4. Integrated spatial planning support systems

For effectively managing the problem of urban sprawl; testing, building and visualising different scenarios, it is imperative to have a robust spatial PSS which would not only aid in managing but also in planning, organising, coordinating, monitoring and evaluating the system in question. Spatial PSS include instruments relating to geo-information technology that have been primarily developed to support different aspects of the planning process, including problem diagnosis, data collection, mining and extraction, spatial and temporal analysis, data modelling, visualisation and display, scenario-building and projection, plan formulation and evaluation, report preparation, enhanced participation and collaborative decision making (Geertman and Stillwell, 2004). Integration of different processes associated with the dynamics of sprawl phenomenon is required for addressing the problem of urban sprawl.

Currently there are few popular systems that try to emulate spatial PSS with an objective to make planning interactive and participatory. Among such existing PSS are What-If? (Klosterman, 1999) and RAMCO (Uljee et al., 1999). Klosterman’s What-If? system is an interactive GIS-based PSS that responds directly to both achieving the ideals of communicative rationality and traditional comprehensive land-use plans. It uses geographic data sets to support community-based efforts to evaluate the likely implications of alternative public policy choices. The package can be customised to a community’s existing geographic data, concerns and desires, providing outputs in easy to understand maps and reports which can be used to support community-based collaborative planning efforts. Given a set of factors and factor weights for determining land suitability, the What-if ? PSS generates projections for future land-use and subsequent land allocation can be based on user requirements. Although this system is claimed to be interactive, the dynamics of the factors and hence their interactions are less transparent, with only the results of a final land-use scenario obtained as output.

         RAMCO (Rapid Assessment for Management of COastal zones) is a prototype information system for regional planning in a generic decision support environment for the management of coastal zones through the rapid assessment of problems (Uljee et al., 1999). The system was developed by integrating GIS, CA and SD. Subsequently, White and Engelen (2000), the developers of RAMCO, also support the integration of GIS, CA and SD with the usage of multi-agent systems for a high-resolution integrated modelling of spatial dynamics of urban and regional systems. This has currently set the standard of technology that can be used for achieving an integrated PSS.

UrbanSim and OBEUS are two other established frameworks with supporting packages for integrated modelling of urban systems. UrbanSim is implemented as a set of packages under Open Platform for Urban Simulation (OPUS) (Waddell et al., 2005). This is fairly comprehensive in the sense that the framework integrates land-use, transportation, economic, demographic and environment variables. However, this framework does not support participatory simulations. The OBEUS (Object-Based Environment for Urban Systems) is more robust and represents an emerging trend to integrate various processes as agent-based models and to simulate them spatially, hence the term ‘geosimulation’ (Benenson and Torrens, 2004).

4.1 Framework for spatial PSS

The framework for a planning and decision making process involving different phases of intelligence, design and decision/choice (Sharifi, 2003) is depicted in Figure 1. The intelligence phase is confined to defining, understanding and assessing the existing situation along with evolving appropriate metrics for quantifying urban sprawl. In the design phase, the dynamics of urban sprawl are captured and subsequently modelled. The design phase concludes with the generation of alternatives. In the decision/choice phase, the review and evaluation of the different policy options are undertaken to arrive at policy recommendations for managing and mitigating the urban sprawl.

Figure 1 : The planning and decision-making process (Sharifi, 2003)

4.2 Evolution of SPSS through integrated SD and agent-based land-use models

Keeping in line with the framework for planning and decision-making suggested by Sharifi (2003), a prototype of the SPSS was developed with the following four components: patterns, processes, causes and consequences. Accordingly, the evolution of the PSS is depicted in Figure 2.

Figure 2 : Evolution of the PSS

Modelling the dynamics of urban sprawl is attempted by a combination of system dynamics and agent-based models over a geospatial domain (Sudhira and Ramachandra, 2007). Initially, the land-use of the region, Bangalore, was characterised using the satellite remote sensing data and the metrics for depicting the patterns were computed accordingly. A multi-stage classification process was employed resulting in classification of the entire landscape into four broad land-use categories. In order to characterise sprawl, some of the metrics computed are: built-up area, built-up density, patchiness (all spatial metrics), population and their densities. Apart from these metrics the percentage of different land-uses were also monitored. Further, to attempt modelling the processes and causes using system dynamics, variables were identified and depicted in a causal loop diagram.

The process of modelling in system dynamics begins with a clear statement of the problem, followed by gathering necessary data and identifying the key variables contributing to the problem. The variables are mapped on to what is referred to as causal loop diagrams (CLDs). With the gathered data and establishing relations with the variables amongst the variables are the stock flow diagrams created. It is possible to simulate the behaviour of the system through a stock flow diagram and plot the behaviour of key variables, test for different hypothesis and study the implications of specific interventions. The problem in question here is the pressure on land due to the extent of city outgrowth or the sprawl. The consequences of the interventions that can be attempted towards addressing this are analysed through this analysis.

