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


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.


Feature data Proximity analysis Spatial relation Spatial neighborhood GIS Decision support 

JEL Classification

C65 D83 I18 



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).


  1. Barouni F, Moulin B (2015) An intelligent spatial proximity system using neurofuzzy classifiers and contextual information. Geomatica 69(3):285–296CrossRefGoogle Scholar
  2. Belnap ND (1977) A useful four-valued logic. In: Modern uses of multiple-valued logic, vol 2 of Episteme. Springer, Dordrecht, pp 5–37Google Scholar
  3. Bera R, Claramunt C (2003) Topology-based proximities in spatial systems. J Geograph Syst 5(4):353–379CrossRefGoogle Scholar
  4. Brennan J, Martin E (2012) Spatial proximity is more than just a distance measure. Int J Hum Comput Stud 70(1):88–106CrossRefGoogle Scholar
  5. Calegari GR, Carlino E, Celino I, Peroni D (2016) Supporting geo-ontology engineering through spatial data analytics. In: Sack H, Blomqvist E, d’Aquin M, Ghidini C, Ponzetto PS, Lange C (eds) The semantic web. Latest advances and new domains: 13th international conference, ESWC (2016) Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings, vol 9678 of Lecture notes in computer science. Springer International Publishing, Cham, pp 556–571Google Scholar
  6. Carbon C-C, Leder H (2005) The Wall inside the brain: overestimation of distances crossing the former Iron Curtain. Psychon Bull Rev 12(4):746–750CrossRefGoogle Scholar
  7. Carlson LA, Covey ES (2005) How far is near? Inferring distance from spatial descriptions. Lang Cognit Process 20(5):617–631CrossRefGoogle Scholar
  8. Carral D, Scheider S, Janowicz K, Vardeman C, Krisnadhi AA, Hitzler P (2013) An ontology design pattern for cartographic map scaling. In: Cimiano P, Corcho Ó, Presutti V, Hollink L, Rudolph S (eds) The semantic web: semantics and big data, 10th international conference, ESWC 2013, Montpellier, France, May 26-30, 2013. Proceedings, vol. 7882 of Lecture notes in computer science. Springer, pp 76–93Google Scholar
  9. Cohn AG, Gotts NM (1996) The ‘egg-yolk’ representation of regions with indeterminate boundaries. In: Burrough PA, Frank AU (eds) Geographic objects with indeterminate boundaries. Francis Taylor, London, pp 171–187Google Scholar
  10. Degenhardt B, Kienast F, Buchecker M (2010) Einflussfaktoren des Naherholungsverhaltens im periurbanen Raum. Schweizerische Zeitschrift für Forstwesen 161(3):75–80CrossRefGoogle Scholar
  11. Denofsky ME (1976) How near is near? A near specialist, vol. 344 of AI Memo. Massachusetts Institute of Technology, Cambridge, pp 1–75Google Scholar
  12. Derungs C, Purves RS (2016) Mining nearness relations from an n-grams Web corpus in geographical space. Spat Cognit Comput 16(4):301–322CrossRefGoogle Scholar
  13. Dolbear C, Hart G, Goodwin J (2007) From theory to query: using ontologies to make explicit imprecise spatial relationships for database querying. In: Winter S, Duckham M, Kulik L, Kuipers B (eds) Proceedings of the 8th international conference on spatial information theory, COSIT 2007, Melbourne, Australia, September 19–23, 2007, vol. 4736 of information systems and applications. Springer, BerlinGoogle Scholar
  14. Efremovič VA (1951) Infinitesimal spaces. Dokl Akad Nauk SSSR (N.S.) 76:341–343Google Scholar
  15. Egenhofer MJ, Mark DM (1995) Naive geography. In: Frank AU, Kuhn W (eds) Spatial information theory: a theoretical basis for GIS, vol. 988 of Lecture notes in computer science. Springer, Berlin, pp 1–15Google Scholar
  16. Ekman G, Bratfisch O (1966) Subjective distance and emotional involvement. A psychological mechanism. Acta Psychologica 25:1–11CrossRefGoogle Scholar
  17. Fisher PF, Orf TM (1991) An investigation of the meaning of near and close on a university campus. Comput Environ Urban Syst 15(1–2):23–35CrossRefGoogle Scholar
