Spatial proximity is more than just a distance measure

Brennan, Juan Arturo; Martin, Éric

doi:10.1016/j.ijhcs.2011.08.006

Cited by 40 publications

(28 citation statements)

References 10 publications

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“…However, the distance-based approaches are not necessarily the only factor that influences human cognitive apprehension of spatial proximity. A suitable model of spatial proximity should acknowledge other factors such as contextual information as proposed by (Brennan and Martin, 2012). The latter work suffers from two main shortcomings: first it only allows to reason about nearness.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, how to apply such an approach to qualitative spatial reasoning was not obvious. (Brennan and Martin, 2006) Later, (Brennan and Martin, 2012) proposed a conceptual framework to qualitatively represent spatial proximity and to enhance the capacity of spatial reasoning systems using contextual information. They consider contextual information as a key element in any model of spatial proximity.…”

Section: Related Workmentioning

confidence: 99%

“…The impact area of an object takes into account both the nature of the object and its surrounding environment. Some examples in (Brennan and Martin, 2012) demonstrate how impact area is more generic than influence area, and how this notion uses contextual information in proximity analysis. Contextual information is defined as any "information collated by an expert who is expected to incorporate all relevant factors into the impact area".…”

Section: Related Workmentioning

confidence: 99%

“…Figure 1. An example of difference between influence areas (Kettani and Moulin, 1999) and impact area (Brennan and Martin, 2012) The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-2, 2014…”

Section: Related Workmentioning

confidence: 99%

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An Intelligent Spatial Proximity System Using Neurofuzzy Classifiers and Contextual Information

Barouni

Moulin

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper, we propose a novel approach to reason with spatial proximity. The approach is based on contextual information and uses a neurofuzzy classifier to handle the uncertainty aspect of proximity. Neurofuzzy systems are a combination of neural networks and fuzzy systems and incorporate the advantages of both techniques. Although fuzzy systems are focused on knowledge representation, they do not allow the estimation of membership functions. Conversely, neuronal networks use powerful learning techniques but they are not able to explain how results are obtained. Neurofuzzy systems benefit from both techniques by using training data to generate membership functions and by using fuzzy rules to represent expert knowledge. Moreover, contextual information is collected from a knowledge base. The complete solution that we propose is integrated in a GIS, enhancing it with proximity reasoning. From an application perspective, the proposed approach was used in the telecommunication domain and particularly in fiber optic monitoring systems. In such systems, a user needs to qualify the distance between a fiber break and the surrounding objects of the environment to optimize the assignment of emergency crews. The neurofuzzy classifier has been used to compute the membership function parameters of the contextual information inputs using a training data set and fuzzy rules.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

An Intelligent Spatial Proximity System Using Neurofuzzy Classifiers and Contextual Information

Barouni

Moulin

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Mais cette proximité ne se réduit pas à un aspect purement spatial (Martinus et Sigler, 2017). Le recours aux technologies de l'information et de la communication peut alors réduire la distance entre des acteurs géographiquement éloignés (Brennan et Martin, 2012).…”

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