Integrating cellular automata and Markov techniques ...

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Integrating cellular automata and Markov techniques to generate urban development potential surface: a study on Kolkata agglomeration Biswajit Mondal, Dipendra Nath Das & Basudeb Bhatta To cite this article: Biswajit Mondal, Dipendra Nath Das & Basudeb Bhatta (2016): Integrating cellular automata and Markov techniques to generate urban development potential surface: a study on Kolkata agglomeration, Geocarto International, DOI: 10.1080/10106049.2016.1155656 To link to this article: http://dx.doi.org/10.1080/10106049.2016.1155656

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Date: 24 March 2016, At: 23:47

Geocarto International, 2016 http://dx.doi.org/10.1080/10106049.2016.1155656

Integrating cellular automata and Markov techniques to generate urban development potential surface: a study on Kolkata agglomeration Biswajit Mondala , Dipendra Nath Dasa and Basudeb Bhattab Centre for the Study of Regional Development, Jawaharlal Nehru University, New Delhi, India; bDepartment of Computer Science & Engineering, Jadavpur University, Kolkata, India

Downloaded by [Jawaharlal Nehru University] at 23:47 24 March 2016

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ARTICLE HISTORY

ABSTRACT

Uncontrolled, yet fragmented peripheral urban expansion has emerged as a menace to urban development. To cope with this rapid urban expansion process, identification of the forces responsible for this rapid urban expansion is a pre-requisite, especially when its threats to habitability are taken into consideration. This study tries to evaluate fragmented uncontrolled urban expansion faced by Kolkata using cellular automata-Markov chain. Urban growth patterns, land use/land cover transformations and spatial allocation correspondence with planning strategy is the main theme of this study. Depending upon the driving forces, the study result indicates a bi-directional urban development potential surface, which might be a result of the biased planning initiative along with middle-class residential demand. This simulation result provides evidence for the planning authority to evaluate the effectiveness of spatial allocation and urban expansion trends and provide flexibility to modify the current allocation scenario.

Received 1 July 2015 Accepted 3 February 2016 KEYWORDS

Kolkata; urban expansion; multi-criteria evaluation; development suitability surface; cellular automataMarkov; urban development potential surface

1. Introduction Urbanization is a form of land transformation (Taubenbock et al. 2009), which is caused by the spatial changes in population and various other socio-economic dynamics (Jokar et al. 2013). Rapid urban expansion induces urban land use change and environmental degradation that can lead to severe effects on inhabitants (Benenson & Torrens 2004; Czamanski 2008; Bhatta 2010). Effective planning is desirable to restrict the urban habitat obliteration. However, incessant evaluation, space allocation, decision-making and flexibility of planning is a complex set of the task towards better urban management. Urban design, or having a zoning scheme for spatial allocation rather than mixed land use, may improve the appearance and proficiency of a city but is unable to resolve the fundamental crisis. Large cities with historic core have severe problems of transportation, commercial expansion and urban regeneration (Hourihan 2001). Most of the large cities in the developing countries (including India) are predisposed by the conventional planning processes that owe their legacies to the colonial past (Pethe et al. 2014). Township and housing development has constantly been an essential apprehension of planning (Massam & Askew 1982; Anthopoulos & Vakali 2012) – which while giving a direction, has nonetheless restricted the imagination of spatial development. Thus, comprehensive

CONTACT Biswajit Mondal

[email protected]

