DEVELOPMENT AND APPLICATION OF SPATIOTEMPORAL ANALYSIS METHODS IN SPATIAL INFORMATION SCIENCE

本研究开发了一套分析现有时空方法无法完全分析的时空域空间对象的方法。本研究选择了四种经常用于表示空间现象的空间对象：1)点，2)线，3)多边形，4)面。随着方法的发展，经验应用也在并行进行。对点的分析是基于对其对应表面的分析：通过空间平滑将点转换成一个表面，并通过峰、坑、色的表面分析方法进行分析。该方法可以同时检测点分布中的局部和全局模式，如果将其与GIS中的图形分析相结合，则工作效率更高。在线路分析中，链接被用作描述随时间变化的基本元素。从时间变化的角度对每个环节进行定量评价，从而对网络的整体变化进行评价。该方法特别适用于道路网变化等离散线变化的分析。除对直线进行分析外，还采用原始分解方法对多边形进行分析——将多边形分布的变化分解为单个多边形的一组变化。将该方法应用于便利店的市场面积分析，得到了一些有益的实证结果，证明了该方法的有效性。曲面分析是基于两种时空奇点自然定义的时空镶嵌。这种方法对一个常见的时空现象给出了两种不同的观点，为不同领域分别研究的时空分析结合提供了一种可能的方法。

This study developed a set of methods for analyzing spatial objects in the spatiotemporal domain that cannot be fully analyzed by existing spatial or temporal methods. This study chose four types of spatial objects frequently used in representing spatial phenomena: l) points, 2) lines, 3) polygons, and 4) surfaces. Along method development empirical applications were also done in parallel.Analysis of points is based on analysis of their corresponding surface: points are converted into a surface by spatial smoothing and analyzed by a surface analysis method which uses peaks, pits, and cols. This method can detect both local and global patterns in point distributions and works more efficiently if it is linked to graphical analysis in GIS.In analysis of lines links are used as primitive elements to describe change over time. Each link is quantitatively evaluated in terms of temporal change, which leads to evaluation of the whole change of a network. This method is appropriate especially for analysis of discrete change of lines such as change of road networks.As well as lines, polygons are analyzed by the primitive decomposition approach- change of a polygon distribution is decomposed into a set of changes of individual polygons. Application of this method to market area analysis of convenience stores yielded some useful empirical findings which demonstrated the validity of the method.Analysis of surfaces is based on spatiotemporal tessellations naturally defined from two types of spatiotemporal singularities. This method gives us two different viewpoints on a common spatiotemporal phenomenon, which shows one possible way of combining spatial and temporal analyzes that have been studied separately in different fields.