Development potentiality of peri-urban region in India: a quantitative analysis on Durgapur Municipal Corporation (DMC)

India's census towns are determined by specific criteria, including a minimum population of 5,000, 75% male working population in non-primary sector, and a population density of 400 person km−2. The urban population has grown significantly between 1951 and 2011 (62.44 million to 377.1 million) with 186% increase of census towns. However, issues like land scarcity, rising living costs, and urban sprawl persist. The peri-urban area serves as a transitional region between rural and urban environments. The study assesses the development potentiality of Durgapur Municipal Corporation (DMC) peri-urban areas using various indicators, including population density, growth rate, household density, labor force, literacy rate, and basic activities. It aims to gain insights into the socio-economic status, infrastructure requirements, and growth opportunities for sustainable regional development. Techniques like TOPSIS, Moran's Index, and hotspot analysis are employed to visualize development concentration and analyze correlation coefficients. The study reveals that the western and southern sectors in DMC have higher development levels due to better accessibility with respect to both roadways and railways, proper availability of natural resources, and so on. This knowledge guides policymakers in developing sustainable, balanced, and equitable growth strategies.


Introduction
The majority of people living in the world now reside in cities, making up more than half of the overall population.Nearly 43.4% of people live in rural areas, compared to 56.6% of people living in cities worldwide as of 2021.Urban population will rise by 68% by 2050, according to the World Urban Population, 2021 report (World urban population, 2021).The socio-economic and environmental effects of such fast urbanisation, particularly in developing nations, have been profound (Karg et al., 2019).In India, census towns are referred to as places with notified urban area committees, corporations, cantonment boards, or municipalities.Census towns must meet a number of requirements, including having a population of at least 5,000, having more than 75% of working males employed in non-agricultural occupations, and having a population density of at least 400 persons km −2 .The Indian Census of 2011 shows that between 1951 and 2011, the urban population rose from 62.44 million to 377.1 million.In contrast to the recognized towns, this saw a growth of only 6%, the number of census towns increased dramatically by 186 between 2001 and 2011.By 2031, 600 million people will live in India's cities, according to UN estimates (Singh, 1967).People move to metropolitan regions for a variety of reasons, including economic opportunity, employment prospects, access to better health care, educational opportunities, and many other aspects (Aijaz, 2019;Paul & Dasgupta, 2012).Cities must contend with issues such as lack of available land, increased living expenses, and population growth in and around the cities.The rural-urban gradient and population migration away from metropolitan cities are persistent (Paul & Dasgupta, 2012).
The peri-urban area, sometimes referred to as the rural-urban gradient zone, is a transitional space between rural and urban settings.These peri-urban zones, which are created as a result of the rapid urbanisation and expansion, serve as a transition zone between rural and urban habitats (Banzhaf et al., 2009).Due to its blend of rural and urban elements, peri-urban can be challenging to define and delineate.Urban elements are frequently seen in rural landscapes in the peri-urban area, which is characterized by a transition zone where agricultural and urban land uses meet (Singh, 1967).The expansion of roads and the advent of new economic opportunities promote unplanned growth in areas surrounding metropolitan centers, which increases the complexity (Paul & Dasgupta, 2012).The urban zone, urban-rural zone, rural-urban zone, and rural region are the four different zones that make up the peri-urban area.According to Yunus (2006), each of these zones reflects a different stage of the rural-urban continuum.Some researchers (Mondal & Sen, 2020) argued that, these areas have a dynamic environment that is transitioning from a rural to an urban setting.On the other hand, maintaining peri-urban areas is frequently a task best left to local level governance, such as Gram Panchayats (Aijaz, 2019).There are a variety of wealthy residents, businesses, governmental agencies, and essential services in the peri-urban area.However, the lack of public services, efficient waste management, unrestricted groundwater abstraction, and health issues in peri-urban areas frequently result in disadvantage (Saxena & Sharma, 2015;Shaw, 2005).
Development potentiality, which refers to opportunities and possibilities for growth, progress, and improvement from various perspectives, including economic, social, environmental, and infrastructural development, is the inherent ability or capacity of a region, area, or resource for positive and sustainable development.It looks at the resources, advantages, and strengths that are already present in a particular region and can be applied there to meet the essential development objectives (Rodrigues & Franco, 2019).It takes into account elements like natural resources, human capital, geographic position, infrastructure, technical advancement, market circumstances, and political framework that might aid in the development process (Saleh et al., 2020).Cities' surrounding areas exhibit traits common to both urban and rural settings, providing special chances for various development objects.Due to their proximity to urban centers, peri-urban areas draw economic activity like industry, commercial businesses, and service sectors (Rauws & Roo, 2011).Urban areas are enticing for company investment and job development because there is availability of land, the expenses are lower when compared to that in the urban areas, and there is access to both urban and rural markets.Urban regions are popular for residential construction because they provide a balance between city conveniences and proximity to nature.People seeking an urban or semi-rural lifestyle may find appeal in the availability of larger lots, more reasonable land costs, and a more laid-back atmosphere (Bhatta, 2009).Opportunities for rural and agricultural development are also present in areas around cities.According to Bai et al. (2011), they can help both urban and rural markets to promote agricultural output, food processing, and agro-industry.Due to this, sustainable food systems can be promoted and agriculture can be integrated with urban activities.
Infrastructure and service provision are additional areas where urban development has a promising and foreseeable future.The need to build transportation networks, infrastructure, medical facilities, educational institutions, and other important services to satisfy the shifting needs of nearby areas has increased with the rise in the population of the surrounding cities (Simon, 2008).Another facet of metropolitan areas' development potential is management of the environment and natural resources.Natural resources and different ecosystems are frequently found in these places, and they can be protected and improved through the use of sustainable practices.This entails putting in place green infrastructure, advocating for environmental protection, and assisting ecotourism projects (Griman et al., Grimm et al., 2008).To benefit from the growth prospects at the outskirts of cities, effective planning and management are required.The sustainable development of peri-urban areas requires balancing the demands of urbanisation with the preservation of rural landscapes, closing infrastructure gaps, guaranteeing fair access to services, and taking environmental sustainability into account (Angel, 2015).Based on their potential for growth, Paul and Chatterjee (2014) classified census town into six groups.Growth poles are basically census towns that have substantial potential for growth, serve as hubs of commerce, and draw capital.The growth centre contributes significantly to the neighborhood's economic development and has a moderate amount of potential.Despite having a poor growth point potential, a growth pole or growth centre nearby can be advantageous.Service centers mostly offer essential services to locals.A small number of service locations offer fundamental services.Rural centers, most of which are situated in rural areas, have a lower likelihood of developing.This classification aids in identifying the distinct tasks and responsibilities of census cities, assisting decision-makers in allocating resources and organizing development initiatives accordingly (Mustăţea, 2013).
This study tries to evaluate the development potentiality of Durgapur Municipal Corporation (DMC) suburbs using several indicators.In order to assess the characteristics that indicate the potential for development of the region, the population density, population growth rate, number of households per 1000 population, labour force, literacy rate, basic activities, centrality, functionality and access to the region are analysed.Ease of use has been considered.These indicators are widely used in the literature to assess and understand the development potential of a region (Paul & Chatterjee, 2014)).A thorough analysis of these factors has been done with the intention of gaining insight into the socioeconomic state, infrastructure requirements, and growth potential of the DMC suburbs.During planning and decision making for regional sustainable development, these indicators should be properly taken into account.The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used in this study to achieve the given aims.Using the Criteria Importance Through Intercriteria Correlation (CRITIC) approach, the weights for the criteria were calculated.Additionally, the Moran's Index and hotspot analysis were carried out to highlight the relationship between development potentiality and its indicators, as well as to depict the spatial concentration of development.These analytical methods enabled a thorough evaluation of the research area's development potential.

