Full text: The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics

ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001 
Michinori HATAYAMA 1 Shigeru KAKUMOTO 2 Hiroyuki KAMEDA 3 
1 Disaster Prevention Research Laboratory, Kyoto University / Japan Digital Road Map Association 
Gokasho, Uji, Kyoto, 611-0011, Japan 
Phone: 81 774 38 4037, Fax: 81 774 38 4044, 
E-mail: hatayama@imdr.dpri.kyoto-u.ac.jp 
2 Frontier Collaborative Research Center, Tokyo Institute of Technology / Central Research Laboratory, Hitachi, Ltd., 
E-mail: kakumoto@crl.hitachi.co.jp 
3 Graduate School of Informatics, Kyoto University / Disaster Prevention Research Institute, Kyoto University / 
Earthquake Disaster Mitigation Research Center, RIKEN 
E-mail: kameda@imdr.dpri.kyoto-u.ac.jp 
KEY WORDS: Spatial Temporal GIS, Data Structure, Census Data 
The temporal data is one of the most important factors for making an analysis of the geographic data, because the entities that compose 
of geographic data change with temporal factor. So, we have developed the Dynamic Management Spatial-temporal Information System, 
called DyMSIS. In this paper, we firstly show how to manage the temporal information. And then, we explain the algorithm to pick up the 
difference of census data between one time and another time. Finally, we demonstrate the example of the temporal analysis of census 
1. Introduction 
Geographic information changes rapidly with time. So temporal 
information may be used to manage geographic data effectively. 
In this paper, a geographic information system with a geographic 
data management function employing temporal information 
(spatial temporal geographic information system) is considered. 
First, the requirements imposed on the proposed system are 
examined. Then, a data base schema to meet those 
requirements is proposed, and a spatial temporal geographic 
information system, DiMSIS, which can handle such a database, 
is described. Finally, we propose a cumulative data management 
method for census data. 
2. Requirements for a spatial temporal geographic 
information system 
[Requirements from the viewpoint of data management] 
(1) Data is in compact form. 
(2) The data structure is simple and easy-to-understand. 
(3) The system can handle temporal information having 
uncertain values. 
[Requirements from the viewpoint of data usage and analysis] 
(1) The system can handle maps for specified times. 
(2) Maps at two time points can be visually compared. 
(3) The system can handle regions of change between two time 
3. KIWI+ {A Spatial Temporal & Simple Topology - Open 
Database Schema: ST2-ODS) 
The KIWI+ format combines the KIWI format proposed in 
ISO/TC204/WG3.2 as a data format for car navigation systems 
and the DiMSIS data structure proposed in [1]. In this format, 
spatial temporal objects can be handled on the basis of temporal 
management using the Space-Time Approach model [2], and by 
describing objects using the implicit description and calculation 
type of data model [3], a compact, easy-to-understand structure 
is obtained. In the following sections, the characteristics of this 
system are described. 
3.1. Database Structure 
The KIWI+ describes all geographical information in the forms of 
two elements: vector for shaping graphic data, and connector for 
relating attribute data. Each element has the spatial and 
temporal factors. The temporal factors can be resolved into four 
factors: generation start time (GS), generation end time (GE), 
extinction start time (ES), and extinction end time (EE) (Figure 1). 
If the dates of generation and extinction cannot be identified, it is 
also possible to represent temporal error using these factors. 
3.2. The concept of feature space 
In order to utilize the data structure explained in 3.1 efficiently 
and to calculate the topological structure, the concept of feature 
space is introduced in the KIWI+ format [3]. 
(1) Definition of feature space 
A set of objects with the same type ID is called a class group. 
In accordance with the objects they comprise, class groups can 
be classified into vector class groups and connector class 
groups. A set of correlated multiple vector class groups and 
multiple connector class groups is defined as a feature space. 
(2) Spatial temporal analysis using feature space 
A feature space has a physical significance as a factor in terms 
of spatial temporal analysis. As such, there are four types of 
feature space: point, line, area, and solid feature space (Figure 
2). Defined as factors in a topological relationship, these 
correspond to points, lines, areas, and solids. Spatial temporal 
analysis is performed in feature space by relating vectors, 
which comprise geometric information, and connectors, in 
which attribute information is linked. Because these 
relationships are calculated in real time when a processing 
command has occurred, it is possible to dynamically 
supplement objects corresponding to topological 

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