation era, scale of study of urban system is shifted from local, regional to national and global but the present paper again considers the local organisation of urban system for few reasons. First, to deep and wide understanding of urbanisation and urban system at micro level i.e. the nature and distribution of small and medium size towns, second, to assess the inequality in urban development i.e. finding out the disparity in the development of infrastructural facilities and services between Statutory and Census type urban centres in India from micro study and third, to facilitate the plan and policy formulation for small and medium size urban understanding the challenges and issues of urban development disparity. So, the objective of the paper is to assess the growth and pattern of urbanisation in Nadia district as well as to evaluate the functional/infrastructural attributes for analysing inequality in urban development.

In India, according to 2001 census, 27.78 per cent of the total population live in urban area whereas it has increased to about 31.16 per cent in 2011. Keeping pace with the country, urbanisation is also significantly growing in West Bengal (Giri 1998; Bhagat 2011) and the urban population is 27.97 per cent in 2001 and it has increased to about 31.87 per cent in 2011. In West Bengal, urbanisation has been spreading in Maldah, Murshidabad, Nadia, Birbhum and Jalpaiguri in spite of their interior location from Kolkata which is the urban magnet of West Bengal (Chakraborty et al. 2015) and it is one of the reasons for taking Nadia district as case study for assessing the district level urbanisation and inequality in urban development. The Nadia district represents the high to moderate level of urbanisation compared to the other districts of the state (Fig. 1) and according to 2011 census, this district shares about 5 per cent of state’s urban population and comprises 27.84 per cent as urban population to its total population.

Fig. 1
figure 1

Source: Based on data of Census of India

Levels of urbanisation of different districts in West Bengal (2011).

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The Census of India (2011) has disclosed a new pattern of urban development across the country which is mainly accentuated by emergence of CTs i.e. the transformation of farm economy to non-farm economy in India (Roy and Pradhan 2018; Roy and Samanta 2018; Kumar 2019; Sahu et al 2019).According to van Duijne et al. (2020), India witnessed a record increase of 2532 new Census Towns during 2001–2011 and they have examined the trend, pattern and process of emergence of this new urban growth with special reference to West Bengal, East India and found a link of road infrastructure with appearance of such new urban in West Bengal. Another study of Guin (2016) has revealed that majority of new towns ([NT]-320 NTs out of 541) which have not urban status in previous census (NIUA, 1988:32 as cited in Guin) have appeared in five districts of West Bengal and Nadia is one of them and the appearance of this NT is the second reason of the selection of the study area. In 2011, Nadia district has reported 65 towns of which 10 are STs and 55 are CTs.

The entire work is segmented into three sections. First section analyses the nature of urbanisation of the test area. In this part, the trend of urban growth of the district from 1981 to 2011 in terms of level of urbanisation, decadal growth, rate of urbanisation is analysed. This section has also focussed on emergence of CTs and city size distribution rules are applied to understand the nature of forces (distribution of resources) operate in the region for such type of urban development. The second section has explored the growth of urban of the test area from perspective of different size class towns (size class based on population) and comparative growth trend of CTs and STs with the possible causes of expansion of this towns. The third section is divided into two subsections; first, the article has given emphasis on location of CTs with respect to their nearest ST and analysed the differences in population, literacy and working characteristics of population. The main focus of the paper i.e. the differences in civic services, facilities as well as quality of life is examined in second part of this section.

Materials and methods

Data source

The data are mainly collected from secondary source i.e. the District Census Hand Book (DCHB), Nadia district of Census of India. The Census of India contains the data of area, household, population, literacy and worker (Total, main, marginal), worker category (Cultivator, agricultural labourer, household industry and others), infrastructure availability (Education, health, financial commercial, recreation) as well as household amenities and assets even at the smallest administrative unit i.e. village, town or ward of towns.