Study area
Durgapur Municipal Corporation (DMC) is located in West Bengal's Paschim Barddhaman district, divided into Purba Barddhaman and Paschim Barddhaman segments.This research aims to identify the development potential zone of DMC's within Durgapur Subdivision, which includes one municipal corporation, four CD Blocks, 41 CTs, and 161 villages (Fig. 1).The population in the southern region of India has grown steadily at a 23.7%rate (Census of India, 1991India, , 2001India, & 2011)), with urban expansion in a linear growth pattern along the National Highway and Railway line constrained by the Damodar River (ADDA, 1980).The Asansol Durgapur Development Authority manages the sub-division, making it the second-largest urban area in the state (Choudhury et al., 2019).Durgapur Municipal Corporation, a notified area since 1962, is surrounded by four community development blocks (Kanksa to the east, Faridpur Durgapur to the north, Ondal to the west, and Pandabeswar to the north-west) and is in the transitional zone between Jharkhand plateau and Ganga-Brahmaputra delta plain (Ghosh et al., 2015).The Durgapur region, with its favorable geographic location and coal and mineral resources, is known for its agriculture and industrial complexes, promoting human habitation and urban growth.This has led to the development of Census Towns, municipal corporations, and municipalities, with excellent infrastructure and transportation.The region is well connected to India via NH-2 and the eastern railway.This study focuses on the Durgapur subdivision to identify the peri-urban area and its dynamic growth, providing valuable insights into the evolving peri-urban zone and potential for further development.

Data base
The crucial data for this study were gathered using the Census of India, which provides a thorough source of demographic, socioeconomic, and geographic information for the whole nation (Burrough, 1986).In order to gather data on a variety of elements of the population, the Indian Government conducts the Census of India every ten years and such data has been utilized in ArcGIS environment.The collection, storage, analysis, and visualization of geographical and non-spatial data are all made possible by the use of the crucial GIS tool.With the use of GIS methodologies and data from the Census of India, demographic, socioeconomic, and geographical data are merged in this study to allow for a thorough review (Doxsey-Whitfield et al., 2015).This holistic approach helps to capture the complexity of the peri-urban environment and provides a solid framework for using TOPSIS and CRITIC techniques to evaluate and rank options based on a number of different criteria.

