## 3 Geospatial Informatics of the Geotechnical Database
### Hot Spot Analysis of the Geotechnical Database
If the distance from neighbor \(j\) to feature \(i\) is within a distance band, \(w_{i,j}=1\), otherwise \(w_{i,j}=0\)[51]. Areas with high Moran's I values are higher than average. Only a single estimate of Moran's I is given in the global version of Moran's I for an overall area. This statistical approach underlines the level of events of each neighborhood characteristic and is thus less susceptible to spatial weighing characteristics than the aggregate level of events for the entire neighborhood [52]. In Daejeon city, because most values in the borehole dataset have similar distance bands, the spatial deviations of Moran's I are not significant for every geo-layer. The kernel density calculates Getis-Ord statistics for the thickness and average \(N_{60}^{exp}\) of each geo-layer for local clusters to identify seismic site effect parameters [53]. The Getis-Ord Gi* of the target geostatistical characteristic value in each geo-layer is analyzed to help identify where hotspots (high-value clusters) and cold spots (low-value clusters) in an area are located [44; 54]. \[G_{i}^{*}=\frac{\sum_{j=1}^{n}w_{i,j}x_{j}}{\sum_{j=1}^{n}x_{j}}\] (3a) \[\begin{split}\text{Z}\Big{(}G_{i}^{*}\Big{)}&= \frac{\sum_{j=1}^{n}w_{i,j}x_{j}-\overline{x}\sum_{j=1}^{n}w_{i,j}^  
where \(G_{i}^{*}\) is the Z-score of patch \(i\), and \(x_{j}\) is the attribute value for patch \(j\). The Getis-Ord statistics were calculated using geostatistical characteristic values for each geo-layer, defined as the ratio of total values to the global total. The spatial clusters, based on confidence distributions, were classified into three \"cold spot\" (90%, 95%, and 99% confidence intervals), three \"hot spot\" (90%, 95%, and 99% confidence intervals), and one \"not significant\" sites. For soil deposits (FL, AS, and WS), the hot spot areas of the geo-layer thickness data were concentrated in the central urban region (elevation \(<\) 10 m; slope \(<\) 12\({}^{\circ}\); mainly quaternary deposits in Figure 1). The northern area was categorized as a hot spot after applying Getis-Ord statistics to WS thickness. In general, there is a strong positive relationship between the geo-layer thicknesses according to the KDE and Getis-Ord Gi* statistic methods. Land surface characteristics in primarily the central and partially the northern urban areas were categorized as the highest density area and as hot spot zones, where spatial correlations between thick soil deposits and high soil stiffness are strong. Even though the DEM and geological map are available, interpolations were restricted to borehole datasets from within the Daejeon administrative division for soil mapping at high spatial resolution, by using sub-group in borehole datasets having high correlation and density. The multi-scale grid zonation for geospatial interpolation was subdivided into two regions: a 20 m \(\times\) 20 m grid for the central zone (elevation \(<\) 10 m; slope \(<\) 12\({}^{\circ}\); area of interest with a hot spot and high K) and 50 m \(\times\) 50 m grid for the others. According to [PERSON] and [PERSON] [23], response parameters were estimated based on the optimized grid at 20 and 50 m intervals. Borehole datasets were also clustered according to the two multi-grid zones. The geospatial interpolation results are resampled into the multi-scale grid.