The urbanisation of the district is assessed by level of urbanisation, rate of urbanisation, decadal growth rate and pace of urbanisation (Sivaramakrishnan et al. 2007) and inequality of urbanisation of statutory town and census town is reviewed by city size distribution and difference of urban infrastructure is assessed by functional weightage analysis and zone of influence delineation. Lastly, disparity in quality of life between statutory towns and census towns is examined by logistic regression model. The details of the methods are:

Measures of degree of urbanisation

i. Level of urbanisation means proportion of total population residing in urban areas (Verma 2006) and higher the value higher the degree of urbanisation. Thus,

level of urbanisation of Nadia District (2011) = population residing in urban area (2011)/total population of the Nadia District (2011) × 100).

ii. Decadal change of the level of urbanisation with respect to initial level of urbanisation is referred as rate of urbanisation and similarly, decadal change of absolute urban population with respect to initial urban population is decadal growth rate. Therefore,

rate of urbanisation (2001–2011) = {level of urbanisation (2011) − level of urbanisation (2001)} / level of urbanisation (2001) × 100.

And decadal growth of urban population (2001–2011) = {urban population (2011) − urban population (2001)} ÷ urban population (2001) × 100.

iii. Pace of urbanisation (also known as Eldridge Index) is the change in the level of urbanisation with respect to maximum possible urbanisation to be achieved (100 per cent) and it is used for better level of analysis than the rate of urbanisation. So, the equation is:

pace of urbanisation (2001–2011) = {level of urbanisation (2011) − level of urbanisation (2001)}/{100-level of urbanisation (2001)} × 100.

iv. Contribution of urban population to total growth is measured as decadal change of urban population with respect to decadal change of total population. Thus,

contribution of urban population to total growth (2001–2011) = {urban population (2011) − urban population (2001)} /{total population (2011)-total population (2001)} × 100.

Assessment/measures of inequalities in urban development

i. City size distribution laws:

Auerbach (1913) postulate the city size distribution law and then, Zipf (1949) put forwarded the rank size distribution for assessing force of unification and diversification and further, rank size rule examined to estimate the conformation, integration, economic development of urban system (Berry 1961; Johnston 1971; El-Shakhs 1972; Haggett 1979; Gregory and Urry 1985; Lucksteada and Devadoss 2014). Indian scholars (Reddy 1969; Barai Daksha 1974; Ramachandran 2000; Verma 2006; Gangopadhyaya and Basu 2009; Kumari 2015) have also applied the rank size relationship to investigate the different perspectives of urban development. To estimate the balance between the force of unification and force of diversification, this paper has applied the zipf’s power law distribution as Pareto distribution. The formula is:

$$\log P_{r} = \log K \, {-} \, b\log r{ ,}$$

where Pr  is the population of the r rank town, K is the population of the 1st rank town, r  is the rank of a town and b  is the slope of the relation of rank and population.

Then, regression equation is used to measure the ‘b’ value. The b value 1 indicates perfect rank size distribution of the towns and balanced, integrated stable urban system with balanced forced of unification and diversification but b value less than 1 indicates the domination of force of diversification and emergence of small towns and vice-versa for value more than 1.

Berry’s reverse rank method of city size distribution is used to assess the nature of distribution of the towns by comparing actual (Pai) and estimated population (Pei) of the towns. The equation is as follows:

$$P_{{{\text{el}}}} = \frac{{\sum P_{{{\text{ai}}}} }}{\sum 1/R},$$

where Pe1 is the estimated population of the first rank town, Pai  is the actual population, R  is the rank of the towns and estimated population of the i rank town (Pei) = Pe1 × reverse rank of the ith town.

ii. Functional importance analysis

Functions and facilities are important indicators of economic growth, quality of life and well-being of people in an urban centre. On the other way, services and infrastructures such as administrative, education, financial, commercial, transportation, communication, water supply sanitation, sewerage, etc. determine the importance and order of towns in regional hierarchy of settlement. For assessing the urban infrastructure, both the higher and lower order functions and facilities are taken into consideration. Broadly, 5 parameters namely education, health, financial, commercial and recreation and 12 sub parameters like primary, middle, secondary, senior secondary school, college, non-formal school, hospital, family welfare centres, national and commercial co-operative bank, agricultural credit society, library facility are considered to compare the infrastructures of STs and CTs.

Weightage combination score (WCS)

For measuring functional importance of the urban centres of the selected district, weightage combination score is computed for all census and statutory towns individually. In computation of weightage combination score, the equation is

$${\text{WCS}} = \mathop \sum \limits_{i = 1}^{n} \left( {W_{i} \times X_{ij} } \right),$$

where Wi is the weightage of the selected parameter and Xij  is the total number of ith function in jth town. In this case, weightage of the each parameter (Wi) is computed using Bhat’s methods (1976) i.e.

$$W_{i} = \, N/F_{i} ,$$

where, N  is the no. of towns in study area and Fi  is the no. of town having ith function.