Selection of variables
The selection process for the variables that will be used to measure the growth points includes defining research objectives, conducting a literature review to find relevant variables, identifying significant growth drivers, consulting experts and stakeholders, evaluating data quality, prioritizing variables based on relevance and data availability, and conducting pilot testing and refinement.By carefully considering these processes, researchers may identify and include factors that describe the fundamental aspects of peri-urban growth and development.This enables a thorough investigation of the potential of urban centers as growth points.For this study's evaluation of the potentialities of metropolitan centers as growth sites, a number of indicators, including population density, population growth rate, the number of houses per 100 people, literacy rate, and working force, were directly gathered from the Census of India (Paul & Chatterjee, 2014).These variables offer crucial socioeconomic and demographic information about the urban areas under investigation.Other criteria, such as regional accessibility, basic activity, centrality, and functionality, were added to form variables (Table 1).Information on the physical and economic characteristics of metropolitan areas is one of these factors.

Weighted analysis
The Criteria Importance Through Intercriteria Correlation (CRITIC) technique was used in this study to alleviate the ambiguity of weightage selection.Another approach used in Multi-criterion Decision Making (MCDM) to ascertain the objective weights of relative relevance for criterion is the CRITIC method, which was initially put out by Diakoulaki et al. (1995).The CRITIC technique offers a methodical way to weigh criteria by taking their connections or correlations into account (Mathew, 2019).It takes into account the idea that criteria are not mutually exclusive and that their relative value may rely on how they relate to other criteria.
Steps to calculate CRITIC.

Normalization of input value
where, a ij is normalized value, y ij is value of criterion j for a particular alternative of i, y jbestvalue andy jworstvalue are an ideal best and ideal worst value of j criterion.Determination of standard deviation for each criterion where, σ j is standard deviation, N is the size of popula- tion, y i each value from the population, μ is population mean.Subsequently, a symmetric matrix with dimensions m × m is constructed.Each element r jk in the matrix denotes the linear correlation coefficient between the vectors and a i .Notably, as the disparity between the scores of alternatives for criteria j and k increases, value of a k decreases.This signifies that a lower r jk value reflects a higher level of disagreement in scores for criteria j and k among the alternatives.Consequently, the (1) sum depicted in Eq. 3 serves as a measure of the conflict or incongruity introduced by criterion j concerning the decision context defined by the other criteria.
In essence, it quantifies the extent to which criterion j deviates from the overall pattern established by the other criteria.
Estimation of the quantity of information with each creation where, C j is the quantity of information of j criterion.
Weightage value of each criterion (3) and W j is weightage value of j criterion and C k is the quantity of information of k criterion.The resultant weighted value (Table 1) has been applied in the TOPSIS method to measure the development potentiality.

Development potentiality analysis
Hwang and Yoon (1981) created the TOPSIS (tool for Order of Preference by Similarity to Ideal Solution) method as a Multi-Criteria Decision-Making (MCDM) tool in 1981.It is a common technique for assessing and ranking options based on a number of criteria or characteristics.The TOPSIS approach enables the simultaneous evaluation of many objectives using a combination of several indexes or criteria (Sahin, 2020).
It is very helpful when making decisions based on the relative importance of various elements after taking into account a variety of circumstances.The fundamental tenet of TOPSIS is founded on the presumption that the best option should have, in Euclidean space, the shortest distance to the ideal best values and the greatest distance from the ideal worst values.The ranking of options according to how close they are to the optimum answer can be determined by TOPSIS by computing these distances (Mostafa et al., 2021).
The TOPSIS approach is utilized in this study to estimate development potentiality value using various variables.To ensure comparable scales, the data is normalized, and matching weights are multiplied by normalized data.The CRITIC method determines the weighted value, reflecting the relative importance of the criteria determined by decision-makers.The weighted normalized values are then multiplied by the weights, determining the ideal best and worst values for each criterion.The ideal best and worst values represent the highest and lowest values for each criterion.The Euclidean distance between the two is calculated, determining the distance between two points in Euclidean space.The development potentiality index is calculated for each alternative, indicating its relative closeness to the ideal solution.Higher development potentiality index values are considered more favorable.TOPSIS is commonly used for regional differences analysis and decision-making, particularly in urban and regional planning.It helps decision-makers evaluate and rank alternatives based on multiple criteria, considering the importance of each criterion.
Steps to calculate TOPSIS.

Normalization of Performance value
where, x ij is normalized value, x ij is performance value of criterion of j for a particular alternative of i.
Integrate weighted with normalized value w j is weightage value of j criterion, v ij is weightage nor- malized value of criterion of j for a particular alternative of i.
Euclidean distance from the ideal best and ideal worst value Here, v + j and v − j are ideal best and ideal worst of weighted normalized value for j criterion.S + i is euclidean distance from ideal best of weighted normalized value for i th alternative, S − i is euclidean distance from ideal worst of weighted normalized value for i th alternative.
Development potentiality index (6)   P i is composite value or development potentiality of i th alternative.

