This paper proposes a Cellular Automata (CA) model to evaluate the urbanization patterns arising from the regulation of urban growth on paddy lands in the Colombo Metropolitan Region (CMR). Most of the historic map data available for the CMR before 1990 are temporally sporadic and spatially incomplete. As an alternative to maps, classified remote sensing data are used to analyze the urbanization process. Logistic regression is applied to derive factors of urbanization and the various relationships among them. The relation between 'urban' and 'non-urban' serves as an explanatory variable. The factors explaining that relationship are calculated by exploratory logistic regression analyses. The probability calculated from the statistical model is used for CA transition with a random number. Several growth patterns are simulated based on a range of transition thresholds to test the CA model. Status quo growth and several growth control scenarios are simulated for the period from 1987 to 2002 based on an optimum threshold. The simulation result of the status quo growth is evaluated with several evaluation methods. The level of agreement between the estimated result from the status quo model and the actual data is 62%, while the multi-scale goodness-of-fit method produces highly accurate values for the given range of resolutions.

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The Japanese Government recently announced a new strategy for the creation of "self-reliant regions" based on aggregation of services and networking between regional cities and surrounding municipalities. This study analyzes the impacts of such scenarios based on the cost savings by residents. It is shown that integration of public facilities into service hubs can lead to substantial reduction in overall travel costs and that this can be enhanced through cross-border cooperation. It is expected that the information gained will provide local governments reference points as they deliberate on how best to proceed in forming local and regional alliances.

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