Creating multiple representation database for population class: Deriving representation levels, object matching, updating

Requirements about geographic information systems have been going beyond the traditional maps when considering the huge amounts of data with various resolutions from different sources. This situation complicates the organization of the data and increasing density of the data appears as a problem that is needed to be solved. Although there is only one world, representing of this reality can change according to the purpose firstly and then scale. Spatial data users having different purposes and their data requirements have increased with every passing day. Resolution and scale values of the data requirements range according to the fields of interest of the users. Variety of user requirements reveals the multiple representation/multiple resolution/multiple scale database conception. This conception means a system that stores a basic dataset enriched as geometric/semantic and less rich dataset(s) derived from basic one in the same database. Studies about multiple representation database have started in America at the end of the 1980. In these studies, it was stated that databases for geographic information systems must be able to support modifications across resolution levels. The studies about multiple representation database like modelling of multiple representation database, object oriented data model for multiple representation database, database design for multiscale geographic information systems have been done in recent years. Keeping different spatial databases for every scale/resolution reveals updating and inconsistent data problems. Major advantage of multiple representation database is the availablity for updating. In multiple representation database system, changing world realities are applied to master database and then these changes are performed to the other levels of the multiple representation database automatically. Basic topics of the multiple representation database system are model generalization and object matching. Because, model generalization and object matching are two main approaches to create a multiple representation database. In multiple representation database, every object must have an identifier information to be able to describe the relation of the objects with each other at different levels. Especially in multiple representation database, identifiers are the records maintainig the relation between the same real world objects at different representation levels. Generalization is still one of the most important issues of the cartography which is the science and art of visualization of world reality on paper, screen or similar media. Particularly, researches on automated generalization, database design for multiple representations with very huge amount of data currently became a research base of cartography. Generalization can be defined as a process of deriving purposes oriented and less detailed dataset at smaller scale or lower resolution from a detailed data source or a dataset at large scale or higher resolution. So, generalization processes can be considered as various modelling operations. Two types of model can be distinguished in geographic information systems. One of these models is digital landscape model, and the other is digital cartographic model. Generalization can affect directly not only the map graphics but also the data. The main objective of model generalization is controlled data reduction for various purposes. While model generalization may also be used as a preprocessing step for cartographic generalization, it is important to note that it is not oriented towards graphical depiction and artistic components. Model generalization contains various transformation processes, so spatial objects can change as geometry, semantic and model during model generalization. Thus, geometry, semantic and data modelling can be considered as variables of the model generalization. Cartographic generalization is the term commonly used to describe the generalization of geospatial data for cartographic visualization. The difference between cartographic generalization and model generalization is that cartographic generalization is aimed at generating visualizations, and brings about graphical symbolization of objects. Therefore, cartographic generalization must also encompass operations to deal with problems created by symbology, such as displacement. After a perfect multiple representation database system is established, updating the datasets stored in this system immediately, quickly, automatically is the main problem must be solved. Establishing this system is also a easier way to pass cartographic generalization. Especially, the agencies which have data provider responsibility as national should design their spatial databases as multiple representation to meet data requirements of the users. National Mapping Agencies are responsible to produce map series at different scales and to retain map series up to date. Since existing datasets have been produced from different sources, this situation reveals an updating problem of the digital map series with a relevant method. Updating the digital map series is a process requiring time and cost because of the volume of data at national level. In order to perform this process more efficiently, datasets with lower resolution can be updated and generalized automatically after master dataset with high resolution is updated manually. In this study, it is aimed to establish the relationship between spatial objects belonging to the same world reality in a multiple representation database and to apply the revisions in master digital landscape model to the other digital landscape models with low resolution (incremental generalization) automatically. In other words, it is aimed to implement incremental generalization. To this end, some tools were developed for application stage of this study. One of the tools named "ModGen" was developed to create a multiple representation database for population object class by using model generalization and object matching techniques. Other tool named "Updating" was developed to implement the revisions in master digital landscape model to the other digital landscape models with low resolution automatically. In this way, rudiments of the topics about updating the digital landscape models and producing the digital cartographic models by using the digital landscape models have been mapped and structured as demonstrated with this study. Because, it is predicted that these topics will be requirements in near future. In this study, creating a multiple representation database with five representation levels (25K-50K-100K-250K-500K) was performed by using base digital landscape model data and model generalization approach. So, we could have a dataset to implement updating digital landscape models automatically and we could perform automatic update by using "Updating" program. This study is the first example application about model generalization and updating in our country.