Spatial autocorrelation analysis
One of the key statistical indicators used in spatial autocorrelation to assess the degree of spatial clustering in a given datasets is Moran's I. Based on the variable of interest, it may quantify how similar and distinct an observation is (Anselin, 1995).Moran's I aids in determining if dissimilar values are scattered or tend to cluster together in space (Getis, 1996).It can be measured through the following equation: where, Z i is the value of variable z of i th alternative; Z is the mean value of z variable; Z j is the value of z variable at all the other locations where = i; σ variance of z vari- able and W ij is the weighted value among the locations i and j.The resultant value of Moran's I are ranging from -1 to + 1, where, positive values of Moran's I indicate spatial clustering, which means that the similar values are more likely to be found near each other whereas the negative values of Moran's I indicate spatial dispersion (Getis, 1996).

Hotspot analysis
Hotspot analysis, also known as spatial clustering analysis, is one of the crucial geospatial analytic methods used to find groups of attributes with high or low values inside a certain geographic area.It is a technique that is frequently used in spatial statistics and geographic information systems (GIS) to find clusters and patterns of things or occurrences that have similar features (Wang & Qiu, 2017).The geographical distribution of data is evaluated using statistical techniques in interval analysis, which also determines whether or not the patterns that are noticed are statistically significant.Researchers and decision-makers can find locations with exceptionally high or low values using the results of hotspot analysis, which can be useful for decision-making, resource allocation, and focused activities.The hotspot analysis has been used in this study to locate high development potential zones in the DMC's peripheral area.The following equation, which has been developed to represent the hotspot analysis, was first introduced by Getis and Ord (1992) as Getis-Ord G * i statistics to show the presence of significant cluster.It can be expressed as follows: (9) where, W ij , is the spatial weighted matrix between obser- vations i and j, x j is value of selected attribute for obser- vation j, n is the total number of observations in the sample, x is the sample mean that x = n j=1 Xj n , and The study used hotspot analysis to evaluate development potentiality using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Criteria Importance Through Intercriteria correlation (CRITIC).Pearson correlation analysis was used to examine relationships between variables, and Moran's I was applied to assess spatial auto-correlation.Hotspot analysis was conducted to identify development concentrations in the outskirts of the DMC.These analyses provided valuable insights into the spatial distribution and clustering of development potentiality, aiding in identifying significant hotspots and potential areas for targeted interventions and policy recommendations.

Population density
Chak Bankola, Parashkol, Debipur, Ondal, and Chak Bankola (Part) are the peri-urban units with the highest population density in the Durgapur Municipal Corporation (DMC).These western areas exhibit high population density, with a manual classification system.Low-density regions have a population density below 500 persons km −2 , while high-density regions have a density above 2000 persons km −2 (Table 2).The study area has a low population density in the eastern part, while a high population density is found in the western part of the periphery region of DMC (Fig. 2a).This is due to factors such as mining areas, multiple railway stations, and industries, which attract a large workforce and economic activities.Additionally, a well-developed road network in the western part enhances accessibility and connectivity, contributing to population growth and density.Transportation infrastructure plays a crucial role in facilitating the movement of goods and people, promoting economic activities and attracting settlements.Debipur, located in the eastern part of DMC, is highly populated due to its proximity to National Highway 2. This accessibility provides better connectivity to economic centers, markets, and services, attracting settlements and economic activities.Factors like land availability, housing affordability, sociocultural aspects, and government policies also significantly affect the population concentration in peri-urban (11) units.A detailed evaluation of the delineated area's livability coupled with its alluring beauty has been done by considering infrastructure development which includes water supply, sanitation, and healthcare facilities.Western areas benefit from mining activities, railway stations, industries, and a well-developed road network, while eastern Debipur has a high population density due to its proximity to NH-2.

Population growth
The population growth rate from 2001 to 2011 was measured and categorized into five classes: very high (> 20.00), high (15.01-20.00),moderate (10.01-15.00),low (5.00-10.00),and very low growth (< 5.00) (Table 2).The study area, including the eastern part of Durgapur Municipal Corporation (DMC), showed a very high growth rate (Fig. 2b).Kumarkhala, Chak Jharia, and Bhadrapur in the western part of DMC had the highest growth rate, indicating significant population dynamics.Shankarpur and Kalinagar in the eastern and northern parts also showed notable population growth.DMC's urban amenities, services, and employment opportunities could boost the population growth in Shankarpur and Kalinagar.Shankarpur's educational institutions, healthcare facilities, and other amenities make it an attractive residential location.Kalinagar's population growth may be driven by land availability, economic opportunities, and improved transportation connectivity.The study found negative population growth in 36 peri-urban units in Rautdihi, Masna, Shyamsundarpur, and Konda, indicating a decline in population.These units are located in the northern part of the study area, near the Ajoy River.Factors such as environmental conditions, limited resource access, and challenges related to river proximity may have contributed to the decline.Additionally, socio-economic factors like limited employment opportunities and inadequate infrastructure development could also contribute to the negative population growth.