  18. Gahegan M (1995) Proximity operators for qualitative spatial reasoning. In: Frank AU, Kuhn W (eds) Spatial information theory: a theoretical basis for GIS. Lecture notes in computer science, vol 988. Springer, Berlin, pp 31–44CrossRefGoogle Scholar
  19. Grütter R, Scharrenbach T, Waldvogel B (2010) Vague spatio-thematic query processing—a qualitative approach to spatial closeness. Trans GIS 14(2):97–109CrossRefGoogle Scholar
  20. Hall MM, Jones CB (2008) A field based representation for vague areas defined by spatial prepositions. In: Methodologies and resources for processing spatial language, Workshop at LREC’2008, Marrakech, Morocco, 31 May 2008, pp 36–41Google Scholar
  21. Helming I, Bernstein A, Grütter R, Vock S (2011) Making close to suitable for web search—a comparison of two approaches. In: Grütter R, Kolas D, Koubarakis M, Pfoser D (eds) Proceedings of the Terra Cognita 2011 workshop on foundations, technologies and applications of the geospatial web, vol. 798 of CEUR workshop proceedings. Sun SITE Central Europe, Aachen Tilburg, pp 101–113Google Scholar
  22. Hernández D (1994) Qualitative representation of spatial knowledge, vol. 804 of Lecture notes in artificial intelligence. Springer, BerlinGoogle Scholar
  23. Hirtle SC, Jonides J (1985) Evidence of hierarchies in cognitive maps. Memory Cognit 13(3):208–217CrossRefGoogle Scholar
  24. Irngartinger C, Degenhardt B, Buchecker M (2010) Naherholungsverhalten und -ansprüche in Schweizer Agglomerationen. Ergebnisse einer Befragung der St. Galler Bevölkerung 2009. Technical report, Eidg. Forschungsanstalt WSL, Zürcherstrasse 111, CH-8903 BirmensdorfGoogle Scholar
  25. Jones CB, Purves RS (2008) Geographical information retrieval. Int J Geograph Inf Sci 22(3):219–228CrossRefGoogle Scholar
  26. Kettani D, Moulin B (1999) A spatial model based on the notions of spatial conceptual map and of object’s influence areas. In: Freksa C, Mark DM (eds) Spatial information theory: cognitive and computational foundations of geographic information science, International conference COSIT’99, Stade, Germany, August 25–29, 1999. Proceedings, vol. 1661 of Lecture notes in computer science. Springer, pp 401–416Google Scholar
  27. Kienast F, Degenhardt B, Weilenmann B, Wäger Y, Buchecker M (2012) Gis-assisted mapping of landscape suitability for nearby recreation. Landsc Urban Plan 105(4):385–399CrossRefGoogle Scholar
  28. Kromrey H, Roose J, Strübing J (2016)Empirische Sozialforschung, 13th edn. UVK Verlagsgesellschaft mbH, Konstanz MünchenGoogle Scholar
  29. Leidner JL (2007) Toponym resolution in text: annotation, evaluation and applications of spatial grounding of place names., Boca Raton, Florida, USAGoogle Scholar
  30. Leidner JL, Lieberman MD (2011) Detecting geographical references in the form of place names and associated spatial natural language. SIGSPATIAL Spec 3(2):5–11CrossRefGoogle Scholar
  31. Maki RH (1981) Categorization and distance effects with spatial linear orders. J Exp Psychol Hum Learn Memory 7(1):15–32CrossRefGoogle Scholar
  32. McNamara TP (1986) Mental representations of spatial relations. Cognit Psychol 18(1):87–121CrossRefGoogle Scholar
  33. McNamara TP, Hardy JK, Hirtle SC (1989) Subjective hierarchies in spatial memory. J Exp Psychol Learn Memory Cognit 15(2):211–227CrossRefGoogle Scholar
  34. Müller K, Steinmeier C, Küchler M (2010) Urban growth along motorways in Switzerland. Landsc Urban Plan 98(1):3–12CrossRefGoogle Scholar
  35. Nielsen TAS, Hovgesen HH (2008) Exploratory mapping of commuter flows in England and Wales. J Transp Geogr 16(2):90–99CrossRefGoogle Scholar
  36. Novel M, Grütter R, Boley H, Bernstein A (accepted) Nearness as context-dependent expression: an integrative review of modelling, measurement and contextual properties. Spat Cognit Comput Interdiscip JGoogle Scholar
  37. Olshavsky RW, MacKay DB, Sentell G (1975) Perceptual maps of supermarket locations. J Appl Psychol 60(1):80–86CrossRefGoogle Scholar