In Bangalore, one of the prominent measures to limit growth is by way of enforcing building height restrictions formalised through the floor area ratio (FAR). These restrictions are one of the popular measures of controlling the zoning of central and several parts of the city. Though the planners in Bangalore have already enforced such restrictions, this in turn has also resulted in increasing the extent of the city outgrowth. SPSS intends to explore the options of relaxations with the FAR to visualize the probable land-use patterns, which would be of interest for planners.

Accordingly, based on the analysis, the key variables identified were: population growth, economic activity, pressure for new housing and industrial areas, land-use zoning, available land, built-up area, level of services and building height restrictions. The typical approach to cause-effect modelling, the ‘disjointed viewpoint’ and ‘linear, control viewpoint’ of one cause leading to one effect, is replaced by a ‘causal-loop, nonlinear feedback viewpoint’ where multiple effects are the result of multiple factors (Sterman, 2004), as also depicted in Figure 3.

Figure 3 : Causal loop diagram for urban sprawl

Analysing the feedbacks generated – reinforcing and balancing, the resultant dynamics reveal that certain practices are actually proving counter-productive. The five feedbacks generated in this system are: (i) reinforcing feedbacks: housing demands (R1); industrial demands (R2); relocation (R3) and infrastructure provision (R4); and (ii) balancing feedback: outgrowth of the city (B1).  The reinforcing feedbacks, R1 and R2, quite natural for most cities, set the demand for development of land into residential layouts/apartments or industrial estates, respectively. As the city grows, there is congestion in central areas forcing residents to relocate in the outskirts. This is captured by the reinforcing feedback, R3. The other reinforcing feedback, R4, for infrastructure provision suggests that augmenting infrastructure in a congested area can be counter-productive. Indeed this insight is well established by the classic Braess paradox (Braess, 1968). This was demonstrated by adding a link within the network to ease congestion would be counter-intuitive in a congested road network. Reinforcing feedbacks often generate exponential growth and then collapse. Systems that are self-regulating or self-correcting are comprised of balancing loops. The only balancing feedback, B1, results in the outgrowth of the city through the process of land-use change limited by the availability of land for development. The other control variable, building height restrictions, suggests that it influences the extent of outgrowth and built-up areas negatively. Bertaud and Brueckner (2004) have shown that in an unrestrictive building height conditions the extent of the city spread is contained with higher densities in central areas.

Further, the causal loop diagram is translated into a stock-and-flow diagram with appropriate numbers for the variables and assumptions. An important assumption relating to the process of land-use change was that economic activity combined with an increase in the workforce (through migration) would lead to higher demand for land for development and hence drive the land-use change. It is observed that it is the market forces operating in the context of a market economy, which are influencing the process of land-use change. Thus the analysis of land-use change has to acknowledge the implications of prevailing market conditions and the supply and demand aspects of land. The stock-and-flow diagram capturing the dynamics of population growth and land-use change is depicted in Figure 4. Essentially the stock-and-flow diagram sets the demand for new built-up areas.

Figure 4 : Stock and flow diagram capturing population growth and land-use change

4.3 Prototype design and implementation

4.3.1 Visualisation environment, design and implementation

The prototype of the spatial PSS has been developed as the BangaloreSim model, implemented using NetLogo (Wilensky, 1999), an agent-based modelling environment developed by the Centre for Connected Learning and Computer Based Modelling, Northwestern University, USA which facilitates encapsulation of processes through rule-based procedures and offers adequate monitors and plots to visualize pattern, model the causes and evaluate the consequences through simulation. The pseudo code of the BangaloreSim model is presented in Figure 5, which outlines the sequence of processes involved in the simulation.

Figure 5 : BangaloreSim model pseudo code

INITIALIZATION Set Parameters -> User Defined or Set from earlier start-up Import land-use data, elevation, transportation networks and CDP (Land-use Policy) Initialise System Dynamics setup - Initialise for Population, Economic Growth Rate (City Development Product), Available Land EACH TIME STEP Check for Simulation End Time and Available Land [Stop run if exceeds either of them] Compute Demand for Land (Inputs from System Dynamics Model – Stock & Flow Diagram) - Population Growth Model Runs -> Current Population - Current Population ->  Population Density -> Land-use Change - Land-use Change ~ Population Density, Available Land, Economic Growth Rate            - Land-use Change ~ Requirement for New Built-up Change Land-use - Obtains Built-up Demand from Land-use Change - Site-Suitability   - Suitability for patches based on Distance from City Centre, Transportation Networks, Weightages for Certain Land-use based & LU Policy, and Proximity to Growth Pole         - Allocate Land   - Check for Built-up Demand [Stop run if exceeds current built-up or No Available Land]   - Check for Maximum Site Suitability in the Neighbourhood (3x3) and allocate Land-use to Built-up Compute Metrics ~ Check for Impacts on Resources and Access to Services Update Views and Draw Plots END Agents are Growth Pole centres influencing land-use in their neighbourhood