Delineation of zone of influence

The zone of influence of each statutory town is delineated to understand the degree of influences of them on surrounding region and it is determined by number of different functions/facilities they provide, their mean threshold population and thereby, proportional composite functional score of an urban centre with respect to that of the district and the area of the district (Prakash Rao 1964).

a. Mean threshold population (mT) of each facility for each ST is calculated using the formula:

$${\text{mT }} = \, P/N_{{\text{f}}} \, ,$$

where mT is the mean population threshold of the particular function (e.g. primary school, hospital, commercial bank, etc.), P is the total population of the concerned ST, Nf is the total number of particular function/facilities in concerned ST.

b. Then, functional weightage of each facility has been calculated with the equation:

$$WF_{i} \, = \, mT_{i} /mT_{1} ,$$

where WFi is the assigned weightage of the ith function; mTi is the mean population threshold of ith function and mT1 is the lowest value among all the mean population thresholds of the functions in a particular ST.

c. Then, all the functional weightages (WFi) are summed up to get the composite score of functional weightages (CFs) of the particular ST or district:

$${\text{CFs}} = \mathop \sum \limits_{i = 1}^{n} WF_{i} \, {.}$$

d. Following the similar method, composite score of functional weightages of each ST and the district is computed separately.

After that, the zone of influence of each towns of hierarchical order has been delineated with the techniques devised by V.L.S. Prokash Rao which is often expressed as-

$${\text{SI}} = \, \left( {{\text{TC}}/C} \right) \times A\; {\text{and }}R = \, \surd {\text{SI}}$$

where SI is the  sphere of influence of each ST, TC is the  composite score of functional weightage of each town and; C is the composite score of functional weightage of the district; A  is the area of the district; R is the radius of the circle which indicates the sphere of influence of function of the particular town. Then, using the radius of the individual ST, hypothetical zone of influence is delineated in Arc-GIS software regarding individual ST as a point data.

Logistic regression model

Logistic regression model is used to estimate the relationship between binary dependent variable and one or more categorical and continuous covariates (Pandey and Joshi 2015; Sarkar and Chouhan 2020; Liu et al.2015). The predicted dependent variable in a logistic regression model is a function of the probability where a particular theme will be in one of the categories (Siddiqui et al. 2018); for instance, the quality of life based on set of scores on predictor variables such as economic condition, drainage system, drinking water facility, etc. The binary logistic regression model is computed with following equations:

$$Y = \, \left( {b_{0} + b_{1} x_{1} + b_{2} x_{2} + \cdots + b_{n} x_{n} } \right)$$
(1)
$$Y = \log \, \left( {P/1 - P} \right)$$
(2)
$$Y = \log \, \left( {P/1 - P} \right) \, = b_{0} + b_{1} x_{1} + b_{2} x_{2} + \cdots + b_{n} x_{n} ,$$
(3)

where x1, x2,….xn are the deriving factors (e,g. drainage facility, drinking water facility, lighting, etc.), Y is linear combination function of explanatory variables, b0 is intercept of the model, b1 b2…bn are the regression coefficient (Siddiqui et al.2018) and Y represent as the odds or likelihood ratio. The procedure has been run in SPSS-21 version.

Logistic regression was performed to assess the impact of several factors on the quality of life (QOL) in CTs and STs of Nadia district. The model contained five independent variables (economic, assets, drinking water facility, drainage system and lighting system).

Independent sample t-test

T-test is a parametric test to compare sample means of a continuous variable for two independent groups (Gerald 2018). In this study, t-test is used to examine whether there is significant difference exist or not in mean quality of life (QOL) of CTs and STs of Nadia district. Total process was conducted through the following steps:

setting of hypothesis: null hypothesis (H0): there is no difference between quality of life score between CTs and STs and alternative hypothesis (H1): there is difference between CTs and STs in quality of life score; setting of significance level: p = 0.05, two-tailed; checking of assumption: [a] scale of measurement (SOM) should be continuous in dependent variable. [b] Independence of observation [c] Random sampling [d] Normality and [d] Homogeneity of variance; computation of statistics: conduct independent sample t-test in SPSS and decision-making: reject null hypothesis (p < 0.05) or Fail to reject null hypothesis (p > 0.05).