© 2016 Informa UK Limited, trading as Taylor & Francis Group


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urban planning should include multi-platform decision-making environment (Yao et al. 2006) where strategies are flexible and are periodically evaluated. Planning and assessment are inseparable and deficient evaluation may lead to the breakdown of the entire planning process (Khakee 1998; Lichfield et al. 1998). Remote sensing coupled with geographic information system (GIS) can be used as an inquiry system for decision-making which can chalk out the loopholes of land use allocation system. The land use change pattern, spatial trend (e.g. Mesev et al. 1995; Zhang & Foody 1998; Kohsaka 2001; Weng 2002; Bhatta 2009; Rozenstein & Karnieli 2011; Pethe et al. 2014), urban growth process (e.g. Li & Yeh 2004; Xiao et al. 2006; Sabet Sarvestani et al. 2011) and long-term monitoring, decision-making and environmental protection (e.g. Massam 2002; Glaeser & Kahn 2004; Huang et al. 2007; Luo & Wei 2009; Rikalovic et al. 2014) have always remained primary concerns in urban land use planning. Apart from mere monitoring and mapping, advanced predictive models such as the Markov chain model (e.g. Weng 2002), CA (e.g. Clarke & Gaydos 1998), logistic-CA (e.g. Allen & Lu 2003; Hu and Lo 2007), CA and partial swarm optimization (e.g. Feng et al. 2011), multi-agent cellular automata (e.g. Manson 2000; Jokar et al. 2013), gravitational field model with CA (e.g. He et al. 2013), variable grid cellular automata (e.g. Van Vliet et al. 2009), cellular automata and system dynamics (e.g. Haase et al. 2012) and CA-Markov (e.g. Liu 2009; Maithani 2010; Guan et al. 2011; Mitsova et al. 2011; Sang et al. 2011; Shafizadeh Moghadam & Helbich 2013) also have significant contribution on land use land cover (LULC) assessment system. Previous researches on urban growth modelling have introduced some physical, socio-economic predictor variables to simulate forthcoming urban expansion scenarios (White & Engelen 1993; Waddell & Evans 2002; Cheng & Masser 2003; Guan et al. 2011; He et al. 2013; Jokar et al. 2013). This study develops an approach towards urban growth modelling of Kolkata Municipal Corporation (KMC) area and its immediate surroundings based on the simulation capabilities of cellular automata Markov chain and multi-criteria evaluation (MCE) techniques. The CA model is useful for environmental-sensitive planning of urban areas (Mitsova et al. 2011). The incorporation capacity of socio-economic factors in the modelling environment is a major hindrance towards urban prediction (Sang et al. 2011; Jokar et al. 2013). Yet, CA-based modelling technique is far superior to the biased regression modelling (Mitsova et al. 2011; Shafizadeh Moghadam & Helbich 2013). The CA mechanism for the dispensation of information is based on its personal rule and external input with an expertise standard in action (Liu 2009). The transition rule of a CA model can precisely plot probable fresh urban cells to set up the spatial demand in cities (Feng et al. 2011; He et al. 2013). Presently, CA rule is the only method that wields physical, socio-economic and infrastructure factors for forecasting urban scenario. Thus, this would help to understand the benefit of socio-economic and infrastructural factors on future land use changes. The primary objective of this study is to analyse the suitable areas susceptible to land use changes by considering the behaviour of different landscape change forces. Driving forces would help us to simulate the 2021 urban development potential surface and the spatial trend in the land use pattern. This study simulates only built-up area because selected forces are mainly associated with built-up expansion. Hence, the simulated result might be distorted if we use the same factors to simulate the other LULC classes. Further, this study also addresses some prime questions: Would pre-planning modelling technique be useful to Kolkata? If the modelling tools are useful, then how can it be used to explain the future urban growth process? Will this urban expansion process decline or increase in future? What is the relation between development suitability and planning area demarcation in Kolkata?

2. Materials 2.1. Study area This study considers the metropolitan core and its immediate periphery within the Kolkata urban agglomeration which is situated on the banks of river Hooghly. The KMC is located on the eastern banks of river Hooghly in the eastern India. The entire study area of 644.72 sq km lies within 22°26′13″N to 22°39′15″N



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Figure 1. Study area map (true colour Google image of KMC and its surroundings) of the year 2013.

latitudes and 88°13′15″E to 88°28′50″E longitudes. The study area consists of 2 municipal corporations, 7 municipalities, 48 census towns and 87 villages (Census of India 2011). This study is spread across four districts – Kolkata, Howrah, North 24 Parganas and South 24 Parganas. In North 24 Parganas, the study covers Rajarhat and Barasat blocks and three municipalities, namely Dumdum, Rajarhat Baranagar and Bidhannagar. The blocks Sonarpur, Maheshtala and Budge Budge are in South 24 Parganas. In the district of Howrah, the study covered Howrah Municipal Corporation (HMC), Bally municipality and the blocks Domjur, Bally-Jaygacha and Sankrail. Finally, under Kolkata district KMC was studied (Figure 1). The KMC itself accounts for 185 sq km, while HMC accounts for 51.74 sq km of the total study area. The total urban and rural population shares are 97.10 and 2.9%, respectively. The 2011 census states that KMC and HMC have 4.49 million, and 1.08 million inhabitants, respectively. The population growth rate in the study area was 1.21% during the period of 1991–2001and it has declined to 0.6% during 2001–2011. KMC and HMC have faced a declining growth rate of −0.17 and 0.67%, respectively, during 2001–2011. In the year 1971, KMC and HMC shared 58.27% of the population in Kolkata urban agglomeration, and KMC itself shared 47.20% of the population. However, in the year 2011, KMC and HMC shared 39.5% population, of which KMC itself accounts 31.86% population. This indicates a decline in the core population (−0.01%) than the periphery (1.79%) population. This phenomenon is a result of high growth (4.49%) of small towns in the periphery than in the urban core.

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2.2. Database A series of cloud-free Landsat TM and ETM + images (1990, 2001, and 2011) with 30 × 30 m resolution have been used in the present study. The data are of fine spatial resolution, which are quite ideal for urban growth modelling (Shafizadeh Moghadam & Helbich 2013). The data have been re-projected to UTM-WGS 1984 Zone 45N projection system. All these images have been corrected and resampled, and then a subset of the image was clipped (containing 898 rows and 814 columns) to generate a LULC for the study area. A set of urban features (municipal and block boundary, primary and secondary transport network, locational attribute, river, protected area, etc.) have been digitized from the Census of India 2011 administrative atlas with the help of Google imagery. Some socio-economic and environmental variables have also been developed from different sources such as the Census of India, West Bengal Registration and field survey (Table 1). Environmental variables for urban growth modelling have been developed by using sub-pixel classifier and ISAT tool (http://coast.noaa.gov/digitalcoast/ tools/isat), and band 6 of ETM + 2011 Landsat imagery.

