scholarly journals INVESTIGATING GEOSPARQL REQUIREMENTS FOR PARTICIPATORY URBAN PLANNING

2015 ◽  
Vol XL-4/W7 ◽  
pp. 11-15
Author(s):  
E. Mohammadi ◽  
A. J. S. Hunter
Keyword(s):  
Urban Planning ◽  
Spatial Reasoning ◽  
Semantic Web Services ◽  
Qualitative Spatial Reasoning ◽  
Topological Methods ◽  
Participatory Gis ◽  
Topological Relationships ◽  
Complex Relationships ◽  
Spatial Entities ◽  
Tools And Methods

We propose that participatory GIS (PGIS) activities including participatory urban planning can be made more efficient and effective if spatial reasoning rules are integrated with PGIS tools to simplify engagement for public contributors. Spatial reasoning is used to describe relationships between spatial entities. These relationships can be evaluated quantitatively or qualitatively using geometrical algorithms, ontological relations, and topological methods. Semantic web services utilize tools and methods that can facilitate spatial reasoning. GeoSPARQL, introduced by OGC, is a spatial reasoning standard used to make declarations about entities (graphical contributions) that take the form of a subject-predicate-object triple or statement. GeoSPARQL uses three basic methods to infer topological relationships between spatial entities, including: OGC's simple feature topology, RCC8, and the DE-9IM model. While these methods are comprehensive in their ability to define topological relationships between spatial entities, they are often inadequate for defining complex relationships that exist in the spatial realm. Particularly relationships between urban entities, such as those between a bus route, the collection of associated bus stops and their overall surroundings as an urban planning pattern. In this paper we investigate common qualitative spatial reasoning methods as a preliminary step to enhancing the capabilities of GeoSPARQL in an online participatory GIS framework in which reasoning is used to validate plans based on standard patterns that can be found in an efficient/effective urban environment.

scholarly journals Qualitative Spatial Reasoning with Directional and Topological Relations

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Sangha Nam ◽  
Incheol Kim
Keyword(s):  
Intelligent Systems ◽  
Spatial Reasoning ◽  
Cognitive Robotics ◽  
Qualitative Spatial Reasoning ◽  
Topological Relations ◽  
Topological Relation ◽  
Wide Range ◽  
Spatial Entities ◽  
Reasoning Algorithm ◽  
Ubiquitous Computing Environments

A wide range of application domains from cognitive robotics to intelligent systems encompassing diverse paradigms such as ambient intelligence and ubiquitous computing environments require the ability to represent and reason about the spatial aspects of the environment within which an agent or a system is functional. Many existing spatial reasoners share a common limitation that they do not provide any checking functions for cross-consistency between the directional and the topological relation set. They provide only the checking function for path-consistency within a directional or topological relation set. This paper presents an efficient spatial reasoning algorithm working on a mixture of directional and topological relations between spatial entities and then explains the implementation of a spatial reasoner based on the proposed algorithm. Our algorithm not only has the checking function for path-consistency within each directional or topological relation set, but also provides the checking function for cross-consistency between them. This paper also presents an application system developed to demonstrate the applicability of the spatial reasoner and then introduces the results of the experiment carried out to evaluate the performance of our spatial reasoner.

PLCA

2012 ◽  
pp. 63-96 ◽  
Author(s):  
Kazuko Takahashi
Keyword(s):  
Graph Theory ◽  
Computational Geometry ◽  
Spatial Data ◽  
Qualitative Data ◽  
Spatial Reasoning ◽  
Research Area ◽  
Qualitative Spatial Reasoning ◽  
Research Areas ◽  
Fruitful Research ◽  
Topology Graph

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.

The Integration of Qualitative Orientation and Named Distances: Application to a Qualitative Navigation Simulator

1997 ◽  
Vol 06 (04) ◽  
pp. 451-480 ◽  
Author(s):  
M. Teresa Escrig ◽  
Francisco Toledo
Keyword(s):  
Spatial Reasoning ◽  
Research Work ◽  
Human Perception ◽  
Positional Information ◽  
Qualitative Spatial Reasoning ◽  
Human Beings ◽  
Operational Model ◽  
Distance Information ◽  
Spatial Concepts ◽  
Definition Of

Human beings reason about different aspects of space (such as relative orientation, cardinal directions, distance, size and shape of objects) quite easily. With the aim of simulating human behavior, several models for these spatial concepts have been developed in the recent years. Cognitive considerations have made these frameworks qualitative, because they seem to deal better with the imprecision that human perception provides. However, an operational model to reason with all these spatial aspects in an integrated way has not been developed, up to now. The first aim of our research work has been the integration of different spatial concepts into the same spatial model which has been accomplished thanks to the definition of an operational model based on Constrain Logic Programming extended with Constraint Handling Rules. Although other aspects of space have been successfully represented by these techniques [2], in this paper we focus our attention in positional information, that is, orientation integrated with distance information. The Constraint Solver developed for managing positional information has a temporal complexity of O(n) 3, where n is the number of spatial landmarks considered in the reasoning process. The second aim of our work is to apply qualitative spatial reasoning to develop a Qualitative Navigation Simulator.

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