Results and discussion

Urbanisation in the selected district: overall trend

From the analysis of the census data (Table1), it has revealed that proportion of urban population to total population of the district is 21.59 per cent in 1981 and it has reached to 27.84 per cent in 2011 with a gradual increasing trend over the decades except 2001. The level of urbanisation in 2001 is lower than its previous decade (1991). In comparison to the national and state average, the district holds lower level of urbanisation over the census periods (1981–2011). Similarly, the rate of urbanisation during 1981–1991 is 4.85 per cent and become negative during 1991–2001 but during the inter-census period 2001–2011, it has recorded rapid increasing rate. However, the rate of urbanisation is higher in the district than state during 1981–1991 but lower during 1991–2001. On the other hand, nation recorded more rate than the state and district over the decades from 1981 to 2001 but the inter-census period 2001–2011, Nadia district unveil an unprecedented rate of urbanisation (30.90 per cent) than the West Bengal (13.95 per cent) and India (12.15 per cent). Though pace of urbanisation does not get influenced by initial level of urbanisation rather considered maximum possible level of urbanisation (100 per cent), it also reveals the same tendency as like the rate of urbanisation with different intensity.

Table 1 Urbanisation of Nadia District (1981–2011)
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The decadal growth of absolute urban population is 53.06 per cent during 1971–1981 and it decreases to 36.25 per cent and 12.35 per cent during 1981–1991 and 1991–2001, respectively, and again growth rate is rapid during 2001–2011 which is measured as 46.89 per cent. Furthermore, contribution of urban population to district’s population growth registered a decelerated trend from 1981 to 2001 but the total growth of the district (more than 80 per cent) is mainly attributed by the urban population in 2011 (Table 1). The absolute number of urban population recorded a gradual increasing but share of urban population to total population slightly decreased in 2001 (22.6 per cent in 1991 and 21.3 per cent in 2001) and thereby, rate and pace of urbanisation have retarded down during 1991–2001.

This retarding trend might be due to declassification of urban ([NM] non-municipal) into villages. Between 1991 and 2001, nine NM towns have declassified as villages (but five villages promoted as CTs) and it is also explicit from the decreasing trend of number and population of class IV, V and VI towns (Table 2). On the other hand, the sudden inflated urbanisation during 2001–2011 might be due to slackening influence of 2001 period and reclassification of new areas as urban in 2011. In 2011 census, large number of villages have upgraded as CTs which contribute the larger share of urban population both in country, state and the district. As per 2011, majority of urban growth (66 per cent) in West Bengal has been attributed by reclassification of villages as forms of CT (Pradhan 2013) and in the selected district, the share of census towns in urban population has been also increased from 19.19 per cent in 2001 to 40.75 per cent in 2011.

Table 2 Size class distribution (no.) and share of ST and CT in urban population (per cent) of Nadia District (1981–2011)
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The rank size curves (Fig. 2a–d) have depicted a criss-cross pattern throughout the census period from 1981 to 2011 and it also indicates the transitional character (emergence of small size towns) of urbanisation of the district in general. The actual population of the first rank city and smaller towns is lower than estimated population and the intermediated towns have higher than estimated. All the curves from 1981 to 2011 intersect the ideal rank size line into two points. Therefore, it can be said that the trend of urbanisation has more or less showed similar pattern with increasing tendency of force of diversification of urban system. From the power law of zipf, ‘b’ value is calculated and the value reveals a fluctuating trend and values are − 1.333, − 1.248, − 1.277and − 1.005 in 1981, 1991, 2001 and 2011, respectively, and it means there is no balance between the force of unification and force of diversification. Moreover, as the value is less than 1, it indicates the dominance of force of diversification which points towards the emergence of small urban centres in the system.

Fig. 2
figure 2

Source:Computed based on data of Census of India

a–d Rank size distribution of town in Nadia district (1981–2011).

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Fast growing census towns in the selected district: transitional urbanisation

Regarding the growth of urban in India, the literatures have observed that ‘there are no startling differences between large and small towns and cities’ (Mohan and Pant 1982, p 1540), similar observation is asserted by Visaria (1997) and Bhagat (2004) while Kundu (2003) visualised that Indian urbanisation is exclusionary in nature that means larger cities are growing in a faster rate with strong resource base. On the other hand, a number of studies have assessed that rapid increase of CTs in India over the past decades was the major cause in the inflated urbanisation of India (Bhagat and Mohanty 2009; Misra 2016). Emergence of this type of urbanisation is known as unacknowledged urbanisation (Pradhan 2013), denied urbanisation (Denis et al. 2012). Guin (2016) studied it as transitional urban growth (shifting of urbanisation growth from large urban centre to emergence or growth of small urban centre) in West Bengal.