3. Methodology The objective of urban growth modelling is not only to simulate developable potential surface, but also to focus on the interaction and influence of individual forces on the spatial expansion process. The urban growth model consists of the following sections; the first section focuses on quantification of land use changes from the classified images. The second section considers the driving forces of individual modelling to derive potential surface for future growth. Finally, all these steps are combined to simulate a developable potential urban surface. Figure 2 illustrates the detailed workflow, which is actually based on an integration strategy of remote-sensing and GIS techniques. 3.1. Spatio-temporal image processing Time series of LULC maps are elementary preconditions for urban growth analysis. After geometric correction, good-quality training samples were collected. Then the time series satellite images were classified using a Maximum-Likelihood algorithm. Finally, all the unclassified pixels were merged into more suitable classes. To evaluate the classification, a total of 250 reference points were generated randomly with a minimum threshold for each category being set to collect 50 reference points (Congalton 1991). This process decimates the pixel bias. Subsequently, the classification accuracy is assessed by measuring Kappa statistics based on error matrix (Congalton 1991; Weng 2010). To evaluate the accuracy of the 2011 classified image, 50 ground reference points were collected with the help of Google imagery. For the 1990 and 2001 images, reference data were collected from land use/land cover maps produced by Kolkata Metropolitan Development Authority (KMDA) and Bhatta (2009). Measures of classification accuracy, including overall accuracy, omission and commission errors, and Cohen’s kappa statistic (Congalton & Green 2008) were derived using Confusion matrices. The GIS Table 1. Description of the collected geospatial and socio-economic data. Data type Socio-economic data

Source Census of India

Year 1991, 2001, 2011 2010, December 2013

Satellite image

West Bengal Registration, Field Survey USGS

Urban features

Vectorized feature

2011

November 1990, 2001, 2011

Dataset Population density, housing density, main workforce, employment Housing price, land price, development cost LANDSAT-TM (1990,2001) and ETM (2011) Building block, transport network, protected areas, river stream, district boundary, locational attribute (metro, rail station, urban centre)



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Figure 2. Workflow of Multi-Criteria CA-Markov (MCCA-Markov) model.

minimum dominant overlay method was applied to evaluate the nature of urban change, and two major classes were extracted from each of the LULC maps. 3.2. Criteria selection and analysis Depending on the five major forces such as locational force, land use force, market force, residential force and a group of resistance force, this study has explored the upcoming urban scenario. Before using MCE, the Cramer’s V was applied to ensure the explanatory power of these forces to drive urban change (Eastman 2012). 3.2.1. Local and land use force (Lf, LLf) Local factors such as distance from primary road, secondary road, distance from railways, distance from cost centre, land use likelihood, distance from metro station and distance from developed area were considered as a driver for decision-making and all these were derived by Euclidian distance measure (Shafizadeh Moghadam & Helbich 2013). 3.2.2. Residential force (Rf) The quality of habitat within the city is the most favoured criterion for residence. Factor analysis was performed to generate a quality of residence (QR) index that represents the probable surface of residential preference. The indicators used for QR assessment are population density, housing density, impervious surface, percentage of green space, temperature, employment rate, percentage of main workers and median housing value. All these indicators were normalized (1) to reduce them to a data range within 0 and 1 (e.g. Cushman et al. 2008; Floridi et al. 2011). Later, the principal component extraction method was applied, and three factors (spacing quality, environment quality and economic quality) were extracted. All factor scores were multiplied by their percentage variance and summed to produce the QR index (e.g. Weng 2010) or termed as residential force (Rf). ⎧ xij Actual −xijmin ⎪ xjmax −xjmin if Iij satisfy higher the value better the condition nIij = ⎨ xijmax −xij actual (1) ⎪ xjmax −xjmin if Iij satisfy smaller the value better the condition ⎩

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tRfij =

n ∑

fsij × Wj

(2)

1


where, tRfij is the residential force (Rf) at time t, fsij is the factor ij score, Wj is the variance explained by factor n. Finally, the Rf was mapped into five categories from high to poor QR. 3.2.3. Market force (Mf) Market force (Mf) is one of the major components that catalyses urban change. To compute the investment potential surface, variables such as housing price, land price and development cost (Dc) were considered. The investment potential surface indicates the area of the developer’s choice (Jokar et al. 2013). It was measured by analysing the investment profit (IP). The investment potential surface is obtained from the residual land value (RL value) which itself was derived from the deduction of development cost from apartment price (Ap). RL value contains land value and profit. Subsequently profit on Dc is derived from the deduction of 10% of Dc to RL value. Furthermore, the developer’s profit is determined by the state government mentioned as 10% cap on profit on Dc and the value is normalized between 0 and 1, termed as a normalize developer’s profit or termed as market force(Mf). It is not an exact measure of investment potential surface, but it can be used as a proxy. Investment potential surface denotes the developer’s interest area, from which they can generate maximum profit because one may assume that they are the profit seekers. Normalized Mf can be expressed as follows (e.g. Jokar et al. 2013):

