PLCA: A Framework for Qualitative Spatial Reasoning Based on Connection Patterns of Regions

PLCA: A Framework for Qualitative Spatial Reasoning Based on Connection Patterns of Regions

Kazuko Takahashi (Kwansei Gakuin University, Japan)
DOI: 10.4018/978-1-61692-868-1.ch002
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Abstract

This chapter describes a framework called PLCA for Qualitative Spatial Reasoning (QSR) based on the connection patterns of regions. The goal of this chapter is to provide a simple but expressive and feasible representation for qualitative data with sufficient reasoning ability. PLCA provides a symbolic representation for spatial data using simple objects. The authors of this chapter define its expression and operations on it, and show the correspondance between the expression and a figure. PLCA also provides semantical reasoning incorporated with spatial reasoning. Moreover, it can be extended to handle shapes of regions. Throughout the study, the authors discovered many topics that relate QSR to other research areas such as topology, graph theory, and computational geometry, while achieving the research goals. This indicates that QSR is a very fruitful research area.
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1. Introduction

Recent advances of computer performance and network infrastructure have increased the opportunities for various users to treat spatial data, such as figures and images. Compared to textual data, spatial data contains more abundant information and provides a representation that is easy to understand. But since spatial data are generally stored and processed as numerical data, their processing requires more memory and time. Therefore, an efficient mechanism for spatial data processing is desired. The more refined the data used are, the clearer and more precise the figure is. However, refined data are not always necessary unless a clear, precise figure is required. It is sometimes sufficient to know the number of objects or the positional relationships of the objects in a figure depending on a user's purpose.

Consider the following problem.

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