The number of STs in India increased from 2758 in 1981 to 4041 in 2011 while CT showed a phenomenal growth i.e. from 1271 in 1981 to 3894 in 2011in India and similarly, urban centres of West Bengal and Nadia district have revealed the rapid growth of number of CT which needs to be analysed. West Bengal witnessed the increasing of number of ST at a slower rate than CT. Between two consecutive census period from 1981to 2011, increase of number of ST ranges from 6 to 11 when CT increased by 80 during 1981–1991 and by 528 [highest in India followed by Kerala (362)] during 2001–2011 in the state. However, during 1991–2001, number of CT in West Bengal declined by 14 points.

In Nadia district, the ST increased from 8 in 1981 to 10 in 2011 but CT depicts a sharp increase from 8 in 1981 to 55 in 2011 having a decline by 4 during 1991–2001 (Table 2). And such emergence of CT indicates transformation of the villages into urban and it invites to investigate the possessing of the facilities and amenities of urban characteristics.

The (Table 2) also shows the number and population share of towns of different size class (class of the town is determined by the population size in Indian census, see Table 2) of the district from 1981 to 2011. Mainly, the class-I and II are represented by statutory towns while the class IV to VI is represented by census towns during 1981–2011.

In the census year 1971, there is no class-I city and in 1981, Nabadwip town has first achieved the class-I category in the district. During 1991 and 2001, another two towns namely Krishnagar and Santipur included in this category and Kalyani due to its higher rate of growth during 1971–2011 (decadal growth rate ranges from 114.4 to 22.5 per cent) enlisted as class-I city in the census year 2011.

From the data analysis, it is found that the proportion of urban population is relatively higher in class-I and class-II but the growth rate is fluctuating due to the transformation of the class of the statutory towns. Another fact is that higher share of these two classes during 1971–1981 might be due to the migration from neighbouring country Bangladesh. According to the Census data, more than 99 per cent migrants of Nadia district are from Bangladesh and during 1971–1981, immigration from Bangladesh in urban area of Nadia district has been increased by 46 per cent (No. of immigrants: 118859 and 173,289 in 1971 and 1981, respectively) and because of this reason, notified areas particularly Kalyani and Gayespur registered unprecedented growth (114.4 per cent and 218.51 per cent, respectively) during this period. This inflated level of urbanisation retarded down the rate of urbanisation in next consecutive periods of the statutory towns. On the other hand, increased proportion of urban population of class IV, V and VI in the census period 2011 mainly is due to the increasing number of census towns after reclassification of towns in 2001. That’s why the class IV and V during 2001–2011 recorded high growth of population (324.9 per cent and 66.7 per cent, respectively).

Differences in urban development

Census towns vis a vis statutory towns in the selected district

According to 2011 census, the lowest population of ST in the district is 23119 (Cooper’s camp-NA) and the highest is 153062 (Krishnagar-M) and area ranges from 1.50–30 sq.km. while population rages from as high as 55,653 (Phulia) to as low as 3982 (Ranaghat-CT) and area ranges from 0.37 to 15.02 sq.km for CT. Areas-wise classification of STs and CTs is given in (Table3).

Table 3 No. of CT and ST as per area
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About 16 per cent of the CT are located within 5 km from Class I city, around 25 per cent at a distance of more than 5 or less than 10 km, 29 per cent within 11–15 km and around 4 per cent at a distance of more than 80 km (Table4).

Table 4 CT as per distance to nearest Class-I town
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Average literacy rate of STs is 80.88 per cent and it is 74.51 per cent for CTs, the work participation rate of CTs is quite same with STs (38.83 and 37.68 per cent, respectively, for STs and CTs) but the noteworthy matter for CTs is that male non-agricultural workers both in main and marginal categories have been increased at higher rate than agricultural sector during 2001–2011. During 2001–2011, male non-agricultural worker increased by 200 per cent and in this case, increasing rate of marginal male non-agricultural worker is higher than main category (Table5).

