Emanuel Leleito

A workshop support tool combining disaster-mitigation performance evaluation and virtual reality for the improvement of densely built-up areas

This study focuses on the difficult issue of building consensus among local residents participating in improvement planning for densely built-up areas with vulnerability to earthquake disasters. As an initial step towards the formulation of a method for building consensus in the participatory planning process, we attempt to clarify the issues involved based on a literature survey and interviews with professionals in the field; then, we examine the main factors affecting consensus building in improvement planning. The results reveal that the lack of performance evaluation of the draft plans for disaster mitigation during workshops as well as an inability to clearly share in visual form images of the prospective townscape after implementation of the plans, are among the main factors negatively affecting consensus planning. From this investigation, the study experimentally develops a tool to meet these challenges by enabling a quantitative evaluation of disaster-mitigation performance; at the same time, this tool can be used to show a virtual 3D image of the future townscape based on the draft plans examined during the workshops.

A cellular automata model incorporating land tax for predicting urban growth

This study looks at the possibility of developing a cellular automata model incorporating tax and geographic conditions for simulating urban growth process with a view of using it in the future as a tool for directing growth in the city of Makassar, Indonesia. The developed model uses land tax values in addition to geographic condition constraints such as rivers, flood prone and nature conservation areas. The CA model with incorporated tax constraints is examined together with a model with only the geographical constraints. Their performance is tested using the multi-scale goodness of-fit. The CA model incorporating the land tax constraints produced slightly better simulation results under different parameter settings as compared with the geographic conditions. A calibrated model with an average prediction accuracy of over 75% becomes the basis for predicting the growth of urban areas in the city of Makassar until the year 2025.