Number of households/ 100 inhabitants
The number of households per 100 inhabitants is a crucial factor in determining development potential.The number of households per 100 inhabitants is classified into five classes: very high (> 20), high (16-20), moderate (11-15), low (5-10), and very low (< 5) (Table 2).The highest number of very high households is found in the western part and near NH-2 in the eastern part of the Durgapur Municipal Corporation (Fig. 2c).Higher household numbers in the western part can be attributed to various factors.The region experienced significant urban growth and residential development due to land availability, housing policies, and infrastructure development.The western part expanded residential areas to accommodate the growing population, resulting in a higher concentration of households.The western part also had economic activities, industrial zones, and employment centers.The region experienced significant urban growth and residential development due to land availability, housing policies, and infrastructure development.The western part expanded residential areas to accommodate the growing population, resulting in a higher concentration of households.The western part also comprised economic activities, industrial zones, and employment centers.
The occupational structure of peri-urban centers is changing due to economic activities and employment patterns.A higher percentage of working force indicates job opportunities and economic activities.These locations may have experienced industrial growth, commercial development, or service-oriented sectors, leading to increased employment prospects.Factors such as infrastructure development, transportation connectivity, and market access also influence the changing occupational character.Improved infrastructure, including roads, transportation networks, and communication facilities, can facilitate the growth of industries, commercial establishments, and service sectors, increasing the demand for a working force.

Literacy rate
The study reveals that the highest literacy rate is found in locations like Tetikhala, Shankarpur, Banagram, Dignala, Kaliganj, and Baska.These areas are scattered in the peri-urban region of DMC.The literacy rate is classified into very high (> 80.00), high (70.01-80.00),moderate (60.01-70.00),low (50.00-60.00),and very low (< 50.00) (Table 2).A high literacy rate indicates a higher level of education and knowledge, crucial for socio-economic development.Individuals' literacy rate is influenced by socioeconomic conditions, income levels, standard of living, and access to basic amenities.Higher socioeconomic conditions provide better access to education and resources, leading to higher literacy rates.These high literacy rates indicate potential for human development, empowerment, and future growth.Factors such as income levels, standard of living, and access to basic amenities also impact educational opportunities and outcomes.

Regional accessibility
Regional accessibility is a pivotal component affecting connectivity and transportation in a region.This study measures accessibility by considering factors like road, city center, railway station, airport, and regional center.
Higher regional accessibility values are observed in locations like Banskopa, Bamunara, Tetikhala, Shankarpur, and Dhubchururia (Fig. 2e).The areas near the Durgapur Municipal Corporation (DMC) offer favorable accessibility and connectivity.Major transportation networks, including important roads, city centres, railway stations, airports, and regional centres, are advantageous to these locations, which include Banskopa, Bamunara, Tetikhala, Shankarpur, and Dhubchururia.This proximity improves commuting convenience, goods and service transportation, and overall connectivity with other regions.Regional accessibility in strategic locations is influenced by well-developed transportation infrastructure, such as roads, railway stations, airports, and regional centers.These facilities enhance mobility, trade, and economic activities, attracting businesses, industries, and individuals seeking convenient access to transportation hubs and regional facilities.

Basic activity
Based on the population who are not in the primary labour force, the values of fundamental activities in each peri-urban centre have been computed.The domestic industry, manufacturing, processing, serving, and repairing workers, and other workers make up the non-primary working group.These elements reflect people who work in a range of secondary economic activities, such as forestry, mining, and other primary industries.As shown in Fig. 2f, the fundamental activity has also been divided into five categories: very high (> 20), high (15.01-20.00),moderate (10.01-15.00),low (5.00-10.00),and very low (5.00) (Table 2).Locations like Kanksa, Ukhra, Kajora, Gopalpur, Debipur, Arra, and others exhibit high levels of basic activities relative to the context of this study.This suggests that there are numerous people engaged in diverse economic activities outside of the basic industries.These areas appear to have a thriving economic environment with a variety of non-primary employment prospects given the high rankings of basic activities.
There are a variety of small-scale industries, manufacturing facilities, service industries, and other economic operations in Kanksa, Ukhra, Kajora, Gopalpur, Debipur, Arra, and other locations.These activities aid in the economic growth of the respective urban areas by generating jobs, money, and other benefits.

Centrality
The degree of services offered by an urban centre in the peri-urban area can be estimated with the use of a measure called centrality.Trade and commercial activities have historically been used to determine an urban center's primary purpose.In this study, however, the category of other workers from the 2011 Census has been taken into consideration as a proxy for determining centrality because data on commercial workers are not available.
The centrality has been divided into five categories based on manual classification: very low (10.00),low (10.00-20.00),moderate (20.01-30.00),high (31.01-40.00),and very high (> 40.00) (Table 2).According to this computation, urban areas like Kanksa, Kajora, Ukhra, Ondal, and others have been shown to have the highest centrality values (Fig. 2g).These areas most typically show a substantial presence of trade, commerce, and service-related economic activities.These peri-urban centers likely play a key role in supplying basic services and serving as commercial hubs within the peri-urban region, according to the high centrality scores.High centrality demonstrates their significance in terms of the various economic activities and service delivery.These peri-urban areas probably draw a sizable amount of trade, commercial and service-related activities.The centrality values show the significant contributions made by these areas in promoting economic interactions and providing for the requirements of the local populace.