Table 5 Literacy and worker status of STs and CTs (per cent)
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Inequality in services, amenities and quality of life of urban centres: census towns vs. statutory towns

Das et al (2019) have assessed the basic amenities and services of urban household in West Bengal and they found a huge disparity between the towns of North Bengal and South Bengal. Similarly, in this research study, an attempt has been made to assess the inequality in basic services and amenities between Statutory towns (especially municipal) and Census Towns (non-municipal). The study shows that more than 80 per cent of urban institutional, health, financial and even recreation facilities is concentrated in statutory towns (Fig. 3). For better and a comprehensive measure of disparity in facilities, a weighted composite score of the factors is used. To measure the functional weightage of the urban centres, the 5 parameters and 12 sub parameters (detail see in methodology) are considered and administrative centre, cinema hall, stadium, park are not regarded because it would give much skewed result as they are found in few municipalities only. However, the combine functional weightage [WCS] =\(\sum\nolimits_{k = 1}^{n} {(W_{i} \times X_{ij}^{{}} )}\) of the towns of the district is categorised into five viz. very low (below 20) low (20–50), medium (50–100), high (100–200) and very high (200–850) The district headquarters, Krishnagar town secure the very high score category (847.52) followed by Nabadwip (337.18) possessing all the facilities with higher number. The high category is comprised of Santipur, Kalyni, Chakdah, Ranaghat municipalities while Gayespur and Birnagar, notified area Taherpur and only three census town Phulia, Kamgachhi and Mira reveal medium score. Rest of the census towns (52) are grouped as low and very low category of infrastructural availability (Fig. 4). Therefore, it is the fact that higher order function and more number of function is concentrate in the municipal towns which are the positive factors of the towns and determine the zone of influence of the towns. Hence, larger zone of influence is identified for krishnagar city (Fig. 5) than other towns.

Fig. 3
figure 3

Source: based on data of Census of India

No. of different facilities in STs and CTs, Nadia District (2011).

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Fig. 4
figure 4

Source: based on data of Census of India

Functional importance of the towns

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Fig. 5
figure 5

Source: based on data of Census of India

Zone of influence of ST, Transport network and CT.

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Not only infrastructural distress, the quality of urban life in CTs has also showed a poorer level compared to STs of the study area and it means that CTs are suffering from lack of water supply, sanitation, electricity, sewerage facilities. CTs also have been remained lag behind in terms of availability of household assets. The census data (2011) have revealed the fact that only 14.46 per cent of the total households of CTs has access to tap water while it is 40 per cent for STs, the figures for treated water are about 11 and 32 per cent, respectively, for CTs and STs (Table 6). There is 10 points’ gap in the source of lightening by electricity (74.49 and 83.96 per cent for CTs and STs, respectively) and 5 point’s gap in latrine facilities (90.56 and 95.23 per cent). Both the towns have poor drainage connectivity and CTs show more poor connection than STs (31.19 and 49.87 per cent). In CTs, out of the total household, only 1.33 per cent possess modern gadgets like television, computer, laptop, telephone, mobile phone, 56 per cent have pucca floor, 62.44 per cent have brunt brick/concrete wall, 38.4 per cent have concrete roof while the values are 3.38, 74 and 50.48 per cent, respectively, for STs (Table 6).

Table 6 Percentage of households having the facilities (2011)
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Logistic regression was performed to assess the impact of several factors on the quality of life (QOL) in CTs and STs of Nadia district and to estimate the differences in QOL between STs and CTs. The model contains five independent variables (economic, assets, drinking water facility, drainage system and lighting system). Omnibus tests of model coefficient value indicated that the model was highly significant for the study (Sig ≤ 0.05). The model as a whole explained between 54.5% (Cox and Snell R square) and 79.9% (Nagelkerke R squared) of the variance in QOL and correctly classified 89.2% of cases (Table-7). The full model containing all predictors was statistically significant, (5, N = 65) = p < 0.001 and indicating that the model was able to measure the predicting variables for QOL in CTs and STs of Nadia district. As shown in Table 8, only three of the independent variables made a unique statistically significant contribution to the model (economic condition, drainage system and drinking water facility) and economic condition is the strongest predictor of reporting QOL in CTs and STs in Nadia district fallowed by water and drainage facility (Tables 7 and 8).

