![]() Integration methods for data and models have been mainly developed for continuous variables in meteorology and oceanography such as temperature and precipitation. If computational speed for integration is fast enough, we can store only observational data and can estimate data at any locations based on the requests. By integrating observational data and models describing underlying mechanisms and structures of object-phenomenon with a GIS, we can provide a GIS-based environment which allow dynamic update of spatio-temporal field of data whenever a new observational data and an improvement of models are given. ![]() In several fields, to improve reliability of spatio-temporal interpolation/ extrapolation in generating quality data, models and/or equations describing an underlying mechanism and structure is integrated with observational data. One of the most fundamental problems which users are facing is the difficulties in generating spatio-temporal filed of quality data for analysis through an interpolation or integration of observational data. Temporal or dynamic analysis of spatial data are needed in various fields such as environmental systems analysis. Through several testing experiments, it showed that GNHC can be a good spatio-temporal interpolation. The evaluation function of GA/HC is defined in the respect to the effect of neighboring spatial-temporal relations on the class change. In GA/HC, a direct coding method and new reproduction and crossover operators are proposed for 3D spatio-temporal data. To optimize the likelihood of spatio-temporal data, a Genetic-Algorithm Hill-Climbing model (GA/HC), which combined genetic-algorithm and Hill-climbing method together to increase the efficiency and quality of optimization, was developed. ![]() ![]() Here we proposed a spatio-temporal interpolation scheme for pixel-based class variable data under the framework of optimization of likelihood. Although interpolate/ integrate methods have been developed for continuous variable data, there are only very primitive interpolation methods such as nearest neighbor interpolation considered about class variable data. To generate time slice data, it is necessary to interpolate/integrate existing global data with different temporal coverage and spatial resolution. *Center for Environmental Remote Sensing,Į-mail : cross-sectional data of arbitrary time slice is a basic function to support temporal operations such as time-series analysis and integration of dynamic models in Global GIS environment. ![]()
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