摘要:

Spatial databases are repositories of representations of the real world. The represented entities have to be observed in the real world and mapped to the database. An observation is interpreted here as a two-level process, consisting of the abstraction of the observed object to a concept and a measurement of the realized concept. Due to the nature of observations, regions representing the location of objects are always imprecise, the explication of a concept succeeds only incompletely, and the measurement is limited in precision. In this paper, the uncertainty in abstraction as well as the imprecision of measurement are modelled statistically. This allows the introduction of the uncertainty of observation into qualitative spatial reasoning. The example used in this paper is the determination of topological relations. The topological relation between two regions becomes uncertain if the regions are imprecise in their location. Hence, the decision about a topological relation is made by maximum likelihood classification. The classification allows a quantitative assessment of the decision by its probability, and by the probability of the alternative relations. The method is useful in data set comparison, data matching, and modelling data quality descriptions.

ISSN:1365-8816    

DOI:10.1080/13658810050057579

出版商:Taylor & Francis Group    

年代:2000

数据来源: Taylor

摘要:

Agronomic soil management and decision-making frequently requires the joint classification of soil variables. Fuzzy set theory is often used to accomplish this task. This paper addresses the issues of objectively defining fuzzy membership functions (FMF) and reducing classification uncertainty with hedge operators. As an example, soil in North-east Thailand was classified according to its inherent potential to support the recovery of a rice crop after a drought spell. The utility of auxiliary information not directly included in the classification was explored. A tree cover density index was employed for an objective definition of the FMF to classify soil organic matter content and plant-available potassium. Mapping units were allocated to classes having low, medium or high availability of these plant nutrients. It was shown that crisp, Boolean style classifications severely misclassify land in all but one class. Adjusted FMF decreased the uncertainty contained in thematic class maps. Single FMF values for soil organic matter and plant-available K were then jointly modelled and the soil classified as having low, medium and high potential for rice plants to recover from drought impacts. The very and more or less hedge operators were applied to increase or decrease the joint FMF values using farmer' knowledge about soil fertility. Overall classification uncertainty using FMF was decreased by 14% if the standard FMF was adjusted and the generated membership values were hedged. It was shown that adjusting FMF influenced the uncertainty components vagueness and ambiguity differently; the former increased slightly but the latter was drastically reduced.

ISSN:1365-8816    

DOI:10.1080/13658810050057588

出版商:Taylor & Francis Group    

年代:2000

数据来源: Taylor

摘要:

This paper presents a development of the extended Cellular Automata (CA), a Voronoi-based CA, to model dynamic interactions among spatial objects. Cellular automata are efficient models for representing dynamic spatial interactions. A complex global spatial pattern is generated by a set of simple local transition rules. However, its original definition for a two-dimensional array limits its application to raster spatial data only. This paper presents a newly developed Voronoi-based CA in which the CA is extended by using the Voronoi spatial model as its spatial framework. The Voronoi spatial model offers a ready solution to handling neighbourhood relations among spatial objects dynamically. By implementing this model, we have demonstrated that the Voronoi-based CA can model local interactions among spatial objects to generate complex global patterns. The Voronoi-based CA can further model interactions among point, line and polygon objects with irregular shapes and sizes in a dynamic system. Each of these objects possesses its own set of attributes, transition rules and neighbourhood relationships. The Voronoi-based CA models spatial interactions among real entities, such as shops, residential areas, industries and cities. Compared to the original CA, the Voronoi-based CA is a more natural and efficient representation of human knowledge over space.

ISSN:1365-8816    

DOI:10.1080/13658810050057597

出版商:Taylor & Francis Group    

年代:2000

数据来源: Taylor

摘要:

In search for methods to handle imprecision in geographical information this paper explores the use of rough classification to represent uncertainty. Rough classification is based on rough set theory, where an uncertain set is specified by giving an upper and a lower approximation. Novel measures are presented to assess a single rough classification, to compare a rough classification to a crisp one and to compare two rough classifications. An extension to the error matrix paradigm is also presented, both for the rough-crisp and the roughrough cases. An experiment on vegetation and soil data demonstrates the viability of rough classification, comparing two incompatible vegetation classifications covering the same area. The potential uses of rough sets and rough classification are discussed and it is suggested that this approach should be further investigated as it can be used in a range of applications within geographic information science from data acquisition and analysis to metadata organization.

ISSN:1365-8816    

DOI:10.1080/13658810050057605

出版商:Taylor & Francis Group    

年代:2000

数据来源: Taylor

ISSN:1365-8816    

DOI:10.1080/13658810050057614

出版商:Taylor & Francis Group    

年代:2000

数据来源: Taylor