Table 7 Model summary
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Table 8 Logistic regression predicting QOL in CTs and STs in Nadia district
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Samples t-test is used to compare the mean quality of life (QOL) score of CTs (n = 55) and STs (n = 10) of Nadia district. The t-test was statistically significant with having significantly higher mean QOL score of STs (M = 0.6708, SD = 0.7882) (mean difference -0.79, 95% CI [− 1.23, − 0.35]) than the CTs (M = − 1.223, SD = 0.6123), t = − 3.6, p < 0.001, two-tailed (Table 9). Therefore, it is evident that there is a significant difference in quality of life between STs and CTs in Nadia districts and thereby, it explicates the disparity in basic services and amenities of STs and CTs.

Table 9 Independent sample t-test predicting QOL score of CTs and STs
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In this case, the fact is that most of the CTs are situated within the sphere of influence of the STs (about 70 per cent of CTs is located within 15 km. distance from class-I towns) and along transport network. So, the people of CTs as commuter engage in non-agricultural activities of STs and thereby increase the male non-agricultural workers of the villages. Guin (2016) has observed that increasing non-farm worker in CTs is due to agricultural distress, not because of dispersal of employment opportunities in his empirical studies. From her empirical work on Singur (CT) in West Bengal, Samanta (2014) has observed the urban shape without infrastructure and services as rural local body or panchayat and she stated ‘transfer of capital from the agricultural sector to the commercial sector has given rise to a large number of census towns in West Bengal’ (Samanta 2014). The census towns of the study area also revealed a higher rate of increase of non-agricultural male worker and decreasing rate female non-agricultural worker.

This type of urbanisation may be assessed from two perspectives that are demand side and supply side perspectives. From the demand side perspectives, it can be stated that development of transport and communication facilities and thereby increasing non-agricultural worker of the rural area who are commuters to the towns lead to the emergence of new urban of census town category. On the other hand, cheap land and labour availability near towns attract firms (Verstappen and Rutten 2015) and population. The study has observed that most of the census towns of the selected district are spotted along the transport network within the sphere of influence of statutory towns and emergence of Census Towns of the study area is mainly influenced by demand side factors which refers to the increasing male non-agricultural workers of the villages as a commuters to the towns.

Therefore, it is evident fact that the census towns are urban only in demographic sense which means these urban centres are only fulfilling the criteria of total population (5000 and more), population density (400 person/sq.km or more) and male non-agricultural population (75 per cent of total worker) but they have been suffering from low-infrastructural development and low quality of life. So, it is the fact that the census towns have contributed in the increased level of urbanisation of the district but have insulated by basic amenities, facilities, infrastructure development and employment generation. The pseudo type urbanisation is the acute problem in development of urban in India at micro level also.

Conclusion and policy implication

Nadia district situated far away from the urban magnet of West Bengal (Kolkata) but the district reveals a higher trend of urban growth. The absolute number of urban population shows a gradual increase but the level, rate and pace of urbanisation as well as decadal growth rate unveil a fluctuating trend due to declassification, refugee migration, reclassification and functional propulsive factors. The striking period for the growth of urbanisation is 2001–2011. In regard of city size distribution, share of urban population was very high for class-I and II till 2001 but 2011 census period registered increasing share of class-III, IV, V and this increasing share is attributed by the emergence of larger number of Census Towns. On the other hand, from the functions and facility point of view, most of the towns of the study area show average availability of infrastructures as well as poor access to amenities and services viz. education, health, financial, water supply, drainage, sanitation, shelter, electricity, etc. But the Census Towns reveal a more distressed condition in terms of infrastructural facilities and socio-cultural amenities than the Statutory Towns of the study area.

A number of urban renewal and redevelopment programmes have been taken in India for improving the availability of basic civic services (housing, sanitation, drinking water, etc.) as well as quality of life even for small and medium sized towns (e.g. UIDSSMT-Urban Infrastructural Development Schemes for Small and Medium Towns). But, the study has brought out the facts that provision of basic services is not adequate and there prevails a significant disparity in access to the services between a statutory town and non-statutory town (Census Town).

Therefore, it requires high attention of the authority to revive the policy and programmes for infrastructural and amenities development for different types of towns depending on their requirement bases along with monitoring and frequent assessment of the implemented programmes to improve the urban quality of life at micro level as well as regional level.