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Journal of Geographical Systems

, Volume 21, Issue 3, pp 367–394 | Cite as

A framework for assisted proximity analysis in feature data

  • Rolf GrütterEmail author
Original Article
  • 92 Downloads

Abstract

This framework for assisted proximity analysis in feature data consists of a hierarchy of proximity classes that use spatial neighborhoods as fundamental building blocks. The instances are spatial relations between isolated objects, or objects in a cluster, sharing the relational properties of reflexivity/irreflexivity and symmetry/asymmetry. The framework proposes ways of generating spatial neighborhoods and includes a discussion of how to deal with the vagueness inherent in nearness relations. It is applied to a realistic use case of epizootic disease outbreak. The framework updates the current state of knowledge in the field by considering: (1) spatial objects in a cluster, (2) spatially coextensive regions, and (3) regions in a partition chain. It relates ways of generating spatial neighborhoods to the proximity classes and introduces a number of yes–no questions to be implemented as a sequence of functions in a GIS system. The objective of the latter is to assist non-expert users, such as decision-makers, in carrying out proximity analyses. This is the first time that such a comprehensive framework has been proposed.

Keywords

Feature data Proximity analysis Spatial relation Spatial neighborhood GIS Decision support 

JEL Classification

C65 D83 I18 

Notes

Acknowledgements

A special thanks to Professor Harold Boley, Ph.D., of the University of New Brunswick, NB, Canada, and Marc Novel of the Swiss Federal Research Institute WSL, Birmensdorf, and the University of Zurich for the extensive discussions of important aspects of this article. The help of Silvia Dingwall, Ph.D. in Applied Linguistics, with language editing is gratefully acknowledged. This research was funded by the Swiss Federal Office for the Environment (FOEN).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Swiss Federal Research Institute WSLBirmensdorfSwitzerland

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