  38. Özçep OL, Grütter R, Möller R (2012a) Dynamics of a nearness relation–first results. In: Bhatt M, Guesgen HW, Davis E (eds) Spatio-temporal dynamics: ECAI 2012 workshop proceedings of STeDy 2012, Montpellier, France, August 27–28, 2012, vol. 030-08/2012 of SFB/TR 8 Report. Universität Bremen/Universität Freiburg, pp 1–7Google Scholar
  39. Özçep OL, Grütter R, Möller R (2012b) Nearness rules and scaled proximity. In: Raedt LD, Bessiere C, Dubois D, Doherty P, Frasconi P, Heintz F, Lucas P (eds) Frontiers in artificial intelligence and applications. Proceedings of the 20th European conference on artificial intelligence (ECAI (2012) Montpellier, France, August 27–31, 2012, vol 242. IOS Press, Amsterdam, pp 636–641Google Scholar
  40. Perler L (2007) Geflügelgrippe: Ursprung - Entwicklung - Ausblick. Eidgenössisches Volkswirtschaftsdepartement EVD, Bundesamt für Veterinärwesen BVET. Accessed 18 July 2019
  41. Purves RS, Clough P, Jones CB, Hall MH, Murdock V (2018) Geographic information retrieval: progress and challenges in spatial search of text. Found Trends Inf Retr 12(2–3):164–318CrossRefGoogle Scholar
  42. Randell DA, Cui Z, Cohn AG (1992) A spatial logic based on regions and connection. In: Nebel B, Rich C, Swartout WR (eds) Proceedings of the 3rd international conference on principles of knowledge representation and reasoning (KR’92). Morgan Kaufmann, San Mateo, pp 165–176Google Scholar
  43. Ratti C, Sobolevsky S, Calabrese F, Andris C, Reades J, Martino M, Claxton R, Strogatz SH (2010) Redrawing the map of Great Britain from a network of human interactions. PLoS ONE 5:e14248CrossRefGoogle Scholar
  44. Robinson VB (2000) Individual and multipersonal fuzzy spatial relations acquired using human-machine interaction. Fuzzy Sets Syst 113(1):133–145CrossRefGoogle Scholar
  45. Rühli L (2012) Gemeindeautonomie zwischen Illusion und Realität: Gemeindestrukturen und Gemeindestrukturpolitik der Kantone. Kantonsmonitoring 4, Avenir Suisse, Zürich. Accessed 18 July 2019
  46. Schockaert S, Cock MD, Kerre EE (2008) Location approximation for local search services using natural language hints. Int J Geograph Inf Sci 22(3):315–336CrossRefGoogle Scholar
  47. Schockaert S, Cock MD, Kerre EE (2009) Spatial reasoning in a fuzzy region connection calculus. Artif Intell 173(2):258–298CrossRefGoogle Scholar
  48. Schuler M, Dessemontet P, Joye D (2005) Eidgenössische Volkszählung 2000. Technical report, Bundesamt für Statistik, Neuenburg, Die Raumgliederungen der Schweiz, p 232Google Scholar
  49. Skoumas G, Pfoser D, Kyrillidis A (2013) On quantifying qualitative geospatial data: a probabilistic approach. In: Proceedings of the second ACM SIGSPATIAL international workshop on crowdsourced and volunteered geographic information, GEOCROWD ’13. ACM, New York, pp 71–78Google Scholar
  50. Wallgrün JO, Klippel A, Baldwin T (2014) Building a corpus of spatial relational expressions extracted from Web documents. In: Proceedings of the 8th workshop on geographic information retrieval, GIR 2014, Dallas/Fort Worth, TX, USA, November 4–7, 2014, pp 6:1–6:8Google Scholar
  51. Williamson T (1994) Vagueness. Routledge, LondonGoogle Scholar
  52. Worboys MF (1996) Metrics and topologies for geographic space. In: Kraak M, Molenaar M (eds) Advances in geographic information systems research II: Proceedings of the 7th international symposium on spatial data handling. Taylor and Francis, London, pp 365–376Google Scholar
  53. Worboys MF (2001) Nearness relations in environmental space. Int J Geograph Inf Sci 15(7):633–651CrossRefGoogle Scholar
  54. Yao X, Thill J-C (2005) How far is too far? A statistical approach to context-contingent proximity modeling. Trans GIS 9(2):157–178CrossRefGoogle Scholar
  55. Yao X, Thill J-C (2007) Neurofuzzy modeling of context-contingent proximity relations. Geograph Anal 39(2):169–194CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Swiss Federal Research Institute WSLBirmensdorfSwitzerland

Personalised recommendations