tMfij =

IPActual − IPmin IPmax − IPmin

(3)

where, IPActual is actual profit, IPmin is minimum profit, IPmax is maximum profit. Thus this criterion denotes the developer’s interest area, where they can generate maximum profit by building residential and commercial buildings and direct the urban expansion. 3.2.4. Resistance force (Rf) Unlike positive forces such as the Lf, LLf, Rf and Mf, some negative factors hinder the spatial expansion process or prohibit developmental and construction activity or urban expansion in certain areas. This resistance imposed by the government is a measure undertaken to protect the areas and reduce ecological degradation. According to Eastman (2012), some major constraints in the land development process are wetland and water bodies, protected natural and artificial parks, river bed area and steep slope area or risk zone within the land boundary. Thus, it is essential to develop restricted and non-restricted areas. In this study, constraints selection was pursued by close examination of the land use change statistics and planning authority’s construction approval guidelines (as the authority has the power to approve and reject allocation decisions). A binary surface was generated at time t with land constraint and drainage constraint, to obtain the resistance surface. 3.3. Urban growth simulation and validation The simulation-based urban growth model utilized in this research incorporates Markov chain and cellular automata. These two techniques have been used to simulate the development potential surface in the immediate future. Markov chain analyses resemble the transition potential area. MCE fuzzy function was also needed to generate a ranked suitability surface through the analytical hierarchical process (AHP; Saaty 1980) and CA-MARKOV is an extension of the MCE procedure. Throughout the iteration in CA process, each and every land cover class is turned into a host category with the contending, adjacent to the claimant categories (Eastman 2012). This strength of character ensures


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that the spatial distribution and the future allocation do not occur randomly in the multi-criteria CA-MARKOV process. 3.3.1. MARKOV chain The MARKOV process describes the probability of the alteration of one state to another. It refers to the state of a system at time 2 that can be predicted by the state of the system at time 1; this would then offer a transition probability to articulate the likelihood of a pixel of a specified class to alter it to any other class in the upcoming state. This uniqueness makes it suitable to function in the land use change process, yet the model fails to produce a two-dimensional spatial surface (Guan et al. 2011). Time series LULC map (1990, 2001, 2011) were categorized into two-time periods (1990–2001 and 2001–2011) to derive two transition matrix for all the LULC categories. The MARKOV technique is used to predict the change undergone by a stochastic process that is state S at time t + 1 which depends on its previous state at time t and is expressed as a conditional probability (e.g. Weng 2002). Markov chain’s conditional distribution states that any future state Xt+1, given the past state X0, X1, …, Xt−1 and the present state Xt, is dependent only on its previous state. A Markov chain with n states S1, S2 … Sn and Pij is a transition probability matrix from state i to j. So the prediction can be solved by the matrix,

 (t+1) =  (t) × Pij

(4)

where,  (t+1) ,  (t) are the states at times t and t + 1, respectively, and Pij is the transition probability matrix. 3.3.2. Cellular automata Specifically, the cellular automata (CA) process creates a ‘spatially-explicit weighting factor’ to generate a spatial distribution. State, cell, neighbourhood, transition rule and discrete time are essential elements of the CA (White & Engelen 1997). The cell is a land use category cell; where land use category is a state, and transition rule uses the standard neighbour function. The neighbourhood filter yields a value of 1 when it completely falls within the existing class unless it is 0. The neighbourhood filter is subsequently multiplied with the final suitability potential maps to re-weight the suitability pixel close to the contiguous area of the same categories (Guan et al. 2011; Mitsova et al. 2011). Here, the neighbourhood denotes the affected change potential cell, and the transition rule helps to transit the cell. This study considered land use maps of 10 years interval to predict the immediate future of 10 years. So the CA iteration time was selected as 10. CA is a discrete dynamic function and can be expressed as follows (Liu 2009),

t + 1DPij = f (tSij , tij )

(5)

where, t + 1DPij is the probability surface, tSij is the development suitability surface derived from MCE techniques using Satty’s Hierarchical weighting process. tij is the neighbourhood effect. 3.3.3. MCCA-Markov model Two-step multi-criteria analysis was undertaken for this study. To generate the development suitability surface, forces are analysed and then standardized, and weight has been assigned according to their importance. During fuzzy standardization, membership function was used more carefully by considering the previous literature (Eastman et al. 1995, 1999; Zhang & Foody 1998; Chen et al. 2010; Al-Yahyai et al. 2012; Shafizadeh Moghadam & Helbich 2013). So, to evaluate the factors that alter land use and shape of urban expansion, spatial multi-criteria analysis can be thought of as a process that combines geographical data into a resultant decision (Rikalovic et al. 2014). Four broad groups of factors (local, economic, spacing, environmental) were selected on the basis of previous studies (Mitsova et al. 2011; Guan et al. 2011; He et al. 2013; Jokar et al. 2013). During MCE, all the factors (local factors) were