Functionality
The functionality of peri-urban centers is assessed based on socio-economic amenities, including educational facilities, medical services, financial institutions, communication and transport linkages, administrative services, and recreational amenities.The study classifies these factors into low, moderate, high, and very high functionality values.The functionality value has been classified based on < 20.00(very low), 20.00-30.00(low), 30.01-40.00 (moderate), 40.01-50.00(high) and > 50.00 (very high) respectively (Table 2).The assessment reveals that urban centers near national highways have the highest functionality values (Fig. 2h).These peri-urban centers offer a diverse range of services and amenities, including educational institutions, healthcare facilities, banking services, efficient communication and transportation infrastructure, administrative support, and recreational options.These factors contribute to their functional significance and ability to meet socio-economic needs of the residents.

Development potentiality
The weightage values from the CRITIC method for the study's variables, such as population density, population growth rate, number of HH/100 residents, working force, literacy rate, basic activity, centrality, functionality, regional, and accessibility, are crucial in determining the relative importance of these variables and in assessing the potential of peri-urban centers as growth points (Ravetz et al., 2012).To comprehend the consequences of these weightage numbers and to determine whether they are suitable or not, a critical examination and debate of them are required.The relative importance of each variable in the decision-making process is indicated by the weightage values derived using the CRITIC approach.According to Lopez-Mosquera and Sánchez (Jesiya & Gopinath, 2019), higher weightage values imply that the related variables have a bigger impact on how the peri-urban centers are rated and evaluated overall.To assure the validity and trustworthiness of these weightage values, it is crucial to look at the justification and logic behind them.Regional accessibility has the greatest weightage value of 16.09 out of the given weightage values, showing that people view it to be important in determining the growth potential of metropolitan centers.This implies that accessibility and connectivity to other areas are important variables for the expansion and development of peri-urban centers.Functionality is also given a lot of weight in the assessment process, receiving a weightage value of 12.75.This emphasizes how crucial it is to have functional services, amenities, and infrastructure in peri-urban centers to enable their development.Comparatively lower weightage values range from 9.25 to 11.49 for factors such as population density, population growth rate, number of HH/100 residents, working population, literacy rate, basic activity, centrality, and others (Table 3).
Although still significant, these factors may have a lesser overall impact on the assessment of the growth potential of peri-urban locations based on the weights they have been given (Nicolae, 2010).The weighted values from the CRITIC technique were used in the second step of the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method in the context of the study on the development potential of the peri-urban area of the DMC (Durgapur Municipal Corporation).This step's goal was to determine each peri-urban center's performance value, which served as a sign of its future development potential.According to the weighted criteria, the performance value derived by TOPSIS shows how close each peri-urban centre is to the optimum solution.Greater development potential is indicated by a higher performance value.Ukhra in Ondal CD Block has the highest performance rating in this scenario, 0.48, indicating that it has the greatest potential for growth out of all the peri-urban centers which have been examined.Talbahari, a peri-urban centre in the Kanksa block, has the lowest performance score, 0.14, compared to the other peri-urban centers, indicating a considerably weaker development potential.The normalization process logically categorizes the peri-urban centers by scaling performance values between 0 and 1 and ensuring comparable scales.This process categorizes and rates these centers based on their development potential, making it easier to identify those with higher or lower development potential.The analysis categorized development potentiality values into six categories: growth poles (> 0.65), growth centers (0.61-0.65), growth points (0.56-0.60), service centers (0.51-0.55), service points (0.45-0.50), and rural centers (< 0.45).Growth poles drive economic growth, growth centers play a significant role in regional development, growth points have moderate potential, and rural centers serve as administrative or commercial hubs (Nicolae, 2010).The analysis reveals 14 peri-urban centers as growth poles, with Ukhra being the top growth pole followed by Kanksa, Kumarkhala, Chak Bankola, Kajora, Gopalpur, and others (Fig. 3).These centers are concentrated in Pandabeswar, Ondal, and Kanksa blocks, mostly situated in the eastern part near NH-2.The study area's easternmost growth poles are adjacent to NH-2.

Hotspot analysis
Hotspot analysis was used in this study to identify locations in the Durgapur subdivision that had a high and low potential for development.Spatial autocorrelation was calculated before the hotspot analysis to evaluate the statistical link between nearby observations in the datasets.In order to determine whether or not the surrounding sites have comparable values that go beyond what is likely to happen by chance, spatial autocorrelation is used.Moran's I was employed in this investigation as a gauge of spatial autocorrelation.Areas with similar development potential appear to be spatially clustered, according to the derived Moran's I value of 0.38, which also suggests that 38% of the data are positively clustered (Fig. 4a).The presence of substantial geographical clustering is confirmed by the matching Z value of 16.42 with a p-value of 0.00 (Wang et al., 2023).Informing decision-making and policy creation with regard to spatially explicit issues, this spatial autocorrelation study offers insightful information about the geographical patterns of development potentiality (Sylla et al., 2019).38 peri-urban units were identified as hotspots with a 99% confidence level by the hotspot analysis (Fig. 4b).Three of these units were situated in the Durgapur Municipal Corporation's (DMC) eastern region, close to NH2, while rest of the units were mainly situated in the western region, close to NH-2 and NH-60 (Zuberogoitia et al., 2014).

