## 1 Introduction  
To evaluate regional geotechnical characteristics, multivariate information should be collected and normalized from the perspectives of geotechnical, spatial analysis, and remote sensing experts. Geospatial data are often limited or irregularly cover an area of interest. The available geospatial data are collected and analyzed based on geographic coordinates built into a geodatabase and processed through interpolation and zonal calculations based on geographic information system (GIS) techniques [1; 2; 3]. The collaborated approaches are feasible for combining the remote sensing based and subsurface in situ information in a map with high spatial resolution and spatial coincidence. In civil engineering and related engineering practices, it is necessary to perform the geotechnical analyses [4]. Heterogeneity in geotechnical data may be governed by discontinuities in soil mass or geotechnical structures, and spatial or statistical biased properties [5]. It is well known that site characterization involves numerous inevitably variable characteristics and uncertainties when data is not directly assessed, but estimated using models and derived from other measured data [6; 7; 8]. Accordingly, the objectives of geotechnical spatial characterization are to determine soil and rock characteristics and to delineate subsurface stratigraphy for geotechnical engineering applications [9; 10]. Thus, the successful implementation of geotechnical spatial characterization requires cooperation between geotechnical engineering and geoinformatics. Methodological approaches for the establishment of geotechnical (or soil) property maps (e.g., engineering geo-layers or maps of in situ surveyed soil properties) with spatially scattered geospatial information provide good techniques for urban geology and development planning [11; 12; 13; 14; 15; 16]. Recently, to demonstrate the multivariate dependencies of soil and rock in geotechnical big data, computational techniques are essential to support the analysis and interpretation of the increasing amounts of geoscientific data and information generated from data mining. The complexity of analyses in geotechnical behavior is due to the multivariable dependencies of soil and rock properties. Therefore, geotechnical mapping and subsurface sampling should have clearly defined spatial correlations that consider outliers and clustering of geotechnical information. Outlier detection is aimed at removing noise, finding possible and relevant information, and identifying optimized data that are distinctly different from others. Outliers in geotechnical spatial properties are difficult to define by statistical approaches, but it is possible to indicate the least predictable or relatively outlying data in the spatial domain using geostatistical modeling [17]. Hot spot analysis is a useful way to classify exactly where geotechnical and geological clustering zones are located [18]. Large metropolitan areas have high spatial variability in topography, geology, and subsurface characteristics as well as various urban developments. In such areas site assessments are largely based on the characteristics of soils and surface geology. Accordingly, it is necessary to comprehensively use surface index maps based on empirical correlations [19; 20] and predict site response characteristics by processing sporadic soil survey data to grasp the site-specific zonal characteristics of highly reliable geo-layers and soil dynamic properties. In the event of an earthquake, near-surface ground motion may have very different intensity and frequency components depending on site effects. Therefore, site-specific response prediction methods provide rapid seismic response through regional seismic disaster prediction, including the seismic susceptibility of structures without numerical site response analysis, and seismic design through site-specific ground motion prediction. They can also be used directly for seismic performance evaluation.