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standardized by a fuzzy membership function (e.g. J-shaped monotonically decreasing, linear function) into a byte-scaled range of 0–255. Except the local force, all the major forces that were selected to generate development suitable surface (Mf, Rf and LLf) were standardized by assigning suitability score to all the pixel values (using re-class and assign module as an alternative to fuzzy standardization). The J-shaped decreasing function explains all linear infrastructures and their behaviour. It has been introduced to show the vast extent of high suitability up to the base control point, after which suitability decreases rapidly with the increase in distance up to second control point. However, it never reaches zeros (Shafizadeh Moghadam & Helbich 2013). For example, the area within 50 m of major road shows high suitability, after which the suitability decreases up to 1000 m, and there is no suitability beyond 1000 m. Similarly, the distance from metro and railway stations shows that suitability decreases from first control point to the second control point. The linear function, on the other hand, is suitable for two factors: distance from cost centre and distance from the developed area. The nearest area to the developed area transforms faster than remote areas due to the urban agglomeration effect. Therefore, according to the linear decreasing function, suitability regularly decreases as distance increases from the developed area. Local factor like distance from cost centre has been considered with all the main centres and distance parameter to generate the cost centre surface. Suitability decreases as travel cost increases with increasing distance. All fuzzy factors were combined with a weighted linear combination by an AHP (Saaty 1980; Eastman et al. 1995; Malczewski 2000). AHP is a systematic approach and is an efficient tool for the complex decision-making process (Eastman et al. 1995; 1999; Malczewski 2004). It helps to obtain the weight by a pairwise comparison process. The importance of these factors was determined by interviewing the local people and expert groups. The consistency ratio was measured to reduce the inconsistency in the pairwise evaluation process. The consistency ratio zero indicates a perfect evaluation, while values greater than 0.1 indicate the presence of inconsistencies in the pairwise comparison process which may not be tolerated. Two-step MCE was implied for local force and development suitable surface by repeating the same process but using different factors. Both Markov and CA use similar kind of rules. However, the difference between the two arises from the fact that CA considers the neighbourhood function as well. MCCA-Markov model generates a surface t + 1DPij and can be expressed as: ) ) (( ∏ ∑ tCnij t + 1DPij = tffaij × Wa × tNij (6) n

a

where, tffaij is the suitability factors (a = 1, 2, …) derived from spatial analysis by using Saaty’s Hierarchical Weighting Process (Wa). tCnij is the constraint factors at i, j. tNij is the neighbourhood effect of cell (i, j) at time t and it can be express as (Feng et al. 2011)

tNij = 𝛼

∑

Bc n×n−1

(7)

where, α is a normalized value used as a weight, Bc is a weighting parameter which indicates a Built-up (urban) cell with corresponding distance zone of neighbourhood n × n, 1 if the cell occupied by built-up; else 0. In each time loop, the transition rule changes a non-urban cell to a highest potential urban cell. During this conversion, the transition rule maintains the Markov-predicted urban land demand and cell transition starts from the highest suitability cell to the lowest suitability until the land demand is met. The neighbourhood effect, on the other hand, generates down weight to suitability surface distant from existing area of each class. Finally, to validate the model, the level of agreement and disagreement function (Pontius et al. 2004) is used by comparing the actual and predicted image of 2011. Validation measure provides several components, such as agreement due to chance, agreement due to quantity, agreement due to location at stratified level, agreement due to location at the grid level, disagreement due to location at stratified level, disagreement due to location at the grid level and disagreement due to quantity. Information regarding quantity is denoted as n = no information, m = medium information, p = perfect information. These seven important expressions and seven


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critical components are defined in Table 5. The expression of the middle column M(m) shows the agreement between comparison map and reference map and 1 − M(m) gives the total disagreement between maps. The N(n) to M(m) sequence shows the agreement, whereas M(m) to P(p) sequence shows the component disagreement (Pontius et al. 2004). Other traditional measures used to verify the simulated result are Kappa index of agreement (K standard), Kappa for no location (K no), Kappa for stratum-level location (K location strata) and Kappa for grid cell-level location (K location). Further, to validate the model, this study calculates relative operating characteristics (ROC). ROC mainly compares suitability image and the simulated built-up image to show the agreement and disagreement. Basically, if the sequence of the suitability values matches perfectly with the sequence in which real land cover change has occurred, then ROC equals to 1.


