Geo-visualisation of Urbanisation in Greater Bangalore

Geo-visualisation of Urbanisation in Greater Bangalore


Bangalore is experiencing unprecedented urbanisation and sprawl in recent times due to concentrated developmental activities with impetus on industrialisation for the economic development of the region. Land use patterns reveal the main concentration areas and can provide invaluable inputs for sustainable city planning

Bangalore is experiencing unprecedented urbanisation and sprawl in recent times due to concentrated developmental activities with impetus on industrialisation for the economic development of the region. This concentrated growth has resulted in the increase in population and consequent pressure on infrastructure, natural resources and ultimately giving rise to a plethora of serious challenges such as climate change, enhanced green-house gases emissions, lack of appropriate infrastructure, traffic congestion, and lack of basic amenities (electricity, water, and sanitation) in many localities, etc. Land use analyses show 584% growth in built-up area during the last four decades with the decline of vegetation by 66% and water bodies by 74%. Analyses of the temporal data reveals an increase in urban built up area of 342.83% (during 1973 to 1992), 129.56% (during 1992 to 1999), 106.7% (1999 to 2002), 114.51% (2002 to 2006) and 126.19% from 2006 to 2010. The study area was divided into four zones and each zone is further divided into 17 concentric circles of 1 km incrementing radius to understand the patterns and extent of the urbansiation at local levels. The urban density gradient illustrates radial pattern of urbanization for the period 1973 to 2010. Bangalore grew radially from 1973 to 2010 indicating that the urbanization is intensifying from the central core and has reached the periphery of the Greater Bangalore. Urban heat island phenomenon is evident from large number of localities with higher local temperatures. The pattern of growth in Greater Bangalore has implication on local climate (an increase of ~2 to 2.5 ºC during the last decade) and also on the natural resources (74% decline in vegetation cover and 66% decline in water bodies), necessitating appropriate strategies for the sustainable management of natural resources.

Bangalore is located in the Deccan Plateau to the south-eastern part of Karnataka, India. Present greater Bangalore occupies an area of 741 sq. km (Figure 1), also known as Silicon Valley and IT corridor in India. Bangalore continued to be a small sustainable green city until the economic liberalisation which led to industrial revolution in 1990’s and advent of the Information and communication sector in 2000s, sudden spurt in population which increased drastically to 8 million in 2011 Further rapid urbanisation which has brought on fundamental land use changes (Ramachandra et al., 2012).

Urban dynamics was analysed using temporal remote sensing data of the period 1973 to 2010. The time series spatial data acquired from Landsat Series, Survey of India (SOI) toposheets of 1:50000 and 1:250000 scales were used to generate base layers of city boundary, etc. City map with ward boundaries were digitized from the BBMP map.
Land use analysis:
This was carried out using data of Landsat satellite using supervised pattern classifier – Gaussian maximum likelihood algorithm using GRASS. Four major types of land use classes were considered: built-up area, forestland, open area, and water body. The results of the analysis is shown in Figure 2 and tabulated in table 1.

Urban density is computed for the period 1973 to 2010 and is depicted below, which illustrates that there has been a linear growth in almost all directions (Figure 2)

This showcased sprawl in the region, hence greater Bangalore with 10 km buffer was also analysed for years 2008, 2010, 2012 (Figure 4 ) and land use share is as tabulated in table 2. This data was further considered for modelling of urban growth for the year 2020.

Modelling and prediction: The growth of Bangalore is visualized for the year 2020 using business as usual scenario. Multi temporal land use information, derived from remote sensing data of 2008, 2010 and 2012 have been used (Ramachandra et al., 2012) for simulation and validation. The land use pattern is evolving dynamically and follows the Markovain random process properties with various constrains that include average transfer state of land use structure stable and different land use classes may transform to other land use class given certain condition (Such as non-transition of urban class to water or vice versa). Thus Markov was used for deriving the land use change probability map for the study region and was applied using Markov module. The probability distribution map was developed through Markov process. A first-order Markov model based on probability distribution over next state of the current cell that is assumed to only depend on current state (Bharath et al., 2013). CA was used to obtain a spatial context and distribution map. CA’s transition rules use its current neighborhood of pixels to judge land use type in the future. State of each cell is affected by the states of its neighboring cells in the filter considered. Besides using CA transition rule and land use transition is governed by maximum probability transition and will follow the constraint of cell transition that happens only once to a particular land use, which will never be changed further during simulation. CA coupled with Markov chain was then used to predict urban land use state in 2020.

Land use [LU] transitions were calculated to predict land use for the year 2012, using markov chain based on 2008 and 2010 LU and CA loop time of 2 years. The Multi criterion analysis was used to generate transition probability areas based on transition rules and constraints for 2010 and 2012 LU data. The transition probabilities from Markov and the transition areas from CA were used to predict land use for the year 2012 (Figure 4.1). The model was scrutinized for allowable error by validating the predicted versus the actual 2012 land use (Figure 4.1). The validation results showed a very good agreement between the actual and predicted 2012 LU with kappa of 0.73.
With the knowledge of 2008 and 2012, LU for 2020 is predicted. CA filter (Figure 4.2) was used to generate spatially explicit contiguity weightage factor to change the state of the cell based on neighborhoods. This prediction has been done considering water bodies as constraint and assumed to remain constant over all time frames.
The simulated land use for 2020 (Figure 5) shows an increase in built up from 48.66 % (2012) to 70.64% (2020). The process of urbanization is observed to be high in the North East direction, near arterial roads and the national/state highways. Visualization of the patterns of urbanisation provides insights required for an effective regional planning to ensure sustainability. The uncontrolled growth will further erode the landscape heterogeneity affecting the natural resources as well as local ecology. This research shows that new urban nuclei will emerge in the next two decades and will be significantly clustered in space, while the outer buffer region will be more fragmented. This endeavor provides invaluable inputs for sustainable city planning. Nevertheless, the exercise is fruitful only when bureaucracy – policy makers, urban planners and city managers take note of the implications of poor planning.

The growth of Bangalore is visualized for the year 2020 using business as usual scenario. The simulated land use for 2020 (Figure 6) shows an increase in built up from 48.66 % (2012) to 70.64% (2020).


The predicted land use reveals of similar patterns of urban growth of last decade. The main concentration will be mainly in the vicinity of arterial roads and proposed outer ring roads. This also showed that the pheripherial regions and the buffer regions which include towns such as Yelahanka, Hesaragatta, Hoskote and Attibele would accommodate most of urban expansions in next decade. Further, an exuberant increase in the urban paved surface growth due to industrial Hubs in south east and north east. The results indicate that the urban area would cover close to 50 to 60 % of the total land use in and surrounding Bangalore.

We thank the Ministry of Environment and Forests, Government of India, Indian Institute of Science and the NRDMS Division, the Ministry of Science and Technology (DST), Government of India for the sustained financial and infrastructure support to energy and wetlands research.

3) Bharath H Aithal, Vinay S and Ramachandra T V, 2013. Modeling and Simulation of Urbanisation in Greater Bangalore, India, Proc. of National Spatial Data Infrastructure 2013 conference, IIT Bombay, November 29-30, 2013, pp 34-50
4) Ramachandra T.V., Bharath A.H. and Durgappa D.S. Insights to urban dynamics through landscape spatial pattern analysis, Int. J Applied Earth Observation and Geoinformation, 18, 2012, 329-343.