| Summary: | Since 2007, more than fifty percent of our planet’s population is living in urban areas. In the coming decade, the rate of urbanization will be fastest in Asia and Africa. Within South Asian countries, urbanization has attained its fastest pace in Pakistan. Urban planners and agencies in Pakistan have tried various spatial plan making solutions to manage urban areas, but none have given the desired results. After a 20% increase in declared urban areas within last two decades, urban planners and policy makers are looking for a more innovative and realistic spatial planning solution, which could adjust to the uncertainties and complexities of real world. This research uses a mixed method approach comprising a two phased survey of professional planners, analyzed through the selective lexicon approach to document planners’ opinions about the reasons behind the poor performance and conformance of spatial plans. This study documents the planners’ understanding of the contemporary concept of ‘scenario planning’. To explore the solution, this paper presents a semi-systematic review of the literature on the application of the ‘scenario method in urban spatial planning’. As a result of this research, a comprehensive digital inventory of all spatial plans ever made in Pakistan has been developed. It has been found that 83% of the urban settlements in Pakistan are growing without a spatial plan and require immediate attention. Furthermore, in terms of the plan making process, twenty-seven major factors contributing to the failure of past plans have been identified and categorized under seven distinct plan making stages. Finally, a new process of spatial plan-making has been proposed, which is the fusion of scenario planning and the traditional plan-making process, backed by digital planning tools. In an era of smart cities and digitization, it is expected that the advancements in scenarios planning, coupled with a new data portal, will assist in addressing the implementation gap in practice, and result in more comprehensive data-driven spatial plans.
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