Relationship with development potentiality and its indicators
The relationship between each of the nine parameters (population density, population growth rate, number of households per 100 residents, working force, percentage of literates, regional accessibility, basic activity, centrality, and functionality) and the development potentiality of the periurban centers was calculated using the coefficient of rank correlation.Values of the correlation coefficient shed light on the direction and intensity of the association.Population density and population growth rate in this study had lower correlation coefficient values, 0.32 and 0.25 respectively, showing a weaker link with development potential.For functionality and geographical accessibility, however, moderate correlation coefficient values of 0.52 and 0.45 were discovered, indicating a moderate amount of association.It is interesting to note that the variables with the highest correlation coefficient values were the number of households per 100 residents, the labour force, the literacy rate, the level of basic activity, and the centrality, with coefficients of 0.71, 0.71, 0.72, 0.69, and 0.74, respectively (Table 4).

Discussion
The study of development potentiality reveals that a major transportation artery significantly facilitates development and accessibility in areas.The eastern part benefits from its proximity to NH-2, providing connectivity to  major urban centers and economic corridors.The western part, however, has a higher number of growth poles due to multiple transportation networks, railway stations, and other factors (Haldar et al., 2023a(Haldar et al., , 2023b(Haldar et al., , 2023c)).
Transportation infrastructures improve regional accessibility, facilitating economic activities and connectivity.
The western part of the region has higher accessibility, enabling the movement of people, goods, and services.Mining areas and industries create income opportunities, attracting population and investment.The Raniganj municipality, located 25 km from DMC's city center, also plays a significant role in the western part's development.
The municipality serves as an intermediate zone between Paschim Barddhaman district's main urban centers, facilitating economic activities and providing services to nearby peri-urban centers (Guérin-Pace, 1995).In the second stage, four growth centers have been identified: Dalurband in Pandabeswar block, Siduli and Ondal in Ondal block, and Panagarh in Kanksa block.These units have higher development potential and are expected to experience significant growth in the future.In the third stage, Chorra, Amlajora, and Harishpur are identified as growth points, with a slightly lower development potential but favorable condition for accelerating the development process.In the fourth stage, 11 peri-urban units are classified as service centers, providing essential services and amenities to the surrounding areas.These hubs for commercial, administrative, and social activities contribute to the region's development.In the fifth stage, 18 peri-urban units are classified as service points, offering essential services and amenities to the local population.These points contribute to the socio-economic development of the peri-urban region by providing access to essential facilities like education, healthcare, and transportation.The majority of peri-urban units are identified as rural centers.
Peri-urban units have lower development potential compared to previous stages due to their rural characteristics and lower infrastructure and services (Goncalves et al., 2017).However, they still support agricultural activities and provide basic amenities to the rural population (Seifollahi-Aghmiuni, 2022).Classifying periurban units into different stages helps understand the varying development potentiality within the study area (Getis, 2010;Paul & Chatterjee, 2014).It is useful to pinpoint important growth hubs, nodes, and service hubs as well as the prevalence of rural centers which is following the study results as assessed by Amirinejad et al. (2018).The analysis shows how the area's population is distributed among the various development potentiality categories.Growth poles are strategically important hubs of the numerous economic activities that house roughly 32.20% of the population and stimulate regional economic growth.With 5.56% and 4.45% of the population, respectively, growth centers and "growth points" contribute to regional growth.7.70% and 8.36% of the population, respectively, are "service centers" and "service points," which offer necessary services.Lastly, 41.72% of people live in "rural centers," which have less potential for development following the results of Paul and Chatterjee (2014) from Indian perspective.
As indicated through hotspot analysis, there are many reasons why there are more peri-urban units in the western half.There are more facilities for education, health care, recreation, finance, communication, transportation, and administration, as well as a higher population density, a larger working population, and a higher literacy rate (Cattivelli, 2021;Haldar et al., 2023b).Additionally, the Raniganj municipality and DMC, two significant urban bodies, have a positive dual impact on the western half.This critical research demonstrates the intricate interactions between urban growth and regional dynamics in the study area by highlighting the significance of infrastructure, amenities, and population factors that lead to the clustering of peri-urban hotspots in the western half which is following the line of research outcome of Amirinejad et al. (2018).With a 99% confidence level, the hotspot analysis revealed 46 peri-urban units, mostly in the northern section of Durgapur Municipal Corporation (DMC), as cold areas.These places have a relatively low potential for development, which can be linked to the uneven distribution of amenities and infrastructure.The study area's infrastructure and amenity distribution are predominantly in favour of the western and lower eastern regions.This infrastructure distribution imbalance may be driven by a number of variables, including  et al., 1998).
The metrics' importance in defining the potential for development in peri-urban centers is demonstrated by the correlation research among the parameters, which indicated a larger positive correlation with development potentiality (Chen et al., 2017;Grimm et al., 2008).Overall, the values of the correlation coefficients offer useful information about the connection between each characteristic and development potential.The higher coefficients show how important variables like household density, workforce, literacy rate, fundamental activities, and centrality have a significant impact on the potential for development of peri-urban centers (Mostafa et al., 2021).Greater urbanisation and residential growth are indicators of enhanced urbanisation, which can result in economic activity and infrastructure growth, both of which can enhance development potential (Shayan & Kim, 2023).A larger working population means more employment options and economic growth, which support development.A population with a higher literacy rate is more likely to be educated and skilled, which eventually improves human capital and promotes economic growth (Liu et al., 2023).The potential for development is further strengthened by the existence of many economic sectors and services, as evidenced by an increase in the basic activities (Haldar et al., 2023b).In addition, a greater centrality value highlights the importance of periurban areas as centers for social and economic activities, leading to an enhanced connectivity and growth possibilities (Haldar et al., 2023c;Mengistie et al., 2023;Shayan & Kim, 2023).