4. Results 4.1. Land use land cover The time series LULC map was produced from classification process which recognized the following five broad classes: Cropland and open land, green spaces and plantation, wetland and water bodies, built-up area and drainage (Figure 3). The Kappa value for classified images of 1990, 2001 and 2011 are 0.82, 0.85 and 0.87, respectively, which is satisfactory for the urban pattern and change analysis (Anderson et al. 2001). A comparison of the time series LULC map of 1990–2011 demonstrates (Figure 3) significant spatial trend in landscape alteration. The total spatial expansion has expanded up to the outskirts of the old city along the major transportation axis. This has led to an increase in the total built-up area from 132.68 sq km in 1990 to 243.94 sq km in 2001 and has reached 341.28 sq km in 2011 along with a substantial simultaneous decrease in cropland and open land, green spaces and plantation area, and wetland and water bodies (Table 2). Figure 3(e) demonstrates 7.82 and 5.23% during 1990–2001 and 8.55 and 7.79% during 2001–2011 of green space and plantation area, and cropland and open land, has contributed to the built-up growth. Real coverage of wetland and water bodies fluctuated throughout this period due to monsoonal rainfall, agricultural and fishing activities. The 1991–2001 period is a critical transition phase. The post-economic reform along with massive infrastructural investment (Shaw & Satish 2007) had created immense demand for land and had simultaneously fostered environmental degradation in the core. The foremost land use alteration in 1990–2001 occurred in the western part of HMC and in the south-west and north-east portion of the study area. During 2001–2011, the major transition took place in the south-eastern and north-eastern outskirt of KMC (Figure 3(d)). The urban spatial growth and population growth are closely correlated. KMC and HMC face a considerable decline in both population and urban spatial growth (Figure 4), whereas the periphery shows a much higher population and urban spatial growth. Large-scale population and built-up growth have occurred in the North 24 Parganas during the decade 1990–2001. Perhaps, one of the major causes of this occurrence is the creation of the new administrative centre, Salt Lake City. The spatial trend (Figure 4) also reveals that North 24 Parganas is one of the major LULC transition area. In South 24 Parganas, Mahestala and Sonarpur area face significant built-up growth but the population growth is insignificant in comparison to North 24 Parganas. During 2001–2011, Rajarhat area saw a decline in both population and urban spatial growth due to the abnormal rise of land prices and real estate business in Rajarhat planning area (Roy 2011). However, during the same period, Sonarpur faced high population growth and built-up spatial growth as it possessed a middle-class favourable environment along with transportation infrastructure and lacked the involvement of the real estate players. The built-up growth rate in Sonarpur, Mahestala, Domjur, Rajarhat is much higher than the household growth in the city, which indicates a highly dispersed situation in the periphery (e.g. Bhatta 2009). Perhaps this peripheral growth has occurred due to the linked peripheral township (Figure 5) (Salt Lake, Baishnabghata-Patuli, East Kolkata and Rajarhat New Town) development project from 1960 to 2011 along with infrastructural development in the periphery of the city (Mondal et al. 2015).

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Figure 3. (a)–(c) Classified images from different dates showing major land use/land cover categories. (d) Comparison of percentage contribution to total built-up change. (e) Spatial trends of built-up growth of core and periphery in two periods (1990–2001, 2001– 2011).

Table 2. Temporal Changes of LULC classes. Area in km² Land use/land cover class Green spaces and plantation Croplands and open lands Wetland and water bodies Built-up Drainage

1990 182.69 234.59 76.8 132.68 17.97

2001 105.92 199.14 78.38 243.94 17.35

2011 79.71 160.21 45.77 341.28 17.76



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Figure 4. Growth rate of population and built-up (core city and its periphery).

4.2. Urban growth simulation 4.2.1. Development suitability surface Population growth, linear infrastructure, socio-economic development and environmental quality are always associated with urban expansion. All these factors are correlated and combinedly affect the landscape change. All the local factors were standardized (Table 3) after that, all the factors were weighted by the AHP measure (Table 3), the consistency ratio was then calculated to verify the weight scheme which is 0.04 for local force and 0.02 for development suitable surface which is below the threshold point of 0.1, all of which indicate a pairwise comparison scheme is logical and consistent (e.g. Saaty, 1980). The weight schemes for the major forces (Table 3) are 0.4212, 0.3339, 0.1362 and 0.1082, respectively (Figure 6). The standardization process (except local force) was decided by expert review, field survey (qualitative judgements) and was finally confirmed by applying the consistency test (0.02). After that, land and water constraints are multiplied with the suitable surface. A combined form of local force is allotted high weight, which produces a suitable surface and has locational advantage and is nearest to the already-developed area, eventually the area enjoys low travel cost and has access to metro rail and major roads. Finally, the development suitability surface is produced by the importance of the forces that are Lf, LLf, Mf, Rf and Rf. Forces like Lf and Mf control human decision-making in comparison to the land use force and residential quality. Depending on all of these forces, Figure 6 shows Rajarhat planning area, Sonarpur and portion of Mahestala are more suitable for development. Suitability surface has been used to generate a development potential urban surface which has been discussed in detail in the following segment. 4.2.2. Predicting urban development potential surface Firstly, the Markov transition probability matrix was derived for the period of 1990–2001 and 2001– 2011 (Table 4). The diagonal of transition probability matrix represents self-replacement, and the off-diagonal shows the transformation probability from each land use class to the other land use class. During 1990–2001 and 2001–2011, self-replacement of the built-up area has remained constant but nearly 111.38 sq km and 80.4 sq km lands were gained from cropland and open land, and green space and plantation area, respectively. The built-up area continuously gains or occupies areas from the other land use classes but is somewhat at a declined rate. However, reduction in cropland and open land remains high. In order to evaluate the model, 1990–2001 LULC map, transition potential matrix of 1990–2001 and development suitability map (produced from local factors) have been combined to generate a simulated 2011 built-up surface. The simulated built-up area of 2011 is 348.68 sq km, which is a little higher than the actual area of 341.28 sq km. The over-prediction is the result of the Markov’s inability to capture the regeneration process and rapid population decline process (out-migration) during 2001–2011(Das & Bhusan 2014). Kappa Index of Agreement shows (Table 5) the amount of