4.3.2 Development of Prototype and Evaluation

Among the key criteria considered during the prototype development was enabling the user-interface for visualisation of land-use along with the other requisite metrics in the common interface. Thus, the entire parameters are presented in a single interface with provisions to change views for different land-uses, sprawl metrics, transportation networks, land-use policy (the comprehensive development plan) and suitability. Subsequently, for a spatial PSS to be effective, it would require the modeller/expert to control relevant parameters for generating scenarios simultaneously enabling dynamic interaction. Considering these key criteria, the prototype developed is depicted in Figure 6. The base year for the model was considered as 2000 with the classified land-use map for the same year serving as the input.

Figure 6 : Prototype of the SPSS

The evaluation of the spatial PSS involved calibration of the suitability weights with mean floor area ratio and location of industrial estates as growth-poles. The FAR was set to 2.75 based on the average permissible ratio of the land-use zoning policy. Considering these model set-up parameters, the suitability weights for different factors like land-uses, distance from city centre and distance from transportation network were calibrated. The calibration for the weights was performed comparing the classified land-use and the simulated land-use for 2006.

In the earlier test simulations (before acknowledging growth poles) the land-use changes could not forecast the land-use for 2006 appropriately. There were other initiatives by the State Government like the creation of an outer ring road, which were captured in the system. The industrial estates were ascribed as a growth pole (GP) agent to capture these changes. The GP agent essentially boosts suitability of any particular land-use in the neighbourhood with the greater probability to change into built-up (see underlined procedure in the pseudo code in Figure 5).

The simulation for year 2006 considering the influence of growth-pole agents and the mean FAR with the calibrated weights for suitability is presented in Figure 7. The comparison of these outputs were also analysed using the Map Comparison Kit 3 (Hagen-Zaker et al. 2006). The simulated land-use (Figure 7(a)) was compared with the classified land-use of 2006 (Figure 7(b)). The Kappa statistic of built-up areas was 0.413 and Kloc was 0.427 with Moran’s I for the simulated and classified image being 0.643. The calibrated model was then used to perform policy analysis.

                

Figure 7 : Classified and simulated land-use of Bangalore for 2006

(a) Classified land-use (2006)                                (b) Simulated land-use (2006)

4.4 Policy analysis

The scope of policy analysis is restricted for evaluating the impact of increasing floor area ratio on extent of sprawl up to year 2015. The effect on sprawl was analysed based on the percentage built-up areas and built-up densities. The simulations revealed that higher FAR resulted in increase of built-up areas (386.6 sq. km, 93.2 per cent) with high densities and the rate of built-up growth declining. However, during the duration of increase in density of built-up areas, the population ‘b-density’ (population over built-up area) depicted ‘U’ type behaviour. Accordingly, the population b-density decreased initially and increased after certain duration of time. This suggests that initially the expansion of the built-up area in spite of FAR continued and only after a threshold, the population chose to locate within the city’s extent over locating in the outskirts. The increase in built-up areas with high densities suggests more congestion in certain parts of city. It is in these parts wherein infrastructure and services needs to be augmented to accommodate the forecasted population b-densities. The policy analysis revealed the probable effects of change in FAR on extent of built-up areas and their densities. The spatial PSS was effective in locating areas with higher densities of built-up area and requiring augmentation of infrastructure and services to relieve of impending congestion in these localities. While this exercise was useful in generating a scenario of urban land-use, there can be many more scenarios generated by undertaking the simulations for different policy settings to understand their respective consequences.

4.5 Drawbacks and recommendations

An important distinction in the spatial PSS was the ability to incorporate the process that lead to land-use changes like creation of growth poles and studying the effect of outgrowth through the FAR. A spatial PSS thus facilitates the analysis of choices, visualisation of such decisions and evaluation of certain policy decisions. However, the success of these will rest on the modellers and planners who can identify and capture these processes to realise the best possibilities from a PSS.

The scale of analysis affects the computational complexity of the system. Higher spatial scales increase the computational resources required to perform the simulations optimally and vice versa. In the current model, the spatial scale is set for 100 metres x 100 metres grid, with yearly time scales. Caution should also be exercised on the choice of scale based on the problem and processes that are modelled.