Concluding remarks
The present study assessed the development potential of peri-urban centers in the Durgapur Municipal Corporation (DMC) area of West Bengal, India.It used a comprehensive methodology, including indicators and analytical techniques, to evaluate factors and identify areas with higher and lower development potential.The findings offer valuable insights into socio-economic conditions, infrastructure needs, and growth prospects, guiding policymakers and planners in formulating sustainable regional growth strategies.This study's datadriven hybrid model provides greater transparency by providing stakeholders with detailed evaluation criteria and reasoning behind site selection, potentially fostering confidence.This study's models streamline decisionmaking by allowing stakeholders to quickly compare sites based on objective criteria, reducing time and expense associated with subjective evaluations.The hybrid model, which combines CRITIC and TOPSIS criteria, can be easily scaled up and modified to address similar site selection issues.The hybrid model used in this study offers benefits but has limitations due to its limited application and may require additional testing for site evaluation.The CRITIC-TOPSIS model, a data-driven technique, holds promise for assessing the development potential of peri-urban regions, requiring accurate and reliable data to avoid potential inaccuracies.The model may be integrated with machine learning techniques like neural networks or decision trees to enhance accuracy and predictive capacity.The model could incorporate probabilistic techniques or fuzzy approaches to enhance its decision-making abilities in uncertain data, enabling it to handle input with varying degrees of uncertainty.The hybrid CRITIC-TOPSIS model can be extended to other renewable energy sources like wind or hydropower, with future research focusing on necessary adjustments.
The TOPSIS method was used to measure development potential, with weighted criteria from the Criteria Importance Through Intercriteria correlation (CRITIC) playing a crucial role.The study found that regional accessibility and functionality are the most critical factors influencing development potentiality in peri-urban centers.These factors emphasize the importance of connectivity and well-functioning services and amenities for enhancing growth prospects.Population density, growth rate, working force, literacy rate, basic activity, and centrality had lower weightage values, indicating smaller impacts on development potential evaluation.The analysis classified peri-urban centers into six categories, with growth poles representing areas of economic activities and significant development potential, accounting for 32.20% of the population.The study examines the development potentiality of peri-urban centers in the DMC region, focusing on growth centers and growth points, service centers and service points, and rural centers.Growth centers and growth points contribute to regional development, while service centers and service points provide essential services.
Rural centers, with 41.72% of the population, serve as administrative or commercial hubs.Factors such as household numbers, working force, literacy rate, basic activity, and centrality show a stronger positive correlation with development potentiality, highlighting their importance in determining growth prospects.
The hotspot analysis helps allocate resources and policy interventions accordingly.The hotspot analysis reveals spatial patterns of development potentiality, with significant clustering in the western part of DMC, where transportation networks, railway stations, mining areas, and industries contribute to the region's development potential.The northern part shows lower development potential, requiring targeted interventions to improve infrastructure and amenities.The hotspot analysis provides a comprehensive understanding of peri-urban centers in the DMC area, identifying areas with higher and lower development potential.The research employs a robust methodology, assessing factors and indicators, to identify areas with higher and lower potential.The findings can help policymakers and planners formulate targeted development strategies, allocate resources efficiently, and promote sustainable growth in the peri-urban region.The study emphasizes the importance of regional accessibility, functionality, infrastructure, and amenities in driving development potentiality and addressing disparities for inclusive and balanced growth.By enhancing growth prospects, the region can achieve more equitable development, ensuring improved socio-economic well-being for the residents.

Fig. 1
Fig. 1 Location of the study area

Fig. 2 a
Fig. 2 a Classification of peri-urban region of DMC and variables of development potentiality (b) population density, (c) population growth, (d) no of HHs/ 1000 population, (e) working force, (f) basic activity, (g) Centrality, (h) functionality population in peri-urban region of Durgapur municipal corporation (DMC)

Fig. 3 Fig. 4 a
Fig. 3 Development potentiality of different units in the peri-urban region of Durgapur municipal corporation (DMC)

Table 1
Selection of variable with its weighted value

Table 2
Assessment of variables for development potentiality analysis

Table 3
Classification of peri-urban unit based on development potentiality value

Table 4
Correlation between development potentiality and its variables