In the midst of the ever-increasing natural and human-induced disasters, where many of the preparedness and mitigation measures show inefficiencies, there is narrow margin for decision-makers to make mistakes by misallocating budgets, designing infeasible reconstruction plans, and in other terms, making decisions not in line with the public preferences. In particular, public participation in post-disaster measures seems undoubtedly necessary to reduce the possible economic, social, political, and cultural conflicts around the stressful community after a major disaster. This paper aims at evaluating the role of public participation in increasing the reconstruction phase efficiency through a case study of the reconstruction process in Bam, a southeastern Iranian city, after the 2003 earthquake. It is attempted to identify the major motivators of the public participation through a combination of quantitative and qualitative studies. Statistical data are generated through a set of questionnaires being filled by a number of 200 randomly selected survivors. The numerical results were then discussed through the Focus Group technique sessions to determine the main contributors to the public participation. It is later found that the answers are found among the performance of the reconstruction authorities, financial policies, emotional resiliency of the survivors, public information mechanisms, public satisfaction, the pace of reconstruction, and temporary housing policies.
In this paper, a cellular automaton model is developed to generate spatio-temporal population maps that estimate population distributions in an urban area in a random working day. The resulting population maps are at 50 by 50 meter spatial resolution and 5 minutes temporal resolution, showing clearly how the distribution of population varies throughout a 24-hour period. Places that are sparsely populated during night-time can be densely populated during day-time. The generated maps can be used to estimate population-at-risk in the wake of major disasters when they occur in an urban area at any time of a day. In addition to assessing exposure to hazards, the resulting maps also reveal the movement patterns, transition trends, peak hours, activity levels, etc. Possible applications, thus, range from public safety, disaster management, transport modeling and urban growth studies to strategic energy distribution planning. The developed cellular automata model assumes that the population transition trends follow similar dynamics and propagation patterns of a contagious disease. Thus the cellular automaton is designed to change the states of each grid cell (Stable/Dynamic) similarly as state changes of an individual being exposed to an infective disease (Susceptible/Infected). The modeling space is further informed by several geographic features, such as the transport routes, land use categories, population attraction points, etc.The model is geosimulated for the city of Trondheim in Norway, where the synthetic day population could be validated upon an estimated day-population map based on the registered work place addresses and employee statistics.
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