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Figure 5. Township building initiative from 1966 to 2011, KMDA.

error avoided by the simulated result (0.81). The ‘K location Strata’ indicates the proportion of grid cell located accurately in the strata (0.83), and the ‘K location’ shows the amount of grid cell located accurately (0.83). Kappa Index of Agreement is greater than 0.8 and is thus acceptable for any simulated outcome (Eastman, 2012). After the accuracy assessment, the model is refitted with 2001 and 2011 land use data, 2001–2011 transition probability area and the development suitability surface to simulate a


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Table 3. Extracted weights based on AHP and fuzzy standardization.


Factors Distance from Cost centre Distance from developed area Distance from metro Distance from railway Distance from major road Distance from minor road

Membership function type Linear Linear J-shaped J-shaped J-shaped J-shaped

Membership function shape Monotonically decreasing Monotonically decreasing Monotonically decreasing Monotonically decreasing Monotonically decreasing Monotonically decreasing

Control point 0–7500 m 100–2000 m 0–1500 m 0–4000 m 50–1000 m 10–100 m

Weight 0.1914 0.2946 0.1703 0.095 0.1371 0.1116

Figure 6. Criteria maps used for the evaluation of development potential surface. The suitability levels are classified based on threshold value. Table 4. Markov transition probability matrix for the period 1990–2001, 2001–2011.

1990–2001

2001–2011

Drainage Built-up Wetland and water bodies Cropland and open land Green space and plantation Drainage Built-up Wetland and water bodies Cropland and open land Green space and plantation

Drainage

Built-up

0.945 0 0.0018 0.0012 0 0.9888 0.0003 0.0065 0 0

0.0128 0.9512 0.0507 0.24 0.2695 0.0044 0.9508 0.1621 0.3829 0.2424

Wetland and water bodies 0.0417 0.0091 0.6056 0.0815 0.0642 0.0063 0.0041 0.5084 0.0218 0.0045

Cropland and open land 0.0006 0.0296 0.2648 0.5606 0.2571 0.0002 0.0372 0.1438 0.5618 0.2647

Green space and plantation 0 0.0101 0.077 0.1167 0.4091 0.0002 0.0077 0.1791 0.0335 0.4884

Note: Diagonal values represents the probability of self-replacement.

2021 development potential surface (Figure 7). ROC measures also validate the MCCA-Markov model, which shows 67% success between predicted cell and suitability surface. However, this study prepares a suitability map by excluding existing built-up land considered as a land use constraint. Hence, the

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Table 5. Classification agreement/disagreement according to ability to specify accurately quantity and allocation. Information of allocation

Perfect[P(x)] Perfect stratum[K(x)] Medium grid[M(x)] Medium stratum[H(x)] No[N(x)]

No[n] P(n) = 0.9813 K(n) = 0.9813 M(n) = 0.8799 H(n) = 0.5000 N(n) = 0.5000

Medium[m] P(m) = 0.9887 K(m) = 0.9887 M(m) = 0.9037 H(m) = 0.5011 N(m) = 0.5011 Information of quantity


Agreement chance Agreement quantity Agreement strata Agreement grid cell Disagree grid cell Disagree strata Disagree quantity

Perfect[p] P(p) = 1.0000 K(p) = 1.0000 M(p) = 0.8948 H(p) = 0.5007 N(p) = 0.5007 0.5 0.0011 0 0.4026 0.0851 0 0.0113

area under the curve shows a value of 0.67 which is better than random (Pontius & Schneider 2001). The period 2011–2021 clearly shows two major focal points of growth, namely Rajarhat planning area and Sonarpur. This study reveals the fact that built-up areas will expand remarkably at Sonarpur and Rajarhat localities in the coming decade. The decadal growth rate of Action area-I will be 90.88%, but Action area-II will grow at a faster rate of 185.63%. It was expected from the beginning of the plan that the New Town would become a million plus town surrounding the KMA to reduce the pressure on KMC and act as an economic growth centre.