The research here is yet to validate the spatial PSS with the government functionaries. Validation of agent-based land-use models has been a contentious issue in recent times. However, recent work by Brown et al. (2005) has attempted to clarify this debate by acknowledging path dependence and bringing out the distinction of achieving predictive accuracy and process accuracy. Consequently, it is important that any spatial PSS should have greater process accuracy and be able to generate patterns resulting out of numerous processes. But planners and decision makers would always want some amount of predictive accuracy informing them what type/pattern of growth will emerge at which locations. Thus spatial PSS should be ideally achieving reasonable predictive and process accuracies. The process accuracy and predictive accuracy of this model is yet to be ascertained. However, the spatial PSS in its current state allows the modeller or experimenter to test for various options and evaluate the consequences.

The contention of the process accuracy can be either addressed by following a similar approach of Brown et al. (2005) or engaging in a focus group evaluation involving key bureaucrats, planners, policy makers and experts who would verify the processes captured in the SPSS and outcomes of the simulation in the SPSS. With such a focus group evaluation, it will also make it beneficial for SPSS to be adopted in their current planning practices.

5. Conclusions and future directions

Proper implementation of master/development plans is a critical aspect in regulating the development of urban areas. Although 1,200 master/development plans for important towns and cities have been prepared in India, their implementation so far has not been satisfactory due to a variety of reasons. City planning mainly addresses the preparation of land-use plans through zoning to cater for projected populations. However, civic authorities also need to plan for meeting the demand for infrastructure facilities and ensuring delivery of basic services. This is dismal in current planning practice since these are normally static master plans or development plans mostly addressing land-use issues. These plans are also less equipped to review and evaluate any policy decisions dynamically by visualising the potential implications of a policy directive and the regions of potential sprawl. It is therefore necessary to support administrators and planners by providing them with better understanding, methods and tools to tackle the problem of urban sprawl. Further, administrators and planners need to be informed of possible areas of sprawl to take corrective actions to mitigate the implications. The problem of urban sprawl is observed to be an outcome of improper planning, inadequate policies and lack of effective governance due to various reasons. The inability of the administration and planning machinery to visualise probable areas of sprawl and its growth is persistent with the lack of appropriate spatial information and indicators.

Thus, in the present context, with the escalating problem of urban sprawl, the evolution of a PSS in the form of the BangaloreSim model is the first step in this direction. The PSS aids in undertaking policy analysis for certain policy measures and its impact on urban land-use. The PSS was also effective in evaluating the influence of FAR on controlling urban sprawl. However, there are numerous challenges for incorporating the complex urban system in a dynamic modelling framework with which to support participatory decision making. The PSS can be used to regularly monitor and check the nature of sprawl for compliance with policy recommendations over time. 

Most PSS allow analysis, evaluation and visualisation only on standalone systems, wherein each stakeholder has to choose/decide the options on same system/platform. This would suggest that all stakeholders have to meet physically to evaluate and decide. Moreover, such initiatives are not normal and very difficult to moderate. In this context, it becomes necessary for a parallel and distributed simulation framework to support PSS, so that all stakeholders and managers/administrators are able to interact, organise, plan, evaluate and decide through a network. Then the challenges are two fold: initially to integrate different models that are required to carry out the simulations, and then to synchronise the model inputs, feedbacks and outputs over space and time. Design of PSS with participatory simulation framework will largely contribute to its ready usage in urban planning and decision-making process.

The PSS is developed on an agent-based modelling environment as a process-based land-use model. The developments in PSS can significantly complement research in temporal geographic information science transforming the temporally static GIS to temporally dynamic systems. The exchange of geospatial data (import and export functions) is now possible for ASCII grid formats. In future, it is imperative that these systems are interoperable and support most of the standard geospatial formats. Provision of PSS over the internet can also imply that they may be accessible to everyone and can become pervasive. Such a web-based PSS can be a very effective tool for monitoring, planning and decision-making encouraging community participation. At the outset, the future development of the PSS can facilitate exchange of standard geospatial data formats and evolve as a web-based system.

Acknowledgements

We thank Indian Institute of Science for financial and infrastructure support. The Global Land Cover Facility (GLCF), Institute for Advanced Computer Studies, University of Maryland, USA and the National Remote Sensing Agency (NRSA), Department of Space, Government of India is duly acknowledged for making available the remote sensing satellite data for the study.

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Additional Reading

Batty, M. (2001) Models in planning: technological imperatives and changing roles, International Journal of Applied Earth Observation and Geoinformation, 3(3): 252-266.
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Wegener, M. (1994) Operational urban models: state of the art, Journal of the American Planning Association, 60: 17-29.