Several powerful natural disasters have recently occurred in different countries around the world. Mitigating the impacts of these disasters has been an important component of urban/land-use planning (Burby et al., 1999). Seismic hazard and urban vulnerability are the two major factors in the assessment of seismic risk. The Peak Ground Acceleration (PGA) is the principal measure of seismic hazard, the Peak Ground Displacement (PGD) represents a minor one, and land-use patterns characterize urban vulnerability. A spatial statistical approach is used to better understand the spatial interactions of seismic hazards and their relationships with land-use patterns. In seismic engineering, spatial autocorrelation (SA) has been rarely discussed, but may cause biased estimations due to omitted variables (Esposito and Iervolino, 2011). A LOSS model is also estimated, that links the economic losses incurred by various activities (housing, commerce, industry, etc.) to PGA and PGD. Next, in order to deal with the uncertainty of earthquakes, an optimization approach has been developed, using the LOSS statistical model, to allocate future land uses.
A set of pseudo-data is generated with an earthquake-engineering simulation model for the city of Taichung, Taiwan, using as input the characteristics of 22 significant historical earthquakes. A PGA model is first formulated as a spatial lag panel (SLP) model based on earthquake magnitude, epicenter-to-site distances, source depth, and neighborhood effects accounting for site geology. A PGD SLP model is also specified to account for neighborhood effects represented by soil liquefaction. The results demonstrate the need to be concerned not only by the PGA/PGD at a specific location, as caused directly by the earthquake, but also by the PGA/PGD at neighboring locations. Then, a seismic loss model is formulated, relating monetary damages to seismic impacts (PGA and PGD) and land uses (residential, commercial, industrial, etc.). By combining the three models, monetary damages can be estimated as a function of land-use patterns, PGA, PGD, their neighborhood effects, and other seismic characteristics. Finally, the statistically-estimated seismic loss function is then used to formulate a seismic land-use optimization model to allocate future land uses under various regional growth rates scenarios while minimizing potential seismic losses. Several location ranking analyses are conducted to generate robust ranking results, at both the township and district levels. These rankings provide planning information for decision makers to allocate future land when confronted with the uncertainty of regional development rates.