5. Discussion and conclusion Inclusion of socio-economic data with the CA-Markov model has a confirmatory impact on results, and this contributes to the bridging of existing research gap in urban expansion scenario of Kolkata. Another interesting fact is that no such detailed work is available on Kolkata that explores the urban development potential surface till date. So, this study may provide a valuable contribution to urban policy and evaluation. This research will also help to investigate the details of the land alteration process, spatial trends of land use change, urban expansion and aggregation pattern. The area with high suitability indicates higher potentiality for alteration within the planning area. The corridors of unexpected growth include the influence of transportation facilities, reverse Mf (low IP) and better QR. Notably, bi-directional spatial growth arises in Kolkata, which are policy-induced suitable surface with the high potentiality to develop. Another growth area includes modified suitability surface with fragmented land use and attributes (decisive factors including transportation, lowland market dynamism and quality residence) dependent residential expansion that has proceeded at a remarkable rate. Finally, it may be concluded that there are majorly three dominant decisive forces – Lf, Mf and township development. Developing a township always includes all types of conveniences such as linear infrastructure, good environment and land market, whereas local force help to induce middle and low-income residential expansion with the absence of vibrant land market in the outskirts of the township. Furthermore, KMC can be better represented as a typical example of ‘urban shrinkage’ because of high out-migration and decreasing rate of employment opportunities (Pal 2006), over-supply of housing and decrease in the residential and commercial land (Sengupta & Tipple 2007), uneven supply and demand of space and infrastructure, unstable market, high dependency on external funding and shortage of municipal expenditure (Shaw 1999). From these above discussions, it might be appropriate to mention that KMC has misplaced its importance as an engine of growth. There has been extensive urban sprawling (Bhatta 2009) which generates large-scale environmental degradation. Poor functioning of local authority and biased policy implementation by local bureaucracy often leads to excessive damage to cropland and green space which itself is an essential ingredient behind land fragmentation. Thus, the growth structure that Kolkata has faced can be categorized into

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Figure 7. Actual built-up area for the year of 1991–2001–2011 and simulated development potential surface 2021.

three directives: (i) linked township development as a radiative growth centred around the old city, (ii) peripheral transformation of green space to cropland to built-up, better known as suburbanization, (iii) private housing investment and local infrastructure fuelled middle-class residential demand in the periphery of township. The MCCA-Markov model is quite useful in spatial simulation because it incorporates various socio-economic and environmental variables. Simulated urban growth depicts the situation of fairly accurate urban expansion. The outcome can be used as a strategic guide by the planning authority to develop an enriched and equitable allocation system. The simulated result of 2021 has captured past


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trends sufficiently. It has predicted that the forthcoming urban growth of the city will decline further unless any significant policy intervention associated with environmental concern, land conservation, economic opportunities and infrastructural investment is undertaken. Building new cities on the periphery is a rational solution. However, it will not be possible without a regional approach for better allocation of urban population and efficient incorporation of the hinterland. Sengupta (2007) demarcated, Kolkata has an exclusionary type of urban spatial planning, which promote the peripheral unplanned residential development (fragmented housing development). Considering above study, urban grid scheme along with some protective measures for redundant land must be enforced to defend the land degradation and the growth of unregistered mini-township enclave in the surrounding areas of the township. Further, dynamic monitoring of urban expansion and micro-level understanding of administration are essential parameters for urban management. Kolkata has been facing township spatial effect on a massive scale. Thus, to acquire an efficient compact land use structure and affordable, healthy living, integrated township development plans must incorporate open spaces and zoning regulations in the form of public–private partnerships (for residential development). The benefit of the township must be analysed against its long-term environmental effects. Numerous incentives in local-level planning for the critical natural area selection and conservation along with the incentive zonal scheme for open-space conservation, housing and commercial are essential for sustainable city development. On the methodological ground, this study explores the urban expansion of Kolkata by using MCCAMarkov model. MCCA-Markov is a widely accepted tool to predict the urban growth. Since the individual indicators are inadequate to explain the urban expansion process of Kolkata, the factor extraction process was applied to reduce the number of indicators into fewer groups of driving factors. This study incorporates some socio-economic factors to improve the acceptability of the model. For example, individual variable like land price is less efficient as a driver variable for urban expansion, whereas factors like economic quality (Percentage of employment, the percentage of main workers and land price) significantly determine the urban bi-directional expansion process. Similarly, the QR index explains this urban expansion process more efficiently. Further, the two-step MCE process helped us to integrate local infrastructure (road, railway and metro rail) with the categorical forces (economic quality and spacing quality). The linear infrastructure can determine the locational advantage and directional land suitability, whereas the categorical variable explains zonal advantage or potentiality. It is to be mentioned that MCCA-Markov model has some limitations: the neighbourhood function produces different land allocation, depending on the Kernel filter size, and one cannot eliminate unnecessary built-up density. Furthermore, the absence of data ignored the detailed urban expansion process that might lead to biased prediction. Finally, the inclusion of village-level or plot-wise data determines more specific suitable land for urban growth. We must keep in mind that a micro view can reduce biased land supply. However, the recurrence of urban growth analysis helps to identify the growth externalities, direction and pattern of change. Therefore, the simulated urban expansion serves as a warning system for future land use change. The future research on ecological impact modelling, housing structural change and the migration scenario would help us to evaluate further the rigorous planning process and the quality of urban life.

Acknowledgements This research was a part of my MPhil dissertation at Jawaharlal Nehru University, New Delhi. This is solely an academic work. We would like to acknowledge Prasenjit Acharya and Tiyali Bose for their comments and discussions. We also appreciate the editor and anonymous reviewer’s helpful and constructive suggestions.

Disclosure statement No potential conflict of interest was reported by the authors.


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ORCID Biswajit Mondal

http://orcid.org/0000-0002-6624-8581


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